Digital terrain analysis and image processing for assessing

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Digital terrain analysis and image processing for assessing
Digital terrain analysis and image processing
for assessing erosion prone areas
A Case Study of Nam Chun Watershed,
Phetchabun, Thailand
Monton Suriyaprasit
March, 2008
Digital terrain analysis and image processing
for assessing erosion prone areas
A Case Study of Nam Chun Watershed, Phetchabun,
Thailand
by
Monton Suriyaprasit
Thesis submitted to the International Institute for Geo-information Science and Earth Observation in
partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science
and Earth Observation, Specialisation: (fill in the name of the specialisation)
Thesis Assessment Board
Prof.Dr. V.G. Jetten (Chair)
Dr. T.W.J. van Asch (External Examiner)
Dr. D.P. Shrestha (First Supervisor)
Dr. D.G. Rossiter (Second Supervisor)
Observer
Drs. T.M. Loran (Course Director AES)
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer
This document describes work undertaken as part of a programme of study at the International
Institute for Geo-information Science and Earth Observation. All views and opinions expressed
therein remain the sole responsibility of the author, and do not necessarily represent those of
the institute.
Abstract
Soil erosion is one of the severe land degradation problems in many parts of the world. This requires
the crucial data to predict critical areas for erosion but they are not easily to acquire in mountainous
areas because of inaccessibility. To assess soil loss in such mountainous area image processing
approach were applied. Suitable image processing approaches were undertaken. The RMMF erosion
model was used considering crucial input parameter such as cover management known as C-factor.
The model then was run by using script in ILWIS 3.3 after all the input parameters were generated.
The results of soil loss 2007 in different land use/cover types were used as standard pattern to study
the effect of land use/cover change in periods 1988, 2000 and 2007 on overall amount of soil loss in
this area. Terrain parameters i.e. slope, flow accumulation were used to map gully formation and
compared with the erosion prone areas that were classified from the results of RMMF erosion model.
The result from land use/cover classification showed the trend of the study area was transformed from
forest to agriculture areas. For C-factor generation, the regression equation (curve estimation) based
on field assessment of C-factor using training values and NDVI gave the satisfy results; adjust R2
(0.78), C.E. (0.77), M.E. (-0.04) and RMSE (0.03). The results from the erosion model illustrated the
highest soil loss occurred in the agriculture areas meanwhile the lowest was found in forest areas.
After applied the rate of soil loss in 2007 as the standard for periods 1988 to 2007, the results
illustrated that the overall amount of soil loss in the study area was raised followed the increasing of
agriculture areas. The critical zones defined most of the gully formation occurred in the same
locations as the RMMF model prediction. These results showed that remote sensing data can use for
assessing erosion prone areas in the areas where accessibility is limited.
Keywords: C-factor, NDVI, RMMF erosion model, critical zones
i
Acknowledgements
I would like to express my gratefulness to the Netherlands government and people for granting me the
scholarship and Land Development Department, Thailand for giving me the chance to study in this
nice country. Special thanks to Mrs. Parida Kuneepong and Mr. Boonrak Patanakanog who did all
effort bringing me here.
I am greatly indebted to my supervisors Dr. D.P. Shrestha and Dr. D.G. Rossitor for their valuable
support, supervision advices and constructive comments. They teach me the real scientific research
and enormous encourage me to improve the quality of my work.
Especial thanks go to all Earth System Analysis staff members. Special gratitude to Prof. V.G. Jetten
who gave the valuable comments during Midterm presentation and Dr. A. Fashad for the parental care
me during whole period of my study.
I would like to thank the staff members of the Land Development Department, Thailand for their
hospitality during the fieldwork.
Many grateful to all my classmates who gave me friendship and cheerfulness during this period:
Sanjaya Devkota who suggested me the professional knowledge in soil science. Kanya Souksakul who
was encourages me and passed very hard time together. Thanks for supporting me and having good
time together.
The deepest appreciation goes to my fiancé Adchara Thongyou for everything she have done for me:
Her love, care, support and patience.
Finally, I would like to express the greatly thanks to my parents and brother for their love and every
good things that make me success. I dedicated this work to them.
ii
Table of contents
1.
2.
Introduction ....................................................................................................................... 1
1.1.
Background .............................................................................................................................1
1.2.
Statement of the problem........................................................................................................1
1.3.
Objectives ...............................................................................................................................2
1.4.
Research Hypothesis...............................................................................................................2
1.5.
Research Questions.................................................................................................................2
Literature review ............................................................................................................... 4
2.1.
2.1.1.
2.1.2.
Land use changes effect soil physical properties....................................................................6
2.3.
Terrain parameters ..................................................................................................................7
2.4.
Digital terrain analysis............................................................................................................8
2.5.
Erosion model .........................................................................................................................9
2.6.
Image processing for removing topographic effect ..............................................................13
2.7.
4.
Soil erosion by water ....................................................................................................................... 4
Factors controlling rate of soil erosion by water.............................................................................. 5
2.2.
2.6.1.
2.6.2.
3.
soil erosion..............................................................................................................................4
Topographic normalization............................................................................................................ 14
Sum normalization ......................................................................................................................... 14
Normalized Difference Vegetation Index (NDVI) ...............................................................14
Study area ........................................................................................................................ 16
3.1.
Location ................................................................................................................................16
3.2.
Climate..................................................................................................................................17
3.3.
Geology.................................................................................................................................18
3.4.
Soils ......................................................................................................................................18
3.5.
Vegetation and land use........................................................................................................18
Material and methods ..................................................................................................... 19
4.1.
Materials ...............................................................................................................................19
4.2.
Research methods .................................................................................................................20
4.2.1.
4.2.2.
4.2.3.
4.2.4.
4.3.
4.3.1.
Land use/cover classification in mountainous areas....................................................................... 24
C-factor mapping ........................................................................................................................... 27
Erosion assessment ........................................................................................................................ 33
Assessing create critical zones for ephemeral gully incision ......................................................... 38
Data analysis .........................................................................................................................40
Laboratory analysis........................................................................................................................ 40
iii
4.3.2.
4.3.3.
5.
Results and discussions ...................................................................................................45
5.1.
5.1.1.
5.1.2.
5.1.3.
5.2.
5.2.1.
Land use classification......................................................................................................... 45
Land use/cover classification result ............................................................................................... 45
Accuracy assessment...................................................................................................................... 47
Trend of land use/cover change from 1988 to 2007....................................................................... 49
C-factor mapping for erosion assessment ............................................................................ 50
Validation of C-factor map ............................................................................................................ 51
5.3.
Soil erosion assessment ....................................................................................................... 52
5.4.
Distribution of soil properties in different land use/cover types ......................................... 54
5.4.1.
5.4.2.
6.
Soil properties analysis .................................................................................................................. 42
Statistical analysis .......................................................................................................................... 43
Distribution of soil organic matter in different land use/cover types ............................................. 54
Distribution of bulk density in different land use/cover types........................................................ 56
5.5.
Assessment of land use/cover change effect on soil erosion ............................................... 57
5.6.
Assessing critical zones for ephemeral gully formation ...................................................... 58
Conclusions and recommendations................................................................................62
6.1.
Conclusions.......................................................................................................................... 62
6.2.
Recommendations................................................................................................................ 62
6.3.
Limitations of the study ....................................................................................................... 63
Reference..................................................................................................................................64
Appendices...............................................................................................................................68
Appendix 1: Field data ...................................................................................................................... 68
Appendix 2: Laboratory analysis ...................................................................................................... 75
Appendix 3: Organic matter 2006 ..................................................................................................... 78
Appendix 4: Regression analysis result summaries between C-factor values and NDVI................. 79
Appendix 6: Histogram of organic matter (a) and bulk density (b) ................................................. 82
Appendix 7: Geopedologic map (Solomon, 2005) used in RMMF model and legend.................... 82
Appendix 8: ILWIS script to run the RMMF model for annual soil loss prediction ....................... 84
Appendix 9: The photographs of gully erosion in the study area ..................................................... 85
iv
List of Figures
Figure 2-1: Flow chart of Revised Morgan –Morgan –Finney model ...................................................12
Figure 2-2: Effect of topography on reflectance ....................................................................................13
Figure 2-3: Effect of topography on the amount of sun illumination ....................................................14
Figure 3-1: The study area in the Phetchabun Province of Thailand ; (a) map of Thailand, (b) map of
Phetcabun province and (c) 3D view of Namchun watershed .......................................................16
Figure 3-2: Average annual rainfall and temperature of the study area.................................................17
Figure 3-3: Land use/cover changed from forest to orchard and cropland ............................................18
Figure 4-1: Flow chart of overall methodology .....................................................................................23
Figure 4-2: Lansat TM March 3, 2007 color composite in 453 BGR ;..................................................25
Figure 4-3: Feature space plot of training samples ................................................................................26
Figure 4-4: Estimation Fc from field......................................................................................................27
Figure 4-5: Estimation Sp from field; (a) Sp around 75% and (b) Sp around 35%...............................28
Figure 4-6: The relationship between annual rainfall and elevation......................................................33
Figure 4-7: Slope map (a) and Aspect map (b) ......................................................................................38
Figure 4-8: Drainage network (a) and catchment (b) extraction............................................................39
Figure 4-9: Critical zones creation.........................................................................................................39
Figure 4-10: Laboratory analysis in ITC; (a) preparing soil samples, (b) walkley - black method, .....40
Figure 4-11: USDA soil texture triangle................................................................................................42
Figure 4-12: SPAW model .....................................................................................................................43
Figure 5-1: Land use/cover classification maps; (a) 1988, (b) 2000 and (c) 2007 ................................46
Figure 5-2: Area in percent in different land use/cover 1988 – 2007 ....................................................46
Figure 5-3: Area of land use/cover change in periods 1988, 2000 and 2007.........................................50
Figure 5-4: C-factor map 2007 derived from NDVI; .............................................................................51
Figure 5-5: The relationship between C-factor prediction and validation from curve estimation.........52
Figure 5-6: Soil loss map 2007 ..............................................................................................................53
Figure 5-7: Erosion prone areas classified from soil loss map 2007 .....................................................54
Figure 5-8: Distribution of soil organic matter (%) in different land use/cover....................................55
Figure 5-9: Distribution of bulk density (g/cm3) in different land use/cover ........................................56
Figure 5-10: Sensitive areas (a) and critical zones (b)...........................................................................58
Figure 5-11: Gully erosion formation prediction with validated gully erosion points ..........................59
Figure 5-12: The areas of erosion level in percent.................................................................................60
Figure 5-13: The areas of gully formation in percent ............................................................................60
Figure 5-14: The comparison between gully erosion prediction and erosion prone areas ....................61
v
List of Tables
Table 3-1: Average monthly rainfall, rainy day and temperature period 36 years (1970 – 2006)....... 17
Table 4-1: Parameters input to the model and their sources ................................................................. 19
Table 4-2: Validation data and sources................................................................................................. 20
Table 4-3: Canopy cover sub factor in different land use/cover ........................................................... 29
Table 4-4: Surface cover sub factor in different land use/cover ........................................................... 29
Table 4-5: Surface roughness sub factor in different land use/cover.................................................... 30
Table 4-6: Annual rainfall at various elevations obtained from ITC .................................................... 33
Table 5-1: Comparison the areas of land use/cover in 1988, 2000 and 2007 ....................................... 47
Table 5-2: Comparison of the accuracy between topographic normalization and sum normalization . 47
Table 5-3: Accuracy assessment of land use/cover classification 2007 ............................................... 48
Table 5-4: Crop calendar for cultivation of Phetchabun Province in 2007........................................... 48
Table 5-5: Land use/cover change in the periods 1988 - 2007 ............................................................. 49
Table 5-6: Comparison between three of C-factor prediction techniques ............................................ 52
Table 5-7: Soil loss prediction in different land use/cover 2007 .......................................................... 53
Table 5-8: Erosion prone areas in different land use/cover .................................................................. 54
Table 5-9: Average soil organic matter content in different land use/cover......................................... 55
Table 5-10: Average bulk density in different land use/cover .............................................................. 56
Table 5-11: Amount of soil loss (t/y) in periods of 1988, 2000 and 2007............................................ 58
Table 5-12: Contingency matrix between Critical zone and Erosion data from fieldwork .................. 59
Table 5-13: Area of erosion prone areas prediction.............................................................................. 59
Table 5-14: Area of gully erosion prediction from critical zones ......................................................... 60
vi
List of Abbreviations
AGNPS
DEM
EUROSEM
FAO
LDD
MMF
NDVI
SLEMSA
RMMF
RUSLE
USDA
USLE
WEPP
Agricultural Non-point source pollution model
Digital elevation model
European Soil Erosion Model
Food and Agriculture Organization
Land Development Department, Ministry of agriculture and cooperatives, Thailand
Morgan, Morgan and Finney Model
Normalized Difference Vegetation Index
Soil Loss Estimation Method for Southern Africa
Revised Morgan, Morgan and Finney Model
Revised Universal Soil Loss equation
United States Department of Agriculture
Universal Soil Loss equation
Water Erosion Prediction Project
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
1.
Introduction
1.1.
Background
Population has been increasing everywhere especially in developing countries, which leads to more
demand for food production. This situation can cause major changes in land use in the coming
decades and demand on higher agriculture production (Stein and Goudriaan, 2000). To support
increased food demand, expansion of agriculture lands is necessary increasing. This leads to
encroachment of sloping and marginal lands in the mountainous areas. Clearing land for agriculture
areas, cutting down forests together with intensive use of land for more products in the sloping land
have led to land degradation.
Land degradation is a process that lowers the capacity of land. According to FAO (1994), there are six
types of land degradation: water erosion, wind erosion, soil fertility decline, salinitation, water
logging, and lowering of the water Table. The unbalance between land resource regeneration rate and
population growth rate leads to lack of suitable land for agriculture (Vargas Rojas, 2004). Unless soil
conservation and management practices are implemented properly, soil erosion can cause loss of plant
nutrient, weak soil aggregation and finally low agriculture production. Improper land use practices in
sloping areas accelerate soil erosion.
Soil erosion not only reduces soil depth, but also reduces the capacities of soil such as water holding
and decrease plant nutrient. In the long term, soil productivity will be decreased. Furthermore it can
cause offsite effects including pollution in water, downstream sediment in river bank and reservoirs. It
is necessary to understand erosion and sedimentation process for soil conservation planning.
1.2.
Statement of the problem
Namchun watershed is in severe problems of deforestation for the long times. The original
characteristic of this mountainous areas are dense forest that have been occupied by local farmers
since long. The areas are changed to cropland where maize is the main crop in. Due to the cultivation
on steep slopes together with removal of vegetation cover, has caused negative effect on soil
properties and its structure. Moreover, improper land use practices in agricultural areas triggering soil
erosion process in the watershed. Although several studies were carried out in past, none of them are
able to predict soil loss and land degradation; leading to look alternatives way analysis.
In order to assess soil erosion, erosion model such as RMMF is one of the alternative ways to
investigate the soil loss rate. By the reason of simplicity of the model and also it was developed for
hill slope, is the reason behind the selection of the model. This model involves with a number of input
parameters. One of the crucial parameters is cover management factor known as C-factor which
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
depends on land use/cover types. To obtain C-factor values and model the soil loss, the classification
of land use/cover is needed. Unfortunately, there are a lot of inaccessible areas causing the major
problem. Remote sensing data seem to be the appropriate solution to derive these essential parameters.
Terrain parameters such as slope that extracted from elevation model (DEM) are also required
parameter in this erosion model. For example, slope gradient is not only using to calculate soil
detachment by runoff but also transport capacity by runoff as well. Due to the lack of erosion data, the
model result could not be validated at this stage, however, the result can be compared by deriving
critical zones threshold. Critical zones threshold is calculated using hydrological parameters such as
flow direction, flow accumulation, slope and catchment area together with flow width.
1.3.
Objectives
The main objective of this research is to assess erosion prone areas in inaccessible mountain areas.
The specific objectives of the research are the following.
To investigate the potential of image processing techniques such as correction of topography
induced constraints for classifying land use/cover in the mountainous area.
To generate cover factor (C-Factor) from remote sensing techniques to be used in erosion
modelling.
To study the distribution pattern of soil properties in different land use/cover types and the
effect of land use/cover change in soil erosion.
To use terrain parameters to map critical zones for gully formation.
1.4.
Research Hypothesis
Land use/cover pattern change from forest to agriculture areas increases overall amount of
soil loss in the study area.
C-factor estimation from NDVI can be improved using field data.
Critical zones for gully formation can be mapped using terrain parameters.
1.5.
Research Questions
What are the image processing techniques that help to remove topographic effect?
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
What is the change pattern of land use/cover to the soil properties? And what role it has in
overall soil erosion in the watershed?
What will be the best technique of generating C-factor from remote sensing data?
Which terrain parameters indicate gully formation?
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
2.
Literature review
2.1.
soil erosion
Among the several land degradation processes, such as soil compactness, soil salinitation, soil acidity,
etc. Soil erosion is a one, that affect environmental and food production. Kruthkul et al. (2001) define
soil erosion as the processes of detachment and transportation of soil. It is caused by erosion agents
including water and wind. Soil erosion can be divided in two groups, natural erosion and accelerated
erosion. Both natural and accelerated erosion appear in the nature, it may slow continue process or
sudden occurs with severe loss of topsoil. However, accelerated erosion also can occur where natural
rate of erosion is increased by human activities. Soil erosion is widespread in mountainous areas due
to steepness of slope, this in combination with improper land use practices including overgrazing
without proper conservation plan. Erosion degrades soil by removing topsoil, decreasing plant
nutrients and rooting depth (Petter, 1992)
2.1.1.
Soil erosion by water
Soil erosion by water can be described in two stages: detachment and transportation of sediments. The
detachment of soil particles is due to raindrop impact, caused by its kinetic energy. Soil particle
detachment is also caused by the scouring effect of overland runoff. The other is transportation of soil
particles by water that can be caused by buoyancy of particles and turbulence of water (Kruthkul et
al., 2001). Water erosion starts in the form of shallow flow which is called sheet erosion. There is no
channel form in this step. It is the removal of topsoil as thin layer. Rill erosion consists of numerous
small channels caused by concentrated overland flow. Rills occur mostly on bare surfaces on sloping
areas, they can be eliminated by tillage operations. The severe permanent erosion form is gully where
huge channels can remove large quantities of topsoil. Unlike rill, gully erosion cannot be annihilated
by normal tillage (Gebrekirstos, 2003).
Accelerated erosion by water can be divided as following (Shrestha, 2007):
1. Sheet erosion is the uniform removal of soil particles where the areas are influenced by effect
of rain splash. The result of this action is detachment and transportation of the detached
sediment by runoff. On slope areas, sheet wash can takes place to remove shallow layer of
soil.
2. Rill erosion is the removal of soil where small linear channels are formed due to concentrated
runoff. This type of water erosion can occur during or suddenly after rainfall. Sometime rills
can be discontinuous and do not thus take part of drainage network. Rills not only act as
erosion process, but also act as transporting agent for the detached particles. Since 30 cm. is
the maximum size for defining rills, they can easily be obliterated by normal tillage operation.
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
3. Gully erosion is developed by scouring effect of concentrated overland flow causing deeply
incised channels. Sometime they are initiated after surface collapse from piping. Definition of
gullies is limited by 30 cm. minimum depth. They can take place during or suddenly after
heavy rainfall. Gullies may have also continuous flow at gullies bottom due to seepage water.
Collapsing of the steep wall can widen gullies and the depth can go deeper, up to a limiting
depth of 30 meters above which they are no longer consider gullies. The direction of gullies
enlargement is upstream during concentrated runoff and head ward erosion due to formation
of small waterfalls. Because of depth themselves, they cannot easily eradicated by normal
tillage operation and can damage infrastructure such as road or building.
2.1.2.
Factors controlling rate of soil erosion by water
The rate of soil erosion by water can be control by following factors (Wall et al., 1987):
1. Rainfall intensity and runoff
Rainfall intensity is considered as an important initial factor in erosion process. The kinetic energy of
rain is highly correlated with soil detachment and eventually to erosion. Hence, erosion and runoff
can be predicted by rainfall intensity (Hammad et al., 2006). The raindrops on soil surface can
collapse soil aggregates and scatter those materials. Heavier materials for instance larger sand and
gravel need more raindrop energy to detach as well as more amount of runoff to remove. Conversely
lighter materials can be easily removed. Surface runoff is formed when rainfall intensity of storm
exceeds the infiltration capacity of soil. Moreover, runoff can occur whenever there is excess water on
a slope that cannot be absorbed into the soil or trapped on the surface. The amount of runoff can be
increased if infiltration is reduced due to soil compaction, crusting or freezing. Runoff from the
agricultural land may be greatest during spring months when the soils are usually saturated, snow is
melting and vegetative cover is minimal (Wall et al., 1987).
2. Soil erodibility
Soil erodibility is used as indicator of soil erosion because it is a measure of soil susceptibility to
detachment and transport by the agents of erosion (Tejada and Gonzalez, 2006). Soil erodibility is
integrated effect of processes that regulate rainfall acceptance and the resistance of the soil to
detachment and subsequent transport of the detached particles. Soil properties including particle size
distribution, structural stability, organic matter content, clay mineralogy and water transmission
characteristics influence these processes (Lal, 1994). Usually, soils have more resistance to erosion if
they have high infiltration rates, high organic matter content and have developed structure. Soil
particle size classes such as very fine sand and silt tend to be more erodible than sand. Clay is also
resistant to erosion due to its consistency. Poor soil structure can occur because of tillage and
cropping practice with low level of organic matter. This may result in compacted soil which increases
soil erosion.. Increasing in runoff and decreasing infiltration rate can be the result from compacted
subsurface layer of soil. Further more, formation of soil crusting can also decrease infiltration rate in
the mean of surface sealing (Wall et al., 1987). Although sometime soil crusting can decrease erosion
such as sheet or rain splash, however, more rill erosion problems can occur because of increasing
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
runoff. Low organic matter content in soil may lead to low nutrient content and poor vegetation cover
that means less protection of soil.
3. Slope gradient and slope length
A factor that is crucial in erosion assessment is slope. Not only slope gradient but also slope aspect,
length and shape of slope are equally important. Generally, steep slope has higher soil loss because
runoff water becomes more erosive and can detach sediment due to increasing flow velocity and high
runoff down the slope. Apart from slope gradient, slope length and aspect also become important
factors. On longer slope, an increased accumulation of overland flow tends to increase rill erosion.
Even steep slope may become less erosive if slope length is short, on the other hand gentle slope can
have more erosion because of longer slope length (Shrestha, 2007).
4. Vegetation
Vegetation cover plays an importance role by protecting soil, thereby minimizing the impact of
raindrop. Moreover, runoff rate also slows down and it provides enough time for surface water to
infiltrate. In completely dense cover situation it can be assumed that there is no erosion because the
impact of all the raindrops will be intercepted. Additionally, dead leaves also provide litters that
maintain organic matter of the soil. Reducing in organic matter with cultivation as soil disturbance
lead to deplete the stability of soil aggregates (Six et al., 2000).
2.2.
Land use changes effect soil physical properties
Human activities impacts on the topography such as changes in land use are often observed with
synchronous changes in erodibility of soils, erosion patterns and suspended sediment concentration
and characteristics in river (Gerald, 2006). Land use changes influence the soil properties such as
bulk density, soil structure, and organic carbon content that affect soil hydraulic properties, including
soil hydraulic conductivity function and water retention characteristics (Zhou et al.). Increase of
vegetation cover increases infiltration capacity of soil by increasing soil organic matter and decreasing
bulk density. Giertz (2005) reported that macro porosity and permeability can be decreased because of
cultivation practices. This can be related to vegetation cover and land use changes.
Land use changes from forest to agriculture, can result in major changes in soil physical properties.
Vegetation cover protects the soil not only against the impact of falling rain but also protects the soil
by its root system. The plant roots help increase soil pore space thus helping in infiltration and better
soil structure. In addition, the dead leaves also increase organic matter content in soil. Modifications
of land use have an important influence on the soil organic matter content (Yadav and Malanson,
2007). Decreasing in organic matter due to cultivation is related to obliteration of macro aggregate.
Soil physical properties such as porosity and bulk density are important factors that can effect
hydraulic conductivity of soil and eventually soil erosion. As negative changes of these factors,
decreasing soil aggregate, porosity, increasing bulk density and reducing infiltration rate can lead to
soil erosion.
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
2.3.
Terrain parameters
Digital terrain parameters or topographic attributes known as or morphometric variable (Shary et al.,
2002) explains about features on the terrain surface that can be described in terms of morphographic
and morphometric attributes (Sharif and Zinck, 1996). Morphographic concept explains about
geometry of geoforms such as shape and profile of the topography, aspect and drainage pattern.
Conversely, morphometry is described about dimensions of the geoforms, including relative elevation,
valley density and slope steepness. They can be derived using digital terrain analysis techniques. The
digital terrain parameters can be divided in to three categories (Hengl et al., 2003):
1. Morphometric parameters are derived directly from the DEM by using filter operation. They can
be grouped as:
· Elevation change gradients: e.g. slope
· Orientation gradients: e.g. aspect, steepest downhill slope, viewshed;
· Curvature gradients: e.g. horizontal or tangent curvature, vertical or profile curvature, mean
curvature
Slope shows the rate of change in elevation in x and y direction. For aspect, it gives azimuth angle of
the sloping surface (orientation of central pixel). Plan curvature is curvature of corresponding normal
section, which is tangential to a contour, positive values give the divergence of flow, conversely
negative indicate concentration of flow. Vertical or profile curvature is curvature of corresponding
normal section, which is tangential to a flow-line. If the normal section concavity is directed up, it
gives negative values. For the positive values that means opposite case. An average of normal section
curvature is called mean curvature. Negative values describe mean-concave landforms, while positive
values refer to mean-convex ones (Hengl et al., 2003).
2. Hydrological or flow-accumulation based terrain parameters are normally used to explain flow of
material over a grid surface (Hengl et al., 2003), for example quantify flow intensity, accumulation
potential or erosion potential. They are Compound topographic Index (CTI), Stream Power Index
(SPI) Sediment Transport Index (STI). In general, CTI reflects the accumulation processes. STI and
SPI reflect erosive power of terrain and overland flow respectively.
Terrain parameters can be very much useful as supporting variable to facilitate spatial prediction of
erosion processes. Martinez-Casasnovas (2003) used digital elevation model as spatial information for
mapping gully erosion. Mati et al. (2000) reported slope steepness and slope length as input
parameters for assessment of erosion hazard in north basin of Kenya. Siepel et al. (2002) developed a
simple water erosion simulation model based on stream power, handles vegetation in terms of contact
cover, and considers the settling characteristics of the eroding sediment. Moreover Menendez-Duarte
et al.(2007) derived slope, flow direction and flow accumulation from digital elevation model to
quantify erosion forms and drainage areas in northern Iberian peninsular.
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
2.4.
Digital terrain analysis
Digital terrain analysis is a process to describe the terrain quantitatively. This process is used to derive
terrain parameters from DEM (Hengl et al., 2003). Some terrain parameters that necessary for this
research including slope aspect, flow direction, flow accumulate were generated from DEM. These
parameters together with drainage network and upper stream area were used to calculate critical zones
for gully formation as following:
Slope aspect
The slope aspect describes the direction of maximum rate of change in the elevations between each
cell and its eight neighbours. It can essentially be thought of as the slope direction. It is measured in
positive integer degrees from 0 to 360, measured clockwise from north (Paron and Vargas, 2007).
Aspects of cells of zero slopes (flat areas) are assigned values of -1.
Flow direction
The flow direction defines the direction of flow from each cell in the DEM to its steepest down-slope
neighbour. The method designated D8 (eight flow directions) for defining flow directions was
introduced by O’Callaghan and Mark (1984). It uses for assigning flow from each pixel to one of its
eight neighbors, either adjacent or diagonal, in the direction with steepest downward slope (David,
1997).
Flow accumulation
Flow accumulation is the total number of cells that would contribute water to a given cell (ESRI,
2007); it defines the amount of upstream area drainage based on the accumulated weights for all cells
that flow into each down slope cell. It is essentially for measuring the upstream catchment area. The
flow direction layer is used to define which cells flow into the target cell. The results of flow
accumulation can be used to create a stream network by applying a threshold value to select cells with
a high accumulated flow (ESRI, 2007).
Drainage network extraction
The Drainage Network Extraction operation extracts a basic drainage network (boolean raster map).
The output raster map showed the basic drainage as pixels with value true, while other pixels have
value false. Depending on the flow accumulation value for a pixel and the threshold value for this
pixel, it is decided whether true or false should be assigned to the output pixel. If the flow
accumulation value of a pixel exceeds the threshold value, the output pixel value will be true; else,
false is assigned (ITC and RSG/RSD, 2005).
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Critical zone
The critical zones or sensitive areas are the areas that prone to ephemeral gully erosion. Generally, the
gully incision is expected to appear when contributing area together with local slope exceed a given
threshold (Jetten et al., 2006). There are many algorithms for determine threshold based on slope
(m/m), upstream area (m2) and flow width (m). A strong correlation was found between the rill crosssections and a power function of slope gradient and contributing area reference.
2.5.
Erosion model
Every soil erosion model try to simplify and represent the complexity of natural processes (Shrestha,
2007). Model building is based on defining the essential factors relating to erosion and soil loss
through obtaining from methodology of field observation, measurement, experiment and statistical
analysis. With increasing computation power of computers, many models have been developed.
However, only one model can not cater and solves various problems (Gebrekirstos, 2003). This is the
reason why many models are available. Users need to understand the concepts behind the models
before applying them. Some models are developed for particular conditions that can not be directly
applied to other locations. Usually, erosion models can be categorized into three groups: empirical,
conceptual and physical based. The distinction between models is not obvious and therefore can be
somewhat subjective. They are likely to contain a mix of modules from each of these categories
(Merritt et al., 2003). The frequently used models are described as follows:
1. The Universal Soil Loss Equation (USLE)
USLE was developed in the 1970s by United States Department of agriculture (USDA). This soil
erosion model used widely within the United States and worldwide (Merritt et al., 2003). The
equations in this model have been developed using statistical analysis of data from 10,000 plots years
from natural run off plots together with 2,000 plot years of artificial rainfall simulators in USA
(Wischmeier and Smith, 1978). Sheet and rill erosion are predicted by using values for indices that
represent the four major factors affecting erosion: R-climatic erosivity, K-Soil erodibility, L- and Stopography, and C and P-landuse. The model has undergone a number of modifications. The model
has also been upgraded to take into account additional information that has become available since the
development of the USLE (Renard, 1997). There are some limitations in this model and can not
identify events as long term erosion. The model can only predicts inter rill erosion, but not gully,
channel or stream bank erosion. It can estimate soil particles movement but ignore deposition. The
accuracy of the equations is bias when using only short-term rainfall records (Merritt et al., 2003).
2. Revised Universal Soil Loss Equation (RUSLE)
This model has the same factors as USLE. It updates the USLE model and incorporates new material
that has been available informally or from scattered research reports and professional journals. It has
been developed to replace the USLE, but it has the same limitations (Gebrekirstos, 2003;
Saengthongpinit, 2004).
9
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
3. SLEMSA (Soil Loss Estimation Method for Southern Africa)
SLEMSA was developed in Zimbabwe, base on USLE. It was developed from the data of the
Zimbabwe highveld to evaluate the result of erosion from various farming systems (Morgan, 1995).
The technique has since been adopted throughout the countries of Southern Africa.
4. Water Erosion Prediction Project (WEPP)
WEPP is a physical based hydrological and erosion model designed to replace the USLE (Laflen et
al., 1991). This model contains two sub-models with hill slope version and watershed version. Hill
slope version can estimate soil detachment and deposition along a hill slope profile and the net total
soil loss is estimated from the end of the slope without considering erosion, transportation and
deposition processes in permanent channels. For watershed version that allows estimation of net soil
loss and deposition over small catchments, it uses for applying to field areas that include ephemeral
gullies which can be farmed over and links these surface erosion processes to the channel network. It
can run for a single storm and on a continuous simulation.
5. Agricultural Non-point source pollution model (AGNPS)
Objective of AGNPS is to compute soil erosion within a watershed. This model is grid cell based and
was developed to estimate runoff quality, with primary emphasis on sediment and nutrient transport
(Young et al., 1989). Since it can be linked to a geographic information system (GIS), its application
in a watershed environment may be more interesting for data integration. Input data for the AGNPS
model include parameters describing catchment morphology, and land use variables and precipitation
data (Merritt et al., 2003). The model extracts topographic variables and land surface characteristics
from basic GIS data layers such as contour, drainage lines and watershed boundaries. The large data
requirements and computational complexity of AGNPS are the limitations of this model.
6. European Soil Erosion Model (EUROSEM)
This model is an event-base model that designs for computing erosion, sediment transport and
deposition over the land surface throughout a storm. It can simulate both rill and inter-rill erosion
including the transport of water and sediment from inter-rill areas to rills. Moreover, it takes into
account effect of leaf drainage and rainfall intercept by vegetation cover. This model can be applied to
individual fields or small catchments (Shrestha, 2007).
7. The Morgan Morgan Finney Model (MMF)
This model was developed to predict annual soil loss from field sized areas on hill slopes. The model
has the simplicity of the universal Soil Loss Equation and yet it covers the advances in understanding
of erosion process (Morgan et al., 1984). This model is a physically based empirical model (Mix
model) and needs less data than most of the other erosion predictive models. This model divides soil
erosion process in two phases including a water phase and a sediment phase. The MMF model can be
easily applied in a raster-based geographic information system (Shrestha, 2007).
10
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
8. Revised Morgan – Morgan – Finney (RMMF)
The RMMF model separates the soil erosion process into two phases: the water phase and the
sediment phase. The water phase determines the energy of the rainfall available to detach soil
particles from the soil mass and the volume of runoff. In the erosion phase, rates of soil particle
detachment by rainfall and runoff are determined along with the transporting capacity of runoff
(Morgan, 2001). The difference from MMF model are the stimulate of soil particle detachment by rain
drop that takes account of plant canopy height and leaf drainage, and a component has been added for
soil particle detachment by flow (Morgan, 2001). The detail of RMMF model can describe as
following (Morgan, 1995):
A. Water phase
Estimation of rainfall energy
Rainfall energy was calculated by using the partitioned rainfall after interception together with energy
of the leaf drainage. First the model computed the proportion of rainfall amount that reach the ground
surface after allowing for rainfall interception to derive effective rainfall. After derived, effective
rainfall then was distributed in two parts. First, rainfall that reached the ground surface after being
intercept by plant canopy as leaf drainage, conversely, second part that rainfall reached the ground
surface without interception. Then kinetic energy was calculated by distributed for effective rainfall
of leaf drainage and direct through fall. Kinetic energy of leaf drainage was a function of plant height
meanwhile kinetic energy of direct through fall was a function of rainfall intensity.
Estimation of runoff
The annual runoff was calculated by using three factors including soil moisture storage capacity,
annual rainfall and mean rainy days. For soil moisture capacity, the calculation was in turn a function
of bulk density, effective hydrological depth, ratio of actual to potential evapotranspiration and soil
moisture content at field capacity.
B. Sediment phase
Estimation of soil particle detachment by raindrop and runoff
The calculation of soil particle detachment was divided in two parts. First, the model considered soil
particle detachment by raindrop impact. Second, soil particle detachment by runoff was taken to
account. Soil particle detachment by raindrop was a function of kinetic energy of effective rainfall and
soil erodibility. The calculation of detachment by runoff was a function of runoff, soil resistance in
turn of surface cohesion, ground cover and slope steepness. In this model assumed that soil
detachment by runoff can only appear where soil was not protected by ground cover. Total particle
detachment ( D ; kg/m2) was finally calculated as a sum of both soil particle detachment by raindrop
and runoff.
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Transport capacity of runoff
The transport capacity was estimated by using runoff, surface cover factor and slope.
Prediction soil loss
The last calculation step of RMMF model was prediction annual soil loss. Total detachment and
transport capacity were compared and erosion rate was the minimum of the result from the
comparison.
The flowchart of RMMF model is shown in Figure 2-1 as following:
Figure 2-1: Flow chart of Revised Morgan –Morgan –Finney model
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
There many studies used erosion model for predict soil loss by modifying various factors. Evaluates
soil erodibility (K) and identifies factors affecting K for calcareous soils was done in Hashtrood City,
northwestern Iran (Vaezi et al.). In the study of Assessment of USLE cover-management C-factors for
40 crop rotation systems on arable farms (Gabriels et al., 2003), The distribution of the rainfall
erosivity over the year was calculated and crop rotation systems were examined, in order to assess the
C-values according to the USLE methodology.
2.6.
Image processing for removing topographic effect
Image processing involves the manipulation of images for the following purpose (Cracknell and
Hayes, 1991):
1. To extract information;
2. To emphasize or de-emphasize certain aspect of the information contained in image ; or
3. To perform statistical or other analyses to extract non-image information
Satellite remote sensing produces very large quantities of digital data that including the mountainous
areas. The image processing is the sensible way to handling vast quantities of information available in
remote sensing data (Harris, 1987). The images of mountainous areas regularly distorted in
radiometric known as topographic effect. As the result from variations of illumination since the solar
and the terrain’s angle, formulates a brightness variation in the images. The topographic effect consist
of the following factors (Hodgson, 1994).
incident illumination —the orientation of the surface with respect to the rays of the sun
existence angle—the amount of reflected energy as a function of the slope angle
surface cover characteristics—rugged terrain with high mountains or steep slopes
Figure 2-2: Effect of topography on reflectance
Source: (Riano et al., 2003)
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
2.6.1.
Topographic normalization
This technique is used to correct the illumination of the sun that influence of topography induced
constraints in the mountainous areas. Topographic normalization is based on Non-Lambertian
Reflectance model, which is operated by using solar azimuth and solar elevation of satellite images.
The model assumed that the observed surface does not reflect incident solar energy uniformly in all
directions (Minnaert and Szeicz, 1961). Instead it needs to take into account variations of terrain.
Minnaert Constant (k)
The Minnaert constant (k) may be found by regression a set of observed brightness values from the
remotely sensed imagery with known slope and aspect values, provided that all the observations in
this set are the same type of land cover. The k value is the slope of the regression line (Hodgson,
1994):
Multi – spectral Sensor
Shadow
Low Illumination
High Illumination
Figure 2-3: Effect of topography on the amount of sun illumination
Source: (Shrestha and Zinck, 2001)
2.6.2.
Sum normalization
Another technique to remove topography effect from satellite images was sum normalization.
According to Shrestha and Zinck (2001), this technique used to minimize the effect of illumination
differences on the surface reflectance. The intensity of each satellite band was normalized by using
sum of illumination of every band then multiplies with constant value.
2.7.
Normalized Difference Vegetation Index (NDVI)
Normalized Difference Vegetative Index (NDVI) is calculated as a ratio between measured
reflectivity in the red and near infrared portions of the electromagnetic spectrum. These two spectral
bands are very affected by the absorption of chlorophyll in leafy green vegetation and by the density
of green vegetation on the surface. Further more, in these two bands, the different between soil and
vegetation is at a maximum. (CGIS, 2004). In general, vegetated areas have high reflectance in the
14
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
near infrared and low reflectance in the visible red. In this index green vegetation are high values ,
water has negative values and bare soil has value around 0. For the middle values, they are indicator
for differences in coverage with green vegetation. The NDVI, as a normalized index, is compensating
changes in illumination conditions, surface slopes and aspect (Lillesand and Kiefer, 1999). NDVI can
be calculated from the following equation.
Equation 1:
Where :
NIR
R
NDVI =
=
=
( NIR − R)
( NIR + R)
a reflectance in the near infrared band
a reflectance in red band
For Landsat TM , the formula can change into following equation.
Equation 2: NDVI =
Where :
(TM 4 − TM 3)
(TM 4 + TM 3)
TM 4
=
Landsat TM band4 (0.76 - 0.90 µ m )
TM 3
=
Landsat TM band3 (0.63 - 0.69 µ m )
Relation between NDVI and C-factor
There are many researches involve with the relationship between NDVI and C-Factor, De Jong (1994)
reported in his PhD thesis on Remote Sensing Applications in Mediterranean areas. By using field
data of 33 plots for statistical analysis, He described that there was a linear relation between NDVI
and USLE C-Factor with a correlation factor of -0.64. In the report of Soil Erosion Risk Assessment
in Italy, Van der Knijff et al. (1999) found the relationship between them in exponential equation.
This seems to be can give more realistic C-factor than linear equation.
15
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
3.
Study area
3.1.
Location
Study area is in Nam Chun watershed in Thailand (Figure 3.1). It falls in two districts namely Lom
Sak and Khoa Khor districts of the Petchabun province in Thailand. It is 400 kilometers north of
Bangkok and 40 kilometers far from the provincial capital. It lies between the latitudes 16 40’ and 16
50’ North and between the longitudes 101 02’ and 10115’ East. The study area covers surficial areas
of 67 km2.
(a) Map of Thailand
(b) Map of Phetchabun province
N
(c) 3D view of Namchun watershed
Figure 3-1: The study area in the Phetchabun Province of Thailand ; (a) map of Thailand, (b)
map of Phetcabun province and (c) 3D view of Namchun watershed
Source: (a) http://www.lib.utexas.edu/maps/middle_east_and_asia/thailand_pol88.jpg
(b) http://www.tat.or.th/travelmap.asp?prov_id=67
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
3.2.
Climate
The climate in the study area is tropical which is characterized by having high humidity, moderate to
high temperature and a distinct climatic variation between dry and wet seasons. This climate is
influenced by the northeast and southwest monsoon. According to Table 3-1, there are three seasons
in this area, dry and hot, wet and hot and relative dry cool periods. The dry and hot period began from
March until April, hot rainy period illustrated from May until October and relative cool period started
on November to February. Average annual rainfall in this area is 1075 mm with 120 rainy days that
are estimated from climatic data in the period of 1970 to 2006. Average annual temperature is 28 oC.
Highest temperature is of 37.5oC in April and lowest temperature is 17.4 oC during winter in
December. Detail climatic data is given in Table 3-1. Figure 3-2 shows average monthly rain and
temperature, derived from Lom Sak Meteorological station’s records that cover 36 years period from
1970 – 2006.
Table 3-1: Average monthly rainfall, rainy day and temperature period 36 years (1970 – 2006)
Month
Rainfall (mm)
Rainy day
Max Temp. (oC)
Min Temp. (oC)
Mean Temp. (oC)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
4.5
22.2
46.5
58.9
159.1
148.1
141.4
197.4
198.3
78.1
15.3
4.8
1
2
5
8
16
17
18
21
19
10
2
1
32.8
34.8
36.6
37.5
35.8
34.0
33.2
32.5
32.9
33.2
32.5
31.6
17.5
19.5
22.0
24.3
25.0
25.1
24.8
24.7
24.5
23.3
20.4
17.4
25.2
27.2
29.3
30.9
30.4
29.6
29.0
28.6
28.7
28.3
26.5
24.5
Total
1074.6
120
34.0
22.4
28.2
Rainfall (mm)
250.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
200.0
150.0
100.0
50.0
Ju
l
Au
g
Se
p
O
ct
N
ov
D
ec
Ja
n
Fe
b
M
ar
Ap
r
M
ay
Ju
n
0.0
Temperature (C)
Source: Lom Sak Meteorological station
Month
Rainfall (mm)
Mean Temp. (C)
Figure 3-2: Average annual rainfall and temperature of the study area.
17
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
3.3.
Geology
According to Mineral Resources Department, Thailand (2006), geology in this area is composed of
uplifted sedimentary rocks of the Korat group in the upper catchments. Various formations were
formed in different periods of time. In Triassic period, the oldest Huai Hin Lat formation consists of
conglomerate, sand stone and shale. The Nam Phong formation contains reddish-brown cross-bedded
sand stone and conglomerate. In Jurassic period, Phu Kradung formation was formed along the scarp
in study area which consists of silt stone, shale and sandstone. The youngest formation is Pha Wihan
which consists of white and pink, cross-bedded sandstone with pebbly layers in the upper beds with
some intercalations of the reddish-brown and grey shale. Quaternary colluvial and alluvial terrace
deposits occur in the lower areas
3.4.
Soils
The main landscapes of the area are the high plateaus, the mountainous areas and the low-lying
narrow valley. The soils are characterized by high clay content categorized mainly in the silt loam to
silty clay loam textures. Soils are mainly of different groups of Inceptisols, Alfisols, Ultisols, and
Entisols (Auanon et al., 2004). The availability of high clay content in the soils may indicate that the
erodibility of the soil tends to be less as far as its physical characteristic is concerned (Morgan, 1995).
3.5.
Vegetation and land use
In the study area, mainly five land use types can be classified namely forest, degraded forest,
cropland, grassland and orchard. The area has undergone heavy deforestation in the recent past. The
forest areas were turned into orchard and cropland (Figure 3-3). Recently reforestation program have
been implemented, some forest tree species such as teak, eucalyptus, gliricidia and leucaena were
planted. Tamarind trees are cultivated in the hill slope areas which are intercroped with maize,
soybeans and mungbeans. For grasslands, the upper catchment is dominated by grass species
Impecata cylindica (Saengthongpinit, 2004; Solomon, 2005).
Figure 3-3: Land use/cover changed from forest to orchard and cropland
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
4.
Material and methods
4.1.
Materials
Materials used in this study are:
Digital elevation model (DEM) resolution 5 meters from Land Development Department
(LDD) Thailand.
Ortho-photo mosaic of the study area from Land Development Department (LDD) Thailand
with scale 1: 4,000 resolution 0.5 meters.
Topographic maps of study area were obtained by with scale 1:50,000 from Land
Development Department (LDD) Thailand.
Satellite images: Landsat TM obtained on March 3, 2007 from Land Development
Department (LDD) Thailand, Landsat TM data obtained on November 2, 2000 from website
of Global Land Cover utility (http://glcf.umiacs.umd.edu/data/landsat/) and Landsat TM data
obtained on November 9, 1988 from ITC.
Geo-Pedological map from ITC (Solomon, 2005).
Table 4-1: Parameters input to the model and their sources
Input data in RMMF
Rainfall data :
Soil data:
Bulk density, BD
Cohesion of the surface soil, COH
Effective Hydrological Depth
of the soil, EHD
Soil moisture content at field capacity, MS
Soil detachability index, K
Sources of data
meteorological stations, previously
researches
calculate from soil texture and OM
from LDD
from LDD
from LDD
from LDD
Landforms: Slope steepness, Critical zone
extract from DEM
Land use/land cover:
Rainfall intercepted by the crop cover, A
Et/Eo
Cover management, C-factor
Canopy Cover, CC (0 -1)
Ground Cover, GC ( 0 -1)
Plant Height, PH
from LDD
from LDD
Derived from NDVI
estimated from field
estimated from field
estimated from field
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Table 4-2: Validation data and sources
Validation data
Validation data :
Ground truth
Erosion data
Sources of data
from field
from field
Softwares applied included:
Microsoft office 2003; Word, Excel, Visio and PowerPoint
SPSS v.15.0 for windows
ILWIS 3.3 Academic
ERDAS IMAGINE 9.1
Arc Pad 7.1
Arc GIS 9.2
SPAW (Soil Plant Atmosphere Water) model v.6.02.75
4.2.
Research methods
To achieve the objective of the study, the methods included image processing, digital terrain analysis,
erosion modeling and statistical analysis is applied. Since the study area is mountainous, the
topographic effects on satellite imagery need to be removed for improving land use/cover
classification. The various techniques were used and the result was compared for accuracy.
In order to model soil erosion, one of the crucial parameter required is C-factor. Remote sensing data
could be used to establish C-factor by correlating with NDVI. Comparison between various
techniques were made and the result was validated with C-factor obtained from field method using
statistical techniques such as adjust R2, coefficient of effectiveness (C.E.), mean error (M.E.) and root
mean square error (RMSE).
Soil properties including soil organic matter content and bulk density were also the crucial factor for
erosion. Laboratory analysis was carried out of the soil samples collected during fieldwork.
Assessment of soil properties in different land use/cover was investigated by using statistical analysis
such as one-way ANOVA.
Result of the erosion model was used to classify erosion prone areas in the watershed. In addition
critical zones derived from terrain parameters computed from digital elevation data were also used
20
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
for finding out whether they could be used to predict erosion prone areas or not. The research process
was divided into three steps as follows:
1. Pre fieldwork
In this stage the main work was focused on reviewed the relevant literature and looking for the
approach applied in data collection method. These included land use/cover assessment, C-factor
estimation, soil data collection and gully erosion investigation. Then material required for data
collection was acquired.
In order to obtain the approximate land use/cover, pre classification was done by using Landsat
satellite image 2000 together with High resolution Ortho-photo map. Then defining sampling area and
sampling design was done by using ArcGIS 9.2.
2. Fieldwork
The fieldwork was done during 3rd – 27th September 2007; reconnaissance survey was performed to
get the general overview of the study area. Identification of the location for sample points were done
and saved coordinates in Compaq iPAQ pocket pc by using Arcpad 7.1 software. Necessary data such
as ground truth as training samples and validation data for supervised classification were collected.
Soil samples were also collected. Simple field estimation followed by laboratory test was done.
Climatic and other secondary data like soil map, digital elevation model, soil parameters were also
collected from Land Development Department and other government offices such as Meteorological
station.
The reconnaissance survey showed that random sampling method could not be accomplished because
of the mountainous and rugged terrain together with heavy rainfall in that period. So, data was
collected only from areas which could be access by car or by foot. Based on representative land
use/cover and landscape, ground truth data and soil samples were collected respectively. During data
collection, some parameters for running the RMMF model were also estimated (Section 4.2.2.1).
Secondary data collecting were included in this fieldwork such as climatic data from meteorological
station. Other data were collected from Land Development Department (LDD), Bangkok. The data
collected from the fieldwork can be described as follows:
Ground truth data
In order to access the accuracy of land use/cover classification and estimated C-factor values,
stratified random sampling were used by selecting representative of each land use/cover type (strata)
that could access by car or by foot and then random points were defined into those strata. At each
observation points, ground truth information of land use/cover, fraction of land surface covered by
canopy (Fc), percentage of land area covered by surface cover (Sp) and plant height were collected
and recorded coordinates by using GPS. A total 263 samples were derived from five land use/cover
class. Each class contained 50 samples exclude forest came with 63 samples. Then the samples in
each class were divided in equally two groups including training and validating by using random
21
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
technique. Each group in each class contained 25 samples except forest had 38 in training and 25 in
validating.
Soil samples
Soil samples were collected together with other members of the ITC fieldwork team (the author, Mr.
Kanya Souksakul, Mr. Raju Sapkota and Mr. Yergalem Naga) at different slope positions (summit,
shoulder, foot slope) and different land use/cover types for characterizing soil properties such as
organic matter and for analyzing soil particle size distribution. A total 177 samples were collected in
plastic bags. Samples were air dried during the fieldwork periods; 100 g of each sample were packed
and labeled. These samples were brought to ITC soil and water laboratory for analysis. Finally only
126 samples within the study area were selected.
Erosion data
Erosion data was collected in locations having gully erosion by visual observation and taking their
coordinates using GPS receiver. Total number of erosion data was from 20 points.
Organic matter content values in Namchun watershed 2006
Organic matter content values in Namchun watershed 2006 were obtained from literature (Neguse,
2007). The total number that covered the study area was 63 values.
Input parameters in RMMF model
Climatic data and soil properties that used as input parameters in the RMMF model from
meteorological station and LDD respectively (Table 4-1).
3. Post fieldwork
This phase consisted of data processing and soil laboratory work followed by accuracy assessment of
classification of land use/cover, C-factor mapping, erosion model and critical zones calculation.
Beside these activities, statistical analysis was done to examine the difference of soil physical
properties between land use/cover classes, investigate soil loss quantities in different periods and
compare the critical zones with observation points of gully erosion.
The overall methodology is illustrated in Figure 4-1. Detail description of the methods is presented in
the following flow chart.
22
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Pre fieldwork
Preparation for fieldwork
Image classification
(Unsupervised)
Material and methods for
data collection
Gathering of available data
Land use/cover clustering map
Identification of target location for sampling
Fieldwork
Data collection
Field data
Gully erosion
points
Secondary data
Soil samples (shared)
Ground truth data
Erosion model
parameters
Land use class, Fc , Sp
Training
Climatic data
DEM , Satellite images
Validating
Post fieldwork
Data analysis
DEM
Rainfall
NDVI
Model parameters
Topographic removal
C-factor
Laboratory analysis
Image classification
(Supervised)
Slope
Organic
matter,
Bulk density
Training
Validation
Critical
zones
Erosion model
only 2007
Land use/cover
only 2007
maps
Soil loss
Validation
Analysis
Gully erosion
points
Compare
Analysis
Erosion prone
areas
Conclusion and
recommenndation
Figure 4-1: Flow chart of overall methodology
23
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
4.2.1.
Land use/cover classification in mountainous areas
Land use/cover maps were created for the years of 1988, 2000 and 2007. These maps were used for
investigating the difference of land use/cover areas in those periods. Based on fieldwork data that
collected in 2007, only land use/cover map 2007 was used as input map for erosion model.
Furthermore, it was very useful for analyzing soil properties such as compactness (bulk density) and
soil organic matters in different land use/cover.
4.2.1.1. Pre classification
Pre classification was done by applying unsupervised classification before getting to the field. The
reason was no prior knowledge about the study area. It could give the idea of approximately land
use/cover and used for sampling planning. This technique is used for classifying an image in a feature
space then analyses and groups the feature space vector into clusters. In this state can only find out the
different appearance in the image for separating in different classes. The user has to define the
maximum the maximum number of clusters, maximum cluster size and minimum distance. Then
computer use these information locates arbitrary vectors as the center points of the clusters. Next step
each pixel is assigned to a cluster by using minimum distance to cluster centroid decision rule. Once
all pixels have been labeled, recalculation of the cluster will takes place is repeated until the proper
clustered center are found and the pixels are labeled accordingly. The iteration stops when the cluster
centers do not change any more. Usually, computer designs to split the clusters when it larger than
maximum size, conversely, clusters are merged in to one if they are closer more than minimum
distance (Janssen et al., 2001). The result from this process is a raster map that each pixel has a class
belongs to the cluster.
4.2.1.2. Topography effect removal
Most of the study area was covered by the mountain therefore It was affected by the terrain such as
illumination variations. This terrain effect caused the severe consequents for land use/cover
classification. The first objective of this study was investigate the potential of image processing
techniques such as correction of topography induced constraints for classifying land use/cover in the
study area. The topography removal techniques were applied and compared. First, all Landsat TM
satellite images had original format in TIFF. They were exported to img format in ERDAS 9.1
software by using function layer stack. Then they were georeferenced and geocoded into the same
map projection of WGS84 datum. Landsat TM image November 9, 1988 had not map projection. On
the other hand, Landsat TM image November 2, 2000 and March 3, 2007 had UTM map projection in
WGS84 datum. A georeferencing technique was applied to Landsat TM image November 9, 1988 by
using Landsat TM March 3, 2007 as referenced image. After that geocoding step was taken place by
using nearest neighbor interpolation. All satellite images were sub mapped for covering only study
area. These operations were accomplished by ERDAS 9.1 software.
Next step, all satellites images were removed topography effect by using both topographic
normalization and sum normalization techniques. For topographic normalization technique, satellites
24
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
images were corrected illumination by using solar azimuth and solar elevation (derived from the
satellite header file) together with DEM by using ERDAS 9.1 software followed the Equation 1
(Colby, 1991) and 2 (Hodgson, 1994) below. Conversely, sum normalization was done in ILWIS 3.3
academic software. All satellite images were exported to mpr format in ILWIS. In this technique, each
band was normalized according to Equation 3 (Shrestha and Zinck, 2001) in map calculation function.
After topographic effect had been removed, color composite was taken place in 453 BGR for each
technique (Figure 4-2 (b), (c)). Then, satellite images in mpr format were exported to img format in
ERDAS 9.1 again for preparing final classification. The equations used in this state are showed as
following.
(a)
(b)
(c)
Figure 4-2: Lansat TM March 3, 2007 color composite in 453 BGR ;
(a) Original image ; (b) Topographic normalization; (c) Sum normalization
Equation 3: BvNormal λ =
Where:
( BvObserved λ cos(e))
(cos(ki ) × cos(ke))
BvNormal λ
= normalized brightness values
BvObserved λ
= observed brightness values
cos(e)
cos(i )
k
Equation 4:
Equation 5:
= cosine of the incidence angle
= cosine of the existence angle, or slope angle
= the empirically derived Minnaert constant
log( BvObserved λ cos(e)) = log BvNormal λ + k log(cos(i ) cos(e))
Bi Normal = (
Bi
) × 255
n
∑B
i =1
Where: Bi Normal
i
= Normalized individual band of any sensor
Bi
= Individual band of any sensor
i
= number of bands (from 1 to n bands)
= Compensation factor
255
25
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
4.2.1.3. Classification
For classification of land use/cover, supervised classification was applied in ERDAS 9.1 software. A
number of five land use/cover classes were considered including forest, degraded forest, agriculture,
grassland and orchard. The classification was divided in two phases. First, 138 training samples
(collected from fieldwork) were defined as training areas in images by assigning a limited number of
pixels of each class. In ERDAS 9.1, signature editor was created for defining the classes. The training
samples in feature space were shown in Figure 4-3. Then the satellite images that had removed
topography effect were added. The relationship between image spectral characteristics and training
samples information were considered. By using AOI (area of interest) tools, the boundaries and
number of pixels for each class were added into signature editor. After that the decision making phase
was taken place, maximum likelihood algorithm was selected because of the advantages of
considering the centre of the clusters together with shape, size and orientation. Finally land use/cover
maps in periods 1988, 2000 and 2007 were classified. Each period was consisted of two maps from
both topographic removal techniques.
Figure 4-3: Feature space plot of training samples
4.2.1.4. Accuracy assessment
Accuracy assessment was applied when classification was finished. The 125 validated data from
fieldwork were used to validate the results of classification through confusion matrix (error matrix).
Moreover, it was used to compare the potential from both topographic effect removal techniques. In
ERDAS 9.1, accuracy assessment function was selected. Validated points with coordinates and land
use classes in text format were imported as true classes. Overall accuracy was computed from
correctly classified pixel divided by total number of pixels checked. The topographic effect removal
26
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
technique that obtained higher accuracy of land use/cover classification was justified to be the
suitable technique. Land use/cover maps from that technique were used for further analysis.
4.2.2.
C-factor mapping
Another objective of this study is to find out the possibility of estimating C-factor from NDVI. For
this purpose C-factor map was derived only for the year 2007 because of validation data. For C-factor
mapping using NDVI, three techniques were applied: De Jong’s equation, Van der Knijff’s equation
and regression method using field assessment of C factor and NDVI (Section 4.2.2.2). The generated
C-factor maps were validated using field data. Statistical analysis was applied such as adjusted R2,
coefficient of effectiveness, mean error and Root mean square error (RMSE).
4.2.2.1. Field method for deriving C factor
The cover-management factor (C-factor) represents the effects of vegetation, management, and
erosion-control practices on soil loss (Toy and Foster, 1998). The C-factor value was estimated using
sub factor from the field together with using some sub factor values from literature, as explained
below. In total 138 samples was collected as training samples and 125 samples were collected for
validation. The C factor estimation is based on sub factor including canopy cover, surface cover
(ground cover), prior land use (PLU) and surface roughness by using RUSLE method (Renard, 1997)
as described below:
Canopy cover and surface cover
Distance between trees
Canopy cover and surface cover were estimated from field in term of fraction of land surface covered
by canopy (Fc) and percentage of land area covered by surface cover (Sp) respectively. Measuring
tape was used to measure the distance between trees. Fc was estimated by using a tape laid on the
ground at the base of the plants and radius measurement of the upper canopy (Figure 4-4).
Combination of one quarter area of each four trees is equal one circle area that gave area of canopy.
Then calculate whole area by using distance between trees. Finally, Fc can was calculated by area of
canopy divide by whole area. For short crops, the radius could be directly measured
Distance between trees
Figure 4-4: Estimation Fc from field
27
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
In the case of Sp, visual interpretation was used to estimate (Figure 4-5) Sp within the Fc estimation
area. For open areas, Sp was estimated by using tape measured exact area of surface cover such as 2 x
2 m2 then visual interpret within that small area.
(a)
(b)
Figure 4-5: Estimation Sp from field; (a) Sp around 75% and (b) Sp around 35%
Plant height was estimated using a measuring tape and clinometer. For short crop, plant height was
measured directly using a measuring tape. But taller crop like tamarind, a clinometer was used to
measure the height. The angles at the top (A1) and bottom (A2) of the plant were measured at a
known distance in this study 5 m was used. These equations were used to derive two heights as
follows:
Where:
Equation 6:
H1 = tan( A1 ) × d
Equation 7:
H 2 = tan( A2 ) × d
H1 , H 2 = height of the top angle and bottom angle
A1 , A2
= top angle and bottom angle
d
= estimation distance
The summation of H1 and H 2 gave the plant height values.
Canopy cover sub factor
Canopy cover was obtained from estimation based on vegetation in land use classes. The dominant
crops in study area were maize, mungbean and soya and orchard based on Tamarind. For forest and
degraded forest, canopy cover was major estimated from teak and bamboo respectively. Natural grass
was considered from grassland. In the field, fraction of land surface covered by canopy (Fc) were
collected and used for calculation. The equation below was employed to derive canopy cover
(Wischmeier and Smith, 1978).
Equation 8:
Where:
28
CC
Fc
H
CC = Fc × e( −0.1× H ) H
=
=
=
canopy cover sub factor (0-1)
fraction of land surface covered by canopy
distance that raindrops fall after striking the canopy (mm.)
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Canopy values that derived from equation above were averaged by different land use/cover as showed
in the Table 4-3 below:
Table 4-3: Canopy cover sub factor in different land use/cover
Land use class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Height (m)
Canopy cover (CC)
19.91
7.52
0.85
0.77
4.28
0.79
0.59
0.51
0.37
0.63
Surface cover sub factor
For second sub factor, surface cover was derived from field data on percentage of land area covered
by surface cover (Sp). The other variables such as random roughness (RU) and an empirical
coefficient (b) were obtained from literature (Renard, 1997). These variables were based on different
types of vegetation and erosional condition. For calculation, surface cover sub factor values were
obtained by equation as follow:
SC = e
Equation 9:
Where:
( − b× Sp×(
0.24 0.08
) )
Ru
SC
b
Sp
=
=
=
surface-cover sub factor
empirical coefficient
percentage of land area covered by surface cover
Ru
=
random surface roughness
Derived surface cover sub factor values were shown in Table 4-4 separated by different land
use/cover.
Table 4-4: Surface cover sub factor in different land use/cover
Land use class
Ru
b
Surface cover (SC)
Forest
Degraded Forest
Agriculture
Grassland
Orchard
0.40
0.25
0.44
0.25
0.34
0.050
0.025
0.035
0.045
0.025
0.26
0.17
0.34
0.12
0.15
Surface roughness sub factor
Soil surface roughness describes the micro variation in the surface elevation across a field resulting
mainly from tillage practices and soil texture (Moreno et al., 2008). This factor controlled most of the
hydraulic and erosion processes and rapid changed caused by the tillage operations, followed by a
slow evolution of the soil structures due to rainfall (Taconet and Ciarletti, 2007). Surface roughness
29
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
sub factor was shown in Table 4-5 and estimated in turn of surface random roughness (RU) as
follows:
Equation 10:
SR
Ru
Where:
SR = e( −0.66×( Ru −0.24))
=
surface roughness sub factor
=
random surface roughness
Table 4-5: Surface roughness sub factor in different land use/cover
Land use class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Ru
Surface roughness (SR)
0.40
0.25
0.44
0.25
0.34
0.90
0.99
0.87
0.99
0.94
Deriving C-factor value
The calculation of C-factor value was done by multiply sub factors together. One more sub factor was
prior land use (PLU). This factor was adapted from the RUSLE guideline (Toy and Foster, 1998).
This factor involved with tillage operation or other soil disturbance that makes the soil more erodible
because of less consolidate and unstable aggregate. For forest and degraded forest, value 0.5 was
given because they were sometime disturbed by human activity such as machinery tillage operation in
degraded forest or forest fire (Toy and Foster, 1998). Conversely, agriculture areas, grassland and
orchard were assigned value 1(Shi et al., 2004). The crop residue in agriculture areas was removed or
burn. Forest fire in dry season frequently occurred in grassland. For orchard, farmer used fertilized or
removed grass. The equation below was applied to derived C-factor values.
Equation 11:
C = PLU × CC × SC × SR
4.2.2.2. Deriving C factor using satellite data (NDVI)
Normalized Difference Vegetation Index (NDVI)
NDVI map was generated from landsat March 3, 2007 in ERDAS 9.1 by spectral enhancement with
indice function. Then this NDVI map was exported to ILWIS 3.3 academic software. The NDVI map
was masked with the boundary of the study area by using map calculation function. Three techniques
were applied to generate C factor using NDVI as follows:
Generating C-factor using De Jong’s (1994) technique
In his PhD thesis, De Jong (1994) reported the used of vegetation indices to extract vegetation
parameters for erosion model. By using field data 33 plots, statistical analysis was done and the result
30
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
showed linear relationship between NDVI and USLE C-factor. The correlation showed negative value
-0.64. In ILWIS 3.3 academic software, map calculation was implied by entering his formula and
generate C-factor map with NDVI map as follows.
Equation 12:
Where:
C = 0.431 − (0.805* NDVI )
C
= C-factor
NDVI = Normalized Difference Vegetation Index
Generating C-factor using Van der Knijff (1999) equation
Van der Knijff (1999) described in his report about soil erosion risk assessment in Italy that estimated
C-factor value derived from linear regression with NDVI quite low. As the same method above, Cfactor map in this state was done by map calculation function in ILWIS 3.3 academic software as
follows:
Equation 13:
Where:
C=e
−α
( NDVI )
( β − NDVI )
C
= C-factor
NDVI = Normalized Difference Vegetation Index
α , β = Parameters that determine the shape of the NDVI-C curve
The value 2, 1 were given to α , β respectively (Van der Knijff et al., 1999).
Generating C-factor from regression equation based on field assessment of C factor using 138
training values and NDVI
In this technique NDVI map of 2007 was crossed with 138 C-factor training values assessed using
field technique. These values were exported to SPSS version 15 for windows format. By using
regression function and curve estimation tool, the relationship was formed and equation was obtained
(Appendix 4(a)). Then, the equation was applied in map calculation function in ILWIS 3.3 to create
C-factor map as follows:
Equation 14: C = 0.227 × e( −7.337× NDVI )
Where:
C
= C-factor
NDVI = Normalized Difference Vegetation Index
4.2.2.3. Validating C-factor mapping based on NDVI
The reliability of generated C-factor map was evaluated by comparing predicted C-factor values from
NDVI with 125 validation data. In ILWIS 3.3 academic software, predicted C-factor value from each
generated techniques (Section 4.2.2.2 above) were crossed with 125 validated C-factor data. Then
31
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
these values were exported to SPSS version 15 for windows software. The regression analysis with
linear estimation was applied to obtain adjusted R2 between predicted values from each generated
technique and validated values. Moreover, coefficient of effectiveness (Morgan, 2005), mean error
(M.E.) and root mean square error (RMSE) were also calculated. The C-factor map that obtained
higher reliability was used as input parameter for calculation transport capacity in the erosion model.
The statistical techniques that used in this state were shown as bellowed:
n
Equation 15: C.E. =
n
∑ ( Xvali − Xval )2 − ∑ ( Xpi − Xval )2
i =1
i =1
n
∑ ( Xval
i
i =1
Where:
− Xval )2
C .E .
=
Xvali
= validated C-factor values
Xval
= mean of validated C-factor values
Xpi
= Predicted C-factor values
coefficient of effectiveness
If C.E. values closer to 1 indicate better prediction.
n
Equation 16:
Where:
M .E . =
∑ ( Xp − Xval )
i
i =1
i
n
M .E .
Xpi
= mean error
Xvali
= validated C-factor values
n
= total numbers of validated C-factor values
= predicted C-factor values
If M.E. values closer to 0 indicate better prediction.
n
Equation 17:
Where:
RMSE =
∑ ( Xp − Xval )
i =1
i
2
i
n −1
RMSE = root mean square error
Xpi
= predicted C-factor values
Xvali
= validated C-factor values
n
= total numbers of validated C-factor values
Smaller RMSE values indicate better prediction more than higher values.
32
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
4.2.3.
Erosion assessment
To estimate soil erosion in this study, RMMF model was selected. From the original equations
(Morgan, 1995), script was created in ILWIS 3.3 academic software. Soil parameters were added in
the attribute table of geo pedological map. On the other hand, plant parameters were added in the land
use/cover attribute table. These parameters were used to create attribute maps subsequently run the
model by using the script. Input parameters of RMMF model are shown in Table 4-1.
4.2.3.1. Estimation of rainfall kinetic energy
Due to the availability of data from only one meteorological station located at Lom Sak, it was not
possible to create rainfall map of the study area. Because of the reason the average annual rainfall data
was used in the erosion model. The data was made available by ITC. The data came from 11
meteorological stations in Phetchabun province. These rainfall data and elevation at meteorological
station were correlated to investigate the variations of rainfall due to elevation. After that regression
technique (Figure 4-6) was applied to obtain the equation, which help to predict rainfall map
(Appendix 4(b)).
Table 4-6: Annual rainfall at various elevations obtained from ITC
Station
Lom Sak
Lom Khao
Khao Kao
Na Sum
Hin Hao
Nam Ko
Lao Ya
Dong Khwang
Khao Kho
Om Kong
Na Ngua
Annual rainfall
Elevation
X-coordinate
Y-coordinate
1089.6
1050.5
1556.1
972.0
837.0
1108.0
1742.0
843.0
1595.0
1045.0
946.0
140
160
720
180
170
170
720
150
920
140
140
740000
738000
715000
737200
736300
732400
716700
732500
713500
730000
729000
1857000
1868000
1854000
1880900
1873700
1857700
1854600
1848400
1840400
1837000
1827700
Figure 4-6: The relationship between annual rainfall and elevation
33
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
The equation for prediction amount of rainfall in study area was showed as follow:
Equation 18:
Where:
R
R = 0.9803 × (elevation) + 840.48
=
Amount of rainfall
The above formula was applied in map calculation function in ILWIS 3.3 software. By using DEM,
rainfall amount whole area of Namchun watershed was predicted. Then rainfall map was classified to
15 classes every 100 meters.
After obtained rainfall map, then rainfall energy was calculated by using the partitioned rainfall after
interception together with energy of the leaf drainage. First the model computed the proportion of
rainfall amount that reach the ground surface after allowing for rainfall interception to derive effective
rainfall. Effective rainfall was calculated by multiply rainfall intercept value range 0 to 1 that got from
literature (Morgan, 1995). Effective rainfall was calculated as following equation:
Equation 19:
Where:
ER
R
A
ER = R × A
= effective rainfall (mm.)
= annual rainfall (mm.)
= rainfall interception (0-1)
Then effective rainfall was divided to two parts, First, rainfall that reached the ground surface after
being intercepted by plant canopy as leaf drainage, conversely, second part that rainfall reached the
ground surface without interception. Plant canopy was added in attribute table of land use/cover for
calculation leaf drainage effective rainfall; the values came from estimation from the fieldwork and
average them per land use/cover classes. The rest of effective rainfall was direct through fall.
Leaf drainage was calculated by using equation below:
Equation 20:
Where:
LD
CC
LD = ER × CC
= leaf drainage (mm.)
= plant canopy (%)
Direct through fall then was calculated by removing leaf drainage from effective rainfall as equation
below:
Equation 21:
Where:
34
DT
DT = ER − LD
= direct through fall (mm.)
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Then kinetic energy was calculated by distributed for effective rainfall of leaf drainage and direct
through fall. Kinetic energy of leaf drainage was a function of plant height meanwhile kinetic energy
of direct through fall was a function of rainfall intensity.
Plant height value estimated from the fieldwork, the average values for each land use/cover class were
added into the land use/cover table. For intensity, the value 25 mm/hr was the reasonable for tropical
countries supported by (Morgan, 2001). Finally two kinetic energy maps were combined together to
produce total kinetic energy map.
Kinetic energy of leaf drainage was calculated by using equation as follow:
Equation 22:
Where:
KE ( LD)
PH
KE ( LD) = LD × (1.58 × PH 0.5 ) − 5.87
= kinetic energy of leaf drainage (j/ m2)
= plant height (m.)
Kinetic energy of direct through fall was computed as follow:
Equation 23:
Where:
KE ( DT )
I
KE ( DT ) = DT × (11.9 + 8.7 log10 I )
= kinetic energy of direct through fall (j/ m2)
= rainfall intensity
Finally, the total kinetic energy ( KETotal ;j/ m2) was obtained from:
Equation 24:
KETotal = KE ( DT ) + KE ( LD)
4.2.3.2. Estimation of runoff
The annual runoff was calculated by using three factors including soil moisture storage capacity,
annual rainfall and mean rainy days. For soil moisture capacity, the calculation was in turn a function
of bulk density, effective hydrological depth, ratio of actual to potential evapotranspiration and soil
moisture content at field capacity.
The parameters to estimate soil moisture storage capacity including soil moisture content at field
capacity (MS), effective hydrological depth (EHD) and ratio of actual to potential evapotranspiration
(Et / Eo) were obtained from literature (Morgan, 1995; Morgan, 2001). Conversely, bulk density was
calculated from soil texture and organic matter that analyzed in ITC soil and water laboratory by using
SPAW software (Section 4.3.2.2), then the average values for each pedological class were added into
the pedological attribute table. The rainfall map that obtained from above and rainy days was used to
calculated mean rainy days. By following script that created in ILWIS 3.3 academic software, these
parameters were automatically generated and calculated annual runoff.
35
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Following equation was used to calculate soil moisture storage capacity:
Equation 25:
Where:
Rc = 1000 × MS × BD × EHD × ( Et / Eo )
Rc
= soil moisture storage capacity (mm.)
MS
BD
EHD
Et / Eo
= soil moisture content at field capacity (%ww.)
= bulk density (g/ cm3)
= effective hydrological depth (m.)
= ratio of actual to potential evapotranspiration
For mean rainy days, it was calculated by using equation below:
Equation 26:
Where:
Ro =
R
Rn
Ro
= mean rainy days
R
Rn
= annual rainfall (mm.)
= number of rainy days in a year
To estimate runoff, Annual runoff was computed by using equation below:
Equation 27:
Where:
Q
Q = R×e
(
− Rc
)
Ro
= annual runoff (mm.)
4.2.3.3. Estimation soil particle detachment by raindrop and runoff
The calculation of soil particle detachment was divided in two parts. First, the model considered soil
particle detachment by raindrop impact. Second, soil particle detachment by runoff was taken to
account.
For soil particle detachment by raindrop, total kinetic energy and soil erodibility parameter were
applied following Equation 28 below. Soil erodibility values were obtained from literature (Morgan,
2001). Combination of both parameters, soil particle detachment by raindrop map was generated
following the script. The other one, soil particle detachment by runoff, was calculated by various
parameters such as annual runoff, slope steepness extracted from DEM, percentage of ground cover
estimated and calculated from field and soil resistance in turn of cohesion derived from literature
(Morgan, 2001). In the end, total soil particle detachment was computed by summation of both soil
particle detachment maps.
36
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Soil particle detachment by raindrop was a function of kinetic energy of effective rainfall and soil
erodibility. The calculation was done as follow:
Equation 28:
Where:
F
K
F = K × KETotal × 10−3
= soil particle detachment by raindrop impact (kg/ m2)
= soil erodibility (g/j)
In this model assumed that soil detachment by runoff can only appear where soil was not protected by
ground cover.
The calculation of soil resistance was showed as follow:
Equation 29:
Where:
Z
COH
Z=
1
(0.5 × COH )
= soil resistance
= surface cohesion (kpa)
Soil detachment by runoff was calculated by applied equation below:
Equation 30:
Where:
H
S
GC
H = Z × Q1.5 × sin S × (1 − GC ) × 10−3
= soil particle detachment by runoff (kg/ m2)
= slope steepness (degree)
= ground cover (%)
Total particle detachment ( D ; kg/m2) was finally calculated as a sum of both soil particle detachment
by raindrop and runoff. The equation was shown as below:
Equation 31:
D=F+H
4.2.3.4. Estimation of transport capacity of runoff
The transport capacity was estimated by using runoff, surface cover factor and slope. Parameters to
estimate transport capacity including surface cover management (C-factor) derived from regression
equation with NDVI, slope steepness extracted from DEM and annual runoff were computed follow
the script. The transport capacity map finally was generated. The equation was showed as below:
Equation 32:
Where:
TC
C
TC = C × Q 2 × sin S ×10 −3
= transport capacity (kg/ m2)
= surface cover factor (C-factor)
37
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
4.2.3.5. Estimation of erosion in turn of soil loss rate
The last calculation step of RMMF model was prediction annual soil loss. Soil loss rate was estimated
by comparing between total soil particle detachment and transport capacity of runoff. The minimum
function was applied following the last step in the script. For the minimum between total soil particle
detachment map and transport capacity map was created to soil loss map. Soil loss estimation was
computed by using equation as follow:
Equation 33:
Sl
Where:
Sl = min( D, TC )
= annual soil loss rate (kg/ m2)
4.2.3.6. Analysis of model result
The model results were analyzed by evaluating descriptive statistic such as mean and standard
deviation was used. Aggregation function in ILWIS 3.3 academic software was applied to average the
soil loss in to different land use/cover.
4.2.4.
Assessing create critical zones for ephemeral gully incision
DEM of the study area available in 225 map sheets were get mosaic in ERDAS 9.1 from which subset
of the study area was created. The DEM was later exported to ILWIS 3.3 format for further analysis.
Terrain parameters; slope gradient and slope aspect (Figure 4-7 (a) and (b)) were extracted by using
filter function and hydro processing function in ILWIS. Finally slope, flow direction, flow
accumulation and catchment area together with flow width were used to define ephemeral gully
erosion by using critical zones concept. The objective of creating critical zones was to compare them
with the erosion prone areas that classified from the results of the erosion model.
(a)
(b)
Figure 4-7: Slope map (a) and Aspect map (b)
38
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Hydrological parameters were used to derive critical zones for ephemeral gulley incision as suggested
by Jetten et al. (2006). By using DEM hydro processing function in ILWIS 3.3 academic software,
flow determination such as fill sink, flow direction and flow accumulation were obtained. Then flow
accumulation was used to calculate drainage network and catchment area (Figure 4-8 (a) and (b) by
using network and catchment extraction function. Sensitive areas after that were derived from these
parameters. The algorithm that use in this study showed as following equation (Desmet and Govers,
1997).
Equation 34:
Where:
A
Fc = S × ( )0.4 > 0.72
w
Fc
= critical threshold
S
A
w
= slope (m/m)
= upstream area (m2)
= flow width (m)
(a)
(b)
Figure 4-8: Drainage network (a) and catchment (b) extraction
Critical zones were classified in two classes; gully erosion and no gully erosion by using threshold
0.72. The overall method for critical zones creation is shown in Figure 4-9 below.
Figure 4-9: Critical zones creation
39
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
4.3.
Data analysis
4.3.1.
Laboratory analysis
Laboratory analysis of the soil samples was accomplished in the ITC soil and water laboratory (Figure
4-10). The objectives for this analysis were determination of soil organic matter and particle sizes
distribution. Preparing soil samples was done by sieving soil samples 100 g pass 2 mm sieve. Weight
approximate 20 g into 1 l beaker to preparing for particle size distribution analysis (Pipette method).
Meanwhile, weight approximate 5g and sieve them pass 0.25 mm sieve, then weight 1 g (with
accuracy 0.01 g) into a 500 ml flask for analyzing % organic matter (Walkley-black method).
(a)
(b)
(c)
Figure 4-10: Laboratory analysis in ITC; (a) preparing soil samples, (b) walkley - black method,
(c) FAO pipette method
4.3.1.1. Walkley-black method
In this method the known volume mixture of potassium dichromate and sulfuric acid at 125 oC was
used to oxidize soil organic matter. After oxidation, titration of residue dichromate was taken place by
using Barium diphenylamine sulphonate as indicator against ferrous sulphate. The soil organic matter
was calculated by using different between the total volume of dichromate and residue volume after
titration. For calculation, the equation used in this step was shown as follow (Van Reeuwijk, 2002):
%C = M ×
Equation 35:
Where:
40
(V 1 − V 2)
× 0.39 × mcf
S
M
V1
V2
S
=
=
=
=
0.39
mcf
= 3 × 10−3 × 100% × 1.3 ( 3 = equivalent weight of carbon)
molarity of ferrous sulphate solution (from blank titration)
ml ferrous sulphate solution required for blank
ml ferrous silphate solution required for sample
weight of air-dry sample in gram
= moisture correction factor
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Conversion of the % carbon to % organic matter was done by multiply empirical factor 2 with %C
Equation 36:
%OM = 2 × %C
4.3.1.2. FAO pipette method
For particle size distribution analysis, pipette method was done. Important part in this analysis was the
pretreatment of the samples aimed at complete dispersion of the primary particles. Consequently,
organic matter needs to be removed. Oxidation of organic matter was done by using
Hidrogenperoxide and water then let them cool down in cold water bath for one night. Next day,
beakers were place on hot water bath (80 oC) and adding 5 to 10 ml of Hidrogenperoxide until
decomposition was complete. Then to remove Hidrogenperoxide, water was added (300 ml) and boil
for 1 hour and allowed them to cool down and settled in beaker. Then siphon off and transferred
sediment to 1 l polythene bottle and added 20 ml of dispersing agent, made the volume to 400 ml
with water and caped the bottle. Next, the bottles were shaken for 16 hrs on an end-over-end shaker at
30 rpm. For determination of sand fractions, sieve suspension through 50 um sieve to the cylinder was
done then added water until reached 1 l, washed sand fraction remained on the sieve into porcelain
dishes. In the method of determination the fractions of silt and clay, shook cylinders and immediately
pipette 20 ml and transfer aliquot the tarred moisture tins for separating silt fractions less than 50 µ m .
After that shook cylinders again and wait for 5 minutes pipette 20 ml again for silt fractions less than
20 µ m and transfer aliquot in the same way as above step. The last pipette was done after shook
cylinders waiting for 5 and a half hrs. Pipette last 20 ml for clay fractions and then transfer aliquot.
After finished pipette method, sand in porcelain dishes and aliquot of silt and clay were dried in oven
overnight. Next day, sand, silt and clay fractions were weighted with 0.001 g accuracy (Van
Reeuwijk, 2002).
The basis of calculations was the oven-dry sample weight after all treatments. It was obtained by these
formulas (Van Reeuwijk, 2002):
Equation 37:
Clay (< 2 µ m) = ( H × 50) − ( Z × 50)
( wt.K )
Equation 38:
Silt (2 − 20 µ m) = (G × 50) − ( Z × 50) − K
( wt.L)
Equation 39:
Silt (20 − 50 µ m) = ( F × 50) − ( Z × 50) − K − L ( wt.M )
Equation 40:
Sand (> 50 µ m) = A
Equation 41:
Sample weight = K + L + M + N (all weight in gram)
( wt.M )
41
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Where:
A
F
=
=
weight of sand fractions
weight 20 ml pipette aliquot of fraction < 50 µ m
G
=
weight 20 ml pipette aliquot of fraction < 20 µ m
H
=
weight 20 ml pipette aliquot of fraction < 2 µ m
Z
=
weight 20 ml pipette aliquot of blank
The proportion amounts of the fractions were calculated by using equation below (Van Reeuwijk,
2002):
4.3.2.
K
×100
Sampleweight
Equation 42:
%clay (< 2 µ m) =
Equation 43:
% silt (2 − 20 µ m) =
Equation 44:
% silt (20 − 50 µ m) =
Equation 45:
% sand (> 50 µ m) =
L
× 100
Sampleweight
M
× 100
Sampleweight
A
×100
Sampleweight
Soil properties analysis
Before analysis, 166 soil organic matter values from laboratory analysis were combined with 63
organic matter values from the dataset 2006 (Neguse, 2007).
4.3.2.1. Soil texture by using USDA soil texture triangle
Following USDA system, soil is separated in three major particle size groups including sand silt and
clay. Clay particles are the smallest size less than 2 µ m meanwhile silt is a medium size in between 2
and 50 µ m . The largest particle is sand, that their sizes in between 50 and 2,000 µ m . Soil texture
refers to proportion of sand, silt and clay relatively found in the same soil sample (Kruthkul et al.,
2001). One of the results from laboratory analysis was percentage of soil fractions. These fractions
were used to calculated soil texture by using USDA soil texture triangle (Figure 4-11).
Figure 4-11: USDA soil texture triangle
Source: http://wps.prenhall.com/wps/media/objects/1411/1445480/FG12_15_wo_arrows.JPG
42
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
On each corner of triangle showed the major texture, the top was clay, lower right was silt and lower
left was sand. In each side of triangle are scaled for the percentage of sand, silt and clay. For clay,
percentage increasing from bottom to top on the left side of triangle and read from left to right across
the triangle. Sand showed the increasing from right to left along the base and read the percentage from
lower right to upper left of the triangle. The last one, silt was increasing from top to bottom and read
the percentage from upper right to lower left of the triangle.
4.3.2.2. Bulk density from soil texture and organic matter by using SPAW software
Bulk density was obtained from SPAW (Soil Plant Atmosphere Water) model v.6.02.75 (Saxton and
Willey, 2007). The parameters that used for calculation were soil organic matter contents and soil
texture. Soil organic matter contents and soil texture were derived from laboratory analysis (Appendix
2). The SPAW software was showed in Figure 4-12.
Figure 4-12: SPAW model
4.3.3.
Statistical analysis
To assess the objective that land use/cover change effect on soil erosion, the soil physical properties
such as bulk density and soil organic matter were investigated in different land use/cover type.
Descriptive statistical approach was employed including arithmetic mean of soil properties per land
use/cover and also standard deviation as well. From table calculation operation in ILWIS, aggregate
function was applied to derive average values of those soil properties in different land use/cover.
After that significant test was applied by using One - way ANOVA function to determine whether
there was the difference between land use/cover or not. If the significant less than 0.05 then a post hoc
approach was done by using Turkey’s HSD technique to examine the significance difference among
the land use/cover type (Appendix 5(a) and (b)). The results of statistical analysis were present in
Chapter 5.4.
43
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
For comparison between the results from soil erosion model and critical zones, a descriptive statistic
such as percentage was applied to compare the areas of erosion prone areas with the areas of critical
zones for gully erosion formation. The results are presented in Chapter 5.6.
44
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
5.
Results and discussions
The main objective of this study is to assess erosion prone areas in inaccessible mountain areas by
applying image processing and digital terrain analysis techniques. The topographic removal
techniques were implemented to improve spectral reflectance of Landsat satellite images obtained in
1988, 2000 and 2007. Image classification was applied to generate land use/cover map. Trend of land
use/cover change was also investigated to know the conversion between land use/cover classes in
those periods. For assessing soil erosion, the erosion model played important role. An assessment of
the relationship between NDVI and C-factor was done to see whether NDVI could be used as
estimator of C-factor in the study area or not. Land use/cover map 2007 was used to predict soil loss
rate and classified erosion prone areas. It was also used to analysis bulk density and soil organic
matter distribution pattern in different land use/cover classes. The results of analysis together with
trend of land use/cover change are reflected in soil erosion. Digital terrain analysis was carried out to
define critical zones for gully formation which was then compared with the erosion prone areas for
confirmation. In this chapter, results are summarized and discussed as follows:
5.1.
Land use classification
Two techniques, topographic normalization and sum normalization, were applied to Landsat satellite
images obtained in 1988, 2000 and 2007. The topographic normalization technique uses solar azimuth
and solar elevation, available in the header file of the image data. In addition to this, digital elevation
data (DEM) is also used. In sum normalization technique, each satellite band is normalized by
summation of all of the bands. The land use/cover classification result after removal of topographic
effect using topographic normalization technique gave higher classification accuracy as compared to
the classification results after performing sum normalization (Section 5.1.2).
However, affect from seasonal influence such as cloud remains in the image. This affects the accuracy
of land use/cover classification. It was not easy to classify orchard from degraded forest because in
some areas the canopy of orchard and degraded forest reflected the same spectral characteristic. In
some cases, some areas of orchard and agriculture areas were mixed because the farmers planted
crops in between the fruit trees. Furthermore, the newly plantation forest in earlier state was easily
classified as degraded forest.
5.1.1.
Land use/cover classification result
Land use/cover classification of 1988, 2000 and 2007 were done by using supervised classification
with the maximum likelihood algorithm in ERDAS 9.1 software. Training samples were separated
from validated data (Section 4.2). The land use/cover maps were classified in five classes including
forest, degraded forest, agriculture, grassland and orchard with total areas 6,658 hectares. In Figure 51 (a), (b) and (c) showed land use/cover classification maps in periods of 1988, 2000 and 2007
45
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
respectively. The comparison of the different land use/cover between those periods is shown in Figure
5-2 as follows.
(a)
(b)
Legend
(c)
Figure 5-1: Land use/cover classification maps; (a) 1988, (b) 2000 and (c) 2007
Area in percent
Area in percent in different land use 1988 - 2007
45
40
35
30
25
20
15
10
5
0
A rea1988
A rea2000
A rea2007
F
DF
A
G
O
Land us e
Figure 5-2: Area in percent in different land use/cover 1988 – 2007
46
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Table 5-1: Comparison the areas of land use/cover in 1988, 2000 and 2007
Land use/cover
class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Total
Area in 1988
Area in 2000
Area in 2007
hectares
percent
hectares
percent
hectares
percent
2699
2663
740
496
60
40.54
40.00
11.11
7.45
0.90
1411
2551
1107
1081
508
21.20
38.33
16.62
16.23
7.62
857
2267
1686
778
1070
12.87
34.04
25.32
11.70
16.07
6658
100
6658
100
6658
100
According to Table 5-1, land use classification map 1988 showed the study area was covered by
natural forest (forest and degraded forest) more than 80% followed by agriculture (11.11%), grassland
(7.45%) and orchard (0.90%) respectively. In 2000, the area of forest decreasing to 21.20% and
degraded forest 38.33% meanwhile agriculture areas increased to 16.62% followed by grassland
7.45% and orchard 7.62%. The land use/cover 2007 showed forest area continue decreasing to
12.87%, degraded forest to 34.04%. Agriculture areas and orchard areas still increased to 25.32% and
16.07% respectively.
5.1.2.
Accuracy assessment
Based on ground truth data was collected in 2007, accuracy assessment was applied to the results of
land use/cover classification 2007 after removing topographic effect. With 125 validated data
collected from fieldwork (Chapter 4.2), the accuracy was calculated for the land use/cover
classification maps from two different techniques for correcting topographic effect. Then the accuracy
was compared for selecting the representative of land use/cover map. The Table 5-2 showed the
comparison of accuracy between both techniques. Because of higher accuracy, the topographic
normalization was the appropriate technique for remove effect from topography. Accuracy of land
use/cover classification map after applying topographic normalization was shown in error matrix in
Table 5-3 as following.
Table 5-2: Comparison of the accuracy between topographic normalization and sum normalization
Topographic normalization
Sum normalization
Classification accuracy
without correcting
topographic effects (%)
74.4
69.1
67.3
Classification accuracy with removal of topography effect (%)
Period
2007
The land use/cover classification without correcting topography effects gave the lowest accuracy
(67.3%). The accuracy could be improved by applying topography effect removal techniques that gave
69.1% and 74.4% respectively. The similar finding was reported by Shrestha and Zinck (2001) that
reducing the topographic effect could improve the classification accuracy. Topographic normalization
technique gave the highest results because it used solar azimuth and solar elevation together with
47
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
DEM to correct the illumination variants meanwhile sum normalization technique only normalized
each satellite image band with sum of illumination and multiply with the constant.
Table 5-3: Accuracy assessment of land use/cover classification 2007
Table 5-4: Crop calendar for cultivation of Phetchabun Province in 2007
Where:
=
=
=
=
the whole period of vegetations and crops growing
starting the planting period (young plants)
the crops and vegetations grow up
the harvesting period of crops and vegetations
Source: Provincial agriculture department of Phetchabun Province, Thailand.
48
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
The overall accuracy for land use/cover map 2007 after applying topographic normalization was
74.4%. The accuracy of classification was acceptable however it could not get higher accuracy
because of some limitations.
The main limitation of the classification was the seasonal influence. Land use/cover map 2007 was
classified from Landsat March 3, 2007 that appeared some clouds in the image. Classification was
done by using spectral reflectance that cloud effect caused misclassified. Furthermore, it was different
season from fieldwork period. Some areas, crops like maize were harvested already in the end of
March and appeared like grassland in September as showed in crop calendar (Table 5-4). Moreover
canopy cover of orchard areas gave the same reflectance like forest areas. Plantation forest areas in
earlier stages also gave the same reflectance as degraded forest.
5.1.3.
Trend of land use/cover change from 1988 to 2007
From the results of classification (Table 5-5) in the periods 1988 to 2007, forest and degrades forest
areas were decreasing especially forest areas loss around 1,842 hectares from 2,699 hectares within 19
years. Degraded forest areas also decreased from 2,663 hectares to 2,267 hectares. Conversely, in
agriculture areas and orchard that increased 946 and 1,010 hectares respectively. These were an
evident that deforestation had occurred in this area. Most of the area has rugged terrains that mean the
deforestation also occurred on hill slope areas, which was replaced by agriculture. This led to soil
erosion problem and severe disaster that caused heavy landslide and flooding in 2001. During field
work it was observed that, orchard and agriculture areas were on the mountain slopes. Although the
government policy is a forestation on sloping land but it lacked of realistic implementation and
responsibility of the villagers and officers. Also the forest areas from plantation project were not
permanent. Teak plantation has the objective for commercial reason; they will be cut down again
when they grow up. The change of land use/cover areas in periods 1988, 2000 and 2007 is shown in
Figure 5-3 as follows:
Table 5-5: Land use/cover change in the periods 1988 - 2007
Land use class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Total
1988 Area
in hectares
2000 Area
in hectares
2007 Area
in hectares
Change area
from
1988 to 2007 in
hectares
2699
2663
740
496
60
1411
2551
1107
1081
507
857
2267
1686
778
1070
-1842
-396
946
282
1010
6658
6658
6658
Change
from the
total area
(%)
-27.67
-5.95
14.21
4.24
15.17
49
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Area change 1988 - 2007
1500
Area in hectares
1000
500
0
F
DF
A
G
O
A rea
-500
-1000
-1500
-2000
Land us e
Figure 5-3: Area of land use/cover change in periods 1988, 2000 and 2007
5.2.
C-factor mapping for erosion assessment
The C-factor map was generated using Landsat TM data obtained on March 3, 2007. The NDVI was
produced and subset to the study area. Three equations were used to calculate C-factor maps from
NDVI. The results were compared with validation data, generated during fieldwork. The C-factor map
derived using De Jong’s equation (De Jong, 1994) (Equation 12) was showed in Figure 5-4 (a). The
range was between 0.020 – 0.431. Meanwhile, Van de Knijff’s equation (Van der Knijff et al., 1999)
(Equation 13) gave more C-factor values with the range between 0.135 – 1.000 (Figure 5-4 (b)). The
last C-factor map was derived from the relationship between training samples of C-factor values from
fieldwork and NDVI (curve estimation, Equation 14). This gave the lowest range between 0.010 –
0.240 (Figure 5-4 (c)). The relationship between training samples of C-factor values from fieldwork
and NDVI was showed in Figure 5-4 (d).
50
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
(a)
(c)
(b)
(d)
Figure 5-4: C-factor map 2007 derived from NDVI;
(a) De Jong’s equation (De Jong, 1994), (b) Van der Knijff’s equation (Van der Knijff et al., 1999),
(c) Regression equation (curve estimation) derived from the relationship between training samples of
C-values and NDVI and (d) the relationship between training samples of C-values and NDVI
5.2.1.
Validation of C-factor map
C factor maps were generated using Landsat TM data of March 2007. Validation of C-factor maps
was done by crossing C-factor maps with 125 validated C-factor values (Section 4.2.2.3). The
statistical techniques (Equation 15 - 17) and adjusted R2 were applied to access the C-factor values
estimation. The result shows that the C-factor map with the highest correlation was the one using the
function derived from the relationship between C-factor training samples and NDVI (Curve
estimation). It gave highest value in adjusted R2 (0.78) (Appendix 4 (c)) and C.E. (0.77) and also
lower values in mean error (-0.04) and root mean square error (0.03) (Table 5-6). The relationship
between C-factor prediction and C-factor validation is shown in Figure 5-5.
51
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
0. 25
Obser ved
Li near
C- pr edi ct
0. 20
0. 15
0. 10
0. 05
0. 00
0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0. 30
C- val i dat e
Figure 5-5: The relationship between C-factor prediction and validation from curve estimation
Table 5-6: Comparison between three of C-factor prediction techniques
C-factor Prediction
techniques
De Jong’s equation
Van de Knijff’s equation
Curve estimation
Statistical techniques
Adjust R2
C.E.
M.E.
RMSE
0.37
0.25
0.78
0.11
0.06
0.77
0.22
0.61
-0.04
0.23
0.62
0.03
As the results from Table 5-6, De Jong and Van de Knijff’s equations gave low correlation and
seemed to give over estimated values for prediction (C.E. = 0.37 and 0.25 respectively). The reasons
could be that De Jong’s equation provided linear relationship between C-factor values and NDVI that
not the realistic characteristic between them. For Van der Knijff’s equation, although the relationship
illustrated in exponential function but the data used for developing were different from the study area.
Furthermore, both of them were developed in semi arid areas zones. Conversely, regression equation
(curve estimation) used C-factor values estimated from the study area as the training data showed high
correlation with NDVI (adjusted R2 = 0.701, Appendix 4(a)). Therefore it gave satisfactory results.
5.3.
Soil erosion assessment
The annual soil loss predictions using data from 2007 (Figure 5-6) ranges between 0 and 61
tons/hectare. Average soil loss was highest (26 tons/hectares/year) in agriculture area and lowest soil
loss rate was found in forest area (0.99 tons/hectare/year). For degraded forest, grassland and orchard,
the soil loss rates were 1.47, 5.39 and 8.76 tons/hectare/year respectively as showed in Table 5-7.
These results proved that vegetation cover strongly influenced erosion process. Due to high vegetation
cover such as in forest and degraded forest area, annual soil loss rate seems to be low, conversely in
agriculture area more erosion because of less vegetation cover. Although orchard area had more
canopy cover as compared to degrade forest or forest area but ground cover was low because farmers
frequently remove grasses. This reason caused more erosion in orchard than degraded forest. For
52
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
grassland, it depended on prior land use/cover types before. It gave less soil loss rates because some
areas the farmer was not removed the residues after harvesting and let the grass growth in the areas.
Table 5-7: Soil loss prediction in different land use/cover 2007
Land use class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Total
Area in percent
Soil loss rate(t/h/y)
SD.
12.87
34.04
25.32
11.70
16.07
0.99
1.47
26.06
5.39
8.76
0.27
0.55
13.52
3.63
0.51
100
Figure 5-6: Soil loss map 2007
The soil loss map then was classified into erosion prone areas using threshold values adapted from
literature (Morgan, 1995; Singh and Phadke, 2006). Five classes were made: very slight (1-4.99
tons/hectares/year), slight (5-9. tons/hectares/year), moderate (10-24. tons/hectares/year), severe (2544.99 tons/hectares/year) and very severe (> 45 tons/hectares/year). The tolerance of soil loss rate that
agriculturist should be concerned was more than 10 ton/hectares/year (Morgan, 1995). This threshold
was applied to Namchun watershed for the difference between slight class and moderate class. The
erosion prone areas map of Namchun watershed 2007 is shown in Figure 5-7.
53
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Figure 5-7: Erosion prone areas classified from soil loss map 2007
Table 5-8: Erosion prone areas in different land use/cover
Land use class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Soil loss rate(t/h/y)
Erosion prone areas
0.99
1.47
26.06
5.39
8.76
Very slight
Very slight
Severe
Slight
Slight
According to Table 5-18, agriculture areas were high erosion level. They fell into severe class
meanwhile orchard and grassland were slight class. Conversely, forest and degraded forest were found
lowest erosion level (very slight). Overall areas had average 20.22 (tons/hectares/year) of annual soil
loss rate. It illustrated more than tolerance soil loss value 10 (tons/hectares/year) and fell in moderate
class.
5.4.
Distribution of soil properties in different land use/cover types
5.4.1.
Distribution of soil organic matter in different land use/cover types
From the Table 5-9 and Figure 5-8, the average of soil organic matter was separated in different land
use/cover types. The lowest average value occurred in agriculture areas (2.24%) and the highest was
in forest area (4.28%). The order from highest to lowest values was forest, degraded forest, orchard,
grassland and agriculture areas respectively.
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Table 5-9: Average soil organic matter content in different land use/cover
Land use class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Average %OM
S.D.
4.28
3.45
2.24
2.36
3.04
0.720
0.877
0.737
1.057
0.596
Figure 5-8: Distribution of soil organic matter (%) in different land use/cover
The statistical analysis showed that there was the difference of soil organic matter contents between
these land use/cover types. This analysis was done by using one-way ANOVA with showed high
significant at 95% confidence level (p < 0.05, F =20.593). Moreover, the further analysis with
Turkey’s HSD test showed multiple comparisons between each pair of land use/cover. There were
significant difference between almost every pair of land use/cover types except forest and degraded
forest, degraded forest and orchard as well as agriculture and grassland (Appendix 5).
Agricultural areas have the lowest soil organic matter content. The main cause of declining in soil
organic matter is due to tillage operation. Beside that tillage also accelerated aeration that caused
rapid and strong oxidation and break down of soil organic matter. Tillage management has a negative
effect on soil organic matter due to human influence. Different tillage systems cause different levels
of soil carbon losses depending on the intensity of the tillage (Iowa State, 2005). For grassland, the
organic matter content was also low. It is possible that these areas had been agriculture area before
and they were changed to grassland recently. Moreover, less soil organic matter content in grassland
came from very little leaf litter. Conversely in forest, degraded forest and orchard, higher organic
matter contents are due to leaf litter. In forest and degraded forest area, high litter coverage regulates
the microbial activity. Litter process helped to restore nutrient cycle including humus formation,
carbon sequestration and soil fertility buildup (Descheemaeker et al., 2006). In Orchard, soil organic
55
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
matter content was relative high as compared to grassland and agriculture areas. This could be the
results from leaf litter together with direct use of fertilizers.
5.4.2.
Distribution of bulk density in different land use/cover types
According to Table 5-10 and Figure 5-9, bulk density was averaged per land use/cover type. Highest
bulk density is in agriculture areas (1.38 g/cm3) and the lowest value occurred in forest and grassland
(1.27 g/cm3). Similar finding were reported by (Celik, 2005) that soils under cultivation had higher
bulk density than soils under forests. The order of highest to lowest values is in agriculture, orchard,
degraded forest, grassland and forest respectively.
Table 5-10: Average bulk density in different land use/cover
Land use class
Forest
Degraded Forest
Agriculture
Grassland
Orchard
Average BD (g/cm3)
S.D.
1.27
1.33
1.38
1.27
1.37
0.035
0.031
0.040
0.038
0.045
Figure 5-9: Distribution of bulk density (g/cm3) in different land use/cover
One-way ANOVA showed significant different of bulk density values between land use/cover at 95%
confidence level (p < 0.05, F = 19.066). The further analysis with Turkey’s HSD test illustrated the
different between each pair of land use/cover except forest and grassland as well as degraded forest,
agriculture and orchard (Appendix 6).
High bulk density values were found in agriculture, orchard and degraded forest areas. This indicator
showed that soils in these areas were more compacted from grassland and forest areas. The
compaction in agriculture and orchard not only came from decreasing of organic matter content but
56
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
also the chemical fertilizers that farmers added in the soil for acceleration the production. There was
a negative relationship between bulk density and hydraulic conductivity. Then hydraulic conductivity
in these areas was decreasing opposite of infiltration rate. The rainwater could not easily penetrate
into deep soil and led to surface runoff. This led to soil erosion in these areas.
Soil properties play an important role for determination of soil erosion. These properties were directly
affects hydraulic properties of soil. Soil organic matter directly effects macro aggregate. Less organic
matter caused decreasing in soil aggregate stability that soil can not hold together in large unit. This
situation gains more chance of soil to easily erode. For bulk density, it directly effect to compactness
of soil. Increasing in bulk density caused soil less hydraulic conductivity that affect the rate of water
can infiltrate into the soil and begin to runoff. Land use change especially forest to agriculture areas
involved in vegetation cover that influenced to these soil properties. Less vegetation cover means less
soil organic matter and increasing bulk density.
As the results from the trend of land use/cover change in periods 1988 to 2007 in Section 5.1.3 above,
the deforestation and expansion of agriculture areas were found. Together with the distribution of soil
properties such as organic matter content and bulk density in different land use/cover types (Section
5.4.1 and 5.4.2), These results could be assumed that if the overall of study areas change from the
forest area into agriculture areas, soil organic matter content will decrease meanwhile bulk density
will increasing. By assuming that, soil organic matter and bulk density distribution pattern in different
land use/cover types were the same in the years 1988 and 2000 (same as in 2007). Then agriculture
areas seem to be prone to erosion. Conversely, forest areas definitely are less prone to erosion. These
could be concluded the erosion increasing in this watershed.
5.5.
Assessment of land use/cover change effect on soil erosion
One of the objectives was to study the effect of land use/cover change on soil erosion. According to
Table 5-5 and 5-7, Trend of land use/cover change in periods 1988 to 2007 and the soil loss rates in
different land use/cover in 2007 were analyzed.
Assuming the estimated soil loss rates of 2007 as being standard erosion rates for different land
use/cover types the amount of soil losses (tons/hectare y) was calculated for 1988 and 2000. Total soil
loss estimated in the Namchun watershed was 29,070.06 (tons/hectare) in 1988 and 44,263.19
(tons/hectare) in 2000 (Table 5-11). Maximum total amount of soil loss occurred in 2007 with
61,684.70 (tons/hectare). Increasing erosion rates are caused by the transformation of natural forest
areas (forest and degraded forest) turned into cultivation areas (agriculture and orchard).
As mention in Chapter 5.4, the expansion of agriculture areas took place on forest areas will increase
the bulk density and decrease organic matter content. These evident could support the assumption of
increasing amount of soil loss above. Both the results from land use/cover change on amount of soil
loss per year and the distribution pattern of soil properties (bulk density and organic matter content)
together with trend of land use/cover change were the important evident that lead to soil erosion.
57
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Table 5-11: Amount of soil loss (t/y) in periods of 1988, 2000 and 2007
Land
use/cover
F
DF
A
G
O
2000
2007
Area (Ha)
Soil
loss(t/y)
Area (Ha)
Soil
loss(t/y)
Area (Ha)
Soil
loss(t/y)
0.99
1.47
26.06
5.39
8.76
2699
2663
740
496
60
2672.01
3914.61
19284.40
2673.44
525.60
1411
2551
1107
1081
507
1396.89
3749.97
28848.42
5826.59
4441.32
857
2267
1686
778
1070
848.43
3332.49
43937.16
4193.42
9373.20
6658
29070.06
6658
44263.19
6658
61684.70
Total
5.6.
1988
Soil loss
rate (t/h/y)
Assessing critical zones for ephemeral gully formation
Sensitive areas for ephemeral gully formation were calculated by using slope, catchment area and
flow width. The range of sensitive areas was between -0.0550 to 2.4457 as shown in Figure 5-10 (a).
These sensitive areas then were classified by using threshold 0.72 into two classes; gully erosion and
no gully erosion (Figure 5-10 (b)). According to land use/cover classification map of 2007, most of
the critical zones areas were found in agriculture areas which are located in the upper part, central part
and bottom right part of study area (Figure 5-10 (b)). Field studies also revealed that agriculture areas
really have a lot of gully erosion. However in forest areas especially plantation forest were also found
gully erosion because of less vegetation cover and distance between the trees quite far.
(a)
(b)
Figure 5-10: Sensitive areas (a) and critical zones (b)
For validation, critical zone maps were crossed with 20 gully erosion validated points as illustrated in
Figure 5-11. Contingency matrix (Table 5-12) was done and showed overall accuracy of 65% for
predicting gully erosion. This result explained that critical zones with the threshold 0.72 seemed to be
acceptable index for prediction the gully erosion formation. However, because of inaccessible areas,
the lack of sufficient validation points was a limitation.
58
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Table 5-12: Contingency matrix between Critical zone and Erosion data from fieldwork
Reference from fieldwork
Total
Gully Erosion
Gully
Critical
zone
No Gully
Total
13
7
13
7
20
20
Overall accuracy = (13/20)x100 = 65%
Gully points
Drainage
Figure 5-11: Gully erosion formation prediction with validated gully erosion points
Due to lack of quantitative data for soil loss validation in the study area it was not possible to validate
erosion level. Simple statistic such as percentage then was used for comparing the areas in percent of
erosion prone areas with the areas in percent of gully erosion formation from critical zones. In terms
of erosion prone areas, the percentage of each erosion level area were calculated as showed in Table
5-13 and Figure 5-12. Most of the study area fell on very slight (83.02%). The other areas was
classified into slight (6.12%), moderate (7.60%), severe (2.79%) and very severe (0.47%) level. On
the other hand, the area of gully erosion formation from critical zones was showed in Table 5-14 and
Figure 5-13. The gully erosion formation area was 5.53% and without gully erosion formation 94.47%
of study area.
Table 5-13: Area of erosion prone areas prediction
Erosion level
Very slight
Slight
Moderate
Severe
Very severe
Total
Area in percent
Area in hectares
83.02
6.12
7.60
2.79
0.47
52831
3893
4833
1777
298
100
63632
59
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Area in percent
Erosion Prone Areas
90
80
70
60
50
40
30
20
10
0
Percent
Very
sligh t
Slight
Moderate Severe
Very
severe
Erosion Level
Figure 5-12: The areas of erosion level in percent
Table 5-14: Area of gully erosion prediction from critical zones
Critical zones
No gully erosion
Gully erosion
Total
Area in percent
Area in hectares
94.47
5.53
60113
3519
100
63632
Area in percent
Gully Erosion Formation
100
90
80
70
60
50
40
30
20
10
0
Percent
No gully
Gully
Erosion
Figure 5-13: The areas of gully formation in percent
The Figure 5-14 showed the comparison between the areas of gully erosion predicted by critical zones
and erosion prone areas classified from soil erosion model. The areas of moderate, severe and very
severe class appeared in the same locations as predicted by critical zones. On the other hand in the
central part of the study area, the erosion class of moderate to very severe contains more areas as
compare to gully formation. The reasons could be soil erosion model considered many factors more
than critical zones, which is primary based on DEM.
60
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Figure 5-14: The comparison between gully erosion prediction and erosion prone areas
However, without the parameters that used in the erosion model, critical zones for gully formation
seemed to be the alternative way to approximately predict the erosion prone areas.
61
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
6. Conclusions and recommendations
6.1.
Conclusions
According to the results discussed in previous chapter, following conclusions can be made:
The topographic effect removal technique was very useful to improve the accuracy of land use/cover
classification. Topographic normalization technique using solar azimuth, solar elevation and DEM
gave the better accuracy than sum normalization technique. It could reduce the illumination variant
which can cause misclassification of land use/cover types in mountainous areas.
Deriving C factor using satellite data (NDVI) approach could be improved with the field data. The
best results came from regression equation based on field assessment of C factor using 138 training
values as compared to the approaches described by De Jong and Van de knijff’s.
The results from soil erosion modeling showed that the overall average annual soil loss rate was
highest in the agriculture areas. The amount of soil loss per year during the period of 1988 to 2007
indicated that change in land use/cover from natural forest to agricultural areas caused more erosion.
The increase of the volume of soil loss followed the expansion of agriculture areas in the study area.
The study shows that agriculture areas have the lowest organic matter content and highest bulk
density of due to land use practices as compared to others. This could support the results from soil
erosion model that showed the highest soil loss rate occurred in agriculture areas.
The critical zones extracted from terrain parameters revealed that most of gully erosion formation
occurred in agriculture areas. The results illustrated the same distribution as the results from soil
erosion model. The critical zones could be mapped using terrain parameters in order to approximate
prediction for the erosion prone areas.
6.2.
Recommendations
Based on the analysis and the result obtained following recommendation has been made:
The accuracy of land use classification in this study obtained was less then 75% , one of the reason is
could be cloud affects on the image, so the high resolution with cloud free images are suggested to
obtain better accuracy consequently to erosion prediction.
Most of the input parameters were acquired from literature. Therefore, more field measurement is
recommended for the other parameters in order to achieve the realistic model results.
62
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
6.3.
Limitations of the study
The available Landsat satellite images used in this study had cloud cover in some areas. This affects
the accuracy of classification and consequently on C-factor prediction. The fieldwork was carried out
in 2007 and the satellite images had acquisition date in 1988, 2000 and 2007. These caused the
reduction of classification and prediction accuracy.
Gathering all the data required in erosion model were not possible due to limitation of time. Therefore
most of the parameters values that used in the erosion model were acquired from literature which may
cause uncertainty on the results. Some data such as rainfall was not local data that could not be
realistic representative of the areas.
The quantitative validation of the annual soil loss prediction was not possible due to lack of the
control erosion plots. The qualitative criteria could not give the values for validating the result from
the erosion model. They explained only which areas were prone to soil erosion
63
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Appendices
Appendix 1: Field data
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
68
X
722984
722984
722996
723005
723004
728026
728075
728102
728060
728038
725176
725202
725250
725257
725294
724747
724757
724753
724738
724743
721826
721825
721802
721845
721825
725136
725161
725170
725150
725135
720196
720184
720193
720185
720192
727963
727672
727409
727509
Y
1856596
1856578
1856605
1856588
1856572
1853681
1853677
1853655
1853621
1853708
1855825
1855785
1855773
1855728
1855806
1855855
1855831
1855814
1855800
1855769
1853286
1853264
1853291
1853283
1853307
1855982
1855978
1855964
1855959
1855966
1853932
1853923
1853944
1853932
1853938
1853390
1853214
1853470
1853540
Land use/cover
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Crop type
Banana
Banana
Banana
Banana
Banana
Banana
Banana
Banana
Banana
Banana
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Maize
Mungbean
Mungbean
Mungbean
Mungbean
Mungbean
Taro roots
Taro roots
Taro roots
Taro roots
Taro roots
Chili
Chili
Chili
Chili
Sc
50
50
50
50
50
60
60
60
60
60
80
80
80
80
80
40
40
40
40
40
60
60
60
60
60
60
60
60
60
60
30
30
30
30
30
40
40
40
40
Fc
55
55
55
55
55
65
65
65
65
65
75
75
75
75
75
70
70
70
70
70
70
70
70
70
70
95
95
95
95
95
40
40
40
40
40
30
30
30
30
Plant Height
2.0
2.0
2.0
2.0
2.0
2.5
2.5
2.5
2.5
2.5
2.0
2.0
2.0
2.0
2.0
1.5
1.5
1.5
1.5
1.5
1.0
1.0
1.0
1.0
1.0
0.2
0.2
0.2
0.2
0.2
0.7
0.7
0.7
0.7
0.7
0.4
0.4
0.4
0.4
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
X
727405
727960
725427
725414
725528
725503
725452
725373
725369
725342
725332
725340
725768
725788
725813
725846
725817
725497
725488
725504
725493
725495
725338
725378
725381
725372
725346
726417
726435
726394
726411
726374
720084
720127
720127
720229
720260
718789
718829
718819
718836
718853
722880
722826
Y
1853503
1853426
1856115
1856093
1856054
1856082
1856096
1856085
1856133
1856122
1856086
1856055
1856049
1856057
1856058
1856036
1856074
1855754
1855718
1855706
1855718
1855746
1856401
1856374
1856393
1856451
1856421
1855581
1855494
1855538
1855602
1855565
1856091
1856111
1856077
1856060
1856074
1856477
1856490
1856471
1856509
1856492
1856637
1856660
Land use/cover
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Agriculture
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Crop type
Maize , Chili , Egg plant
Maize , Chili , Egg plant
Maize (Havested)
Maize (Havested)
Maize (Havested)
Maize (Havested)
Maize (Havested)
Maize (Havested)
Maize (Havested)
Maize (Havested)
Maize (Havested)
Maize (Havested)
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Sc
45
45
90
90
90
90
90
85
85
85
85
85
80
80
80
80
80
65
65
65
65
65
75
75
75
75
75
80
80
80
80
80
60
60
60
60
60
70
70
70
70
70
15
15
Fc
70
70
0
0
0
0
0
0
0
0
0
0
75
75
75
75
75
70
70
70
70
70
89
89
89
89
89
78
78
78
78
78
64
64
64
64
64
75
75
75
75
75
70
70
Plant Height
0.3
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.0
6.0
6.0
6.0
6.0
7.0
7.0
7.0
7.0
7.0
8.0
8.0
8.0
8.0
8.0
7.5
7.5
7.5
7.5
7.5
7.0
7.0
7.0
7.0
7.0
8.0
8.0
8.0
8.0
8.0
6.0
6.0
69
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
70
X
722910
722881
726730
726692
726657
726583
726412
726266
726294
726379
726406
726324
726356
726379
726460
726353
722778
722787
723252
723207
723228
723211
723237
724211
724181
724170
724192
724205
722870
725104
725445
725430
725405
725431
726472
726189
726159
726203
726192
726259
726166
726123
726073
726211
Y
1856610
1856665
1855424
1855363
1855345
1855449
1855704
1855476
1855407
1855369
1855416
1855490
1855488
1855499
1855536
1855645
1856685
1856634
1856644
1856532
1856554
1856579
1856599
1856526
1856531
1856550
1856510
1856480
1855686
1855972
1855697
1855693
1855652
1855642
1855680
1855477
1855460
1855444
1855424
1855502
1855932
1855888
1855901
1855988
Land use/cover
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Degrade Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Crop type
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
Bamboo
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
Sc
15
15
80
80
80
80
80
77
77
77
77
77
68
68
68
68
40
40
95
95
95
95
95
20
20
20
20
20
15
65
65
65
65
65
80
80
80
80
80
65
65
65
65
65
Fc
70
70
75
75
75
75
75
66
66
66
66
66
72
72
72
72
70
70
80
80
80
80
80
70
70
70
70
70
70
85
85
85
85
85
90
90
90
90
90
85
85
85
85
85
Plant Height
6.0
6.0
7.3
7.3
7.3
7.3
7.3
6.7
6.7
6.7
6.7
6.7
7.0
7.0
7.0
7.0
6.5
6.5
12.0
12.0
12.0
12.0
12.0
8.0
8.0
8.0
8.0
8.0
8.0
9.0
9.0
9.0
9.0
9.0
7.0
7.0
7.0
7.0
7.0
9.0
9.0
9.0
9.0
9.0
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
X
726139
726080
726090
726030
725962
725999
723954
723983
724043
724080
723978
724062
724062
724001
723775
724081
724090
724071
724124
724078
724170
724462
724450
724418
719795
719779
719817
719768
719775
720892
720875
720870
720899
720897
724490
721707
721722
721707
721708
721724
721454
721469
721476
721442
Y
1855953
1855987
1856029
1856127
1856010
1856055
1856474
1856468
1856437
1856325
1856375
1856304
1856288
1856217
1856149
1856325
1856681
1856720
1856701
1856672
1856652
1856730
1856772
1856723
1853992
1853981
1854002
1853986
1853990
1855840
1855823
1855843
1855817
1855803
1856705
1853092
1853103
1853107
1853069
1853088
1853252
1853232
1853242
1853228
Land use/cover
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Forest
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Crop type
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
Eucalyptus
Eucalyptus
Eucalyptus
Eucalyptus
Eucalyptus
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
TEAK
Teak (plantation)
Teak (plantation)
Teak (plantation)
Teak (plantation)
Teak (plantation)
Teak (plantation)
Teak (plantation)
Teak (plantation)
Teak (plantation)
Teak (plantation)
TEAK
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Sc
70
70
70
70
70
70
95
95
95
95
60
10
10
10
10
10
15
15
15
15
15
20
20
20
20
20
20
20
20
25
25
25
25
25
20
60
60
60
60
60
80
80
80
80
Fc
80
80
80
80
80
80
80
80
80
80
70
55
55
55
55
55
45
45
45
45
45
50
50
50
45
45
45
45
45
55
55
55
55
55
50
85
85
85
85
85
90
90
90
90
Plant Height
8.7
8.7
8.7
8.7
8.7
8.7
10.0
10.0
10.0
10.0
8.0
20.0
20.0
20.0
20.0
20.0
18.0
18.0
18.0
18.0
18.0
19.0
19.0
19.0
10.0
10.0
10.0
10.0
10.0
16.0
16.0
16.0
16.0
16.0
19.0
1.0
1.0
1.0
1.0
1.0
1.5
1.5
1.5
1.5
71
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
72
X
721445
721001
720970
720942
720952
720968
721751
721785
721773
721811
721790
724309
726078
726055
726099
727252
726112
725042
725039
724676
725099
724906
721580
721618
721593
721619
721574
718770
718800
718796
718773
718751
727554
727544
727558
727591
727577
722100
722095
722106
722102
722085
725590
725583
Y
1853241
1855707
1855706
1855681
1855671
1855677
1856736
1856734
1856765
1856726
1856704
1856697
1855755
1855772
1855964
1856197
1855724
1857340
1857445
1857680
1857358
1857298
1853320
1853298
1853281
1853242
1853254
1856349
1856323
1856301
1856300
1856282
1853631
1853663
1853649
1853652
1853626
1853953
1853941
1853928
1853967
1853945
1855987
1855980
Land use/cover
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Orchard
Orchard
Crop type
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Grass
Longan
Longan
Sc
80
70
70
70
70
70
70
70
70
70
70
70
80
80
80
80
80
70
70
70
70
70
60
60
60
60
60
100
100
100
100
100
80
80
80
80
80
65
65
65
65
65
80
80
Fc
90
85
85
85
85
85
90
90
90
90
90
90
95
95
95
95
95
90
90
90
90
90
85
85
85
85
85
100
100
100
100
100
95
95
95
95
95
85
85
85
85
85
16
16
Plant Height
1.5
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.3
1.3
1.3
1.3
1.3
1.4
1.4
1.4
1.4
1.4
2.0
2.0
2.0
2.0
2.0
1.0
1.0
1.0
1.0
1.0
1.2
1.2
1.2
1.2
1.2
3.5
3.5
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
X
725595
725594
725588
725110
725111
725096
725071
725097
724673
724686
724667
724665
724636
725585
725602
725611
725601
725621
725450
725471
725425
725356
725393
724578
724542
724508
724586
724560
724629
724602
724581
724548
724524
721269
721283
721284
721303
721308
721515
721469
721479
721555
721543
724883
Y
1855978
1855968
1855963
1855988
1856002
1855998
1855957
1855977
1856045
1856019
1855994
1855970
1855959
1855926
1855910
1855861
1855842
1855826
1856009
1856050
1856029
1856035
1856003
1856347
1856325
1856345
1856368
1856360
1855844
1855837
1855843
1855816
1855827
1853403
1853413
1853388
1853403
1853429
1853724
1853713
1853685
1853737
1853741
1856140
Land use/cover
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Orchard
Crop type
Longan
Longan
Longan
Tamarind , Maize
Tamarind , Maize
Tamarind , Maize
Tamarind , Maize
Tamarind , Maize
Tamarind , Mango
Tamarind , Mango
Tamarind , Mango
Tamarind , Mango
Tamarind , Mango
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Tamarind
Sc
80
80
80
10
10
10
10
10
40
40
40
40
40
20
20
20
20
20
5
5
5
5
5
50
50
50
50
50
40
40
40
40
40
90
90
90
90
90
70
70
70
70
70
60
Fc
16
16
16
50
50
50
50
50
70
70
70
70
70
50
50
50
50
50
28
28
28
28
28
10
10
10
10
10
30
30
30
30
30
50
50
50
50
50
45
45
45
45
45
25
Plant Height
3.5
3.5
3.5
7.0
7.0
7.0
7.0
7.0
8.0
8.0
8.0
8.0
8.0
7.0
7.0
7.0
7.0
7.0
4.5
4.5
4.5
4.5
4.5
3.0
3.0
3.0
3.0
3.0
5.5
5.5
5.5
5.5
5.5
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
4.0
73
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
260
261
262
263
X
724871
724876
724909
724909
Y
1856167
1856196
1856167
1856187
Land use/cover
Orchard
Orchard
Orchard
Orchard
The coordinate systems: WGS84 UTM zone 47
74
Crop type
Tamarind
Tamarind
Tamarind
Tamarind
Sc
60
60
60
60
Fc
25
25
25
25
Plant Height
4.0
4.0
4.0
4.0
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Appendix 2: Laboratory analysis
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
X
721704
718457
719273
718572
719685
719397
720672
720767
727294
720656
724812
726844
725583
720292
724453
723210
718863
725908
726233
724567
725117
724573
722564
724887
717953
717231
725907
726107
726307
725707
725907
726107
726307
723507
723707
723907
724107
724307
724507
724707
724907
725107
Y
1854384
1858568
1858839
1858070
1853870
1854158
1855632
1855741
1855868
1855915
1856076
1856081
1856224
1856341
1856377
1856582
1856647
1856812
1857002
1857236
1857340
1857350
1857702
1857850
1858230
1858512
1856428
1856428
1856428
1856228
1856228
1856228
1856228
1856028
1856028
1856028
1856028
1856028
1856028
1856028
1856028
1856028
clay%
21.3
23.4
22.7
18.5
20.6
26.2
25.3
27.0
22.1
29.3
14.4
23.0
23.6
24.7
17.6
14.4
15.0
21.3
20.3
19.7
21.8
25.6
21.4
21.1
23.2
21.0
21.6
21.2
23.1
24.0
24.4
24.2
23.7
23.2
20.1
18.9
24.3
18.6
18.4
21.4
23.0
24.1
silt<20 %
32.3
34.8
35.9
33.6
32.3
33.7
34.4
32.9
33.3
33.8
33.7
34.0
34.2
34.2
30.3
31.6
26.0
34.8
28.5
31.9
33.5
34.2
33.1
33.7
36.0
32.6
33.8
36.1
36.8
33.2
30.6
33.7
36.4
33.6
31.4
30.4
35.3
34.6
33.9
33.4
34.3
35.4
silt<50 % sand%
32.1
14.4
37.3
4.5
34.2
7.2
41.3
6.6
36.6
10.5
33.2
7.0
36.4
4.0
34.7
5.4
37.5
7.1
32.3
4.6
38.6
13.3
37.0
6.0
37.1
5.2
36.9
4.3
33.9
18.2
39.8
14.2
31.1
27.8
39.2
4.7
31.3
20.0
36.6
11.8
37.7
7.0
36.6
3.6
36.9
8.7
38.6
6.6
35.1
5.6
37.5
8.9
36.3
8.2
37.8
4.9
38.1
2.0
33.3
9.5
40.6
4.4
36.8
5.3
37.7
2.2
37.5
5.7
34.9
13.5
37.0
13.7
35.8
4.5
38.9
7.8
37.9
9.7
33.7
11.6
38.1
4.6
35.0
5.6
Texture
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silty clay loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
BD
1.35
1.44
1.33
1.35
1.35
1.30
1.40
1.30
1.33
1.36
1.40
1.32
1.32
1.46
1.38
1.41
1.43
1.37
1.37
1.36
1.33
1.39
1.34
1.34
1.32
1.34
1.33
1.24
1.38
1.32
1.25
1.30
1.34
1.32
1.36
1.36
1.31
1.36
1.36
1.34
1.46
1.31
OM
0.79
0.93
0.99
1.04
0.98
1.00
1.28
1.43
1.64
1.49
2.36
2.15
2.45
0.54
0.79
1.73
0.69
1.94
2.39
3.22
2.19
1.33
2.43
2.19
1.15
1.17
2.78
3.37
1.62
1.85
3.16
2.03
2.07
1.91
1.01
1.21
2.55
2.32
1.63
2.18
0.72
2.71
75
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
76
X
725307
725507
725707
725907
722707
722907
723107
723307
723507
723707
723907
724107
724307
724507
724707
724907
725107
725307
725507
725707
722707
722907
723107
723307
723507
723707
723907
724107
724307
724507
724707
724907
725107
725307
723107
723307
723507
723707
723907
724907
725107
725307
720977
718182
Y
1856028
1856028
1856028
1856028
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855828
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855628
1855428
1855428
1855428
1855428
1855428
1855428
1855428
1855428
1853525
1857435
clay%
22.6
19.8
22.4
22.4
14.4
23.6
26.3
21.1
11.9
20.5
21.9
22.7
25.4
23.6
26.9
25.4
20.5
25.2
22.9
20.3
16.1
22.5
16.1
23.4
16.7
18.9
24.9
14.2
23.2
25.2
24.5
24.7
20.3
21.9
19.1
26.2
22.7
27.7
22.3
20.6
23.0
25.1
18.6
20.4
silt<20 %
33.4
30.6
32.3
32.9
28.8
35.4
37.5
32.2
31.5
32.7
33.2
34.4
32.0
34.3
35.0
34.2
32.8
34.4
31.0
32.2
29.7
32.9
36.9
35.3
34.2
34.4
35.8
31.8
33.7
35.6
34.5
34.3
34.5
36.1
35.6
32.7
35.2
35.3
35.6
33.0
35.7
34.7
31.7
30.8
silt<50 % sand%
37.8
6.3
33.1
16.5
35.1
10.2
35.0
9.7
31.6
25.3
35.6
5.5
34.0
2.2
34.3
12.3
33.7
22.9
35.6
11.2
34.1
10.8
35.7
7.3
36.0
6.6
36.1
5.9
33.1
5.0
34.6
5.8
35.8
10.9
35.0
5.4
33.2
13.0
33.6
14.0
30.8
23.4
35.7
9.0
35.3
11.7
38.5
2.9
34.9
14.2
38.7
8.0
35.8
3.5
36.4
17.5
34.9
8.2
34.2
5.0
37.2
3.8
36.3
4.8
36.1
9.1
35.0
6.9
36.0
9.3
37.8
3.4
38.7
3.4
34.7
2.3
38.1
4.1
32.5
13.9
36.2
5.1
37.1
3.0
37.3
12.3
35.4
13.5
Texture
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silty clay loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
BD
1.32
1.36
1.33
1.33
1.43
1.32
1.35
1.35
1.45
1.35
1.34
1.33
1.31
1.32
1.30
1.31
1.35
1.31
1.34
1.35
1.41
1.33
1.38
1.37
1.38
1.35
1.31
1.41
1.32
1.31
1.31
1.32
1.35
1.33
1.35
1.33
1.37
1.42
1.35
1.35
1.32
1.40
1.36
1.35
OM
1.13
1.28
2.20
1.68
1.87
1.25
1.79
2.14
0.94
2.40
3.84
2.05
2.48
2.32
1.80
0.84
1.08
1.59
1.52
2.22
0.98
2.35
3.24
1.70
4.31
2.86
2.36
2.90
3.15
2.47
2.38
2.30
1.05
1.76
2.15
2.09
1.77
0.76
2.12
2.48
1.57
1.29
1.06
1.20
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Sample
X
Y
clay%
silt<20 %
87
718161
1856805
23.5
34.7
88
717254
1854755
18.1
34.7
89
719767
1852180
30.1
34.0
90
722749
1856685
17.8
32.0
91
725055
1855342
13.4
29.7
92
723697
1856135
11.6
31.1
93
726981
1853595
22.1
34.3
94
726287
1853355
21.1
33.1
95
729868
1854855
21.6
35.0
96
729407
1855045
20.8
33.2
97
718309
1854365
22.5
34.6
98
720202
1852595
27.1
35.3
99
721687
1854095
26.7
35.0
100
719103
1856625
19.2
32.5
101
720320
1856045
16.9
30.8
102
724030
1856835
19.4
27.8
103
723180
1857145
22.5
35.2
104
729831
1855885
18.8
29.7
105
726132
1855475
23.7
33.4
106
728131
1853646
18.6
32.6
107
728787
1855815
14.6
30.2
108
728949
1856065
19.8
34.0
109
724392
1857755
26.0
35.8
110
727655
1854298
21.1
30.4
111
726814
1854094
18.2
33.2
112
724148
1855466
22.0
32.7
113
727425
1853063
18.8
31.2
114
724697
1856525
24.3
33.8
115
724030
1856835
22.0
33.6
116
725716
1856665
25.6
34.9
117
719145
1852825
21.2
33.9
118
716860
1857949
25.4
35.1
119
716972
1858409
23.7
33.7
120
724922
1851675
24.8
35.3
121
725355
1853035
17.6
34.8
122
717309
1857535
22.1
34.3
123
717563
1857872
12.0
32.4
124
724805
1855868
10.3
28.8
125
729367
1854605
15.8
29.5
126
719385
1853715
17.8
32.3
The coordinate systems: WGS84 UTM zone 47
silt<50 % sand%
36.7
5.0
31.2
16.1
35.1
0.8
36.9
13.2
33.9
23.1
36.3
21.1
38.8
4.8
38.2
7.7
38.7
4.7
33.5
12.5
31.0
11.9
37.0
0.6
37.5
0.8
38.3
9.9
36.0
16.2
31.4
21.4
37.1
5.2
33.0
18.4
32.2
10.6
36.1
12.6
36.2
19.1
35.7
10.5
35.2
3.0
29.9
18.6
38.6
10.0
36.9
8.3
37.2
12.8
35.4
6.4
35.7
8.8
35.8
3.7
36.5
8.4
36.7
2.8
34.1
8.5
33.9
6.0
39.7
7.9
37.8
5.8
39.3
16.4
32.6
28.3
28.7
26.0
37.0
12.9
Texture
Silt loam
Silt loam
Silty clay loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silty clay loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
Silt loam
BD
1.32
1.38
1.41
1.37
1.43
1.45
1.34
1.34
1.39
1.35
1.34
1.40
1.44
1.36
1.39
1.38
1.32
1.37
1.33
1.37
1.41
1.35
1.41
1.36
1.36
1.33
1.36
1.31
1.33
1.42
1.34
1.41
1.32
1.31
1.36
1.33
1.44
1.48
1.41
1.37
OM
2.17
0.57
0.62
1.52
0.88
0.92
2.20
1.76
1.70
2.45
2.84
1.03
0.69
0.33
0.32
0.88
0.68
1.13
0.57
2.69
2.10
4.05
1.14
2.35
1.62
1.61
1.50
1.19
1.97
0.95
1.14
1.05
1.25
2.19
2.62
2.38
1.39
2.54
1.37
1.95
77
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Appendix 3: Organic matter 2006
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
X
727967
720676
717773
729180
729158
729062
718772
724780
724793
724778
724719
728208
722790
723113
718171
718156
718233
724582
724903
724627
724584
728112
720906
729116
725324
725288
725295
726071
726057
726435
728139
726422
Y
1854046
1856122
1857973
1855194
1854570
1854497
1858082
1855717
1855722
1855825
1855844
1853907
1857516
1857471
1857716
1857772
1857700
1856787
1856659
1856523
1856368
1853935
1855807
1854508
1856109
1856054
1855924
1856766
1856626
1855476
1854025
1855589
OM
2.18
2.16
2.01
3.36
2.48
3.56
0.4
3.71
2.22
1.11
1.41
2.5
2.87
2.37
0.74
2.46
1.38
2.09
3.15
4.25
3.17
4.28
2.75
3.52
2.82
1.86
2.96
3.9
3.36
3.71
3.59
3.67
The coordinate systems: WGS84 UTM zone 47
78
Sample
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
X
727049
727562
726909
726790
727140
727892
719589
721906
721745
721883
721908
721184
721651
725994
717714
722401
722493
720369
720189
720438
720455
720917
720920
727060
725500
725493
724716
725538
725509
721916
721826
Y
1855790
1856627
1854863
1854212
1855678
1853768
1855523
1853148
1852981
1852835
1852948
1853470
1853090
1855948
1858018
1853647
1853643
1854488
1854198
1855915
1855884
1855665
1855820
1855705
1855805
1855735
1856668
1855743
1855657
1855636
1855639
OM
3.52
5.49
4.51
2.52
3.63
2.33
1.61
3.6
5.01
6.42
3.29
5.35
3.02
5.12
2.91
4.66
2.32
1.78
4.25
2.32
2.37
4.74
2.92
3.21
2.18
2.57
2.88
4.21
5.41
1.74
2.91
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Appendix 4: Regression analysis result summaries between C-factor values
and NDVI
a) Regression between C-factor and NDVI
Model Summary
R
R Square
Adjusted R
Square
.839
.703
.701
The independent variable is NDVI.
Std. Error of
the Estimate
.422
ANOVA
Sum of
Squares
df
Mean Square
F
Sig.
Regression
57.316
1
57.316
322.056
.000
Residual
24.204
136
.178
Standardized
Coefficients
t
Sig.
Beta
-.839
B
-17.946
Std. Error
.000
15.158
.000
F
54.160
Sig.
.000
Standardized
Coefficients
t
Sig.
Beta
.926
B
7.359
Std. Error
.000
14.509
.000
Total
81.520
137
The independent variable is NDVI.
Coefficients
Unstandardized
Coefficients
B
-7.337
NDVI
Std. Error
.409
(Constant)
.227
.015
The dependent variable is ln(C_factor).
b) Regression between annual rainfall and elevation
Model Summary
R
R Square
Adjusted R
Square
.926
.858
.842
The independent variable is Elevation.
Std. Error of
the Estimate
126.057
ANOVA
Regression
Sum of
Squares
860612.200
df
1
Mean Square
860612.200
Residual
143012.180
9
15890.242
Total
1003624.380
10
The independent variable is Elevation.
Coefficients
Unstandardized
Coefficients
Elevation
B
.980
Std. Error
.133
(Constant)
840.486
57.927
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
c) Regression between C-factor predictions from curve estimation and C-factor validation
Model Summary
R
.884
R Square
.781
Adjusted
R Square
.779
Std. Error of
the Estimate
.027
The independent variable is Cvalidate.
Appendix 5: One-way ANOVA analysis summaries
a) One-way ANOVA analysis of Organic matterin different land use/cover
ANOVA
%OM
Sum of
Squares
df
Mean Square
F
Sig.
Between Groups
56.825
4
14.206
20.593
.000
Within Groups
108.999
158
.690
Total
165.825
162
Post Hoc Tests
Multiple Comparisons
Dependent Variable: %OM
Tukey HSD
(I) Land use
Forest
Degrade Forest
Std. Error
Degrade Forest
Agriculture
.83306
2.03689(*)
.30954
.30643
Grassland
1.91939(*)
Orchard
.060
.000
Lower
Bound
1.6872
2.8825
.30643
.000
1.0738
2.7650
1.24389(*)
.30791
.001
.3942
2.0936
-.83306
.30954
.060
-1.6872
.0211
Grassland
Orchard
1.20383(*)
1.08633(*)
.41083
.19081
.19081
.19318
.000
.000
.214
.6773
.5598
-.1222
1.7304
1.6129
.9439
Forest
-2.03689(*)
.30643
.000
-2.8825
-1.1913
Degrade Forest
-1.20383(*)
.19081
.000
-1.7304
-.6773
Forest
Grassland
Orchard
Grassland
Forest
Degrade Forest
Agriculture
Orchard
-.11750
.18572
.970
-.6300
.3950
-.79300(*)
-1.91939(*)
-1.08633(*)
.18815
.30643
.19081
.000
.000
.000
-1.3122
-2.7650
-1.6129
-.2738
-1.0738
-.5598
.11750
.18572
.970
-.3950
.6300
Orchard
-.67550(*)
.18815
.004
-1.1947
-.1563
Forest
-1.24389(*)
.30791
.001
-2.0936
-.3942
.19318
.18815
.18815
.214
.000
.004
-.9439
.2738
.1563
.1222
1.3122
1.1947
Degrade Forest
-.41083
.79300(*)
.67550(*)
* The mean difference is significant at the .05 level.
Agriculture
Grassland
80
Sig.
Upper
Bound
-.0211
1.1913
Agriculture
Agriculture
95% Confidence Interval
Mean
Difference (IJ)
(J) Land use
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
b) One-way ANOVA analysis of Bulk density in different land use/cover
ANOVA
BD
Sum of
Squares
df
Mean Square
F
Sig.
Between Groups
.114
4
.028
19.066
.000
Within Groups
.076
51
.001
Total
.190
55
Post Hoc Tests
Multiple Comparisons
Dependent Variable: BD
Tukey HSD
(I) Land use
Forest
(J) Land use
Degrade Forest
Agriculture
Grassland
95% Confidence Interval
Mean
Difference (IJ)
Std. Error
-.06083(*)
-.10923(*)
.02055
.02032
Sig.
.036
.000
Upper
Bound
-.1189
-.1667
Lower
Bound
-.0027
-.0518
-.00462
.02032
.999
-.0621
.0528
Orchard
-.10385(*)
.02032
.000
-.1613
-.0464
Forest
.06083(*)
.02055
.036
.0027
.1189
Agriculture
Grassland
Orchard
-.04840(*)
.05622(*)
-.04301
.01546
.01546
.01546
.023
.006
.056
-.0921
.0125
-.0867
-.0047
.0999
.0007
Forest
.10923(*)
.02032
.000
.0518
.1667
Degrade Forest
.04840(*)
.01546
.023
.0047
.0921
Grassland
.10462(*)
.01514
.000
.0618
.1474
Orchard
Forest
Degrade Forest
.00538
.00462
-.05622(*)
.01514
.02032
.01546
.996
.999
.006
-.0374
-.0528
-.0999
.0482
.0621
-.0125
Agriculture
-.10462(*)
.01514
.000
-.1474
-.0618
Orchard
-.09923(*)
.01514
.000
-.1421
-.0564
Forest
.10385(*)
.02032
.000
.0464
.1613
.04301
Agriculture
-.00538
Grassland
.09923(*)
* The mean difference is significant at the .05 level.
.01546
.01514
.01514
.056
.996
.000
-.0007
-.0482
.0564
.0867
.0374
.1421
Degrade Forest
Agriculture
Grassland
Orchard
Degrade Forest
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Appendix 6: Histogram of organic matter (a) and bulk density (b)
Nor mal Di s t r i but i on
Nor mal Di st r i but i on
8
25
Mean =2. 83
St d. Dev . =1. 012
N =163
15
10
Mean =1. 33
St d. Dev . =0. 059
N =56
6
Fr equency
Fr equency
20
4
2
5
0
0
0. 00
2. 00
4. 00
%
OM
(a)
6. 00
1. 20 1. 25 1. 30 1. 35 1. 40 1. 45 1. 50
BD
(b)
Appendix 7: Geopedologic map (Solomon, 2005) used in RMMF model and
legend
82
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Source: (Solomon, 2005)
83
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
Appendix 8: ILWIS script to run the RMMF model for annual soil loss
prediction
// ILWIS script to run RMMF model for annual soil loss prediction
// Create attribute maps of A, Et/Eo, CC, GC, PH, EHD
A.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.A)
Et_Eo.mpr {dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse 2007area,Landuse.tbt.ET_EO)
CC.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.CC)
GC.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse 2007area,Landuse.tbt.GC)
PH.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.PH)
EHD.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.EHD)
// Calculate Kinetic Energy
ER = Rainfall*A
LD = ER*CC
DT = ER - LD
KE_DT = DT*(11.9 + (8.7*(log(25))))
KE_LD = LD*(15.8*(PH^0.5))-5.87
KE_Total = KE_DT + KE_LD
// Calculate mean rainy days
Ro = Rainfall/120
// Create attribute maps of MS , BD , K , COH
MS.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.MS)
BD.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.BD)
K.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.K)
COH.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.COH)
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DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
// Calculate soil particle detachment
RC = 1000*MS*BD*EHD*Et_Eo
Q = Rainfall*(exp(-RC/Ro))
F = K*KE_Total*0.001
Z = 1/(0.5*COH)
H = Z*(Q^1.5)*(SIN(DEGRAD(Slope))) *(1-GC)*0.001
D = (F+ H)
// Calculate transport capacity
TC = (Cmap2007*(Q^2)*(SIN(DEGRAD(Slope)))*0.001)
// Calculate soil loss rate
Soilloss2007 = min(D,TC1)
Appendix 9: The photographs of gully erosion in the study area
85