Digital terrain analysis and image processing for assessing
Transcription
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 vii 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 1 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? 2 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? 3 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. 4 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 5 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. 6 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. 7 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). 8 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. 11 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 12 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) 13 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 16 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 18 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 19 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. 54 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 Reference Auanon, T., Krirawee, K., Mornchareon, L. and Sngauntrakul, K., 2004. Risk Assessment of Disaster Area from Landslides (in Thai language), Faculty of Engineer Mahidol University, Phetchabun. Celik, I., 2005. 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CATENA, In Press, Corrected Proof. 67 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 79 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 81 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) 84 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