home    land degradation    project outcome    Gallery Flooding Flood Hazard Assessment Pilot Area • The Pilot Areas • Climate • Cover factor (C) estimation • Digital terrain analysis • Relationship between soil properties • Relationship of soil properties with different land use/cover types Methodology • Methodologies • LISEM Results • Result Soil Properties and Land Use • Runoff rate for different land cover types • Model calibration and validation • Sensitivity analysis • Land use scenarios • Rainfall size scenarios • Flooding Scenario - 2 years return period • Flooding Scenario - 10 years return period • Flooding Scenario - 20 years return period • Flooding Scenario - 50 years return period • Summary tables for each flood characteristic • Flood hazard mapping • Conclusions and Recommendation Erosion Erosion Assessment Pilot Area • The Pilot Areas • Climate • Cover factor (C) estimation • Digital terrain analysis • Relationship between soil properties • Relationship of soil properties with different land use/cover types Methodology • Methodologies • Erosion Models Result • Results • C-Factor Mapping for erosion assessment • Soil erosion assessment • Assessment of land use/cover change effect on soil erosion • Assessing critical zones for ephemeral gully formation • Erosion Assessment by Different Models • The recommended erosion models • RMMF Landslide Case I • Landslide hazard assessment • The area (Pilot 3) • Landslide Mapping in Wang Chin using digital image analysis Techniques. • Results • Discussions Case II • Doi Ang Khang (Ang Khang) (Pilot area 4) • Landslide mapping in Ang Khang using aerial photos • Landslide Inventory Mapping • Landslide modelling and Susceptibility mapping • Landslide Susceptibility Maps from the three Scenarios (models) • Comparison between Ang Khang and Wang Chin (with respect to weights of evidence modelling using morphometric parameters) Salinization Case I • Pilot area II • Soil salinity • Salinization • Geomorphology • Soils • Geopedologic map • Results Case II • CASE II - The study of Yadav, R.D. (2005), • Methodology • Crop Simulation Model • PS 123 • Results • Mathematical (Regression) Model for Crop Yield Estimation • Conclusion Case III • Case III • Modeling Salinization • Geostatistics and Interpolation (GIS and Kriging) • Model Simulation and Prediction of Salinity • Simulated Depth to water table • Tenth Years Prediction • Twenty Years Prediction • Conclusion Runoff • Surface Runoff • Methodology • Modelling Runoff • Modelling Runoff Using Empirical Approach • Modelling runoff using the Semi-physical approach • Estimation with the Curve Number Method • Results and Discussion • Generation of Scenarios • Comparison of runoff rates with soil loss map • Conclusions • Presentation of papers based on project work at international • Workshop in Lomsak • Hua Hin seminar • Training of LDD staff • GIS and RS based predictive soil mapping • Soil erosion assessment • Landslide hazard assessment • Flood hazard assessment • Soil salinity study • List of completed MSc. theses on project GEOPEDOLOGIC MAP GP map is the basic layer in the ‘SIS’, which formulates the general framework for the research. It was produced through three main steps Photo interpretation. Digitising analog photo interpretation Fieldwork Aerial photo interpretation (API): The photoset contains eight aerial photographs, which cover approximately 300 Km2 (30,000 ha). The photos were organized in two photo-runs, and formulated six stereo-pairs were interpreted according to the ‘Geopedologic approach’[37]. The result shows that there are two major Landscapes, namely Valley, and Peneplain. Ten relief types, two lithological units (making use of the geological map and reliable literature), and finally fourteen landforms were distinguished. Statistics reveals that there are 77 delineations (at the level of landform). The minimum delineation is approximately six hectares, while the maximum size is approximately 3800 hectares. The interpretation was done over four transparencies, and joined manually using the ‘lighting table’. Converting API map to digital coverage: The method described by (Rossiter et al., 2002) was followed. The four-centre photos (which represents the stereo-model) were scanned with 300 dpi resolutions, while the overlays were scanned with 150dpi. The orthorectification was done using the existing DTM in the background, and with several common ground control points between the topographic map and the aerial photos. The maximum error was 4.6 pixels (18.4 m) from an orthophoto model with eleven tie points, and the minimum 3.1 pixels (12.4m) from an orthophoto model with eight tie points. This error is considered acceptable, because Eight years difference between the aerial photographing date and the date topographic maps were produced (Topo maps were produced in year 1991, while the aerial photos were taken in 1999) The difficulty of defining the common tie points in the study area. (a flat topography without sufficient infrastructure, the major use cover is cassava or paddy fields) Final scale of the GP map (1:50,000). The aerial photos were resampled to a grid with four-meters pixel size. After Orthorectifying the four aerial photos the rectification accuracy was checked by overlaying the streams and roads layer (generated from the topographic map), and relatively stable landmarks (even though there are some evidences that they have changed in a few locations) to enhance the distribution of the tie points (by adding more) to achieve homogenous accuracy all over the photos. The overlays were georeferanced using an Affine transformation to fix rotation and scale distortion. The maximum error was 1.8 pixels, and the minimum of 0.5 pixels with six tie points. The rectified interpretation layer was then scanned and digitised, using the on-screen digitising facility of ILWIS. The overlays were resampled to a grid with 8.5 m pixel size. Finally the vector layer was checked for topological errors, and turned to polygon coverage by labeling the polygon using GP classification with last four hierarchal levels to enable reclassifying the GP layer at any of those levels. The generated DTM was used to check the plausibility of the GP units, and the result shows high compatibility between the GP units and the DTM especially in the flat region (Northeast part), while the majority of the rectification errors accumulate at the southwest ridge.   Figure 1 : (a) Final aerial photo mosaic, (b) GP map at the final hierarchal level (Landform) Table 1 : Geopedologic legend Landscape Relief Type Lithology Landform GP Code Peneplain (Pe) Ridge Sedimentary rocks Korat group Top complex Pe111 Side complex Pe112 Slope-facet complex Pe113 Summit Pe114 Tread riser complex Pe115 Glacis Sedimentary rocks Korat group Tread riser complex Pe211 Vale Sedimentary rocks Korat group Slope complex Pe311 Lateral Vale Sedimentary rocks Korat group Side complex Pe411 Bottom-Side complex Pe412 Bottom complex Pe413 Depression Sedimentary rocks Korat group Basin Pe511 Valley (Va) Flood Plain Alluvial deposits Levee - Overflow complex Va111 Old Terraces Alluvial deposits Overflow - Basin complex Va211 New Terraces Alluvial deposits Overflow - Basin complex Va311 References Rossiter, D.G. and T. Hengl (2002). Technical note: Creating geometrically-correct photointerpretations, photomosaics and base maps for a project GIS., ITC: Enschede. Development of Methodologies for Land Degradation Assessment Applied to Land Use Planning in Thailand ^go to top