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 CONCLUSION Some selected measured soil salinity and fertility variables (EC, pH, CEC and OM) were used to estimate their variation in geopedologic units at “relief type” and at “landscape” levels, and also their spatial variation and dependency to estimate the area affected by salinity. Soil salinity and soil fertility varied significantly within and between geopedologic units at “relief type” and “landscape” levels. Moving average gave best interpolation result in salinity and fertility modelling. PS123 simulates crop yield that correlates significantly with farmers yield. This was not true for the case of CropSyst. Both models simulate crop yield with different degree of salinity (sensitivity analysis). Now a days, the development of GIS and remote sensing based models are used in various fields of studies. The GIS and remote sensing tools and techniques are commonly applied in natural resources management. These were also applied in order to compare the results obtained from the deterministic models. Result of a case study of applying image transformation by means of band rotation to enhance soil spectral reflectance and combining the result in geopedologic image interpretation is shown in figure.   Figure 1 : Classification of saline soils using image transformation, DEM and geopedologic interpretation (Shrestha et al., 2005) References Shrestha, D.P., Soliman, A.S., Farshad, A. and Yadav, R.D., 2005. Salinity mapping using geopedologic and soil line approach, 26th Asian Conference on Remote Sensing, Hanoi, Vietnam, pp. 6. Development of Methodologies for Land Degradation Assessment Applied to Land Use Planning in Thailand ^go to top