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 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 from De Jong’s equation (De Jong, 1994). The range was between 0.020 – 0.431. Meanwhile, Van de Knijff’s equation (Van der Knijff et al., 1999) gave more C-factor values with the range between 0.135 – 1.000. The last C-factor map was derived from the relationship between training samples of C-factor values from fieldwork and NDVI. Figure 1 : 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 The C factor map was then validated by crossing C-factor maps with 125 validated C-factor values. The statistical techniques 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) and C.E. (0.77) and also lower values in mean error (-0.04) and root mean square error (0.03) . The relationship between C-factor prediction and C-factor validation is shown in Figure 2 . Figure 2 : The relationship between C-factor prediction and validation from curve estimation References De Jong, S.M., 1994. Application of Reflective Remote Sensing for Land Degradation Studies in Mediterranean Environment, University of Utrecht, Utrecht. Van der Knijff, J.M.f., Jones, R.J.A. and Montanarella, L., 1999. Soil Erosion Risk Assessment in Italy. EUROPEAN COMMISSION DIRECTORATE GENERAL JRC JOINT RESEARCH CENTRE Space Applications Institute European Soil Bureau, http://eusoils.jrc.it/ESDB_Archive/pesera/pesera_cd/pdf/er_it_new.pdf, pp. 58. Development of Methodologies for Land Degradation Assessment Applied to Land Use Planning in Thailand ^go to top