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 COVER FACTOR (C) ESTIMATION Soil surface cover factor is used in assessing soil erosion hazard. One of the objectives of the study is to estimate the cover factor (C) and compare it with typical values used in Thailand, so as to ascertain the reliability of the methodology used by Land Development Department (LDD). For a given land use, LDD used a simple method based on percentage vegetative cover, including mulch and roughness (Yazidhi, 2003). In this study, estimation of the cover factor was based on sub factors i.e. canopy cover, surface cover and surface roughness following the RUSLE method (Renard, 1997). 1. Generation of sub-factor Canopy cover The derivation of canopy cover (CC; 0-1) for the annual land use was based on maize, coriander and mungbean, which are the dominant crops in the study area. Canopy factor for orchard was based on mango and tamarind, for grassland on natural grass, for planted tree on Teak plantation. For degraded forest the canopy cover was based on bamboo and other forest tree. The cropping pattern in Lomsak is that maize is planted in the first season, followed by mungbean in the second season. Coriander is planted on September . Table 1 : canopy cover sub factor for different landuse Landuse PLU CC SC SR Derived C C(LDD) Annual crop 1 0.51 0.48 0.87 0.213 0.47 Orchard 1 0.68 0.16 0.94 0.102 0.15 Grassland 1 0.23 0.03 0.99 0.007 0.015 Planted forest 0.5 0.92 0.38 0.90 0.157 0.088 Degraded forest 0.5 0.60 0.15 0.99 0.045 0.048 2. Generation of surface cover sub factor Surface cover was derived from field data on ground cover (sp; %), random roughness (RU) and an empirical coefficient (b). For annual crop a coefficient value of 0.035 was used. Other coefficient values are as follows : degraded forest (0.025), planted tree (0.05), orchard (0.025) and grassland (0.045). The coefficients are based on erosion site conditions and different kinds of plants (Renard, 1997). Table 2 : Surface cover subfactor for different landuse Landuse Sp Ru b SC Annual crop 22.22 0.44 0.035 0.48 Orchard 76.11 0.34 0.025 0.16 Grassland 75.45 0.25 0.045 0.03 Planted forest 20.00 0.40 0.050 0.38 Degraded forest 75.50 0.25 0.025 0.15 3. Generation of surface roughness sub-factor Surface roughness affects soil erosion through the impact of residue effectiveness. The surface roughness sub factor (SR; 0-1) is estimated as a function of the surface’s random roughness (RU), which is defined as the standard deviation of the surface elevations when changes due to land slope or non-random tillage marks. Table 3 : Surface roughness sub-factor for different landuse Landuse Rn SR Annual crop 0.44 0.87 Orchard 0.34 0.94 Grassland 0.25 0.99 Planted forest 0.40 0.90 Degraded forest 0.25 0.99 4. Generation of C-factor value The final C-factor values is calculated by multiplying prior landuse (PLU) factor, SR, SC and CC.The prior land use (PLU) value of 1 was assigned for annual crop, orchards and grassland because the crop residue is either removed or burned, while in orchards farmers use herbicide or remove grass. Grassland is put on fire in the dry season. For planted forest and degraded forest the PLU value of 0.5 was assigned, because it is disturbed sometimes from forest fire or human activity. Table 4 : Derived C-factor LDD C-factor for different landuse Landuse Sp Ru b SC Annual crop 22.22 0.44 0.035 0.48 Orchard 76.11 0.34 0.025 0.16 Grassland 75.45 0.25 0.045 0.03 Planted forest 20.00 0.40 0.050 0.38 Degraded forest 75.50 0.25 0.025 0.15 Development of Methodologies for Land Degradation Assessment Applied to Land Use Planning in Thailand ^go to top