Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models

This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km2, respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km2, respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km2. Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha−1 year−1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha−1 year−1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future

Assessment and enchacment of SRTM and ASTER GDEM using lidar digital elevation model

In the past decade, significant advances in global elevation modelling have been made with the release of the space-borne SRTM “Shuttle Radar Topography Mission” and ASTER “Advanced Spaceborne Thermal Emission and Reflection Radiometer” elevation data sets. Since a number of applications may rely solely on SRTM and/or ASTER GDEM, it is important to assess the quality of these DEMs using accurate data and precise techniques as well as finding simple but applicable approaches to improving these valuable free access DEMs. To doing so, present study has focused on comprehensive methods include DEM error quantification, DEM error distribution pattern as well as DEM statistical enhancement model. After LiDAR, SRTM and ASTER GDEM data preparation, at the first step, validation of SRTM v4.1 and ASTER GDEM v.2 have been examined using LiDAR information as truth data. Various visual, empirical, and analytical approaches were used as methods. Determination the impacts of terrain characteristics on SRTM and ASTER GDEM DEMs error distribution was next stage which have been done using determination of slope and aspect effects on DEM error distribution pattern in study area. Subsequently, techniques for SRTM and ASTER GDEM enhancement have been investigated by testing varied interpolation approaches on output DEM’s quality. Regression model as a next method have been applied on interpolated DEMs for acquiring better-enhanced results. Finally, to evaluate the effects of improvements approaches on SRTM and GDEM, quantification of DEM errors was done again, and amount of RMSE and statistical parameters was calculated for enhanced SRTM and ASTER GDEM. To sum up, Results could showed us, despite last version of ASTER GDEM has finer pixel size and its validation team claims its vertical accuracy is near to 17 meter (while for our case study 28 meter), still SRTM has better quality and is more reliable than GDEM. Improved RMSE of near 15 m versus 28 meter in original data for ASTER GDEM and 10 Meter opposite 19 meter for SRTM were the results of DEM enhancement in this study