Assessment of Vertical Accuracy for TanDEM-X 90 m DEMs in Plain, Moderate, and Rugged Terrain

Land Cover monitoring is an essential task for a better understanding of the ecosystem’s dynamicity and complexity. The availability of Remote Sensing data improved the Land Use Land Cover mapping as it is routine work in ecosystem management. The complexity of the Mediterranean ecosystems involves a complexity of the surrounding environmental factors. An attempt to quantitatively investigate the interdependencies between land covers and affected environmental factors was conducted in Nisos Elafonisos to represent diverse and fragile coastal Mediterranean ecosystems. Sentinel-2 (MSI) sensor and ASTER Digital Elevation Model (DEM) data were used to classify the LULC as well as to draw different vegetation conditions over the designated study area. DEM derivatives were conducted and incorporated. The developed methodology is intended to assess the land use land cover for different practices under the present environmental condition of Nisos Elafonisos. Supervised classification resulted in six different land cover clusters and was tested against three different environmental clusters. The findings of the current research pointed out that the environmental variables are independent and there is a vertical distribution of the vegetation according to altitude.

Abstract Rising Incidents of landslide at district Mandi is issue of concern in Himachal Pradesh. Every year many people losses their life and property in these landslide event. This study is conducted with aim to preparation of landslide susceptibility zonation map of district Mandi using method of frequency ratio. Causative factor of landslide involved in preparation of Landslide susceptibility zonation map is Lithology, Slope, Drainage density, Aspect and Land use land cover. Slope, Drainage density, Aspect map are extracted through digital elevation model. Source of Digital elevation model used here is based on SRTM data whereas lithology map is based on data of geological survey of India. Land use land cover map is extracted by images of Landsat 8 satellite. Total of 52 existing landslides are used to model final map. LSZ map show 40.42% area is falling under medium susceptibility class, 34.5 % under low and 25.07% is under high susceptibility class which cover tehsils Mandi, Chachyot, Thunag and some part of Padhar, Aut and Bali Chowki. Further to validate these result areas under curve (AUC) method is use which give prediction rate of 76.06%.

This study investigates the accuracy of various DEMs (SRTM DEM, ASTER GDEM, and ALOS PALSAR DEM) for the area of the designing Pskom water reservoir (recommended to construction in Pskom River, in Tashkent region. DEMs are compared for the study area using the Global Mapper application and selection Ground Control Points (GCP). The RMSE we calculate is the most easily interpreted statistic as the square root of the mean square error because it has the same units as the quantity drawn on the vertical axis. Results show that SRTM based measurements of ground control points (GCPs) exhibit RMSE of 15.72 m while ASTER DEM based measurements exhibits and RMSE of 18.47 m, ALOS PALSAR exhibit RMSE of 14.02 m for the Water reservoir located in the plain. There are AOS PALSAR outperforms SRTM and ASTER DEM in detecting vertical accuracy. Based on the capabilities of the Global Mapper program, we can build the longitudinal profile of the approximate location where the dam can be built in each DEM and compare. The results obtained show that the dam height is 187 m at ALOS PALSAR DEM, 168 m at ASTER GDEM, and 175 m at SRTM. The study found that using ALOS PALSAR data in the design of the proposed Pskom Reservoir for construction leads to a more accurate result. Comparing the DEMs data shows that there is more difference between the vertical accuracy; the horizontal accuracy level is almost the same. The results were obtained using ALOS PALSAR data in determining the storage volume (W=479368568 m3) and area (F=8.31 sq., km) of the water reservoir.