Land degradation (LD) and deserti cation is a serious ecological, environmental, and social-economic threat in the world, and there is a demanding need to develop accountable and reproducible techniques to assess it at different scales. In this study to assess LD and deserti cation with the help of Remote Sensing (RS) and Geographical Information System (GIS) in the study region for the period of past 29 years i.e., from 1990 to 2019. The severity of LD and deserti cation was assessed quantitatively by collecting twelve soil samples in the study region, and analyzing the eleven soil Physico-chemical parameters and these values have made correlated with Digital Number (DN) values with LANDSAT 8 satellite image. The land cover analysis of LANDSAT imagery revealed that the water body slightly increased from 0.29% in 1990 to 0.46% in 2019, and built-up-land increased from 2.87% in 1990 to 5.31% in 2019. Vegetation is decreased from 52.03% in 1990 to 28.57%. Fallow land, degraded land, and deserti ed lands are increased at alarming rates, respectively 13.71% to 26.35, 18.57% to 22.31%, and 12.53% to 17.00%. It is also established that the multi-temporal analysis of change detection data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 29 years, considerable progress has been made in the respective research.
The data deals with the functions that automatically extracted lineaments from the Cartosat, ASTER and SRTM of Digital Elevation Model (DEM) of different spatial resolutions, in the software ArcGIS 10.4. The extracted lineaments result shows the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) DEM gives the lowest number of lineaments reflects Cartosat and SRTM (Shuttle Radar Topography Mission) DEM shows a medium number of lineaments. Cartosat DEM is most appropriate for extraction of contours precisely rather than ASTER and SRTM. This study reveals the Cartosat DEM data is best to use extraction of lineaments in the Indian provinces, offers at most comprehensive geological structural info amongst all the data sets. The extracted lineaments lengths and densities are determined by the statistical method. Based on the data generated lineament density and rose diagram. Cartosat DEM data are the best suited for studying very small areas as through geological and structural information can be mined by using this data.
The conservation and sustainable advancement of soil and water assets is one of the fundamental standards for improvement of arid and semi-arid regions of India. The present study is underway to evaluate the Artificial Groundwater Recharge Zones (AGRZ) in the semi-arid region of Anantapur district, Andhra Pradesh, India using Remote Sensing (RS), Geographical Information System (GIS) and Analytical Hierarchy Process (AHP) technique. The comparative weights were assigned to different thematic layers with the help of the decision making tool of AHP. A set of eight thematic layers influence groundwater potential (GWP) is determined based on their corresponding weights, which depend on a Saaty’s 9 points scale. These weights are normalized using AHP technique to identify the AGRZs. Five AGRZs were recognized as very low, low, moderate, good and very good, depending on its suitability to identify the sites for groundwater recharge. About 4.29 % (8.96km2) and 17.70 % (36.95km2) area in the region show very good and good potentials of artificial groundwater recharge, respectively. On the other hand 61.59% (128.60km2), 11.94% (24.94km2) and 4.48 % (9.35km2) area showed moderate, poor and very poor potentials. Overall accuracy of AGRZ map is 82.05%. 92 check dams, 19 percolation tanks and 7 check walls were found suitable in the region. The effectiveness and prediction ability of the method depends on integrity of the criterion used. AHP based methodology can be useful for precise and reliable analysis and predictions of groundwater in semi-arid regions of India.
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