Assessment of land degradation using comprehensive geostatistical approach and remote sensing data in GIS-model builder

• Main Authors: Mohamed A.E. AbdelRahman, A. Natarajan, Rajendra Hegde, S.S. Prakash
• Format: Article
• Language: English
• Published: Elsevier 2019-12-01
• Series: Egyptian Journal of Remote Sensing and Space Sciences
• Online Access: http://www.sciencedirect.com/science/article/pii/S1110982317303617

This study was conducted to assess Land Degradation (LD) status under different land use. Geostatistical technique used to interpolate spatially distribution of soil physical, chemical and biological properties. Salinity indices were applied on Hyperspectral and Multispectral Data to predict the salt affected areas. Arc GIS model-builder implemented to integrate the available LD methodologies and produce the overall degradation map of the study area in Chamrajanagar district (CDK), Karnataka, India. Remote sensing data was found to be useful tools to map land resources, especially in the areas where accessibility is limited like mountains. This study determined spatial distribution by calculating different soil properties for soils profiles. For LD calculations, eighteen soil profiles were dug and 79 samples were analyzed. This along with the parameters taken into consideration i.e., soil, slope, rainfall, DEM, land use, and land characteristics maps. It was found by adopting the logical criteria that LD of CDK categorized as very high, high, moderate, low and very low. The result of this research work could be potentially used as a useful tool to guide policy decision makers for sustainable land resource management in CKD. Based on the imagery interpretation and soil map unit description, hotspots were identified for representing different types of degraded soils. From the physical and chemical characteristics of pedons studied, it has been found that soils of CDK are exposed to degradation in the surface and sub surface horizons. Keywords: Chamrajanagar district, Hyperspectral, Multispectral, Geostatistical, GIS spatial model, Land degradation