Identification of land degradation hotspots in semiarid region of Anantapur district, Southern India, using geospatial modeling approaches

• Published in: Modeling earth systems and environment Vol. 6; no. 3; pp. 1841 - 1852
• Main Authors: Kumar, B. Pradeep, Babu, K. Raghu, Rajasekhar, M., Ramachandra, M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2020; Springer Nature B.V
• Subjects: Change detection; Chemistry and Earth Sciences; Classification; Computer Science; Conflicts; Desertification; Detection; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Ecosystems; Environment; Environmental degradation; Geographical information systems; Ground truth; Hot spots; Identification; Image classification; Image detection; Land cover; Land degradation; Logging; Math. Appl. in Environmental Science; Mathematical Applications in the Physical Sciences; Modelling; Monitoring systems; Normalized difference vegetative index; Original Article; Physics; Semi arid areas; Statistics for Engineering; Tabulation; Vegetation; Vegetation index; India; Land degradation; Geographical information system; NDVI; Remote sensing; Desertification
• ISSN: 2363-6203; 2363-6211
• DOI: 10.1007/s40808-020-00794-x

Land degradation and desertification has been graded as a foremost environmental dispute for the coming eras. Thus, the surveillance and early detection of degradation is a major objectives for a number of scientific and policy organizations, with RS and GIS techniques being a candidate choice for the development of monitoring systems. This study was carried out as an attempt to develop a new modeling approach for identification of desertified hotspots based on normalized difference vegetation index (NDVI) in semiarid region of Anantapur district, Southern India. A supervised classification was carried out on the six reflective bands for the past 28 years, i.e., from 1990 to 2018 individually with the aid of ground truth data. Ground truth information collected during field data and land cover map of 2005–2006 was used to assess the accuracy of the classification results. Using ancillary data, visual interpretation and expert knowledge of the area through GIS further refined the classification results. Post-classification change detection technique was used to produce change image through cross-tabulation. Changes among different NDVI classes such as water bodies, agricultural fields, vegetation, degraded lands, desertified lands were assessed. During the study period, a very severe land degradation has taken place as a results reveals that 31.93% of the land in this region is facing the land degradation and desertification conditions; in that 17.34% of the land face the severe land degradation and 14.59% of the land have been desertified. These changes in land cover led to vegetation degradation and water logging in part of the study area.