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Desertification vulnerability index—an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India

2017; Wiley; Volume: 29; Issue: 1 Linguagem: Inglês

10.1002/ldr.2850

1099-145X

S. Dharumarajan, Thomas F. A. Bishop, Rajendra Hegde, Surendra Singh,

Soil Geostatistics and Mapping

Land Degradation & DevelopmentVolume 29, Issue 1 p. 150-161 RESEARCH ARTICLE Desertification vulnerability index—an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India Subramanian Dharumarajan, Corresponding Author Subramanian Dharumarajan sdharmag@gmail.com orcid.org/0000-0002-0614-2101 ICAR-National Bureau of Soil Survey and Land Use Planning, Hebbal, Bangaluru, 560024 India Correspondence S. Dharumarajan, ICAR-National Bureau of Soil Survey and Land Use Planning, Hebbal, Bangaluru 560024, India. Email: sdharmag@gmail.comSearch for more papers by this authorThomas F.A. Bishop, Thomas F.A. Bishop School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, 2006 AustraliaSearch for more papers by this authorRajendra Hegde, Rajendra Hegde ICAR-National Bureau of Soil Survey and Land Use Planning, Hebbal, Bangaluru, 560024 IndiaSearch for more papers by this authorSurendra Kumar Singh, Surendra Kumar Singh ICAR-National Bureau of Soil Survey and Land Use Planning, Amaravati Road, Nagpur, 440033 IndiaSearch for more papers by this author Subramanian Dharumarajan, Corresponding Author Subramanian Dharumarajan sdharmag@gmail.com orcid.org/0000-0002-0614-2101 ICAR-National Bureau of Soil Survey and Land Use Planning, Hebbal, Bangaluru, 560024 India Correspondence S. Dharumarajan, ICAR-National Bureau of Soil Survey and Land Use Planning, Hebbal, Bangaluru 560024, India. Email: sdharmag@gmail.comSearch for more papers by this authorThomas F.A. Bishop, Thomas F.A. Bishop School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, 2006 AustraliaSearch for more papers by this authorRajendra Hegde, Rajendra Hegde ICAR-National Bureau of Soil Survey and Land Use Planning, Hebbal, Bangaluru, 560024 IndiaSearch for more papers by this authorSurendra Kumar Singh, Surendra Kumar Singh ICAR-National Bureau of Soil Survey and Land Use Planning, Amaravati Road, Nagpur, 440033 IndiaSearch for more papers by this author First published: 21 November 2017 https://doi.org/10.1002/ldr.2850Citations: 24Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract There is a need for the up-to-date assessment of desertification/land degradation maps that are dynamic in nature at different scales for comprehensive planning and preparation of action plans. This paper aims to develop the desertification vulnerability index (DVI) and predict the different desertification processes operating in Anantapur District, India, based on machine language techniques. Climate, land use, soil, and socioeconomic parameters were used to prepare DVI by a multivariate index model. The computed DVI along with climate, terrain, and soil properties was used as explanatory variable to predict the desertification processes by using a random forest model. About 14.2% of the area was created as a training dataset in 9 places for modeling and remaining area was tested for prediction of desertification processes. We used desertification status map (DSM) of Anantapur District prepared under Desertification status mapping of India–2nd cycle as a reference dataset for calculation of accuracy indices. Kappa and classification accuracy index were calculated for training and validation datasets. We recorded overall accuracy rate and kappa index of 85.5% and 75.8% for training datasets and 71.0% and 51.8% for testing datasets. The results of variable importance analysis of random forest model showed that DVI was the most important predictor followed by potential evapotranspiration and Normalized Difference Vegetation Index for prediction of desertification processes. The results from this work given new insight into using the existing knowledge on prediction of desertification in unvisited areas and also quick update of DSM maps. Citing Literature Volume29, Issue1January 2018Pages 150-161 RelatedInformation