A multinomial logistic model-based land use and land cover classification for the South Asian Association for Regional Cooperation nations using Moderate Resolution Imaging Spectroradiometer product

Ram Kumar Singh (), Vinay Shankar Prasad Sinha (), Pawan Kumar Joshi () and Manoj Kumar ()
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Ram Kumar Singh: TERI School of Advanced Studies
Vinay Shankar Prasad Sinha: TERI School of Advanced Studies
Pawan Kumar Joshi: Jawaharlal Nehru University
Manoj Kumar: Forest Research Institute (FRI)

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 4, No 65, 6106-6127

Abstract: Abstract The land-use and land-cover (LULC) mapping is crucial for the management and planning of a nation. The land use and land cover are the two separate terminologies often used interchangeably. We mapped LULC for the nations of South Asian Association for Regional Cooperation represented by Afghanistan, Bangladesh, Bhutan, India, Nepal, the Maldives, Pakistan and Sri Lanka covering an area of ca. 5,100,000 km2. Mapping LULC is a cumbersome process for a larger area; nevertheless, in recent decades the satellite-based Earth observation has provided a great impetus and various approaches have been adopted to map LULC. In this study, we used the multinomial logit model (Mnlogit) to classify Moderate Resolution Imaging Spectroradiometer (MODIS)-derived time-series data for mapping LULC. The MODIS-archived images for the years 2004–2007 and 2016 were used to map LULC for corresponding years. For the same years, maps were available from IGBP, FAO and NR Census, India, for comparison; hence, these years were selected. The classification of images for each of the years was found to be more consistent and accurate in comparison with the existing LULC maps of IGBP and FAO, and hence a robust approach. The method can be adopted to map global resources. We achieved overall accuracy ranging from 76 to 82% with kappa statistic (K-hat) ranging from 0.64 to 0.73, which is better than previous works. The study demonstrates an effective way to utilize remote sensing data for mapping LULC for a larger area with reasonable accuracy.

Keywords: Agriculture; Forestry; NDVI; LULUCF; Logistic model; Savitzky–Golay (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00864-1

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