ArticleHeliyon2019
Multi-temporal Land Use Land Cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multi-sensor satellite image calibration strategy.
Article in Heliyon, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Analyzing Land Use/Land Cover Changes Using Google Earth Engine and Random Forest Algorithm and Their Implications to the Management of Land Degradation in the Upper Tekeze Basin, Ethiopia.TheScientificWorldJournal · 2024Article
- Article
- Habitat suitability modeling for the endangered Bengal slow loris (Nycticebus bengalensis) in the Indo-Chinese subregion of India: a case study from southern Assam (India).Primates; journal of primatology · 2022Article
- Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India.Environmental monitoring and assessment · 2021Article
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Abstract
Multi-temporal and multi-sensor satellite data calibration is an inherent problem in remote sensing-based applications. If multiple satellite scenes cover the study area, it is difficult to compare and process the images for change detection and long-term trend analysis of the same day and/or seasons from different satellites or sensors. Moreover, the validation of all the past images is a challenge due to unavailability of past ground truth datasets. The proposed calibration paradigm in this study is based on radiance normalization in the spatial and spectral domain to ease the alignment of multiple images into an identical radiometric foundation. In this study, an intuitive radiometric correction technique (at a daily, monthly and yearly scale) was proposed, aimed at all Landsat sensors' datasets for long-term Land Use Land Cover (LULC) trend analysis for a dry semi-arid river basin in western India, facing drought conditions. The post-calibration mosaiced images were smooth, and meaningful LULC classification results could be obtained easily for all the years. The LULC change dynamics were analyzed and compared for the years 1972, 1980, 1991, 2001, 2011 and 2016 in Shivna River Basin. During these study periods, wasteland was found to be the most altered class, followed by agricultural land and forest. The spatial extent of agricultural land was found to decrease linearly, while forest cover showed an exponential decrease; a linear increase was observed in wasteland. Though during the 44 years study period (1972-2016), 241.48 km
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