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

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 km2 area was converted to agricultural land from wasteland, but more than double that land was converted to wasteland from agricultural land; alarmingly, 5.18% (8.12% in 1972 and 2.94% in 2016) forest cover decreased. The existing forest cover in 2016 is approximately one-third compared to 1972. The present work provides a generic framework for the calibration of multi-temporal and multi-sensor satellite images for long-term LULC trend analysis, which can be adopted for other satellite datasets