Vol18 No.1: 126-140
【Title】Topographically derived subpixel-based change detection for monitoring changes over rugged terrain Himalayas using AWiFS data
【Author】Vishakha SOOD1; Hemendra Singh GUSAIN2; Sheifali GUPTA1; Sartajvir SINGH3*
【Addresses】1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India; 2 Snow and Avalanche Study Establishment, DRDO, Chandigarh 160017, India; 3 Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, 174 103, India.
【Corresponding author】Sartajvir SINGH
【Citation】Sood V, Gusain HS, Gupta S, Singh S (2021) Topographically derived subpixel-based change detection for monitoring changes over rugged terrain Himalayas using AWiFS data. Journal of Mountain Science 18(1). https://doi.org/10.1007/s11629-020-6151-y
【Abstract】Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards. Conventional techniques generally focus on per-pixel based processing and overlook the sub-pixel variations occurring especially in case of low or moderate resolution remotely sensed data. However, the existing subpixel-based change detection (SCD) models are less effective to detect the mixed pixel information at its complexity level especially over rugged terrain regions. To overcome such issues, a topographically controlled SCD model has been proposed which is an improved version of widely used per-pixel based change vector analysis (CVA) and hence, named as a subpixel-based change vector analysis (SCVA). This study has been conducted over a part of the Western Himalayas using the advanced wide-field sensor (AWiFS) and Landsat-8 datasets. To check the effectiveness of the proposed SCVA, the cross-validation of the results has been done with the existing neural network-based SCD (NN-SCD) and per-pixel based models such asfuzzy-based CVA (FCVA) and post-classification comparison(PCC). The results have shown that SCVA offered robust performance (85.6%-86.4%) as compared to NN-SCD (81.6%-82.4%), PCC
(79.2%-80.4%), and FCVA (81.2%-83.6%). We concluded that SCVA helps in reducing the detection of spurious pixels and improve the efficacy of generating change maps. This study is beneficial for the accurate monitoring of glacier retreat and snow cover variability over rugged terrain regions using moderate resolution remotely sensed datasets.
【Keywords】Topographic correction; Change vector analysis (CVA); Subpixel-based change detection (SCD); Western Himalayas.