Qualitative And Quantitative Analysis Of Topographically Derived Cva Algorithms Using Modis And Landsat-8 Data Over Western Himalayas, India

The change detection via remote sensing offers a cost-effective solution to monitor the earth surface variations and manage natural resources. Change vector analysis (CVA) is one of the most appropriate suitable amongst various change detection techniques that attract the interest of researchers due to its potential and overall applicability. However, the effectiveness of recently developed CVA methods is yet to be explored over the mountainous region like Himalayas with most commonly used satellite sensors such as MODIS and Landsat-8. In the present analysis, we have performed the qualitative and quantitative analysis of advanced CVA algorithms such as improved CVA (ICVA), posterior probability-based CVA (CVAPS), median CVA (MCVA) and fuzzy-based CVA (FCVA). Two experiments were conducted on different study sites of western Himalayas (Himachal Pradesh, India) using topographically corrected MODIS and Landsat-8 datasets. The experimental outcomes of change maps confirm the effectiveness of FCVA (86.80?87.6%) as compared to MCVA (78.4?83.6%), CVAPS (82.8?84%) and ICVA (73.6?80.4%). This study provides a comprehensive framework to explore the potential of different CVA algorithms to detect the land use and land cover changes especially over rugged terrain region.