MONITORING VEGETATION DENSITY USING SPECTRAL VEGETATION INDICES: A CASE STUDY OF MALAM JABBA REGION, DISTRICT SWAT, PAKISTAN
Keywords: GIS, Satellite Remote Sensing, Digital Elevation Model, Vegetation density monitoring, Spectral Indices
Abstract. The limited forest resources with a higher deforestation rate per annum, Pakistan ranks the second highest in Asia. FAO reported that the annual forest cover change rate during 1990–2000 was −1.8% and increased to −2.2 % between 2000–2010. Most of Pakistan's total forest resources, dominantly natural forest, are situated in the Northern regions. Stepping into the corridor of the 21st century, the Spatio-temporal analysis has been evolved using Satellite Remote Sensing data aided with Geographic Information System) GIS) platforms. The study is carried out over the mountainous vegetation land of Malam Jabba, district Swat, Khyber Pakhtunkhwa, Pakistan. Due to varying topography and the region being part of the agro-forestry zone, drastic changes were observed in vegetation and built-up areas. The vegetation cover has been identified and classified based on elevation throughout the area. This study has provided essential insights into vegetation cover change over a period of four decades. Vegetation cover is classified into high to very high, medium, and low to very low. The Landsat and the SRTM DEM satellite imageries were exported to the ERDAS software for pre-and post-processing, and for further analysis ArcGIS 10.5 was used, where the vegetation density change for each period was computed from the pixels by using vegetation indices like VCI, NDVI, and SAVI. The results show a significant decline from 1980 to 2010 in vegetation density in the Northwestern direction; however, an increasing trend can be seen in 2020 due to awareness and the Government’s Billion Tree Tsunami initiative. Such studies can significantly benefit researchers and decision-makers interested in satellite remote sensing for forest and other vegetation cover monitoring and management at a regional scale.