Assessing land use/land cover change detection of north-eastern watersheds of Kashmir valley using GIS and remote sensing techniques

Author:

Gull Sarvat1ORCID,Shah Shagoofta Rasool1,Dar Ayaz Mohmood1

Affiliation:

1. Department of Civil Engineering, National Institute of Technology, Srinagar 190006, India

Abstract

Abstract Land use changes in the Kashmir Himalayas must be mapped and monitored for long-term development and efficient planning. This work uses geospatial technologies such as remote sensing and geographic information system to track changes in land cover trends in four main watersheds in the Kashmir Valley's north-eastern Himalayas from 2003 to 2013. Land cover maps were created using images from the Landsat-5 Thematic Mapper. The watershed's land use/land cover (LULC) maps were created using supervised classification utilizing the maximum likelihood classifier (MLC). Over the previous decade, the LULC in the study watersheds has undergone a series of intricate changes as a result of deforestation, climate change, and agroforestry growth. A total of 11 major LULC classifications were discovered, indicating that forests are the most common land use in all four watersheds. Forest cover, river beds, water bodies, non-perennial snow, and glaciers have all decreased significantly, whereas scrubland, horticulture, rock mass, built-up areas, barren land, and agriculture have all increased significantly, except for Sindh watershed, where the scrubland class has decreased by 5.97% from 2003 to 2013. The study's methodology and conclusions point to crucial policy implications for long-term LULC management in the Kashmir Himalayas' Madhumati, Arin, Sindh, and Lidder watersheds.

Publisher

IWA Publishing

Subject

Water Science and Technology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The Himalayas in the Anthropocene;The Himalayas in the Anthropocene;2024

2. Assessment of Land Utilization Pattern and Their Relationship with Surface Temperature and Vegetation in Sikkim, India;Advanced Remote Sensing for Urban and Landscape Ecology;2023

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