Analyzing Land Use Change and Fragmentation Using Earth Observation Data: A Case Study of the Ken River Basin, India

Land use/land cover (LULC) are a critical concern due to their significant impact on ecosystems, biodiversity, and climate patterns. The objective of this study is to understand the dynamics of land use and land cover (LULC) changes and quantify the fragmentation in the Ken River Basin using open-access remote sensing data and the FRAGSTATS software. Landsat images from 1995, 2015, and 2022 were utilized to analyze changes over these distinct time periods. We employed supervised classification using the maximum likelihood method to produce detailed land use and land cover maps. The analysis identified five land use classes: water bodies, forest, barren land, cultivable land, and built-up land, with cultivable land emerging as the most dominant class, followed closely by forest cover. To quantify the land cover classes, various landscape metrics at the class level were employed. The results reveal a concerning trend: both forest and cultivable land classes are experiencing increasing fragmentation over time. This rising fragmentation poses significant risks to the ecological integrity and sustainability of the Ken River Basin. By quantifying long-term land cover changes, this study assesses the effectiveness of conservation efforts and utilizes remote sensing and GIS techniques to inform and enhance best management practices in the region.

Authors

Institutions

Publication Details

Journal
Asian Journal of Environment & Ecology
Published
2025-04-07
DOI
https://doi.org/10.9734/ajee/2025/v24i4689
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analyzing Land Use Change and Fragmentation Using Earth Observation Data: A Case Study of the Ken River Basin, India

Arnab Kundu, Rajesh K. Pandey, Ankush V. Lal, Mukesh Kumar et al.
Asian Journal of Environment & Ecology
Flood Risk Assessment and Management
article

Analyzing Land Use Change and Fragmentation Using Earth Observation Data: A Case Study of the Ken River Basin, India

Arnab Kundu, Rajesh K. Pandey, Ankush V. Lal, Mukesh Kumar, Easterlily Shylla
article en

Abstract

Land use/land cover (LULC) are a critical concern due to their significant impact on ecosystems, biodiversity, and climate patterns. The objective of this study is to understand the dynamics of land use and land cover (LULC) changes and quantify the fragmentation in the Ken River Basin using open-access remote sensing data and the FRAGSTATS software. Landsat images from 1995, 2015, and 2022 were utilized to analyze changes over these distinct time periods. We employed supervised classification using the maximum likelihood method to produce detailed land use and land cover maps. The analysis identified five land use classes: water bodies, forest, barren land, cultivable land, and built-up land, with cultivable land emerging as the most dominant class, followed closely by forest cover. To quantify the land cover classes, various landscape metrics at the class level were employed. The results reveal a concerning trend: both forest and cultivable land classes are experiencing increasing fragmentation over time. This rising fragmentation poses significant risks to the ecological integrity and sustainability of the Ken River Basin. By quantifying long-term land cover changes, this study assesses the effectiveness of conservation efforts and utilizes remote sensing and GIS techniques to inform and enhance best management practices in the region.

Asian Journal of Environment & EcologyVol. 24(4)
Sam Higginbottom Institute of Agriculture (IN), Bankura University (IN)
Openalex Percentile: Top 92%
Flood Risk Assessment and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.