Assessing spatiotemporal LULC changes using NDVI and EVI in a freshwater swamp forest of Bangladesh

The Lakshmi Baor Freshwater Swamp Forest (LBFSF) is a vital freshwater swamp ecosystem in northeastern Bangladesh that is renowned for its biodiversity and ecological significance. However, this unique habitat faces significant degradation owing to both natural and human influences, which threatens the biodiversity and livelihoods of local communities. This study investigated land use and land cover (LULC) changes in the Lakshmi Baor Swamp Forest from 2000 to 2020 to understand the extent and drivers of forest degradation. Using Landsat 7 and Landsat 8 data, we applied two vegetation indices, the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), to assess the changes in forest cover over four distinct periods: 2000–2003, 2003–2018, 2018–2020, and 2000–2020. Land cover was categorized into forest, sparse vegetation, bare land, and water using maximum-likelihood supervised classifiers, and post classification change detection was employed to track LULC dynamics over time. The analysis revealed a substantial decrease in forest area, with the NDVI-based classifications showing a decline from 59.68 to 43.77% and the EVI classifications showing a decrease from 59.54 to 41.57%. Conversely, bare land expanded significantly, with the NDVI increasing from 14.44 to 26.71% and the EVI increasing from 9.47 to 34.42%. Although the water area slightly increased, sparse vegetation exhibited minimal changes in the NDVI assessments but decreased according to the EVI. These findings highlight critical trends in forest loss and land degradation, which have direct consequences for biodiversity, ecosystem services, and local livelihoods. This study highlights the efficacy of the NDVI, which achieved an 3.64% greater overall accuracy than the EVI in assessing LULC changes. The observed degradation patterns underscore the urgent need for targeted conservation. We recommend designating the Lakshmi Baor Swamp Forest as a protected zone and implementing sustainable resource management practices to safeguard the ecosystem. These insights provide a scientific basis for policymakers and conservationists to develop strategies to mitigate further ecological losses and to support sustainable development in the region.

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Publication Details

Journal
Discover Forests
Published
2025-09-15
DOI
https://doi.org/10.1007/s44415-025-00037-w
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00
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article

Assessing spatiotemporal LULC changes using NDVI and EVI in a freshwater swamp forest of Bangladesh

Mrityunjoy Kunda, Chan-Su Yang, Natasa Tasnia Shipra, Iftekhar Ahmed Fagun et al.
Discover Forests
Remote Sensing in Agriculture
article

Assessing spatiotemporal LULC changes using NDVI and EVI in a freshwater swamp forest of Bangladesh

Mrityunjoy Kunda, Chan-Su Yang, Natasa Tasnia Shipra, Iftekhar Ahmed Fagun, Sree Juwel Kumar Chowdhury, Ahmed Harun‐Al‐Rashid
article en

Abstract

The Lakshmi Baor Freshwater Swamp Forest (LBFSF) is a vital freshwater swamp ecosystem in northeastern Bangladesh that is renowned for its biodiversity and ecological significance. However, this unique habitat faces significant degradation owing to both natural and human influences, which threatens the biodiversity and livelihoods of local communities. This study investigated land use and land cover (LULC) changes in the Lakshmi Baor Swamp Forest from 2000 to 2020 to understand the extent and drivers of forest degradation. Using Landsat 7 and Landsat 8 data, we applied two vegetation indices, the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), to assess the changes in forest cover over four distinct periods: 2000–2003, 2003–2018, 2018–2020, and 2000–2020. Land cover was categorized into forest, sparse vegetation, bare land, and water using maximum-likelihood supervised classifiers, and post classification change detection was employed to track LULC dynamics over time. The analysis revealed a substantial decrease in forest area, with the NDVI-based classifications showing a decline from 59.68 to 43.77% and the EVI classifications showing a decrease from 59.54 to 41.57%. Conversely, bare land expanded significantly, with the NDVI increasing from 14.44 to 26.71% and the EVI increasing from 9.47 to 34.42%. Although the water area slightly increased, sparse vegetation exhibited minimal changes in the NDVI assessments but decreased according to the EVI. These findings highlight critical trends in forest loss and land degradation, which have direct consequences for biodiversity, ecosystem services, and local livelihoods. This study highlights the efficacy of the NDVI, which achieved an 3.64% greater overall accuracy than the EVI in assessing LULC changes. The observed degradation patterns underscore the urgent need for targeted conservation. We recommend designating the Lakshmi Baor Swamp Forest as a protected zone and implementing sustainable resource management practices to safeguard the ecosystem. These insights provide a scientific basis for policymakers and conservationists to develop strategies to mitigate further ecological losses and to support sustainable development in the region.

Discover ForestsVol. 1(1)
Korea Institute of Ocean Science and Technology (KR), Sylhet Agricultural University (BD), Culture Resource (EG), Korea University of Science and Technology (KR)
Openalex Percentile: Top 76%
Remote Sensing in Agriculture
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