Land-Use and Land-Cover Change Detection in a North-Eastern Wetland Ecosystem of Bangladesh Using Remote Sensing and GIS Techniques

Abstract

Lakshmibaur-Nalair Haor, a freshwater wetland ecosystem is situated in the north-eastern region of Bangladesh. This place hosts the second largest freshwater swamp forest in Bangladesh. Containing rich biodiversity, this unique area experiences significant landscape changes. This study examines land-use and land-cover (LULC) changes between 1989 and 2019 in the Lakshmibaur-Nalair Haor area by operating Landsat multispectral imageries through remote sensing (RS) and geographic information system (GIS) techniques. The changing status of the haor was analyzed by initiating normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI). The unsupervised classification technique was implemented to classify these images into five major classes (vegetation, cropland, bare soil, shallow water, and deep water bodies) using threshold values of NDVI and MNDWI. After accuracy assessment, the post-classification comparison method was performed to evaluate the change detection. This study demonstrates that this valuable area lost ~ 2208.6 ha (37.54%) of the deep water body and 489.6 ha (8.34%) of vegetation over the last 3 decades. However, it has gained about 1729 ha (29.39%) of cropland, 2673 ha (45.44%) of shallow water and 1124 ha (28%) of bare soil. Such changes indicate significant human interventions such as expansion of croplands with increased population pressure. Gradual change of deep water into shallow water over time is enabling local community to expand agricultural lands and activities during the dry season. This study’s findings are useful in understanding and tracking changes in wetlands in Bangladesh and other similar settings.

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

“…Shwarnali Bhattacharjee employed the Normalized Difference Vegetation Index (NDVI) and thresholding for land cover classification and change detection [107]. Heena Kaldane used unsupervised Normalized Difference Ratio (NDR) as a change index (CI) and then applied thresholding to extract binary masks for changed and unchanged areas [108].…”

Un-planned Urban Growth Monitoring from 1991 to 2021 of Aizawl City, North-east India by Multi- Temporal Changes and CA-ANN Model

“…Several studies were conducted based on dynamic changes of land use and land cover with land cover change detection and supervised classification (Akyürek et al 2018;Seyam et al 2023) and accuracy assessment using the kappa coefficient (Tewabe and Fentahun, 2020;Hussain and Karuppannan, 2023), and prediction by satellite image of landsat-7 and landsat-8 using cellular automated and Markov chains (Abijith and Saravanan, 2022;Wang et al 2021). Deep learning (DL)-based method (Song et al 2021), supervised classification, NDVI method (Pande et al 2021) including field verification and Google Earth Professional (Kamel, 2020), NDWI method (Raut et al 2020), MNDWI method (Bhattacharjee et al 2021), transition matrix method (Bagwan and Sopan, 2021), post classification matrix (Kouhgardi et al 2022; Márquez-Romance et al 2022; Das and Angadi, 2022), classprior object-oriented conditional random field (COCRF) method (Shi et al 2020), maximum likelihood classifier (MLC) method (Kumar and Jain, 2020; Saini et al 2019;Sarif and Gupta, 2022), Siamese global learning framework (Zhu et al 2022), preprocessing and classification and accuracy assessment (Thakur et al 2020;Mondal et al 2021;Mondal et al 2022), using various satellite imagery such as Landsat, MODIS, Sentinel and SPOT. The advantage of Landsat satellite data is the free accessibility of multi-temporal time series since 1972 (Lu et al 2019).…”

Application of GIS and Remote Sensing to Identify Changes in Surface Water Bodies and Wetland Depletion in the Kumasi Metropolis, Ghana.

“…Remote sensing and geographic information systems (GIS) are powerful tools that have been used to monitor and assess the changes in surface water bodies and wetlands [4]; [5]; [6]. Remote sensing is the science of acquiring data about the Earth's surface using sensors onboard satellites, aircraft, or drones [7]; [8].…”