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Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.
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http://dx.doi.org/10.1016/j.heliyon.2024.e32625 | DOI Listing |
Environ Manage
September 2025
TEMSUS Research Group, Catholic University of Ávila, Ávila, Spain.
Forests have been increasingly affected by natural disturbances and human activities. These impacts have caused habitat fragmentation and a loss of ecological connectivity. This study examines potential restoration pathways that reconnect the five largest forest cores in the Castilla y León region of Spain.
View Article and Find Full Text PDFEnviron Monit Assess
September 2025
School of Civil Engineering, Putian University, Putian City, 351100, China.
Land degradation (LD) is a critical environmental challenge caused by human activities and climate change. Reversing degraded land requires effective LD monitoring. The UN Sustainable Development Goal (SDG) indicator 15.
View Article and Find Full Text PDFEnviron Monit Assess
September 2025
Institute of Earth Sciences, Southern Federal University, Rostov-On-Don, Russia.
Sustainable urban development requires actionable insights into the thermal consequences of land transformation. This study examines the impact of land use and land cover (LULC) changes on land surface temperature (LST) in Ho Chi Minh city, Vietnam, between 1998 and 2024. Using Google Earth Engine (GEE), three machine learning algorithms-random forest (RF), support vector machine (SVM), and classification and regression tree (CART)-were applied for LULC classification.
View Article and Find Full Text PDFMar Pollut Bull
September 2025
CSIR-National Institute of Oceanography, Dona Paula, Goa, 403004, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
The Indian Sundarban Delta (ISD), located at the confluence of the Ganga-Brahmaputra-Meghna river system along India's eastern coast, is among the world's most geomorphologically dynamic and environmentally vulnerable deltaic systems. Over the past five decades, the region has undergone substantial morphodynamic changes driven by natural forces such as relative sea-level rise, wave action, and sediment flux, as well as anthropogenic factors like upstream water regulation via dams and barrages. This study examines the long-term evolution of shoreline and island morphology across the ISD from 1972 to 2025 using multi-temporal Landsat datasets under consistent tidal conditions.
View Article and Find Full Text PDFEnviron Monit Assess
September 2025
Indira Gandhi Conservation Monitoring Centre, World Wide Fund-India, New Delhi, 110003, India.
Understanding the intricate relationship between land use/land cover (LULC) transformations and land surface temperature (LST) is critical for sustainable urban planning. This study investigates the spatiotemporal dynamics of LULC and LST across Delhi, India, using thermal data from Landsat 7 (2001), Landsat 5 (2011) and Landsat 8 (2021) resampled to 30-m spatial resolution, during the peak summer month of May. The study aims to target three significant aspects: (i) to analyse and present LULC-LST dynamics across Delhi, (ii) to evaluate the implications of LST effects at the district level and (iii) to predict seasonal LST trends in 2041 for North Delhi district using the seasonal auto-regressive integrated moving average (SARIMA) time series model.
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