article · Applied Sciences
Rapid urbanisation in Gelephu, Bhutan, has prompted an assessment of land use, land cover, and vegetation health to understand local environmental changes. Using ten-metre resolution Sentinel-2 satellite imagery alongside a Random Forest classifier, land transitions and Normalised Difference Vegetation Index dynamics were analysed between 2016 and 2023. The classification framework achieved an area under the curve accuracy of up to 0.89. During the seven-year period, urban areas grew by 5.65 percent in Gelephu and 15.05 percent in the municipal entity of Gelephu Thromde, reflecting intense infrastructure development. Crucially, the vegetation assessment identified a 75.11 percent reduction in healthy vegetation across Gelephu. These findings provide baseline evidence to assist environmental protection strategies, municipal planning, and ecosystem balancing aligned with national forest conservation targets.
Balancing infrastructure expansion with ecosystem preservation is a central challenge for fast-growing mountain settlements. By measuring land cover changes and tracking vegetation health at high resolution, this research provides measurable evidence of the environmental costs of urban growth. Municipal authorities and environmental regulators can use such data to introduce targeted zoning policies and protect critical forest cover.
The methodology provides an applied and tested monitoring pipeline combining satellite data and machine learning for local spatial analysis. Potential users include municipal planning agencies, geospatial analytics providers, and environmental consultancies seeking cost-effective tools to audit land development and vegetation loss. Because the workflow uses existing satellite infrastructure and proven algorithms, it is near-market and readily adaptable for decision-support software in municipal management.
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Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.
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DOI: 10.3390/app14041578
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