article · ENVIRONMENTAL SYSTEMS RESEARCH
Tracking land use and land cover dynamics over time provides vital evidence for sustainable landscape and watershed management. Using the Google Earth Engine platform alongside machine learning algorithms and Landsat satellite imagery, land dynamics across a thirty-year period between 1993 and 2023 were evaluated for the Robit watershed. Six distinct categories were mapped: agricultural land, grazingland, shrubland, built-up areas, forest, and bareland. Among the tested classification techniques, Random Forest models incorporating auxiliary variables such as spectral indices and topographic data achieved superior accuracy compared with Support Vector Machines and Classification and Regression Trees. While agricultural land remained the most prevalent category, it experienced contraction alongside forest and shrubland losses. Conversely, built-up areas, bareland, and grazinglands expanded. These transitions were driven by demographic growth, wood extraction, charcoal production, and unregulated settlements, providing baseline data to guide sustainable watershed planning.
Accurate monitoring of environmental change shows how human settlement and resource collection deplete natural landscapes over decades. Applying machine learning to cloud-based satellite data allows environmental managers to observe these shifts rapidly and reliably. This evidence helps communities spot land degradation trends, support conservation interventions, and design policies that protect critical watersheds from unsustainable loss.
The research represents an applied and tested methodology operating within the open-source Google Earth Engine platform. It provides a functional analytical framework for public land management agencies, environmental consultancies, and regional spatial planning authorities seeking to monitor land degradation. While it is not a turnkey commercial software product, the workflow can be directly adopted by geospatial practitioners to generate environmental intelligence for watershed management programmes.
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A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.
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DOI: 10.1186/s40068-024-00366-3
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