ArticleScientific reports2026
Comprehensive modelling of coastal landuse and land cover (LULC) dynamics and future changes using random forest, GEE, and MLP-Markov chain integration: a geo-computational approach.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Coastal landuse / land cover (LULC) faces dynamic transformations driven by natural and human activities. This study investigates decadal LULC dynamics and their future projections (2034, 2044, and 2054) along the Ernakulam-Alappuzha coastal stretch of Kerala, India, using Google Earth Engine (GEE)- based time-series Landsat ETM + and OLI images from 2004, 2015, and 2024. GIS-based machine learning (ML) techniques, including Random Forests (RF), Support Vector Machines (SVM), and Classification and Regression Trees (CART), were used to extract LULC features. The spectral indices, such as NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), MNDWI (Modified Normalized Difference Water Index), NDBI (Normalized Difference Built-up Index), NDBaI (Normalized Difference Bareness Index), BSI (Bare Soil Index), and UI (Urban Index), were incorporated to enhance the future LULC trends. Among all ML algorithms, the RF achieved the highest classification accuracy with 95.46%, 92.38%, and 93.94% for 2004, 2015, and 2024, respectively. The LULC patterns indicate pronounced urban expansion, with built-up areas increasing from 109.93 km
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