Vijay, A.Varija, K.2026-02-042024Environmental Monitoring and Assessment, 2024, 196, 5, pp. -1676369https://doi.org/10.1007/s10661-024-12633-yhttps://idr.nitk.ac.in/handle/123456789/21146Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.BiodiversityData handlingExpansionForestryImage enhancementLand useMachine learningRandom forestsRegression analysisRemote sensingSatellite imageryTopographyVegetation mappingBare soil indicesClassification treesGoogle earth engineGoogle earthsKeralaLand use and land coverModified normalized difference water indexNormalized difference build-up indexNormalized difference vegetation indexNormalized difference water indexNormalized differencesRegression treesEnginesclassificationGISland coverland use changemachine learningNDVIregression analysisremote sensingsoftwarespatiotemporal analysisaccuracyArticlebare soil indexclassification and regression treeclimate changeelectromagnetic spectrumelectromagnetismfarming systemgovernmentimageryland uselandslidelearning algorithmmodified normalised difference water indexnear infrared spectroscopynon-governmental organizationnormalised difference vegetation indexnormalised differences built up indexpredictive modelrandom foresttopographytrainingurban areaurbanizationvalidation processwater pollutionanimalenvironmental monitoringLepidopterasoilIndiawaterAnimalsEnvironmental MonitoringMachine LearningRemote Sensing TechnologySoilWater