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The surface of the earth is rapidly changing every day due to certain natural reasons and other impacts by society. Over the last few decades, the hottest topics in the field of remote sensing and GIS (geographic information system) environments have evolved from observing the nature of the earth. Owing to the enlargement of several worldwide modifications related to the nature of the earth, land use/land cover (LU/LC) change is considered as the matter of utmost importance in the natural atmosphere, and it has also become an interesting area to be studied by the researchers. As there is a lack of review articles in the land use/land cover change analysis process, we presented a comprehensive review which may help the researchers to proceed further. This paper deals with the most frequent methods used by researchers on various processes like pre-processing, classification, and prediction of time series satellite images for analyzing the LU/LC changes using satellite images. The generic flow of the LU/LC change analysis process and the challenges faced during each process by the researchers are discussed. Varied resolutions of the environmental image captured by remote sensing satellites for analyzing the LU/LC changes are discussed. Various LU/LC classes depending on change in the earth’s surface are also studied and the constraint used in each application is stated. The importance of this review lies in the motivation for future researchers to work on the LU/LC change analysis problem effectively.

Continuous monitoring and observing of the earth’s environment has become interactive research in the field of remote sensing. Many researchers have provided the Land Use/Land Cover information for the past, present, and future for their study areas around the world. This research work builds the Novel Vision Transformer–based Bidirectional long-short term memory model for predicting the Land Use/Land Cover Changes by using the LISS-III and Landsat bands for the forest- and non-forest-covered regions of Javadi Hills, India. The proposed Vision Transformer model achieves a good classification accuracy, with an average of 98.76%. The impact of the Land Surface Temperature map and the Land Use/Land Cover classification map provides good validation results, with an average accuracy of 98.38%, during the process of bidirectional long short-term memory–based prediction analysis. The authors also introduced an application-based explanation of the predicted results through the Google Earth Engine platform of Google Cloud so that the predicted results will be more informative and trustworthy to the urban planners and forest department to take proper actions in the protection of the environment.

Researchers are developing new methods to analyze changes in satellite data across various locations using remote sensing and geographic information systems (GIS). Land Surface Temperature (LST) maps are important indices for understanding changes in global land use and land cover (LU/LC). This study introduces the ALST-W (Adaptive Land Surface Temperature of Water Bodies) index to investigate the impact of water bodies on the LST map of the non-forest-covered Javadi Hills region, India, using Landsat 9/8 images for 2020, 2022, and 2024. The ALST-W results were compared with reference maps from Google Earth Engine (GEE), and the findings showed a good average accuracy of 95.06%. This study introduces the new index of the ALST-W, which displays the temperature data for high and low vegetation, along with the water bodies in a single raster map. The information from this work helps communities and policymakers understand environmental changes and take informed actions to protect vegetation and water bodies from significant future loss.

The land-use/land-cover (LU/LC) information can be extracted through continuous monitoring and observation of the global environment in the field of RS and GIS (remote sensing and geographic information system). With many inventions on satellite technologies, RS plays a crucial role throughout the world, and the researchers had shown their interest in finding the past, present, and future LU/LC information using the RS satellite data. In this research work, the non-forest- and forest-covered changes of Javadi Hills located in India were simulated and predicted using the hybrid machine learning models. The Markov chain–artificial neural network with cellular automata (MC–ANN–CA) and Markov chain–logistic regression with cellular automata (MC–LR–CA) were used and compared using the actual LU/LC maps of 2009, 2012, and 2015 along with the spatial variables (slope, aspect, hill shade, and distance road map). The results of the comparative analysis between the predicted and actual map of 2015 had shown a higher percentage of correctness in the MC–ANN–CA model for the spatial variables like slope, aspect, and distance road map. The LU/LC for 2021 and 2027 was predicted using the MC–ANN–CA model. By 2021, the forest-covered area will decrease by nearly − 0.38%, and the non-forest-covered area will increase by 0.79%. By 2027, forest-covered areas will decrease by − 0.52%, and non-forest-covered areas will increase by 1.06%, respectively, indicating the impacts of human and urbanization on LU/LC in Javadi Hills.

Researchers have been actively investigating the statistics about Land Use/Land Cover worldwide for several decades. This research examines the Javadi Hills in India, a region known for its natural significance, rich forest resources, and essential water content within the forest and non-forest landscapes. The LISS-III satellite imageries were used in this work to explore the Land Use/Land Cover statistics in water bodies, vegetation, and bare soil for the periods of past (2012, 2015, 2018, 2021), present (2024), and future (2027), with the selected timeframe ensuring a consistent three-year gap between each period to facilitate data availability and forecast future trends. We implemented the novel Fuzzy Shuffled Frog Leaping Optimization-based Convolutional Long Short-Term Memory (FSFLO-ConvLSTM) model, which integrates the Normalized Difference Water Index (NDWI) as a fuzzy membership function and spatial mapping variable. This method significantly enhances the classification accuracy (96.84%) and prediction accuracy (95.73%). The importance of this research was using NDWI to improve accuracy and create a spatial map, which helps in understanding changes in vegetation and water bodies. The overall statistics of past (2012–2021), present (2024), and future (2027) Land Use/ Land Cover assist the urban designers, government representatives, and concerned forestry officers to safeguard the environment, especially the vegetation and water bodies. 

Land Use/ Land Cover change detection is an inspiring, interesting task to be performed worldwide. The real-time satellite data of the earth's surface and its different findings can be studied with the assistance of Remote Sensing and Geographic Information Systems. This work builds the novel Fuzzy Swin Transformer-based LU/LC classification model using the LISS-III satellite data of Yelagiri Hills. In the proposed work, the LU/LC features extracted from fuzzy clustering were used as the input training patches when executing the Swin Transformer model. The training patches assist the transformer in finding the LU/LC classification map with good computational complexity and accuracy. The Simple Random, Cluster, Systematic, and Stratified Random sampling methods were used to validate the accuracy of the acquired LU/LC classification map. The proposed Fuzzy Swin Transformer model attains good results with an average classification accuracy of 98.43% using Simple Random Sampling, 97.45% using Stratified Random Sampling, 97.36% using Systematic Sampling, and 96.97% using Cluster Sampling. The LU/LC change detected in this work was considered an important source of information to support the concerned land resource planners in taking necessary action to preserve the land cover, exclusively for the forest-covered areas of the hill stations.