Land use changes have been demonstrated to exert a significant influence on urban planning and sustainable development, particularly in regions undergoing rapid urbanization. Tehran Province, as the political and economic capital of Iran, has undergone substantial growth in recent decades. The present study employs sophisticated Geographic Information System (GIS) instruments and the Google Earth Engine (GEE) platform to comprehensively track and analyze land use change over the past two decades. A comprehensive analysis of Landsat images of the Tehran metropolitan area from 2003 to 2023 has yielded significant insights into the patterns of land use change. The methodology encompasses the utilization of GIS, GEE, and TerrSet techniques for image classification, accuracy assessment, and change detection. The Kappa coefficients for the maps obtained for 2016 and 2023 were 0.82 and 0.87 for four classes: built-up, vegetation cover, barren land, and water bodies. The findings suggest that, over the past two decades, Tehran Province has undergone a substantial decline in ecological and vegetative areas, amounting to 2.4% (458.3 km2). Concurrently, the urban area and the barren lands have expanded by 287.5 and 125.5 km2, respectively. The increase in water bodies during this period is likely attributable to the reduction of vegetation cover and dam construction in the region. The present study demonstrates that remote sensing and GIS are excellent tools for monitoring environmental and sustainable urban development in areas experiencing rapid urbanization and land use changes.

Currently, coal resource-based cities (CRBCs) are facing challenges such as ecological destruction, resource exhaustion, and disordered urban development. By analyzing the landscape pattern, the understanding of urban land use can be clarified, and optimization strategies can be proposed for urban transformation and sustainable development. In this study, based on the interpretation of remote sensing data for three dates, the landscape pattern changes in the urban area of Huainan City, a typical coal resource-based city in Anhui Province, China were empirically investigated. The results indicate that: (1) There is a significant spatial-temporal transformation of land use, with construction land gradually replacing arable land as the dominant land use type in the region. (2) Landscape indices are helpful to reveal the characteristics of land transfer and distribution of human activities during a process. At the landscape type level, construction land, grassland, and water bodies are increasingly affected by human activities. At the landscape composition level, the number of landscape types increases, and the distribution of different types of patches becomes more balanced. In addition, to address the problems caused by the coal mining subsidence areas in Huainan city, three landscape pattern optimization strategies are proposed at both macro and micro levels. The research findings contribute to a better understanding of land use changes and their driving forces, and offer valuable alternatives for ecological environment optimization.

Soil salinization is a difficult challenge for agricultural productivity and environmental sustainability, particularly in arid and semi-arid coastal regions. This study investigates the spatial variability of soil electrical conductivity (EC) and its relationship with key cations and anions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, CO₃²⁻, HCO₃⁻, SO₄²⁻) along the southeastern coast of the Caspian Sea in Iran. Using a combination of field-based soil sampling, laboratory analyses, and Landsat 8 spectral data, linear Multiple Linear Regression and Partial Least Squares Regression (MLR, PLSR) and nonlinear Artifician Neural Network and Support Vector Machine (ANN, SVM) modeling approaches were employed to estimate and map soil EC. Results identified Na⁺ and Cl⁻ as the primary contributors to salinity (r = 0.78 and r = 0.88, respectively), with NaCl salts dominating the region’s soil salinity dynamics. Secondary contributions from Potassium Chloride KCl and Magnesium Chloride MgCl₂ were also observed. Coastal landforms such as lagoon relicts and coastal plains exhibited the highest salinity levels, attributed to geomorphic processes and anthropogenic activities. Among the predictive models, the SVM algorithm outperformed others, achieving higher R² values and lower RMSE (RMSE_Test = 27.35 and RMSE_Train = 24.62, respectively), underscoring its effectiveness in capturing complex soil-environment interactions. This study highlights the utility of digital soil mapping (DSM) for assessing soil salinity and provides actionable insights for sustainable land management, particularly in mitigating salinity and enhancing agricultural practices in vulnerable coastal systems.

This study investigated the changing land use patterns and their impacts on ecosystem in the Teesta River Basin of northwestern Bangladesh. Although anthropocentric land use patterns, including agricultural land use, settlements, built areas, and waterbody loss, have been increasing in the Nilphamari district, by negatively affecting local ecosystems, they have not been identified by prior research. Limitations of contemporary literature motivated me to work on this crucial ground in the Teesta River Basin in Northwestern Bangladesh. This study applied a mixed research approach to identify the study objectives. Firstly, the land use and land cover (LULC) changes which occurred between 2000 and 2020 were detected using satellite imagery and supervised classification method. In addition to the detection of LULC changes, the study explored the people’s perceptions and experiences about the ecosystem changes resulted from the LULC changes over the last 20 years, conducting stakeholders’ consultations and household surveys utilizing a semi-structured questionnaire. The findings indicated that waterbodies in Nilphamari district have significantly decreased from 378 km² in 2000 to 181 km² in 2020. In the same way, the vegetation coverage has reduced 187 km² between the years 2000 and 2020. On the contrary, agricultural lands (croplands) have increased from 595 km² to 905 km² and settlements have increased from 81 km² to 206 km² between the years 2000 and 2020. From the chi-square test, it was found a significant association between ecosystem change and biodiversity loss. It was further identified that waterbody decreases have significant impacts on aquatic ecosystems. The results of this study also indicated that due to the introduction of foreign tree species, local and native species have been significantly decreasing over the time. This study emphasizes the non-anthropocentric and inclusive land use policy implications for protecting life on land and preserving the aquatic ecosystem in Bangladesh.

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.