Urbanization is one of the biggest challenges for developing countries, and predicting urban growth can help planners and policymakers understand how spatial growth patterns interact. A study was conducted to investigate the spatiotemporal dynamics of land use/land cover changes in Salem and its surrounding communities from 2001 to 2020 and to simulate urban expansion in 2030 using cellular automata (CA)–Markov and geospatial techniques. The findings showed a decrease in aerial vegetation cover and an increase in barren and built-up land, with a rapid transition from vegetation cover to bare land. The transformed barren land is expected to be converted into built-up land in the near future. Urban growth in the area is estimated to be 179.6 sq km in 2030, up from 59.6 sq km in 2001, 76 sq km in 2011, and 133.3 sq km in 2020. Urban sprawl is steadily increasing in Salem and the surrounding towns of Omalur, Rasipuram, Sankari, and Vazhapadi, with sprawl in the neighboring towns surpassing that in directions aligned toward Salem. The city is being developed as a smart city, which will result in significant expansion and intensification of the built-up area in the coming years. The study’s outcomes can serve as spatial guidelines for growth regulation and monitoring.

— Lithological mapping is a crucial factor in identifying and mapping the spatial distribution of minerals. It aids in accurately defining the most promising primary prospects for local exploration. The differentiation of rock units across a wider region is likely to be attributed to remotely sensed satellite data. Therefore, the research focuses on utilizing remote sensing methods to create a geological map for a specific area in Salem district, Tamil Nadu, by employing HYPERION and ASTER satellite images. Various techniques, such as Band Ratio (BR), Spectral Angle Mapper (SAM), Minimum Noise Fraction (MNF), Mixture Tuned Mapped Filtering (MTMF), Spectral Feature Fitting (SFF), and Support Vector Machines (SVMs), are utilized to classify lithological units, which are crucial for data analysis. The outcomes of these methods will be compared to field-mapped geological boundaries to assess accuracy. In the final phase, a highly precise geological map is produced by combining remote sensing data with on-site investigations. The application of these approaches holds significant potential for enhancing geological mapping and mineral exploration in hard-to-reach areas.

Unrestrained urbanisation is a significant challenge to sustainable development, particularly in rapidly developing countries like India. Widespread informal urbanisation in resource-poor areas leads to inadequate urban physical infrastructures like housing, transportation, water supply, and waste management. In this context, generating cost-effective information on urban sprawl patterns can assist planners in proactively enhancing physical Infrastructure and efficiently orienting urban development. However, the existing research on urban sprawl has predominantly focused on the large megapolises, leading to an extensive knowledge gap on non-metro regions that also experience significant urban development pressure. This study addresses this gap by focusing on the Salem region in Tamil Nadu, India. The study uses freely available remote sensing data and GIS-based landscape metrics to describe Salem and nearby urban centers' growth patterns from 2000 to 2020. The study methodology includes three primary analyses: i) Buffer analysis to fix the urban growth threshold around each town; ii) Centroid shift analysis to identify the macro urbanisation pattern; and iii) Landscape metrics analysis for micro-level urban growth characterisation. The study results show that the neighbouring towns influence Salem's growth. The Omalur's directional growth and proximity suggest it could become a Salem satellite town in the coming decades. These findings provide essential inputs for planning a more sustainable Salem city and contribute to the current literature gap on the sprawl characteristics of India's non-metro cities.

Blue and Green Infrastructure (BGI) offers a nature-based approach to managing stormwater in urban areas by mimicking natural hydrological processes. With rapid urbanization leading to increased impervious surfaces and runoff-related issues such as flooding and groundwater depletion, there is a pressing need to identify potential sites for BGI implementation in vulnerable regions. This study proposes a low-cost, GIS-based methodology to map surface depressions using Cartosat DEM and updated Land Use/Land Cover (LULC) data, processed entirely through open-source QGIS tools. Suitable depressions, filtered by minimum area and land use criteria, were categorized for specific BGI types such as rain gardens, bioswales, wetlands, and urban agriculture. The analysis revealed that the highest potential for urban agriculture and parks was found in the peri-urban zones surrounding Salem, while stormwater retention ponds and bioswales were concentrated in areas like Rasipuram, Sankari, and the Salem-Thirumangalam corridor. The resulting spatial layers provide decision-makers with a prioritized set of BGI sites in the Salem region. This approach serves as a replicable model for integrating BGI into urban planning for sustainable water management.

Oil spills are among the most devastating marine disasters, posing significant ecological and economic threats. One recent large-scale incident occurred in the Mediterranean Sea near Syria, where approximately 15,000 tons of fuel leaked from a thermal power plant in the coastal city of Baniyas on August 23, 2021. Regardless of the cause, the immediate need following such incidents is the accurate estimation of the spill area to plan mitigation efforts, including labour and resource allocation. Synthetic Aperture Radar (SAR) data, particularly from the Sentinel-1 mission, has proven effective for oil spill detection due to its all-weather, day-and-night imaging capability. Among various polarization modes, VV polarization has been widely recognized as suitable for marine oil spill studies. This study investigates the performance of two machine learning algorithms, namely K-Means Clustering (unsupervised) and Random Forest (supervised), for detecting and delineating oil spill extents using Sentinel-1 SAR imagery. It aims to assess how effectively each algorithm can differentiate between oil-contaminated areas and clean water surfaces, providing useful insights for selecting the most appropriate method for operational oil spill monitoring and response.