Urbanization significantly alters city landscapes, exacerbating the Urban Heat Island (UHI) effect, where cities experience significantly higher temperatures compared to their rural surroundings due to impervious surfaces and heat emissions. As cities expand, mitigating UHI and improving urban thermal comfort becomes critical. Urban parks play a key role in cooling cities, typically being 1–2°C cooler than surrounding areas, with temperature differences of up to 7°C. This study evaluates the cooling potential of seven urban parks in Kolkata, India, using Landsat 8 for Land Surface Temperature (LST) and Sentinel 2 for vegetation mapping. The study assessed Park Cooling Intensity (PCI) along with related metrics such as Cooling Range & Temperature Drop Amplitude.It also examines how internal park characteristics, including vegetation density, impervious surface fraction, water bodies, & tree height, influence LST. Results show PCI ranges from 0.75–6.84°C, with an average temperature reduction of 2.87°C in park surroundings. Factors like tree height and water bodies enhance PCI, while a negative correlation exists between PCI and park area, suggesting that park size alone does not maximize cooling benefits. Vegetation density and water features are crucial for optimizing PCI, indicating that well-designed park features are more important than park size for UHI mitigation. These findings guide urban planners and policymakers in designing parks that maximize cooling effects, reduce UHI, and enhance urban climate resilience. Strategic planning of park elements can foster healthier, more sustainable cities capable of adapting to climate change and rapid urbanization.

Frequency of extreme weather events such as cloudbursts, heatwaves etc. have increased as an outcome of changing climate. Identification of the pattern of extreme temperature events is important since it governs various events such as heatwaves, wildfires, droughts, storms, coldwaves etc. Moderate Resolution Imaging Spectroradiometer (MODIS) provides Land Surface Temperature (LST) data at 1 kilometre of spatial resolution at daily interval that can help in the identification and mapping of the anomalies in the temperature at pixel level. This study proposes a global-scale daily long-term thermal anomaly detection tool made using Google Earth Engine (GEE) App. This open source tool with the name of ‘Deviation from Mean’ uses the MODIS LST data available from 2000 till date to detect temperature anomaly based on the deviation of temperature of any day (chosen by the user) from the long-term climatological mean. It also generates a time-series plot of temperature values of any pixel for any date for last 24 years i.e. 2000–2023 in the graphical form to analyze the variation in the temperature over the time. A case study has also been done using the tool to highlight the thermal anomaly experienced over the Indian sub-continent during March-April, 2022 and 2023. This tool is capable of providing thermal anomaly information at global, regional as well as local level that can help in taking region-specific mitigation measures.

Climate change has exacerbated the intensity as well as frequency of forest fire events in the Indian state of Uttarakhand. The present study focusses on undertaking forest fire risk mapping across the state by utilizing geospatial technology along with Google Earth Engine. Ten parameters were identified that have a strong influence in determining fire prone areas. The Analytic Hierarchy Process (AHP) was then implemented for the development of the risk map in which criteria weights were assigned to the parameters based on their ability to influence a forest fire event. The analysis revealed that out of the total forest area, 24.22% is under ‘very high’ risk zone, 29.24% is under ‘high’ risk zone, 18.23% is under ‘moderate’ risk zone, 7.69% is under ‘low’ risk zone and 20.62% is under ‘very low’ risk zone of forest fire. Further study was carried out to determine fire risk levels in populated regions and in some of the most critical nature reserves having high ecological importance which reveals that ‘very high’ and ‘high’ risk zones have greater population density indicating the influence of anthropogenic activities on forest fire occurrence. The results additionally indicate that four national parks and wildlife sanctuaries are particularly vulnerable to forest fires at present which is a source of concern and requires intervention from the stakeholders.

Understanding the distribution of aboveground biomass (AGB) is vital for evaluating carbon stocks & ecosystem dynamics, especially in regions with diverse landscapes like Indian subcontinent. This study evaluates three machine learning models—Random Forest (RF), Gradient Tree Boosting (GTB), & Classification and Regression Trees (CART)—for predicting AGB across the subcontinent. Independent variable in these models is AGB, while dependent variables include a range of vegetation & topographic layers: Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, Fraction of Photosynthetically Active Radiation, land cover, elevation, aspect, slope, & hillshade. These predictors are essential for capturing ecological & topographical characteristics that influence biomass distribution. The models were evaluated using coefficient of determination (R2) & Pearson's correlation coefficient (r) to assess predictive accuracy. RF emerged as most accurate, with an R² value of 0.834 & r value of 0.913, effectively capturing the spatial variability in AGB across subcontinent’s diverse ecosystems, which was then used to predict AGB for 2023. The predictions reveal significant spatial variation in biomass density, reflecting region's diverse ecological zones & land-use patterns. In India, high biomass densities are found in Himalayan foothills, northeastern states, & Western Ghats, while arid regions like Rajasthan & Gujarat have lower values. Pakistan generally exhibits low biomass densities, with higher values near the northern border with India. Nepal & Bhutan show high densities in their forested regions, particularly in the mid-hills, high mountains, & Eastern Himalaya. Bangladesh has moderate to low biomass densities. In Sri Lanka, central highlands & southwestern rainforests have highest biomass densities, while the more arid northern & eastern regions exhibit lower values. This study highlights the importance of using robust machine learning models like RF to accurately capture spatial patterns of biomass distribution, which is crucial for forest management, carbon accounting, & biodiversity conservation in the Indian subcontinent.

The Amur Falcon (Falco amurensis) is a migratory raptor known for its long-distance journeys from eastern Russia and northern China to southern Africa, passing through various stopovers, including northeastern India and Southeast Asia. The Amur Falcon's migration spans diverse habitats and climatic zones, offering insights into its dynamics. However, climate change, habitat loss, and bio-climatic variability increasingly threaten its breeding and stopover sites. To date, no comprehensive study has analyzed how bio-climatic factors influence migration patterns across such a broad range. This study explores the bio-climatic factors influencing the falcon's migration and habitat suitability using remote sensing, GIS, and machine learning models—Maximum Entropy (MaxEnt) and Random Forest (RF). It evaluates 56 bio-climatic variables, such as temperature, precipitation, solar radiation, wind speed & water vapour pressure. Species occurrence data from citizen science is used to train and validate models. RF showed higher accuracy (AUC=0.98) than MaxEnt (AUC=0.96) and identified 6.69% of global land as moderately to highly suitable for the falcon, compared to MaxEnt’s 2.07%. The analysis also revealed potential habitats outside the bird's natural migration route, including parts of North America, South America, and Oceania. Key factors affecting habitat suitability included mean temperature, precipitation, and solar radiation. This research emphasizes the importance of bio-climatic data in understanding species distribution and migration patterns, offering valuable insights for conservation planning and avian ecology.