The increasing demand for tropical timber from natural forests has reduced the population sizes of native species such as Cedrela spp. because of their high economic value. To prevent the decline of population sizes of the species, all Cedrela species have been incorporated into Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The study presents information about the modeled distribution of the genus Cedrela in Peru that aims to identify potential habitat distribution of the genus, its availability in areas protected by national service of protected areas, and highlighted some areas because of their conservation relevance and the potential need for restoration. We modeled the distribution of the genus Cedrela in Peru using 947 occurrence records that included 10 species (C. odorata, C. montana, C. fissilis, C. longipetiolulata, C. angustifolia, C. nebulosa, C. kuelapensis, C. saltensis, C. weberbaueri, and C. molinensis). We aim to identify areas environmentally suitable for the occurrence of Cedrela that are legally protected by the National Service of Protected Areas (PAs) and those that are ideal for research and restoration projects. We used various environmental variables (19 bioclimatic variables, 3 topographic factors, 9 edaphic factors, solar radiation, and relative humidity) and the maximum entropy model (MaxEnt) to predict the probability of occurrence. We observed that 6.7% (86,916.2 km²) of Peru presents a high distribution probability of occurrence of Cedrela, distributed in 17 departments, with 4.4% (10,171.03 km²) of the area protected by PAs mainly under the category of protection forests. Another 11.65% (21,345.16 km²) of distribution covers areas highly prone to degradation, distributed mainly in the departments Ucayali, Loreto, and Madre de Dios, and needs immediate attention for its protection and restoration. We believe that the study will contribute significantly to conserve Cedrela and other endangered species, as well as to promote the sustainable use and management of timber species as a whole.

Palmer Drought Severity Index (PDSI) is the most effective and well-acknowledged drought severity index that particularly determines the long-term drought conditions over the forest and other terrestrial ecosystems. However, the sensitivity of PDSI has not been explored yet based on productivity (i.e., Gross Primary Productivity (GPP)), biophysical parameters (i.e., biomass—Leaf Area Index (LAI) and Enhanced Vegetation Index (EVI) and greenness content—Normalized Difference Vegetation Index (NDVI)), and absorbed solar radiation by plants (i.e., fraction of Absorbed Solar Radiation (fAPAR)) over a humid-subtropical forest ecosystem. In this study, the sensitivity of the PDSI was analyzed through uncertainty and error estimation modeling from long-term (2015–2019) MODIS GPP and reflectance data using Google Earth Engine (GEE) over a humid-subtropical forest region of Arunachal Pradesh, India. It was experimentally observed that EVI was the most sensitive parameter to the PDSI in long-run observation based on a low uncertainty (2.39–3.01%) and error (0.07–0.12) compared to the other parameters. Besides, EVI had a strong agreement with PDSI compared to GPP, NDVI, LAI, and fAPAR, where the Pearson’s r ranged from −0.87 to −0.63, except 2015. Hence, it is stated that EVI is the simple, effective, and most complementary indicator for assessing the PDSI over the forest regions of a tropical ecosystem. This study showed that EVI might be a promising tool for effectively evaluating long-term drought impacts on the forest ecosystem that indicates the actual water deficit-induced stress conditions.

A proper geomorphic study of a region can be useful in understanding past and present environmental circumstances and analyzing potential environmental risks. Careful analysis of morphodynamic processes and existing diagnostic landforms reveal several aspects about the origin, characteristics and possible pattern of morpho-climatic interactions on the landscape over temporal scale, which helps significantly in proper terrain evaluation from societal welfare and integrated management point of view, including environmental risk assessment and disaster management. This paper has made a thorough geomorphic investigation based on intensive fieldwork and multi-sourced remote sensing data to characterize the lateritic soil profile and landforms of the study area in respect to their morphology and Physico-chemical properties at the different sites of land degradation to understand the outcome of morphoclimatic interactions on the landscape over time, and to evaluate the severity of operation with pedo-geomorphic constraints in the lateritic environment for sustainable management purposes. It is found that the region is highly sensitive to weathering, mass movement and denudational activities, mainly caused by rainwater erosion which has resulted into varied landforms including well-developed rills and gullies, lateritic ridges, isolated residual hills etc and generated a unique identity to this part of the lateritic region. The study also suggested a model for the development of geomorphic landforms in a lateritic terrain based on past and present morphoclimatic interactions, nature of physiography, lithology, soil characteristics and other biotic and abiotic elements. The region is also found to be a subject to moderate to severe land degradation due to the active geomorphic processes in operation in tropical regions and inherently poor physical and chemical formation of the existing soil profile and radical conversion of land uses as observed at cadastral level leading towards irresistible desertification. Annual topsoil loss amount has been calculated using the Universal Soil Loss Equation method. Three sample Mouzas namely Ballavpur, Shyambati and Chawpahari jungle have been assessed to be having 36.98%, 71.42% and 61.73% of degraded land in respect to their total village area. Various conservative measures like stabilization of gully heads and beds through reforestation and afforestation with cutting earth plug, brush fills and check dams; improvement of the irrigation network, suitable dryland farming etc. have been recommended to arrest the desertification process.

Fragmentation or the breakdown of landholdings to smaller parcels has an adverse impact on crop yields and productivity because of its uneconomic operational sizes. This comprehensive review reflects the insights into the complex dynamics of small landholding (SLs) in India by leveraging earth observation (EO) based sensing technology through synthesizing existing literature, methodologies and outcomes, and technological advancements along with its challenges and limitations. This study aims to summarize the current state-of-the problems, management, and utilization of EO technology for mapping, monitoring, and parametric assessment of small-scale agricultural land holdings in India. The review also discussed about different sensing platforms, and how to utilize their varied spectrums for identifying and characterizing SLs at different geographies of India. India, with its deeply rooted agricultural tradition dating back thousands of years, has agriculture as the primary occupation for a significant portion of its 1.4 billion population. However, a significant issue in Indian agriculture is the fragmentation of croplands into small landholdings, which results in the division of agricultural land into smaller and often uneconomical parcels. Therefore, accurate delineation and EO based sensing of SLs in India is highly necessary for precise monitoring of crop health, soil conditions, and water usage and many more, which can significantly improve productivity on small, uneconomical parcels of land to boost nations food security. The Authors believe with the efficient intervention of EO based sensing technology in SLs, farmers can make more informed decisions and maximize their yields by making more effective use of their resources. Furthermore, Earth observation can help prevent crop losses and improve food security in a nation where a large portion of the population is dependent on agriculture by helping with the early detection of pests and diseases. Our study will support the decision-making process and policy formulation in Indian agriculture system by providing comprehensive insights from EO-based sensing perspectives. Finally, this will help to create more productive and sustainable farming methods, which will be advantageous to both farmers and the national economy.

The rapid pace of urbanization worldwide has significantly altered local and global weather-climate interactions. Expanding urban areas modify land-atmosphere interactions, leading to changes in temperature, precipitation patterns, and extreme weather events. To better understand and mitigate urban-induced weather and climate effects, long-term, frequently updated urban datasets are essential. Such datasets enable accurate monitoring of urban expansion and its impact on atmospheric processes, ultimately improving weather forecasting capabilities. The Weather Research and Forecasting (WRF) model is a widely used numerical weather prediction tool, yet its urban representation remains constrained by the limited availability of continuous, high-resolution urban data. The accuracy of weather forecasts, particularly in and around urban areas, is dependent on how well the model represents urban land cover and surface characteristics. In this study, the authors present Normalized Difference Urban Index+ (NDUI+) dataset, a 30-meter, long-term, continuously updated urban dataset designed to enhance urban representation within WRF. This dataset uses AI-calibrated DMSP-VIIRS nighttime light images merged with Landsat NDVI (Normalized Difference Vegetation Index) to generate Normalized Difference Urban Index (NDUI) metric from 1999 to present. The integration of NDUI+ into WRF improves the characterization of urban areas, leading to more precise simulations of weather conditions. Results demonstrate that the enhanced urban representation significantly refines key meteorological variables such as temperature, humidity, and wind speed, yielding more reliable and accurate forecasts. The improved model performance underscores the necessity of incorporating high-resolution, frequently updated urban datasets to advance weather prediction capabilities, especially in rapidly urbanizing regions. By bridging the gap between urban data availability and numerical weather modeling, this study highlights the critical role of urban datasets in improving the accuracy of weather forecasts and understanding micro and meso-level urban-climate interactions.