Spatial variabilities and drivers of land use and land cover (LULC) change over time and are crucial for determining the region’s economic viability and ecological functionality. The North-Western Himalayan (NWH) regions have witnessed drastic changes in LULC over the last 50 years, as a result of which their ecological diversity has been under significant threat. There is a need to understand how LULC change has taken place so that appropriate conservation measures can be taken well in advance to understand the implications of the current trends of changing LULC. This study has been carried out in the Baramulla district of the North-Western Himalayas to assess its current and future LULC changes and determine the drivers responsible for future policy decisions. Using Landsat 2000, 2010, and 2020 satellite imagery, we performed LULC classification of the study area using the maximum likelihood supervised classification. The land-use transition matrix, Markov chain model, and CA-Markov model were used to determine the spatial patterns and temporal variation of LULC for 2030. The CA-Markov model was first used to predict the land cover for 2020, which was then verified by the actual land cover of 2020 (Kappa coefficient of 0.81) for the model’s validation. After calibration and validation of the model, LULC was predicted for the year 2030. Between the years 2000 and 2020, it was found that horticulture, urbanization, and built-up areas increased, while snow cover, forest cover, agricultural land, and water bodies all decreased. The significant drivers of LULC changes were economic compulsions, climate variability, and increased human population. The analysis finding of the study highlighted that technical, financial, policy, or legislative initiatives are required to restore fragile NWH regions experiencing comparable consequences.

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant hydraulic systems, can lead to tree mortality events, and may reduce forest diversity, making forests more vulnerable to subsequent forest disturbances, such as forest fires or pest infestations. Although early warning metrics (EWMs) derived using satellite remote sensing data are now being tested for predicting post-drought plant physiological stress and mortality, applications of unmanned aerial vehicles (UAVs) are yet to be explored extensively. Herein, we provide twenty-four prospective approaches classified into five categories: (i) physiological complexities, (ii) site-specific and confounding (abiotic) factors, (iii) interactions with biotic agents, (iv) forest carbon monitoring and optimization, and (v) technological and infrastructural developments, for adoption, future operationalization, and upscaling of UAV-based frameworks for EWM applications. These UAV considerations are paramount as they hold the potential to bridge the gap between field inventory and satellite remote sensing for assessing forest characteristics and their responses to drought conditions, identifying and prioritizing conservation needs of vulnerable and/or high-carbon-efficient tree species for efficient allocation of resources, and optimizing forest carbon management with climate change adaptation and mitigation practices in a timely and cost-effective manner.

Purpose: Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas. Materials and methods: In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data. Results and discussion: Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R²_adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R² = 0.99), and the SVM model was the lowest (R² = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model. Conclusion: The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy.

Target characterization is an essential aspect of polarimetric decomposition. This technique is capable of categorizing polarimetric signatures for different types of targets based on the scattering mechanisms they follow, enabling straightforward physical interpretation of the targets. The geometric anomalies associated with human‐made targets escalate the degree of randomness in the scattering process, which causes scattering ambiguity for such targets. The second‐order model descriptors do not relate to the actual physical structure and yield predominant volume scattering power. Such urban targets are decomposed as natural targets leading to irrelevant decomposition results. The methods developed to curb the problem are unable to maintain the consistency in the decomposition modeling as they underestimate volume scattering powers for natural landcover. A hybrid decomposition model is proposed herein to solve the problem of predominant volume scattering observed from urban targets by preserving volume scattering powers for natural targets. The model uses eigenvalue‐based decomposition parameters and polarimetric interferometric synthetic aperture radar (PolInSAR) coherence to decompose ambiguous targets. The proposed model has been tested on NISAR UAVSAR PolInSAR data acquired over the Greenville region, MS, USA. The proposed model has increased the double‐bounce scattering from the urban targets and enhanced the volume scattering from natural landcover as well. By comparing the results with existing decomposition models, it is observed that the proposed model gives a more robust representation of the landcover than the compared decomposition models.

The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation, and carbon sequestration. Conventional direct BD measurement methods are often time-consuming, costly, and laborious. In contrast, the use of pedotransfer functions (PTFs) based on readily available soil properties offers a cost-effective alternative approach that can provide indirect quick estimates of soil BD. Although several region-specific PTFs are available, none is tailored for use in the hilly North-Western Himalayas (NWH) area. Thus, this work developed a novel PTF to estimate the soil BD of 300 soil samples collected from the NWH area and analyzed by stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT). The site-specific SR-based equation performed better than the PLSR model by explaining up to 72.2% variability of BD and achieving a coefficient of determination (R²) of 0.73, a mean absolute error (MAE) of 0.0379 g cm⁻³, and a root mean square error (RMSE) of 0.0487 g cm⁻³ for the BD prediction. Soil organic matter and clay contents were the most significant contributors, showing the highest importance in the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation (R²: 0.006–0.71, RMSE: 0.0649–0.405 gcm^− 3, MAE: 0.0512–0.395 g cm^− 3). The promising SR equation and PTF developed in this work showed high accuracy and excellent suitability for stakeholders and professionals in the study area and the scientific community as a reliable BD estimator for hilly soils. This study introduces proposed the first-ever site-specific PTF for BD estimation in hilly soils, contributing to scientific challenge by enhancing soil database and improving BD predictions in complex terrains worldwide. In terms of global significance, the outcomes of this study are essential for soil quality and vulnerability studies, resilience indicators estimates carbon sequestration initiatives, and digital soil mapping, and can also be used to construct a BD database for hilly soils across the globe. Graphical Abstract The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation and carbon sequestration, and soil quality, vulnerability and resilience. Conventional direct methods to measure soil BD are often time-consuming, costly and laborious, whereas pedotransfer functions (PTFs) based on readily available soil properties offer an efficient alternative approach that provides indirect quick estimates of soil BD. However, none of the several region-specific PTFs currently available, is tailored for use in the hilly North-Western Himalayas (NWH) area. A novel PTF was developed in this work to evaluate the BD of 300 soil samples collected from agriculture, horticulture, forest and fallow hilly soils in the North West Himalaya area. Stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT) were tentatively applied for BD estimation of the studied soils, showing that the site-specific SR-based equation performed the best. Various soil properties were evaluated in the models, with soil organic matter and clay contents being the most significant contributors to the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation for the hilly soils studied in this work. In conclusion, the SR equation and PTF developed in this work show excellent and promising suitability for stakeholders and professionals in the study area and for the global scientific community as a reliable BD estimator for hilly soils. Furthermore, the results of this work can be used for soil management, land use planning, and climate change mitigation in fragile ecosystems like the Himalayas.