Abstract: The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes in the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats and, often, heritage sites. This study evaluates usage of Sentinel-2 time-series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different veg-etation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation as-semblages is assessed using the Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral com-ponents, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated to the predictor space - spectral components versus vegetation indices. We contribute insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant as-semblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and plant conservation in such landscapes.

Abstract: This study presents a national-scale mapping of Ecosystem Functional Groups (EFG) in the Republic of Korea using the IUCN Global Ecosystem Typology (GET), integrated with national-level spatial datasets, satellite imagery, and a Random Forest (RF) classifier. By incorporating locally relevant ecological data, the original typology was refined to resolve issues of overgeneralization and spatial overlap. The resulting map delineates 20 distinct ecosystem types, offering improved spatial accuracy and better alignment with the actual land extent. To evaluate the potential of EFG classification, the RF model was trained on seasonal satellite composites and environmental variables, achieving an overall accuracy of 80%. Elevation and temperature were found to be the most influential predictors, effectively distinguishing ecological patterns across diverse landscapes. This integrated approach supports consistent tracking of ecosystem changes and helps address the limitations of static or infrequently updated spatial datasets. The developed EFG map supports biodiversity conservation by providing a practical foundation for national spatial planning and contributing to Red List of Ecosystems assessments, in line with the goals of the Global Biodiversity Framework.

Abstract: The semi-Mediterranean (SM) and semi-arid (SA) regions, exemplified by the Kurdo-Zagrosian forests in western Iran and northern Iraq, have experienced frequent wildfires in recent years. This study has proposed a modified Non-Negative Matrix Factorization (NMF) method for detecting fire-prone areas using satellite-derived data in SM and SA forests. The performance of the proposed method was then compared with three other already proposed NMF methods: Principal Component Analysis (PCA), K-means, and IsoData. NMF is a factorization method renowned for performing dimensionality reduction and feature extraction. It imposes non-negativity constraints on factor matrices, enhancing interpretability and suitability for analyzing real-world datasets. Sentinel-2 imagery, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Zagros Grass Index (ZGI) from 2020 were employed as inputs and validated against post-2020 burned area derived from the Normalized Burned Ration (NBR) index. Results demonstrate NMF’s effectiveness in identifying fire-prone areas across large geographic extents typical of SM and SA regions. The results also revealed that when elevation was included, NMF_L1/2 sparsity offered the best outcome among the used NMF methods. In contrast, the proposed NMF method provided the best results when only Sentinel 2 bands and ZGI were used.

Abstract: In this contribution, we propose a novel parallel patch-wise sparse residual learning (P2SR) algorithm for resolution enhancement based on fusion of hyperspectral imaging data (HSI) and multispectral imaging data (MSI). The proposed method uses multi-decomposition techniques to extract spatial and spectral features to form a sparse dictionary. The spectral and spatial characteristics of the scene encoded as a dictionary enables reconstruction through a first order optimization algorithm to ensure an efficient sparse representation. The final spatially enhanced HSI is reconstructed using sparse-dictionary features from low resolution HSI and applying a MSI regulated guided filter to enhance spatial fidelity while minimizing artifacts. P2SR is deployable on a high-performance computing (HPC) system with parallel processing, ensuring scalability and computational efficiency for large HSI datasets. Extensive evaluations on three diverse study sites demonstrate that P2SR consistently outperforms traditional and state-of-the-art (SOA) methods in both quantitative metrics and qualitative spatial assessments. P2SR displays superior spatio-spectral reconstruction contributing to sharper spatial features, reduced mixed pixels, and enhanced geological features. Importantly, we show that P2SR preserves critical spectral signatures such as Fe²⁺ absorption and improves the detection of fine-scale environmental and geological structures. P2SR’s ability to maintain spectral fidelity while enhancing spatial detail makes it a powerful tool for high-precision remote sensing applications, including mineral mapping, land-use analysis, and environmental monitoring.

Abstract: Urbanization has transformed natural landscapes, resulting in increased land surface temperatures and the intensification of Urban Heat Island (UHI) effects. This study explores the relationship between land use/land cover (LULC) changes and land surface temperature (LST) from 2017 to 2024, using satellite data from Landsat and Sentinel. Results from supervised classification reveal a 50.9% increase in built-up land, from 21,256 hectares in 2017 to 32,099 hectares in 2024, accompanied by a 6.3% decline in woodland. Analysis of LST data highlights rising temperatures in urbanized and deforested areas, with LST peaking at 36.96 °C in 2020 before slightly decreasing to 31.03 °C in 2024, potentially influenced by increased rainfall. However, hotspots of elevated LST persist, indicating sustained thermal stress. The Urban Thermal Field Variance Index (UTFVI) showed worsening ecological conditions, particularly in densely urbanized zones. The study highlights the pressing need for integrating Urban Heat Island (UHI) considerations into urban planning, as elevated urban temperatures threaten public health and escalated energy consumption. Additionally, the research aligns with Sustainable Development Goal 11 (SDG 11), emphasizing the creation of inclusive, safe, resilient, and sustainable cities. By providing policymakers with key UHI indices, this study contributes to climate-resilient urban environments, mitigating heat risks through green infrastructure and sustainable urban design.

Abstract: Accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-Range Photogrammetry (CRP) is widely used in 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, Novel View Synthesis (NVS) technology such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using different imaging devices. We then performed dense reconstruction of forest stand using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. Photogrammetric method is less suited to more complex forest stands, resulting in tree point cloud models with issues such as excessive canopy noise, wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, especially in reconstructing canopy regions. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings suggest that NVS methods have significant potential for 3D reconstruction of urban forests, providing further technical support for forest resource visualization, inventory and management tasks.

Abstract: The bay-marsh boundary is a dynamic topographic feature. Advances in remote sensing provide cost-effective, high-resolution mapping technologies to detect change at fine scales. However, many questions remain about how to best apply these technologies and quantify change at bay-marsh boundaries. We combine UAS photogrammetry and boat-mounted echosounding to map the bay-marsh boundary over two consecutive years and use a previously collected Lidar dataset for decadal timescale comparisons. We evaluate accuracy, lateral and volumetric change rates, and how this approach compares to traditional methods of quantifying change. Results indicate an elevation uncertainty of 0.07 m for the topobathymetric DEMs. Volumetric erosion rates between marsh shorelines were -0.78 0.22 and -0.25 0.15 m3m-1yr-1 at an 8-year and annual sampling interval respectively. Lateral erosion rates were -1.57 0.15 and -1.54 0.07 m yr-1 at an 8-year and annual sampling interval respectively and were weakly correlated with volumetric change rates, even within morphologically similar sections of the marsh edge. Measured volumetric change rates of the marsh edge were reasonably estimated with limited vertical data at decadal timescales, suggesting that this change could be approximated with an RTK system. However, high-resolution mapping remains essential for assessing annual, event-driven, or small-scale change.

Abstract: Wildfires serve both ecological and destructive roles—supporting biodiversity and nu-trient cycling while also threatening ecosystems and economies, especially as climate change drives increases in fire frequency and intensity. This study investigates the impact of wildfires and subsequent vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE), remote sensing techniques were applied to assess burn severity and post-fire re-growth. The analysis utilized Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery. Burn severity was classified through differenced NBR (dNBR), while vegetation recovery was monitored via differ-enced NDVI (dNDVI) and multi-year NDVI time series. Results show that low-severity zones recovered more quickly than high-severity areas, which often failed to regain pre-fire vegetation levels. These findings suggest a potential shift from forested areas to shrubland or mixed vegetation in severely burned zones. Key recovery influencers in-clude climate variability, soil erosion, and repeated fire exposure. This study highlights the value of remote sensing for post-fire assessment and emphasizes the need for adaptive land management and ecological restoration strategies to support long-term ecosystem resilience in Mediterranean fire-prone landscapes.

Abstract: Macroalgae are an integral part of estuarine primary production, however their excessive growth may have severe negative impacts on the ecosystem. Although it is generally believed that algal blooms may be caused by a combination of excessive nutrients and temperature, their occurrences are hard to predict, and quantitative monitoring is a logistical challenge which requires development of reliable and inexpensive technique. This can be achieved by implementation of processing algorithms and indices on multi-spectral satellite images. Tuggerah Lakes estuary on the Central Coast of NSW was studied because of the regular occurrences of blooms, primarily of green filamentous algae. The detection of algal blooms based on the red-edge effect of the chlorophyll provided consistent results supported by direct observations. Floating Algae Index (FAI) was chosen as the most accurate index detecting algal blooms in shallow areas. Logistic regression was implemented where FAI was used as a predictor of two clusters, “bloom” and “non-bloom”. FAI was calculated for multi-spectral satellite images based on pixels of 20x20 meters, covering the entire area of the Tuggerah Lakes. Seven sample points (pixels) were chosen, and the optimal threshold was found for each pixel to assign it to one of the two clusters. Logistic regression model was trained for each pixel; then the optimal parameters for its coefficients and the optimal classification threshold were obtained by cross validation and bootstrapping. Probabilities for classifying clusters as either “bloom” or “non-bloom” were predicted with respect to the optimal threshold. The resulting model can be used to estimate probability of macroalgal blooms in coastal estuaries allowing quantitative monitoring through time and space.

Abstract: Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct application in large-scale 3D scene reconstruction. To address this, this paper proposes a novel grid-based scene segmentation technique in the process of reconstruction. Sparse point clouds, acting as an indirect input for 3DGS, are first processed by Z-Score and percentile-based filter to prepare the pure scene for segmentation. Then, through grid creation, grid partitioning, and grid merging, it forms rational and widely-applicable sub-grids and sub-scenes for training. Followed by integrating Hierarchy-GS's LOD strategy, this method achieves better large-scale reconstruction effect within limited computational resources. Experiments on multiple datasets show that this method matches others in single block reconstruction and excels in complete scene reconstruction, achieving superior results in PSNR, LPIPS, SSIM, and visualization quality.

Abstract: Remote sensing observations of snow-covered areas (SCA) are important for monitoring and modeling energy balances, hydrologic processes, and climate change. For an agricultural field, we produced 15 snow cover maps from UAS imagery during a snowmelt period. SCA maps were used to characterize snow cover patterns, validate satellite snow cover products, translate satellite Normalized Difference Snow Index (NDSI) to fractional SCA (fSCA), and downscale satellite SCA observations. Compared to manually delineated SCA, the UAS SCA accuracy was 85%, with shadows and ice causing misclassifications. During snowmelt, UAS-derived maps of bare ground patches exhibited self-similarity, behaving as fractal objects over scales from 0.01 to 100 m2. As a validation tool, the UAS-derived SCA showed that satellite observations accurately captured the fSCA evolution during snowmelt (R2 = 0.93-0.98). A random forest satellite downscaling model, trained using 20 m Sentinel-2 NDSI observations and 20 cm vegetation and terrain features, produced realistic (>90% accuracy), high-resolution SCA maps. Relative to traditional Sentinel-2 SCA, downscaling snow cover improved performance during periods of patchy snow cover and produced more realistic bare patches. UAS optical sensing demonstrates the potential uses for high resolution snow cover mapping and recommends future research avenues for using fine scale UAS SCA maps.

Abstract: Characterization of soil attribute variability often requires dense sampling grids, which can be economically unfeasible. A possible solution is to perform targeted sampling based on previously collected data. The objective of this research was to develop a method for mapping soil attributes based on Management Zones (MZs) delineated from Sentinel-1 radar data. Sentinel-1 images were used to create time profiles of six indices based on VV (vertical-vertical) and VH (vertical-horizontal) backscatter in two agricultural fields. MZs were delineated by analyzing indices and VV/VH backscatter bands individually through two approaches: (1) fuzzy k-means clustering directly applied to the indices' time series, and (2) dimensionality reduction using deep-learning autoencoders followed by fuzzy k-means clustering. The best combination of index and MZs delineation approach was compared with four soil attribute mapping methods: conventional (single composite sample), high-density uniform grid (one sample per hectare), rectangular cells (one composite sample per cell of 5 to 10 hectares), and random cells (one composite sample per cell of varying sizes). Leave-one-out cross-validation evaluated the performance of each sampling method. Results showed that combining VV/VH index and autoencoders for MZs delineation provided more accurate soil attribute estimates, outperforming the conventional, random cells, and often the rectangular cell method.

Abstract: Urbanization is reshaping landscapes and posing unprecedented sustainability challenges, necessitating more integrative approaches to urban observation. This review synthesizes recent advancements in traditional remote sensing and emerging social sensing technologies, emphasizing their convergence within urban science. A systematic thematic analysis of 669 peer-reviewed articles highlights methodological progress, practical applications, and theoretical innovations arising from this integration. Traditional remote sensing effectively captures urban physical features but lacks insights into human behaviors. Conversely, social sensing, leveraging digital traces from social media and mobile data, introduces essential human-centered dimensions into urban monitoring. The fusion of these complementary paradigms through advanced data analytics and multimodal integration has produced transformative methodologies, enhancing urban resilience frameworks, functional zone delineation, and real-time disaster responses. Despite significant progress, the integration faces persistent challenges, including data heterogeneity, representational bias, ethical concerns, and scalability limitations. Moving forward, this review argues for a conceptual shift toward a dynamic, systems-based urban observatory framework, underpinned by hybrid metrics and ethical standards. Such a framework will enable a more holistic, responsive, and equitable approach to urban governance and sustainability.

Abstract: Urban expansion has a major impact on local climate conditions, particularly through rising land surface temperatures (LST) and the intensification of climate risk zones. With an emphasis on LST variation with abnormal urban growth, this study uses Geographic Information System (GIS) approaches to assess the spatial relationship between urban expansion and urban climate risk zones which offer meaningful characteristics in quantifying urban expansion with the affecting thermal pattern and urban climate risks. Using satellite-derived thermal imagery, land use data and air quality data, the temperature distribution and air quality level across various urbanized and non-urbanized regions in Dhaka City Corporation (DCC) and Rajshahi City Corporation (RCC) has been illustrated as well as a comparison between these cities. Findings show that LST intensified by urban expansion increases the climate risk zones, environmental sensitivity and localized heat stress in DCC and RCC from 2016 to 2024. The study showcases the necessity of integrating GIS-based urban climate risk assessments into urban planning to alleviate the adverse impacts of rising temperatures and ensure sustainable development.

Abstract: Total Precipitable Water (TPW) is a key variable of atmospheres, and its spatiotemporal distribution is of great importance in global climate change. This paper addresses the TPW retrieval over both sea and land surfaces from the data acquired by the Microwave Humidity Sounder II (MWHS-II) on Fengyun 3D (FY-3D) satellite. First, Back Propagation neural network (BPNN) algorithms are developed with the spatiotemporal matching samples of the MWHS-II data versus the fifth-generation European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. Then, the TPWs between 65°S and 65°N over both sea and land surfaces are retrieved from FY-3D MWHS-II data in 2022. Finally, the TPWs retrieved in this work are validated with the radiosonde TPWs over both sea and land surfaces, and they are also compared to the F18 Special Sensor Microwave Imager Sounder (SSMIS) TPWs over sea surfaces. The results indicate that the BPNN algorithms developed in this work are valid and accurate. The mean error (ME), the root mean square error (RMSE) and mean absolute error (MAE) of the TPWs retrieved in this work against the radiosonde TPWs are -1.17 mm, 3.46 mm and 2.63 mm over sea surfaces, respectively, and they are -0.80 mm, 4.04 mm and 3.13 mm over land surfaces, respectively. The TPWs retrieved in this work are much more accurate than the F18 SMMIS TPWs.

Abstract: Remote sensing data, particularly from Sentinel-2 satellites, offers valuable insights into surface mining activities. This study evaluates the effectiveness of Sentinel-2 imagery in detecting and monitoring sand and gravel extraction sites across five mining areas in Schleswig-Flensburg, Germany, from 2015 to 2019. Three machine learning algorithms—Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN)—were compared to determine the most accurate classification approach. Models were trained using three different training data scenarios to assess their performance. While RF and SVM demonstrated greater robustness than ANN, SVM outperformed the others when validated against ground truth data. Consequently, an optimized SVM-based classification model was developed and implemented in R to analyze temporal changes in the study areas over five years. The findings highlight the potential of integrating Sentinel-2 imagery with machine learning techniques for accurate and efficient monitoring of surface mining activities, offering a scalable approach for environmental management and land-use planning.

Abstract: The Weather Research and Forecasting (WRF) model is used to study the transport of a passive scalar plume, representing smoke from biomass burning in South America (SA). In this context, the Mellor–Yamada–Nakanishi–Niino (MYNN), Yonsei University (YSU), and Bougeault-Lacarrere (BouLac) Planetary Boundary-Layer (PBL) schemes were utilized. A total of three simulations were conducted, one for each PBL scheme, starting on August 15, 2019, and ending on August 20, 2019. This period was characterized by a high concentration of smoke, which were transported to the southern and southeastern regions of South America. During this period, the direct impact of these particulates was observed in the São Paulo Metropolitan Area (SPMA). To determine the positions of the passive tracers, data from the Fire Information for Resource Management System (FIRMS) were used to identify the regions with the highest Fire Radiative Power (FRP) during the study period. Based on this analysis, three locations in different areas were selected. To analyze the transport, synoptic fields were examined during the study period to provide a better description of the atmospheric dynamics. On the mesoscale, the impact of diffusivity was assessed for each PBL scheme. For validation, surface data from the Light Detection and Ranging (LiDAR) system, located in the São Paulo region was utilized. It was observed that one of the tracers reached the SMPA in all three simulations. However, when analyzing the other tracers, the BouLac scheme failed to transport the particulates to this region. Dynamically, the low-level jet played an active role in transporting the tracers across the southern part of domain and subsequently toward the southeastern region.

Abstract: One of the most important inputs for land use planning and risk management applications is a reliable Digital Elevation Model (DEM). This problem becomes more critical in flat areas where low-precision models cannot represent the terrain configuration in sufficient detail. In many areas 3around the world, this information is not available, which makes it necessary to formulate a strategy to solve this scarcity of information. This paper presents a methodological proposal for improving the vertical accuracy of global coverage digital elevation models (DEM) in flat areas with limited data. The correction procedure is based on adjusting the altimetric differences between the digital elevation model and surveyed topographic points with GNSS-RTK, also taking into account the land covers in the different areas. The Ranchería river delta in Riohacha, La Guajira, Colombia, was selected as a case of application of the proposed methodology. The correction methodology was applied to two global coverage satellite DEMs FABDEM and SRTM. The results revealed a significant reduction of the RMSE error of up to 53.03% in the FABDDEM and 59.07% in the SRTM. The proposed methodology proves to be an easily applicable alternative in another area with similar characteristics, requiring basic information and not requiring large computational capabilities or training in sophisticated algorithms, which gives it great potential for replication. This substantial improvement in altimetric accuracy has crucial implications for natural risk management and decision-making in floodplain areas with limited information because DEM accuracy is essential for proper hydrodynamic modeling, and the improvements obtained with the proposed methodology have the potential to increase the reliability 18 of models used in flood prediction and management in similar flat areas. This advancement can positively impact critical decision-making, risk management, and the protection of vulnerable communities in such areas.

Abstract: Ambient fine particulate matter of size less than 2.5 µm in aerodynamic diameter (PM2.5) is a key component of ambient air pollution that has been linked to numerous adverse health outcomes. Reliable estimates of PM2.5 are important for supporting epidemiologic and health impact assessment studies. Precise measurements of PM2.5 are available through networks of monitors, however these are spatially sparse and temporally incomplete. Chemical transport model (CTM) simulations and satellite-retrieved aerosol optical depth (AOD) measurements are two data sources that have been used to develop prediction models for PM2.5 at fine spatial resolutions with increased spatial coverage. As part of the Multi-Angle Imager for Aerosols (MAIA) project, a geostatistical regression model has been developed to bias-correct AOD, followed by Bayesian ensemble averaging to gap-fill missing AOD values with CTM simulations. Here we present a suite of statistical software (available in the R package ensembleDownscaleR) to facilitate the adaptation of this modeling approach to other settings and air quality modeling applications. We describe the Bayesian ensemble averaging approach, model specifications, estimation methods and evaluation via cross-validation that are implemented in the software. We also provide a case study of estimating PM2.5 using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available at GitHub, data is made available at Zenodo, and the ensembleDownscaleR package is available for download at GitHub.

Abstract: This study explores the growing application of 3D remote sensing in geohazard studies, particularly for rock slope monitoring. It highlights Street View Imagery (SVI) as a cost-effective alternative to traditional methods like UAVs. By comparing 3D point clouds generated from SVI data over time, researchers can track rockfall progression and quantify retreat volume. A case study in Koto Panjang cliff, Indonesia, demonstrates this approach. Utilizing seven years of SVI data, the study reveals a total rockfall retreat of 5,270 m3, with an average retreat rate of 7.53 m3/year. Structural analysis identified six major discontinuity sets and confirmed inherent instability within the rock mass. Kinematic simulations using SVI-derived data further assessed rockfall trajectories and potential impact zones. Results indicate that 40 % of simulated rockfall deposits accumulated near existing roads, with significant differences in distribution based on scree slope angles. This emphasizes the role of scree slope in influencing rockfall propagation. In conclusion, SVI presents a valuable tool for 3D point cloud reconstruction and rockfall hazard assessment, particularly in areas lacking historical data. The study showcases the effectiveness of using SVI in quantifying rockfall volume and identifying critical areas for mitigation strategies, highlighting the importance of scree slope angle in managing rockfall hazard.