Search Results (196)

Stromboli’s volcanic activity fluctuates in intensity and style, and periods of heightened activity can trigger hazardous events such as crater collapses and lava overflows. This study investigates the volcano’s explosive behavior surrounding the 19 May 2021 crater-rim failure, which primarily affected the N2 crater and partially involved N1, by integrating high-frequency thermal imaging and high-resolution unmanned aerial system (UAS) surveys to quantify eruption parameters and vent morphology. Typically, eruptive periods preceding vent instability are characterized by evident changes in geophysical parameters and by intensified explosive activity. This is quantitatively monitored mainly through explosion frequency, while other eruption parameters are assessed qualitatively and sporadically. Our results show that, in addition to explosion rate, the spattering rate, the predominance of bomb- and gas-rich explosions, and the number of active vents increased prior to the collapse, reflecting near-surface magma pressurization. UAS surveys revealed that the pre-collapse configuration of the northern craters contributed to structural vulnerability, while post-collapse vent realignment reflected magma’s adaptation to evolving stress conditions. The May 2021 events were likely influenced by morphological changes induced by the 2019 paroxysms, which increased collapse frequency and amplified the 2021 failure. These findings highlight the importance of integrating quantitative time series of multiple eruption parameters and high-frequency morphological surveys into monitoring frameworks to improve early detection of system disequilibrium and enhance hazard assessment at Stromboli and similar volcanic systems. Full article

Collaborative sensing between low-altitude remote sensing and ground-based mobile mapping lays the theoretical foundation for multi-platform 3D data fusion. However, point clouds collected from Airborne Laser Scanners (ALSs) remain scarce due to high acquisition and annotation costs. In contrast, while autonomous driving datasets are more accessible, dense annotation remains a significant bottleneck. To address this, we propose Ground to Altitude (GTA), a weakly supervised domain generalization (DG) framework. GTA leverages sparse autonomous driving data to learn robust representations, enabling reliable segmentation on airborne point clouds under zero-label conditions. Specifically, we tackle cross-platform discrepancies through progressive domain-aware augmentation (PDA) and cross-scale semantic alignment (CSA). For PDA, we design a distance-guided dynamic upsampling strategy to approximate airborne point density and a cross-view augmentation scheme to model viewpoint variations. For CSA, we impose cross-domain feature consistency and contrastive regularization to enhance robustness against perturbations. A progressive training pipeline is further employed to maximize the utility of limited annotations and abundant unlabeled data. Our study reveals the limitations of existing DG methods in cross-platform scenarios. Extensive experiments demonstrate that GTA achieves state-of-the-art (SOTA) performance. Notably, under the challenging 0.1% supervision setting, our method achieves a 6.36% improvement in mIoU over the baseline on the SemanticKITTI → DALES benchmark, demonstrating significant gains across diverse categories beyond just structural objects. Full article

Accurate quantification of photosynthetically active radiation absorbed by chlorophyll (${\mathit{fAPAR}}_{\mathit{Chl}\mathit{a}}$) and the corresponding fluorescence quantum efficiency (FQE) is critical for understanding vegetation productivity. In this study, we investigate the retrieval of pigment-specific effective absorbance and Sun-Induced Chlorophyll Fluorescence (SIF) using both airborne hyperspectral imagery (HyPlant) and ground-based field spectroscopy (FloX) over a well-irrigated alfalfa field in northeastern Spain. Spectral unmixing techniques, including Constrained Least Squares (CLS), Potential Function (POT), and Bilinear (BIL) models, were applied to disentangle pigment and background contributions. The CLS approach was identified as the most robust, balancing reconstruction accuracy with physical plausibility. We derived ${\mathit{fAPAR}}_{\mathit{Chl}\mathit{a}}$ from the abundance-weighted pigment absorbance and combined it with spectrally-integrated SIF to calculate FQE. Comparisons between airborne and ground-based measurements revealed strong agreement, highlighting the potential of this combined methodology. The study demonstrates the applicability of advanced spectral unmixing frameworks for both airborne and proximal sensing data, providing a reliable baseline for photosynthetic efficiency in a healthy crop and establishing a foundation for future stress detection studies. Full article

The use of unmanned aerial vehicles (UAVs) as mobile sensor platforms has grown significantly in recent years, including applications where drones emulate radar targets or serve as dynamic measurement systems. This paper presents a novel approach to time-synchronized UAV trajectory planning for radar environment simulation. The proposed method considers a UAV equipped with a software-defined radio (SDR) capable of reproducing the radar signature of a simulated airborne object, e.g., a high-maneuverability or high-speed aerial platform. The UAV must follow a spatial trajectory that replicates the viewing geometry—specifically, the observation angles—of the reference target as seen from a ground-based radar. The problem is formulated within a factor graph framework, enabling joint optimization of the UAV trajectory, observation geometry, and temporal synchronization constraints. While factor graphs have been extensively used in robotics and sensor fusion, their application to trajectory planning under temporal and sensing constraints remains largely unexplored. The proposed approach enables unified optimization over space and time, ensuring that the UAV reproduces the target motion as perceived by the radar, both geometrically and with appropriate signal timing. Full article

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy. Full article

This research presents a comprehensive analysis of the spatial extent and temporal change in benthic habitats within the Puck Lagoon in the southern Baltic Sea, utilizing integrated machine learning classification and multi-sourced remote sensing. Object-based image analysis was integrated with Random Forest, Support Vector Machine, and K-Nearest Neighbors algorithms for benthic habitat classification based on airborne bathymetric LiDAR (ALB), multibeam echosounder (MBES), satellite bathymetry, and high-resolution aerial photography. Ground-truth data collected by 2023 field surveys were supplemented with long temporal datasets (2010–2023) for seagrass meadow analysis. Boruta feature selection showed that geomorphometric variables (aspect, slope, and terrain ruggedness index) and optical features (ALB intensity and spectral bands) were the most significant discriminators in each classification case. Binary classification models were more effective (93.3% accuracy in the presence/absence of Zostera marina) compared to advanced multi-class models (43.3% for EUNIS Level 4/5), which identified the inherent equilibrium between ecological complexity and map validity. Change detection between contemporary and 1957 habitat data revealed extensive Zostera marina loss, with 84.1–99.0% cover reduction across modeling frameworks. Seagrass coverage declined from 61.15% of the study area to just 9.70% or 0.63%, depending on the model. Seasonal mismatch may inflate loss estimates by 5–15%, but even adjusted values (70–94%) indicate severe ecosystem degradation. Spatial exchange components exhibited patterns of habitat change, whereas net losses in total were many orders of magnitude larger than any redistribution in space. These findings recorded the most severe seagrass habitat destruction ever described within Baltic Sea ecosystems and emphasize the imperative for conservation action at the landscape level. The methodology framework provides a reproducible model for analogous change detection analysis in shallow nearshore habitats, creating critical baselines to inform restoration planning and biodiversity conservation activities. It also demonstrated both the capabilities and limitations of automatic techniques for habitat monitoring. Full article

Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an $RMSE$ of 5.52 m, and an $MAE$ of 4.08 m. The XGBoost model obtained $r$ = 0.716, $RMSE$ = 5.55 m, and $MAE$ = 4.10 m using the same data combination as the Random Forest model, which produced $r$ = 0.706, $RMSE$ = 5.63 m, and $MAE$ = 4.16 m. The multi-source comprehensive fusion technique produced the greatest results; however, including either Sentinel-1 or Sentinel-2 enhances model performance, according to comparisons across multiple data combinations. This work presents an efficient technological strategy for monitoring forest height in complex terrains, thereby providing a scalable and robust methodological reference for supporting sustainable forest management and large-scale ecological assessment. The proposed multi-source spatiotemporal fusion framework, coupled with systematic model evaluation, demonstrates significant potential for operational applications, especially in regions with limited LiDAR coverage. Full article

This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi-arid and arid basins. We hypothesized that water-sensitive spectral indices correlate with gamma-ray hotspots in arid regions of Mongolia, where natural radionuclide distribution is influenced by hydrological processes. Leveraging historical car-borne gamma spectrometry data collected in 2008 across the Sainshand and Zuunbayan uranium project areas, we evaluated twelve spectral bands and five established moisture-sensitive indices against radiation heatmaps in Naarst and Zuunbayan. Using Pearson and Spearman correlations alongside two percentile-based overlap metrics, indices were weighted to yield a composite performance score. The best performing indices (MI—Moisture Index and NDSII_1—Normalized Difference Snow and Ice Index) guided the derivation of ten new ND constructs incorporating SWIR bands (B11, B12) and visible bands (B4, B8A). The top performer, NDGRI = (B4 − B12)/(B4 + B12) achieved a precision of 62.8% for detecting high gamma-radiation areas and outperformed benchmarks of other indices. We established climatological screening criteria to ensure NDGRI reliability. Validation at two independent sites (Erdene, Khuvsgul) using 2008 airborne gamma ray heatmaps yielded 76.41% and 85.55% spatial overlap accuracy, respectively. Our results demonstrate that NDGRI effectively delineates gamma radiation hotspots where moisture-controlled spectral contrasts prevail. The index’s stringent acquisition constraints, however, limit the temporal availability of usable scenes. NDGRI offers a rapid, cost-effective remote sensing tool to prioritize ground surveys in uranium prospective basins and may be adapted for other radiometric applications in semi-arid and arid regions. Full article

Tropospheric ozone (O₃), a secondary pollutant of mounting global concern, emerges from complex, nonlinear photochemical reactions involving nitrogen oxides (NO_x) and volatile organic compounds (VOCs) under dynamically evolving meteorological conditions. Accurately characterizing and effectively regulating O₃ formation necessitates not only precise and multi-dimensional precursor observations but also modeling frameworks that are structurally coherent, chemically interpretable, and sensitive to regime variability. Despite significant technological progress, current research remains markedly fragmented: observational platforms often operate in isolation with limited vertical and spatial interoperability, while modeling paradigms—ranging from mechanistic chemical transport models (CTMs) to data-driven machine learning approaches—frequently trade interpretability for predictive performance and struggle to capture regime transitions across heterogeneous environments. This review provides a dual-perspective synthesis of recent advances and enduring challenges in the VOC–O₃ research landscape. We first establish a typology of ground-based, airborne, and satellite-based VOC monitoring systems, evaluating their capabilities, limitations, and roles within a vertically structured sensing architecture. We then examine the evolution of O₃ modeling strategies, from empirical and semi-mechanistic models to hybrid frameworks that integrate physical knowledge with algorithmic flexibility. By diagnosing the structural decoupling between observation and inference, we identify key methodological bottlenecks and advocate for a system-level redesign of the VOC–O₃ research paradigm. Finally, we propose a forward-looking framework for next-generation atmospheric governance—one that fuses cross-platform sensing, regime-aware modeling, and policy-relevant diagnostics into an integrated, adaptive, and chemically robust decision-support system. Full article

Debris flows pose significant risks to mountainous regions, and quick, accurate volume estimation is crucial for hazard assessment and post-disaster response. Traditional volume estimation methods, such as ground surveys and aerial photogrammetry, are often limited by cost, accessibility, and timeliness. While remote sensing offers wide coverage, existing optical and Synthetic Aperture Radar (SAR)-based techniques face challenges in direct volume estimation due to resolution constraints and rapid terrain changes. This study proposes a Super-Resolution Shape from Shading (SRSFS) approach enhanced by a Non-local Piecewise-smooth albedo Constraint (NPC), hereafter referred to as NPC SRSFS, to estimate debris-flow erosion volume using single high-resolution optical satellite imagery. By integrating publicly available global Digital Elevation Model (DEM) data as prior terrain reference, the method enables accurate post-disaster topography reconstruction from a single optical image, thereby reducing reliance on stereo imagery. The NPC constraint improves the robustness of albedo estimation under heterogeneous surface conditions, enhancing depth recovery accuracy. The methodology is evaluated using Gaofen-6 satellite imagery, with quantitative comparisons to aerial Light Detection and Ranging (LiDAR) data. Results show that the proposed method achieves reliable terrain reconstruction and erosion volume estimates, with accuracy comparable to airborne LiDAR. This study demonstrates the potential of NPC SRSFS as a rapid, cost-effective alternative for post-disaster debris-flow assessment. Full article

The Dead Sea is one of the most saline terminal lakes on Earth, and few organisms survive in this harsh environment. In some onshore spring pools, active and diverse microbial communities flourish. In the geological past, microbial-rich environments left their marks in the form of stromatolites. Stromatolites are studied to better understand the appearance of life on Earth and potentially on other planets. Hyperspectral methodologies have been shown to be useful for detecting structures in stromatolites. In an effort to characterize the biosignatures and chemical composition inherent to stromatolites, we created a spectral classification scheme for distinguishing between stromatolites and their bedrock environment—typically carbonatic rocks, mostly dolomites. The overarching aim comprises the development of an automated hyperspectral reflectance method for detecting the presence of stromatolites. We collected and measured 82 field samples with an ASD spectrometer and used our spectral dataset to train three machine learning algorithms (linear regression, K-Nearest Neighbor, XGBoost). The results show the successful detection of stromatolites, with all three prediction methods giving high accuracy rates (stromatolite > 0.9, bedrock dolomite > 0.8). The continuum removal and spectral ratio technique results identified two significant spectral regions, ~1900 nm (water) and ~2310–2320 nm (carbonates), that allow one to differentiate between stromatolites and dolomites. This study establishes the grounds for the automated detection of a fossilized livable environment in a carbonatic terrain based on its hyperspectral reflectance data. The results have significant implications for future mapping efforts and emphasize the feasibility of automated mapping, extending the data acquisition to airborne or satellite-based hyperspectral remote sensing technologies to detect life forms in extreme environments. Full article

We applied UAV (Unmanned Aerial Vehicle) and ALS (Airborne Laser Scanning) remote sensing methods to identify terrain obstacles encountered during timber extraction in the skidding process with the aim of proposing accessibility solutions to the inner parts of forest stands using skidding trails. At the Vítovický žleb site, located east of Brno in the South Moravian Region of the Czech Republic, we analysed the accuracy of digital terrain models (DTMs) created from UAV LiDAR (Light Detection and Ranging), RGB (Red–Green–Blue) UAV, ALS data taken on site and publicly available LiDAR data DMR 5G (Digital Model of Relief of the Czech Republic, 5th Generation, based on airborne laser scanning, providing pre-classified ground points with an average density of 1 point/m²). UAV data were obtained using two types of drones: a DJI Mavic 2 mounted with an RGB photogrammetric camera and a GeoSLAM Horizon laser scanner on a DJI M600 Pro hexacopter. We achieved the best accuracy with UAV technologies, with an average deviation of 0.06 m, compared to 0.20 m and 0.71 m for ALS and DMR 5G, respectively. The RMSE (Root Mean Square Error) values further confirm the differences in accuracy, with UAV-based models reaching as low as 0.71 m compared to over 1.0 m for ALS and DMR 5G. The results demonstrated that UAVs are well-suited for detailed analysis of rugged terrain morphology and obstacle identification during timber extraction, potentially replacing physical terrain surveys for timber extraction planning. Meanwhile, ALS and DMR 5G data showed significant potential for use in planning the placement of skidding trails and determining the direction and length of timber extraction from logging sites to forest roads, primarily due to their ability to cover large areas effectively. Differences in the analysis results obtained using GIS (Geographic Information System) cost surface solutions applied to ALS and DMR 5G data DTMs were evident on logging sites with terrain obstacles, where the site-specific ALS data proved to be more precise. While DMR 5G is based on ALS data, its generalised nature results in lower accuracy, making site-specific ALS data preferable for analysing rugged terrain and planning timber extractions. However, DMR 5G remains suitable for use in more uniform terrain without obstacles. Thus, we recommend combining UAV and ALS technologies for terrain with obstacles, as we found this approach optimal for efficiently planning the logging-transport process. Full article

Quantifying forest structure to assess changing wildfire risk factors is critical as vulnerable areas require mitigation, management, and resource allocation strategies. Remote sensing offers the opportunity to accurately measure forest attributes without time-intensive field inventory campaigns. Here, we quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery. In addition, we compared UAS–SfM estimates with those derived using similar methods applied to Airborne Laser Scanning (ALS) 3D point clouds as well as traditional ground-based measurements. Canopy cover estimates were similar across remote sensing (ALS, UAS-SfM) and ground-based approaches (r² = 0.79, RMSE = 16.49%). Compared to ground-based approaches, UAS-SfM point clouds allowed for correct detection of 68% of trees and estimated tree heights were significantly correlated (r² = 0.69, RMSE = 5.1 m). UAS-SfM was not able to estimate canopy base height due to its inability to penetrate dense canopies in these forests. Since canopy cover and individual tree heights were accurately estimated at the plot-scale in this unique bioregion with diverse topography and complex species composition, we recommend UAS-SfM as a viable approach and affordable solution to estimate these critical forest parameters for predictive wildfire modeling. Full article

This study reviews research from 2010 to 2023 on the integration of airborne laser scanning (ALS) metrics with satellite and ground-based data for forest monitoring, highlighting the potential of the combined use of ALS and optical remote sensing data in improving the accuracy and the frequency. Following an in-depth screening process, 42 peer-reviewed scientific manuscripts were selected and comprehensively analyzed, identifying how the integration among different sources of information facilitate frequent, large-scale updates, crucial for monitoring forest ecosystems dynamics and changes, aiding in supporting sustainable management and climate smart forestry. The results showed how ALS metrics—especially those related to height and intensity—improved estimates precision of forest volume, biomass, biodiversity, and structural attributes, even in dense vegetation, with an R² up to 0.97. Furthermore, ALS data were particularly effective for monitoring urban forest variables (R² 0.83–0.92), and for species classification (overall accuracy up to 95%), especially when integrated with multispectral and hyperspectral imagery. However, our review also identified existing challenges in predicting biodiversity variables, highlighting the need for continued methodological improvements. Importantly, while some studies revealed great potential, novel applications aiming at improving ALS-derived information in spatial and temporal coverage through the integration of optical satellite data were still very few, revealing a critical research gap. Finally, the ALS studies’ distribution was extremely biased. Further research is needed to fully explore its potential for global forest monitoring, particularly in regions like the tropics, where its impact could be significant for ecosystem management and conservation. Full article

The ongoing advancement of unmanned aerial vehicles (UAVs) and the evolution of small-scale cameras have bridged the gap between traditional ground-based surveys and orbital sensors. However, these systems present challenges, including limited coverage area, image stabilization constraints, and complex image processing. In water quality monitoring, these difficulties are further compounded by sun glint effects, which hinder the construction of accurate orthomosaics in homogeneous water surfaces and affect radiometric accuracy. This study focuses on evaluating these challenges by comparing two distinct airborne imaging platforms with different spectral resolutions, emphasizing Total Suspended Solids (TSS) monitoring. Hyperspectral airborne surveys were undertaken utilizing a pushbroom system comprising 276 bands, whereas multispectral airborne surveys were conducted employing a global shutter frame with 4 bands. Fifteen aerial survey campaigns were carried out over water bodies from two biomes in Brazil (Amazon and Savanna), at varying concentrations of TSS (0.6–130.7 mg L⁻¹, N: 53). Empirical models using near-infrared channels were applied to accurately monitor TSS in all areas (Hyperspectral camera—RMSE = 3.6 mg L⁻¹, Multispectral camera—RMSE = 9.8 mg L⁻¹). Furthermore, a key contribution of this research is the development and application of Sun Glint mitigation techniques, which significantly improve the reliability of airborne reflectance measurements. By addressing these radiometric challenges, this study provides critical insights into the optimal UAV platform for TSS monitoring in inland waters, enhancing the accuracy and applicability of airborne remote sensing in aquatic environments. Full article