Search Results (405)

ICESat-2/ATLAS photon-counting LiDAR faces several challenges in regional-scale forest aboveground biomass (AGB) estimation. These challenges include sparse sampling, signal saturation, terrain effects, and limited model generalization. To solve these challenges, this study proposes a new synergistic multi-source remote sensing framework for regional-scale AGB estimation by integrating terrain-corrected ICESat-2 canopy height and forest-type heterogeneity. The framework combines structural, spectral, textural, topographic, and climatic information derived from multiple remote sensing datasets to improve biomass estimation accuracy and model robustness across different forest types. In this paper, multi-source datasets were integrated, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission (SRTM), WorldClim, and a terrain-corrected canopy height model (CHM). Subsequently, candidate features were derived such as spectral, textural, topographic, and climatic variables. In terms of the terrain-corrected CHM, canopy structural parameters were extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL08 data after terrain correction based on a high-resolution DEM. Footprint-level AGB samples were first generated using ICESat-2-derived canopy structural parameters through four regression approaches, including Multiple linear regression, Stepwise multiple regression, Ridge regression, and Lasso regression. These generated AGB samples were then used as response variables for subsequent regional-scale modeling. To build accurate AGB estimation model, key features were first identified using correlation analysis. To account for forest structural heterogeneity, three models including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed for regional AGB mapping. To evaluate the performance of the proposed AGB estimation model by integrating terrain-corrected canopy height and forest-type heterogeneity, this study conducted AGB estimation at the Harvard Forest (HARV) site in the United States. The experimental results show that forest-type-specific modeling improves model adaptability and robustness. Among the models (RF, XGBoost and SVM), RF achieved the best performance, with an average coefficient of determination of 0.694. The optimized model was applied to produce a 30 m resolution AGB map. The validation was conducted using airborne LiDAR-derived AGB referenced results. The validation shows that an overall coefficient of determination (R²) of 0.606 and a root mean square error (RMSE) of 16.53 Mg ha⁻¹. These results demonstrate that the proposed new synergistic AGB estimation framework, which integrates terrain-corrected ICESat-2 canopy height with forest-type-specific modeling, provides an accurate and reliable solution for regional-scale forest biomass mapping and carbon stock assessment. Full article

This study conducted a six-month time-series micro-topographic analysis using high-resolution UAV LiDAR technology to precisely characterize the complex terrain changes in regressive tidal creeks within coastal wetlands. To overcome the unique challenges posed by vegetation-dense regressive tidal flats, the LiDAR Penta Return (5-pulse) mode was applied, yielding high-density point cloud data with an average of 174 pts/m². The analysis successfully reproduced the bare earth surface beneath the vegetation canopy at sub-centimeter-level precision, overcoming the limitations of conventional optical surveying, and enabled quantitative detection of micro-topographic changes of ±25 cm or greater. Time-series analysis based on the DEM of Difference (DoD) revealed spatiotemporally asymmetric erosion and deposition patterns concentrated at the lower elevation zone (0.0–2.0 m) and slope boundaries of the regressive tidal creek. However, the apparent large elevation changes in the lowest, intermittently inundated creek-bed zone (including a maximum of about 3.7 m between the summer surveys, T2–T1) were found to scale monotonically with the tide level at the time of each flight, indicating that they are governed by the tide-dependent water-surface return rather than by genuine bed erosion. After excluding this water-affected zone, the consistently sub-aerial surface showed only modest net change over the six-month period, indicating that the regressive tidal creek adjusts gradually rather than through abrupt large-magnitude erosion and deposition. This study presents the essential value of high-precision time-series monitoring for assessing the geomorphic stability of coastal wetlands in an environment where extreme weather events under climate change are increasing in frequency. Full article

A significant low-level wind shear event that occurred at Xining Caojiabu Airport on 10 April 2019 was comprehensively analyzed. The analysis utilized data from the airport’s ground automatic weather observation system (AWOS), lidar detection data, ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and ETOPO2v2 topographic data from the National Oceanic and Atmospheric Administration (NOAA). The analysis focused on the evolution of meteorological elements during the wind shear, lidar characteristics, large-scale environmental features, and the main influencing systems. The results indicate that this was a typical “easterly backflow” low-level wind shear event, representing a special type of cold-frontal low-level wind shear, with the wind shear occurring in the prefrontal area as the cold front approached the airport. During the passage of the wind shear, the AWOS stations at Runways 29 and 11 sequentially recorded pressure increases and temperature decreases, reflecting the gradual intrusion of cold air from east to west into the airport. Lidar Plan Position Indicator (PPI), Range-Height Indicator (RHI), and Doppler Beam Swinging (DBS) modes revealed that the wind shear appeared as convergence between southeast and northwest winds, with an impact on the airport that moved from east to west and from bottom to top, belonging to a meso-γ-scale system. The evolution of the sea-level pressure field, pressure-change field, frontogenesis function, and temperature advection indicated that cold air first moved eastward along the Hexi Corridor and then poured back into the Huangshui River Valley through the topographic gap at the eastern end of the Qilian Mountains. The easterly wind converged with the westerly wind, and the topographic funneling effect strengthened the easterly backflow and promoted its westward advance, leading to the occurrence of low-level wind shear. The large-scale influencing systems of this event included a transverse trough over Mongolia at 500 hPa, an upper-level frontal zone, an upper-level jet stream, and a surface cold front. The favorable conditions for the formation of this “easterly backflow” low-level wind shear were the strengthening of baroclinicity in the upper-level frontal zone, intensified cold advection, momentum downward transport induced by the upper-level jet and ageostrophic secondary circulation, and the easterly backflow and wind speed enhancement caused by the special topography. Full article

The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelized prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains. Full article

Virtually all spaceborne topographic lidars to date have used a single beam, with the exception of the ATLAS lidar on NASA’s ICESat-2 satellite, which split the beam into 3 “strong” and 3 “weak” beamlets distributed perpendicular to the along-track path of the satellite. This approach has provided high-resolution along-track surface measurements but relatively poor resolution cross-track measurementswithin a given surface area. The present paper attempts to resolve this discrepancy by (1) transmitting and scanning a single Gaussian beam and (2) imaging the return onto a 14 × 14 pixelated, single-photon sensitive, detector array, thereby providing between 100 and 196 measurements per pulse, depending on the solar background. Besides enhancing the lidar’s capability to penetrate tree canopies and water bodies, the proposed single-beam approach provides one to two orders of magnitude more measurements per pulse with equal spatial resolution in boththe along-track and cross-track directions. At the 10 kHz pulse rate of the ATLAS laser on NASA’s ICESat-2 satellite, this implies between 1 and 2 million topographic measurements per second. The maximum surface area observable by a single pulse increases with the laser peak power defined by the ratio of the pulse energy to the temporal pulsewidth. Larger surface areas per pulse result in more time for cross-track scanning while still maintaining contiguous along-track mapping. Two scanning methods appear to be feasible: (1) circular scans using individual but temporally coordinated wedge scanners for the transmitted and received beams, and (2) unidirectional linear scans utilizing Acousto-Optic Deflectors. The circular scan approach is probably easier to implement, but it also requires additional post-processing to obtain an accurate contiguous 3D image of the planetary terrain. Full article

Extreme precipitation and compound hazards can increase the risk of inundation and accidental release of untreated effluent from water treatment facilities, with potential downstream impacts within a short emergency-response window. Few studies have linked site-scale surface-runoff behavior, feasible emergency-response scenarios, and resilience-based decision support for critical water infrastructure. This study presents a GIS-based scenario-comparison framework that couples high-resolution surface-runoff simulation with an AHP-informed resilience interpretation to evaluate untreated effluent runoff and temporary flood-defense strategies at a water treatment plant in Jeollabuk-do, South Korea. A 1 m digital elevation model derived from drone-based LiDAR data was used in ArcGIS Pro to simulate two-dimensional unsteady surface-runoff propagation, producing water-depth and flow-velocity fields at 30 s intervals over 20 min. Three scenarios were compared under identical topographic, release, and hydraulic assumptions, no response, primary defense-line deployment, and secondary defense-line deployment, adding a 335 m barrier along the downstream road. Under the no-response scenario, released water reached the river after approximately 6 min, with a cumulative river inflow of 329.27 m³. The primary defense line reduced cumulative river inflow by 16.8%, and the secondary defense line by 78.2%, while delaying river arrival to 8 min and 30 s. An approximate surface-water balance and time-series analysis showed that the defense lines primarily redistribute water into temporary upstream storage rather than eliminate it. The simulation-derived indicators were linked to four resilience components whose relative importance was estimated using the Analytic Hierarchy Process (AHP) from 205 expert and practitioner responses, which identified recovery speed as the highest-priority component; the weighted normalized indicators are summarized as a transparent scenario-level composite resilience indicator that increases from the no-response to the primary and secondary defense-line scenarios. Because the stormwater drainage network, pollutant transport, and operational deployment uncertainties were not explicitly modeled, the results should be interpreted as a comparative assessment of water-volume transport risk rather than a deterministic prediction of inundation or pollution impact. Within these stated assumptions, the results indicate that a strategically placed secondary defense line can substantially reduce downstream river inflow and secure additional response time, providing preliminary decision support for disaster-risk reduction and emergency-response planning at critical water infrastructure. Full article

Forest disturbances significantly affect the terrestrial carbon cycle, yet high-resolution detection, driver attribution, and carbon loss quantification remain challenging in cloudy and complex terrains. Here, we investigated the Northeast China and Southwest Hengduan Mountains forest regions from 2021 to 2024. We developed a Bayesian Model Averaging (BMA) framework integrating multi-source remote sensing (Sentinel-1/2, Landsat 8/9) and multi-algorithm ensembles (LandTrendr, CCDC, 1D-CNN) to extract 10 m disturbance features. Automated driver attribution and carbon loss quantification were achieved utilizing the Fire Information for Resource Management System (FIRMS), Dynamic World, and GEDI L4B LiDAR data. Validation yielded overall spatial accuracies of 91.15% in the Northeast and 89.62% in the Hengduan Mountains, with corresponding ensemble F1-Scores of 0.92 in both regions. Results indicated the disturbed area in the Northeast (1084.58 ha) significantly exceeded the Hengduan region (133.48 ha). Natural degradation dominated both regions (Northeast: 72.25%; Hengduan: 88.43%), though the Northeast experienced more wildfires and anthropogenic activities. Topographically, Northeast disturbances clustered on low-lying, gentle landscapes, whereas Hengduan events occurred on steep, high-altitude terrains. Due to denser per-pixel carbon storage, the Hengduan area exhibited higher carbon emission costs per unit area. Ultimately, this framework provides a quantitative technical foundation supporting high-resolution forest conservation and spatial evaluations for carbon neutrality commitments. Full article

LAI is a critical parameter for forest management and global ecosystem monitoring. GEDI provides global-scale vegetation structure data, yet its L2B LAI product often exhibits systematic biases. This study investigates the Maoer Mountain forest in China, utilizing a total of 60 validated GEDI footprints as the primary dataset. To address the limitations of the standard GEDI L2B algorithm, which assumes a horizontally uniform canopy, we integrated a four-scale geometric optical model to characterize canopy clumping effects. This model was employed to simulate the geometric proportions of sunlit/shaded canopy and ground components within each footprint to derive a footprint-specific clumping index, thereby refining the gap rate estimates. The accuracy of the revised leaf area index was rigorously verified by using the measured data from the sample plots in the Maoer Mountain area. The results indicate that the original GEDI L2B data underestimates LAI, with a mean absolute error (MAE) of 1.79 m²/m², a root mean square error (RMSE) of 1.47 m²/m², and a bias of −1.25 m²/m². After correcting for canopy clumping, accuracy improved significantly, reducing the MAE to 0.65 m²/m² and the RMSE to 0.82 m²/m², while effectively mitigating underestimation. These findings demonstrate that accounting for non-uniform canopy distribution effectively reduces errors, providing a robust methodological basis for high-precision LAI retrieval using spaceborne lidar. Despite these improvements, this method still has certain limitations: the model’s performance is constrained in extremely steep terrain due to waveform aliasing and in fragmented vegetation areas where sub-footprint heterogeneity is high. Future research should incorporate topographic corrections and multi-source data fusion to enhance the model’s robustness in complex landscapes. Full article

Large-scale landscape transformation in mountainous regions during the Late Iron Age remains insufficiently integrated into broader debates on European urbanism. In southwestern Transylvania, extensive slope terracing came to define the spatial core of the Dacian political centre. This study examines the scale, organization, and social implications of this engineered landscape using high-resolution LiDAR data and spatial modelling. Over 4000 anthropogenic terraces were identified, and their spatial patterning was analysed through Kernel Density Estimation (300 m and 800 m radii) in order to evaluate intensity gradients and territorial articulation. The results indicate compact nuclei of high terrace concentration embedded within a broader, yet continuous, system structured along ridge corridors and circulation routes. The spatial correlation between terrace density and elevated architectural features suggests differentiated building practices and hierarchical organization within a territorially extensive settlement pattern. Rather than representing isolated fortified sites, the Dacian mountain core emerges as an integrated and infrastructurally connected landscape. These findings support the interpretation of the area as a form of Late Iron Age low-density urbanism, in which habitation, mobility, and social differentiation were materially embedded in large-scale topographic modification. Full article

Human modification of tidal embayments, estuaries, and deltas through polders, dykes, and embankments has profoundly altered sediment dynamics and coastal morphology worldwide. Mont-Saint-Michel Bay (northwestern France) exemplifies a macrotidal system affected by large-scale land reclamation, accelerated infilling, rapid saltmarsh expansion, and progressive loss of the insular character of the World Heritage abbey. To restore its maritime setting, a large-scale restoration programme initiated in the 1990s combined engineering measures with nature-based management, including embankment removal, managed retreat, and controlled hydraulic flushing. Future morphodynamic evolution was initially assessed using a movable-bed physical model complemented by numerical simulations. Here, a 22-year LiDAR dataset is used to quantify post-restoration topographic changes and sediment budgets, and evaluate model performance. The results show enhanced erosion and deepening of tidal flats around Mont-Saint-Michel, indicating effective sediment export, together with spatial redistribution of salt marshes that maintained the overall ecological value of the bay. Discrepancies between model predictions and field observations reflect both the difficulty of reproducing long-term channel migration variability and evolving hydro-meteorological forcing conditions, as well as differences between the initially modelled restoration scheme and the engineering works ultimately implemented. This study provides a rare multi-decadal comparison between pre-project morphodynamic forecasts and post-restoration observations. The results highlight both the potential and the limitations of long-term morphodynamic forecasting in non-stationary tidal systems undergoing anthropogenic modifications and climate-driven environmental change, emphasising the importance of long-term monitoring and adaptive management strategies. Full article

Full-waveform hyperspectral LiDAR offers a new approach for precise forest ecological monitoring by simultaneously acquiring the three-dimensional structure and continuous spectral information of targets. However, uncertainty in the backscattering cross-section and the inseparability of the reflectance coefficient lead to systematic underestimation of multi-echo reflectance retrieved using traditional methods. This limitation significantly hinders quantitative applications. The existing multi-echo reflectance correction using neighborhood single-echo reflectance (MCNS) method provides an effective solution by establishing proportional models between similar targets, laying an important foundation for the extraction of multi-echo reflectance. However, its applicability in complex forest scenes is limited due to its dependence on specific vegetation single-echo samples. To address this, an iterative correction method based on ground reflectance baseline, namely Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR (BGE-ICMER), is proposed. Using ground single-echo reflectance as a stable baseline, a multi-target energy distribution model is constructed based on energy conservation, and backscattering cross-section proportions for each echo are iteratively solved to recover true reflectance. Validation using a high-fidelity dataset generated by the Large-Scale remote sensing data and image Simulation framework (LESS) confirmed the effectiveness of the proposed method. This dataset encompasses three typical tree species with vegetation layers ranging from two to four, incorporates micro-topographic ground surfaces and ten spectral channels from 500 to 1000 nm, thereby capturing the structural and spectral complexity of real forests. The results showed that coefficients of determination (R²) between the corrected and true reflectance exceeded 0.9560, with an RMSE below 0.0418 and MAE below 0.0360. The average relative error was reduced from 26.66% to 10.07%, representing a 62.22% improvement in accuracy. Even in the most challenging scenarios with four-layer vegetation occlusion within this dataset, no significant error accumulation occurred. These results demonstrate the robustness and effectiveness of the proposed method for multi-echo reflectance extraction. This study lays a foundation for more accurate forest biochemical attribute assessment and enables the vertical characterization of multiple targets using high-resolution spectral reflectance. Full article

Accurately mapping tree canopy heights of savanna ecosystems, which account for around 20% of the terrestrial land surface, is of great importance for global biomass estimation, carbon cycling, and biodiversity. The spaceborne lidar of Global Ecosystem Dynamics Investigation (GEDI) has great potential for measuring tree canopy heights in sparse savanna ecosystems due to its implicit three-dimensional structural information. However, the accuracy of the GEDI system may be affected by the random geolocation errors. In this study, we aim to develop a reliable method to mitigate the impact of low-quality and position-biased GEDI footprints. Then we generated 30-m resolution wall-to-wall mapping of tree canopy heights for 2020 by combining GEDI L2A footprints with spatially continuous multi-source information in the Kruger National Park, South Africa. Moreover, we explored the explanatory ability of multi-dimensional features derived from optical, radar, topographic, and artificial intelligence-based images and conducted a comparative analysis of relevant products. Validation results confirmed that integrating quality indicators, incorrect ground elevation estimation assessment, and optical and radar features could significantly improve the accuracy of GEDI-based tree canopy height estimation in savannas (i.e., Pearson’s r = 0.51, RMSE = 3.88 m, N = 6276). Compared to existing products, the model trained on comprehensively filtered footprints exhibited higher agreement with reference canopy height model data and lower estimation errors (i.e., Pearson’s r = 0.66, RMSE = 4.09 m, N = 10,469). We also found that features incorporating red-edge bands exhibited higher explanatory ability. This study showcases GEDI-based mapping of savanna tree canopy heights and provides a foundation for future large-scale research on savanna ecosystems. Full article

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article

Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east–west transect at ~1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to ~15 m, revealing a laterally continuous high-amplitude reflector at ~10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography. Full article

Coastal beach–dune systems along the Western Black Sea Coast represent geomorphologically complex and ecologically valuable environments that have been increasingly affected by long-term urbanisation and recreational pressure. This study examines the geomorphological settings, sedimentary connectivity and associated Natura 2000 dune habitats within two urbanised beach–dune systems, Pobeda (Burgas) and Asparuhovo (Varna), to improve their cadastral documentation and support objective conservation assessment. The analysis is based on high-resolution UAS-LiDAR surveys, complemented by UAS photogrammetry and field observations, allowing detailed three-dimensional characterisation of dune landforms, surface morphology and habitat patterns. The results identify foredune-dominated system architectures in both study areas, with the Pobeda (Burgas) and Asparuhovo (Varna) beach–dune systems comprising embryonic dunes, established foredune ridges and low-relief foredune plains, variably developed and spatially fragmented as a result of long-term urbanisation and recreational pressure, and spatially associated with dune habitats. Despite substantial anthropogenic modification, these elements remain recognisable, although locally fragmented and morphologically degraded. Subtle topographic changes related to trampling, informal access routes and surface compaction were detected, particularly affecting foredune crests and foredune plains, with implications for sediment transport continuity and habitat stability. The study shows that conventional habitat inventories alone are insufficient for capturing such changes. Integrated geomorphological and habitat analysis based on UAS-LiDAR provides a reliable framework for accurate mapping, conservation status assessment and informed consideration of coastal dune systems within the Natura 2000 network and related protection schemes. Full article