Search Results (219)

Landscape character change in mountain traditional villages is difficult to assess from a single perspective because roof-material replacement, material–color deviation, and visual exposure vary under complex terrain and settlement configurations. This study develops a multi-source UAV framework for reproducible, spatially explicit assessment of courtyard-scale visual–material integrity (CI), a spatially observable component of landscape character integrity. Using 749 courtyards in nine nationally designated traditional villages in Shangluo, China, the framework integrates UAV orthophotos, 3D mesh models, and point-cloud data to derive material penetration rate (PR), material–color conflict (CC), and standardized visual exposure (VC). PR and CC represent baseline material–color loss, whereas VC is incorporated as an exposure-amplification condition. Formula-structure sensitivity analysis against blinded ratings of 50 sampled courtyards showed that the proposed formulation had the highest rank consistency with expert judgments (Spearman’s ρ = 0.949). The frozen framework also showed a strong association with blinded professional ratings in geographically independent Longnan villages (ρ = 0.912, p < 0.001). XGBoost–SHAP identified courtyard impervious-surface ratio, primary material, and roof form as the leading model-associated predictors of CI variation. The framework supports courtyard-scale CI assessment, priority screening, and repeat-survey reassessment under comparable acquisition conditions. Full article

Deep-seated gravitational slope deformations (DGSDs) are large, slow-moving landslides that strongly influence mountain landscape evolution and slope stability. Despite their geomorphological importance, no standardized regional geospatial dataset describing DGSDs and their associated gravity-induced landforms has been available for the Friuli Venezia Giulia Region (NE Italy). In this study, a dataset including DGSDs and their gravity-induced landforms that affect the mountainous sector of the above-mentioned region is presented. The dataset was produced through the integrated interpretation of high-resolution LiDAR-derived digital elevation models, orthophotos, European Ground Motion Service (EGMS) InSAR data, landslide inventories, and uncrewed aerial vehicle (UAV)-assisted field surveys. It is distributed as a GeoPackage containing DGSD boundaries together with point, line, and polygon layers representing gravity-induced landforms, as well as an attribute table describing the geometry, morphometry, geology, auxiliary data, metadata, and associated landforms of each mapped DGSD. Its standardized and interoperable structure facilitates visualization, querying, comparison, and integration with other geospatial datasets, providing a reproducible resource for future geomorphological investigations and database updates. Full article

Automated airport pavement inspection requires reliable instance segmentation models for detecting and quantifying thin cracks under real operating conditions. Building on a previously established UAV-AI workflow for airport pavement crack detection and quantification, and on an earlier investigation of sealed-crack class definition using YOLO11, the present study addresses a subsequent research question by comparing two recent model configurations based on fundamentally different computer vision paradigms. YOLO26 was selected for its deployment-oriented convolutional architecture, computational efficiency, and mechanisms aimed at improving small-target handling, whereas RF-DETR was selected for its transformer-based architecture and DINOv2-pretrained backbone; these provide fine-grained visual representations and exploit broader contextual information. The two configurations were assessed using the same dataset of 24,768 annotated images and compared in terms of computational demands, independent test-set performance, and field-based crack length reliability. Field validation was performed on two airport taxiways representing different surface conditions: taxiway Nibbio, mainly affected by active longitudinal and transverse cracks with limited interference from sealed cracks, and taxiway November, characterized by the coexistence of active and sealed cracking patterns. YOLO26 showed a lower computational demand, requiring approximately one hour and 20 compute units, compared with approximately six hours and 80 compute units for RF-DETR. RF-DETR achieved a higher mAP50 and recall on the test set and lower model error index values on both taxiways, indicating better crack length recovery. However, on taxiway November, it also showed higher hallucination index values, revealing greater sensitivity to visually ambiguous sealed cracks. These findings indicate that model selection should consider pavement surface conditions, computational constraints, and the operational consequences of missed cracks and false-positive detections. The specific contribution of the present study is therefore the extension of the previously established UAV-AI framework from workflow development and class definition analysis to the comparative evaluation of recent convolutional and transformer-based model configurations under real airport pavement conditions. Full article

Mountain sandy patches are typical indicators of aeolian degradation in arid and semi-arid zones; however, few studies have systematically analyzed their static spatial morphological features and statistical correlations with environmental variables. Taking the Sarbulak River Basin in the Ili River Valley of Xinjiang as the study area, this study extracts multiple morphological metrics of mountain sandy patches from high-resolution UAV orthophotos and adopts the XGBoost-SHAP framework combined with correlation analysis to quantitatively analyze patch morphological traits and their statistical links with environmental predictors. The main results are as follows: (1) Elongated geometry dominates mountain sandy patches with diverse auxiliary shapes, and the average major axis of all patches reaches 16 m. Every pair of morphological indicators shows significant positive correlations at p < 0.01 level. (2) The model’s relative predictive importance varies markedly across predictors. Wind speed ranks first with a normalized SHAP contribution of 34.7%, followed by precipitation (18.7%), NDVI (13.0%), and grazing intensity (9.0%). The four predictors jointly account for over 75% of total predictive signals and constitute a wind–water–vegetation–grazing statistical association system. All predictors show obvious nonlinear responses to mountain sandy patch occurrence with distinct statistical thresholds. (3) Strong combined statistical correlations exist between wind speed, precipitation, NDVI, temperature, elevation, and grazing intensity, and multi-variable combinations correspond to a higher probability of large-scale sandy patches. This paper summarizes key threshold intervals derived from SHAP dependence curves: patches tend to expand when wind speed ranges from 2.10 to 2.15 m/s; precipitation below 219.7 mm presents negative correlations with patch distribution; NDVI within 0.17–0.29 corresponds to positive marginal associations with sandy patch occurrence; grazing intensity exceeding 3.60 SU/ha matches frequent patch enlargement; and areas above 645.9 m elevation display higher patch prevalence. Full article

Online incremental orthophoto generation with multiple unmanned aerial vehicles (UAVs) remains challenging, as it requires accurate, efficient, and scalable mapping from distributed aerial observations. In this paper, we present a centralized GNSS-assisted multi-UAV 3D Gaussian Splatting SLAM framework for online incremental orthophoto mapping. Each UAV independently performs visual odometry to build local submaps, which are first aligned into a unified global coordinate system using GNSS constraints and further refined via inter-agent visual loop closures for improved cross-agent consistency. To enable scalable and high-quality mapping, we introduce two complementary Gaussian map maintenance modules: plane-guided grid-based collaborative densification, which improves mapping quality and accelerates convergence under multi-UAV conditions, and visibility-aware adaptive pruning, which effectively controls redundancy and memory usage. These components allow efficient joint optimization within a unified Gaussian representation. Experiments on multiple aerial datasets using video-derived image frames captured by consumer-grade UAV cameras demonstrate that the proposed system provides a favorable trade-off between geo-consistency, visual fidelity, and efficiency compared with existing methods. Quantitatively, the proposed method achieves a GCP RMSE of 2.32 m, completes multi-UAV orthophoto generation within 3.3–5.5 min, and reduces the total mapping time by approximately 35–55% compared with the corresponding single-UAV setting, while supporting online tracking and incremental orthophoto updates with bounded latency and memory consumption. Full article

On 18 May 2026, a M5.2 double earthquake struck the Taiyangzhen area of Liunan District, Liuzhou City, Guangxi, China, triggering shallow surface collapses in this karst terrain. We conducted three UAV orthophoto surveys of the meizoseismal area on 20 May, 23 May, and 24 May, and interpreted 17 collapse monitoring units from the imagery. The total collapse area increased from 75.3 m² on 20 May to 499.1 m² on 23 May, and further to 553.9 m² on 24 May. On 20 May, only 7 of 17 units exhibited collapses; by 23 May, all 17 units were affected. Among them, 7 pre-existing collapse patches expanded, and 10 new collapses emerged between 20 and 23 May. Depth measurements revealed measurable depths of 0.18–7.60 m for 7 collapses, and all 6 units with bi-temporal depth data showed continued deepening from 23 to 24 May. Ponding water was observed in up to 10 units, consistent with the 98.7 mm of rainfall recorded during 18–24 May. Multi-temporal UAV surveys reveal that post-seismic surface collapse development in karst terrain extends well beyond the mainshock, with both rapid expansion of pre-existing failures and delayed emergence of new collapses driven by the coupled effects of seismic weakening and hydrologic forcing. Full article

Open-pit mines contain rapidly changing terrain, discontinuous bench structures, and mixed artificial–natural objects, which complicate automated three-dimensional mapping. This study presents a dual-module workflow for UAV LiDAR point clouds. Module A characterizes local geometry using normal and curvature descriptors, constructs local plane support through RANSAC fitting, and detects candidate bench-line points using an angular-gap criterion, followed by regional grouping and Kalman-filter refinement. Qualitative overlay with the orthophoto showed coherent correspondence with principal platform–slope transitions. Module B segments buildings, roads, and vegetation using a PointNet++ network enhanced by local Transformer self-attention and inverted residual feature transformation. Under a fixed spatial hold-out setting, the network achieved an overall accuracy of 97.6% and a mean intersection over union of 96.4%. It obtained the highest overall accuracy, mean intersection over union, and class-wise intersection over union among the selected baselines, whereas Point Transformer achieved a slightly higher mean class accuracy. The two independently operated modules provide complementary structural and semantic information for open-pit mine mapping. Broader applicability requires reference-based bench-line assessment and evaluation across additional mines and survey periods. Full article

Monitoring the spatial distribution of oyster reefs in an accurate, efficient, and flexible manner is crucial for assessing changes in nearshore fishery habitat environments. However, traditional optical remote sensing methods are often affected by illumination variation, tidal fluctuation, and spectral confusion in complex intertidal environments. An automated extraction framework based on the SAM (Segment Anything Model) using UAV (Unmanned Aerial Vehicle) DOM (Digital Orthophoto Map) imagery is proposed to achieve high-precision oyster reef identification and area estimation. Multi-resolution UAV imagery was processed, and key SAM parameters were systematically optimized under different illumination conditions. The results show that spatial resolution significantly influences segmentation accuracy, and appropriate resolution selection improves both stability and reliability. Based on UAV data acquired in 2025, the total oyster reef area in the study region was estimated to be 2.24 ha. After parameter optimization, segmentation accuracy improved from 93.22% to 97.61% in illuminated areas and from 93.30% to 96.67% in shaded areas. The proposed method demonstrates strong robustness under varying environmental conditions and effectively enhances boundary detection accuracy. A scalable and reliable approach is provided for coastal habitat monitoring and offers new insights into automated object extraction in complex remote sensing imagery. Full article

This study assesses the capabilities of UAV-based Earth observation for analyzing marginalized communities, using Roma settlements in southeastern Slovakia as a case study. Marginalized populations are often underrepresented in official spatial datasets, resulting in a limited understanding of their living conditions, infrastructure needs, and environmental risks. To address this gap, we propose a multi-scalar, UAV-based observational approach that bridges the limitations of coarse satellite imagery and logistically constrained ground surveys. High-resolution RGB and thermal imagery were acquired across three settlements with varying spatial characteristics and processed using photogrammetric workflows to generate detailed orthophotos and spatial products. The results demonstrate that UAV data with centimeter-level spatial resolution enable precise mapping of settlement morphology, infrastructure, waste distribution, and thermal inequalities. Furthermore, UAV observations enable change detection and environmental risk assessment at scales that are not achievable with conventional remote sensing. However, the study also highlights critical operational and ethical challenges, including regulatory constraints, privacy concerns, and the need for community engagement. By integrating technical evaluation with socially sensitive research practices, this work proposes a methodological framework for responsible UAV deployment in marginalized contexts. The findings underscore the potential of UAV-based observation to improve spatial visibility and support evidence-based planning while emphasizing the importance of ethical implementation. Full article

River ecological restoration in lowland plain basins is often constrained by fragmented river networks, degraded riparian zones, eutrophication risk, and intensive human disturbance. Conventional monitoring approaches rarely connect watershed-scale dynamics with responses from typical restoration units, limiting quantitative evaluation and the separation of direct project outcomes from broader environmental variability. To address this gap, this study developed a collaborative satellite–unmanned aerial vehicle (UAV)–unmanned surface vehicle (USV) monitoring framework and applied it to the Nihe River Basin, China, a lowland plain river undergoing systematic restoration under the Shan-shui Initiative. The framework combines Sentinel-2 time-series imagery, high-resolution Gaofen-1, Gaofen-2, and Jilin-1 imagery, UAV orthophotos, USV observations, and auxiliary environmental datasets. Unlike single-scale monitoring approaches, it links watershed-scale indicators, including water-body dynamics, chlorophyll-related eutrophication risk, riparian ecological background, and soil-water conservation capacity, with unit-scale diagnosis of riparian buffer and riverine wetland restoration. Results showed that river water-body area increased from 37.78 km² to 40.59 km² during 2021–2024, while normalized difference chlorophyll index (NDCI)-based eutrophication risk improved in 9.12% of the monitored river area and degraded in only 0.47%. Riparian vegetation cover remained high, whereas regional soil-water conservation capacity declined due to climatic factors, revealing asynchronous responses between local recovery and regional background conditions. At the unit scale, riparian buffer restoration enhanced buffer continuity and near-bank water quality, as reflected by decreased chemical oxygen demand (COD), increased dissolved oxygen (DO), and limited ammonia nitrogen (NH₃-N) improvement. Riverine wetland restoration promoted land-use adjustment and ecological spatial reorganization. This cross-scale evidence chain supports adaptive management of inland river and wetland restoration projects. Full article

In the context of dynamic environmental changes, accurate geospatial information is fundamental for evidence-based decision-making in land-use planning. As urban areas undergo rapid structural transformations, characterizing their spatial morphology becomes essential for assessing ecosystem conditions and identifying pressure points within the urban–rural gradient. Drawing on the indicators for ecosystem condition and pressure recommended by the Mapping and Assessment of Ecosystem Services (MAES) framework, reflecting their trends, this study presents a methodology for comprehensive geospatial mapping of urban and peri-urban morphology, using the Functional Urban Area (FUA) of Burgas, Bulgaria, as a case study. The approach enables multi-scale spatial analysis (regional and local), integrates the structure and functions of urban ecosystems, and reveals the spatial heterogeneity of complex socio-economic systems. At the regional level, ecosystems within the FUA were identified using the national land-use/land-cover database. At the local level, within the city of Burgas, urban morphology was classified by combining building and land-cover types into 14 distinct urban morphological zones (local climate zones—LCZs) using high-resolution unmanned aerial vehicle (UAV)-based orthophotos. This precise spatial data allowed for a detailed assessment of the balance between pervious and impervious surfaces within each LCZ. By integrating Google Earth Engine (GEE) data, the appropriate conditions and pressure indicators in the case study are assessed. Regional ecosystem pressure is effectively captured through the spatial distribution of the Final Pressure Index (IPr). Concurrently, the Urban Ecosystem Performance Index (UEPI) highlights sharp spatial polarization, with critical stress concentrated in the industrial and port zones of the urban core. The results provide policy-makers and stakeholders with critical insights into current pressures and environmental changes in urban and peri-urban ecosystems, offering a robust foundation for evidence-based management and climate change adaptation strategies. Full article

Reliable pavement condition assessment using UAV-derived orthophotos remains challenging under manual flight conditions, where acquisition parameters are not predefined and photogrammetric quality is highly operator-dependent. This study evaluates how UAV flight configuration influences orthophoto quality and operational usability for road infrastructure assessment in real-world manual survey scenarios. Eight flight treatments combining altitude (30–40 m AGL), flight speed (low/normal), and image capture interval (2–3 s) were tested over an urban–peri-urban road segment in Misantla, Veracruz, Mexico, using a DJI Air 3S platform. Orthomosaic quality was assessed through ground sampling distance (GSD), tie-point density, multiplicity, RMS reprojection error, dense cloud size, orthomosaic continuity, and a criteria-based interpretability index supported by field observations. Results show that while altitude controls spatial resolution, resolution alone is insufficient for reliable pavement assessment. Configurations with higher image overlap and photogrammetric redundancy (notably Treatment 1 (T1) and Treatment 3 (T3)) achieved superior geometric consistency, reduced seam artifacts, and improved detection of subtle surface irregularities. In contrast, reduced-overlap configurations produced complete but less interpretable orthomosaics. The study provides experimentally validated operational guidelines for optimizing UAV flight parameters under manual conditions, bridging the gap between controlled photogrammetric theory and practical infrastructure monitoring. Full article

Accurate registration of UAV-derived thermal infrared orthophotos and RGB orthophotos is essential for multi-sensor geospatial analysis, but it remains challenging because thermal imagery generally has lower spatial resolution, weaker texture, and less distinct structural information than RGB imagery. This study comparatively evaluated five histogram equalization methods—histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE), brightness-preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), and minimum mean brightness error bi-histogram equalization (MMBEBHE)—for improving AKAZE-based registration of land surface temperature (LST) orthophotos to reference RGB orthophotos. High-accuracy RGB orthophotos generated using GNSS-surveyed ground control points were used as the geometric reference. Thermal data were acquired twice at each of two study sites with contrasting surface characteristics and processed into LST orthophotos. Each histogram equalization method was applied to the LST orthophotos, after which keypoints and descriptors were extracted using AKAZE, tentative correspondences were established, outliers were removed using RANSAC, and an affine transformation was estimated from the inlier correspondences. Here, an inlier denotes a tentative match that remained geometrically consistent after RANSAC-based outlier rejection. The estimated transformation was then applied to the source LST raster to preserve radiometric values in the final corrected product. Performance was assessed using the number of detected keypoints, tentative matches, RANSAC-verified inliers, matching efficiency, reproducibility, and exploratory statistical analysis. Among the five methods, BBHE consistently produced the highest number of inliers and the best matching efficiency at both study sites, while also showing the lowest variability between repeated acquisitions. These results indicate that brightness-preserving histogram equalization is particularly effective for thermal–RGB orthophoto registration and can improve the reliability of UAV-derived thermal mapping products for geomatics applications. Full article

Rapid detection of wilt-affected pine crowns in mountainous forests is hindered by occlusion, self-shadowing, and heterogeneous backgrounds in conventional nadir products. We evaluated whether oblique UAV RGB imagery improves crown-level detection relative to nadir imagery under matched site, season, sensor, and workflow conditions. The workflow was designed for rapid post-flight screening of geotagged UAV photographs. Paired nadir orthophotos and 45–70° oblique photographs were acquired over pine stands in Wenshan Prefecture, Yunnan, China, and organized into D1 (nadir), D2 (oblique), and D3 (simple mixed-view concatenation). Three YOLO11 detectors were trained for crown shoot damage ratio (SDR)-derived operational classes: early-stage (SDR < 50%), severely damaged (SDR ≥ 50%), and withered (needle-free dead crowns). A paired crown-level RGB subset (n = 20 crowns observed in both views) was analyzed as supporting evidence for view-dependent appearance differences. The oblique-image model (D2) achieved the highest validation performance, with precision of 0.994, recall of 0.991, F1-score of 0.989, mAP@0.5 of 0.995, and mAP@0.5:0.95 of 0.880. The paired subset showed a significant multivariate RGB profile difference between views (Hotelling’s T2 = 58.91, F = 3.10, p = 0.044), driven mainly by reduced Excess Green and greater dispersion of blue-related traits under oblique viewing. These results indicate that oblique UAV photographs retain additional crown-edge, lateral-structure, and chromatic context for detecting wilt-affected pine crowns. Oblique RGB imagery therefore provides a practical, low-cost input for rapid forest health surveillance and targeted field verification in rugged pine landscapes. Full article

The aim of this study was to examine the potential of multispectral imaging derived from unmanned aerial vehicles (UAVs) for detecting the spread of mistletoe (Viscum album ssp. austriacum L.) in Scots pine stands and to assess the information potential of selected vegetation indices in mistletoe detection. UAV campaigns were performed in the Niepołomice Primeval Forest (Niepołomice Forest District, Regional Directorate of the Polish State Forests National Holding, Kraków, Poland). A fixed-wing UAV Trinity F90+ (Quantum Systems GmbH) equipped with a five-band multispectral MicaSense RedEdge-M camera and an RGB Sony UMC-R10C camera was employed. The number of trees infected by mistletoe, as well as the quantity and area of mistletoe biogroups, were derived based on the classification of true multispectral orthophotos using a support vector machine (SVM) classifier. The spectral information potential assessment identified NIR (B5) as the most important single spectral source of information, while the greatest information potential among vegetation indices was found in NormG, CIG, and GRVI. The mistletoe classification of the 22.5-ha compartment revealed 1735 mistletoe biogroups covering a total area of 489 m², with 58.6% of the 2917 detected tree crowns identified as infected (Kappa = 0.74). The results confirm that UAV-based multispectral data, particularly when combined with green-sensitive vegetation indices, enable effective differentiation of mistletoe from host tree crowns. The integration of the near-infrared (NIR) band further enhanced classification performance. This study evaluates UAV-based multispectral and RGB imagery for detecting common mistletoe (Viscum album ssp. austriacum) in Scots pine stands. The information potential of 22 vegetation indices was assessed to identify the most effective spectral features for mistletoe classification. Full article