Search Results (594)

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

Identifying habitat characteristics is essential for conserving critically endangered species. When quantifying species habitat characteristics, ignoring data types may lead to misunderstandings about species’ specific habitat requirements. This study focused on the critically endangered Chinese pangolin in Guangdong Province, China, and divided the study area into 600 m × 600 m grids based on its average home range. The burrow number within each grid was obtained through line transect surveys, with burrow numbers/line transect lengths used as direct indicators of habitat utilization intensity. The relationships with sixteen environmental variables, which could be divided into three categories, including topographic, human disturbance and land cover composition, were quantified using the GAM method. We also converted continuous data into binary data (0, 1), constructed GAMs and compared them with habitat utilization intensity models. Our results indicate that the habitat utilization intensity model identified profile curvature and slope as primary factors, showing a nonlinear response to profile curvature (Edf = 5.610, p = 0.014) and a positive relationship with slope (Edf = 1.000, p = 0.006). The presence–absence model emphasized distance to water (Edf = 1.000, p = 0.014), slope (Edf = 1.709, p = 0.043) and aspect (Edf = 2.000, p = 0.026). The intensity model explained significantly more deviance, captured complex nonlinear relationships and supported higher model complexity without overfitting. This study demonstrates that habitat utilization intensity data provides a more ecologically informative basis for in situ conservation (e.g., identifying core habitats), and the process from habitat selection to habitat utilization should be integrated to reveal species’ habitat characteristics. Full article

The Three Gorges Reservoir Area (TGRA) is an important ecological barrier and cultural intermingling zone in the upper reaches of the Yangtze River, and its traditional villages carry unique information about natural changes and civilisational development, but face the challenges of conservation and development under the impact of modernisation and ecological pressure. This study takes 112 traditional villages in the TGRA that have been included in the protection list as the research objects, aiming to construct a cultural landscape identification framework for the traditional villages in the TGRA. Through field surveys, landscape feature assessments, GIS spatial analysis, and multi-source data analysis, we systematically analyse their cultural landscape type systems and spatial differentiation characteristics, and then reveal their cultural landscape types and spatial differentiation patterns. (1) The results of the study show that the spatial distribution of traditional villages exhibits significant altitude gradient differentiation—the low-altitude area is dominated by traffic and trade villages, the middle-altitude area is dominated by patriarchal manor villages and mountain farming villages, and the high-altitude area is dominated by ethno-cultural and ecologically dependent villages. (2) Slope and direction analyses further reveal that the gently sloping areas are conducive to the development of commercial and agricultural settlements, while the steeply sloping areas strengthen the function of ethnic and cultural defence. The results indicate that topographic conditions drive the synergistic evolution of the human–land system in traditional villages through the mechanisms of agricultural optimisation, trade networks, cultural defence, and ecological adaptation. The study provides a paradigm of “nature–humanities” interaction analysis for the conservation and development of traditional villages in mountainous areas, which is of practical value in coordinating the construction of ecological barriers and the revitalisation of villages in the reservoir area. Full article

Non-Newtonian debris flows represent a critical challenge for hydraulic infrastructure in mountainous regions, often causing significant damage and service disruption. However, current models typically simplify these flows as Newtonian, leading to inaccurate design assumptions. This study addresses this gap by comparing the hydraulic behavior of Newtonian and non-Newtonian flows in an alluvial fan, using the Amoray Gully in Apurímac, Peru, as a case study. This gully intersects the Interoceánica Sur national highway via a low-water crossing (baden), making it a relevant site for evaluating debris flow impacts on critical road infrastructure. The methodology integrates hydrological analysis, rheological characterization, and hydraulic modeling. QGIS 3.16 was used for watershed delineation and extraction of physiographic parameters, while a high-resolution topographic survey was conducted using an RTK drone. Rainfall-runoff modeling was performed in HEC-HMS 4.7 using 25 years of precipitation data, and hydraulic simulations were executed in HEC-RAS 6.6, incorporating rheological parameters and calibrated with the footprint of a historical event (5-year return period). Results show that traditional Newtonian models underestimate flow depth by 17% and overestimate velocity by 54%, primarily due to unaccounted particle-collision effects. Based on these findings, a multi-barrel circular culvert was designed to improve debris flow management. This study provides a replicable modeling framework for debris-prone watersheds and contributes to improving design standards in complex terrain. The proposed methodology and findings offer practical guidance for hydraulic design in mountainous terrain affected by debris flows, especially where infrastructure intersects active alluvial fans. Full article

Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5θ²) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency. Full article

As a typical reforested region of the Loess Plateau, the Caijiachuan watershed plays a vital role in ecological security and resource management. This study evaluates the spatiotemporal variations in key ecosystem services—namely soil retention, water yield, carbon storage, and habitat quality—between 2002 and 2024 using the InVEST model, calibrated with field-measured rainfall, carbon density, and high-resolution land use data derived from integrated remote sensing and field surveys. Statistical analyses based on the R language reveal dynamic trade-offs and synergies among these services. The results show that: (1) soil retention, carbon storage, and habitat quality have steadily improved, while water yield shows an overall upward trend with significant spatial heterogeneity; (2) a consistent and significant trade-off exists between carbon storage and water yield (average R² ≈ 0.28), while other ecosystem service interactions are relatively weak; (3) climatic variability, topographic heterogeneity (e.g., slope and elevation), and vegetation structure are key drivers of these trade-offs. This study provides scientific evidence to support ecological management and policy formulation in reforested areas of the Loess Plateau. Full article

This paper explores ways to improve the coordinate transformation of maps from the First and Second Military Surveys of the Austro-Hungarian Monarchy using airborne laser scanning (ALS) data. The paper analyses the current positional accuracy of georeferenced maps from the first two military mappings from available spatial data sources. Several areas of interest with different terrain ruggedness (plain, undulated terrain, mountains) were selected for analysis to investigate whether terrain ruggedness has an impact on the accuracy of these maps. The next part of the paper deals with the georeferencing of military mapping maps using current, mid-20th-century maps and ALS data using affine and second-degree polynomial transformations. The paper concludes with a statistical analysis and evaluation of the potential of ALS data for solving this type of problem. The results obtained in the paper indicate that ALS data can be a suitable source for finding control points to transform early topographic maps. Full article

Orientation entropy serves as a critical metric for assessing the directional disorder of urban street networks. However, conventional two-dimensional (2D) approaches neglect vertical variations, limiting their applicability in cities with complex terrains. This study proposes a three-dimensional (3D) orientation entropy framework by integrating elevation data, providing a more comprehensive assessment of urban spatial complexity. We developed a computational workflow combining ArcGIS 10.8 for spatial data extraction and Python 3.10.10 for entropy calculation. A case study in Chongqing, China, explores the relationship between 3D orientation entropy and residents’ perceptions of spatial disorder through a small-scale survey. Although no statistically significant correlation was observed, the findings suggest emerging patterns and underscore the necessity of multidimensional frameworks in evaluating urban spatial experience. This research contributes a novel metric to urban design assessment, particularly in topographically diverse environments, and offers a foundation for future empirical studies. Full article

This study utilizes UAV-based LiDAR to analyze doline microtopography within a karst mountainous terrain. The study area, ‘Gulneomjae’ in Mungyeong City, South Korea, features steep slopes, limited accessibility, and abundant vegetation—conditions that traditionally hinder accurate topographic surveying. UAV LiDAR data were acquired using the DJI Matrice 300 RTK equipped with a Zenmuse L2 sensor, enabling high-density point cloud generation (98 points/m²). The point clouds were processed to remove non-ground points and generate a 0.25 m resolution DEM using TIN interpolation. A total of seven dolines were detected and delineated, and their morphometric characteristics—including area, perimeter, major and minor axes, and elevation—were analyzed. These results were compared with a 1:5000-scale DEM derived from the 2013 National Basic Map. Visual and numerical comparisons highlighted significant improvements in spatial resolution and feature delineation using UAV LiDAR. Although the 1:5000-scale DEM enables general doline detection, UAV LiDAR facilitates more precise boundary extraction and morphometric analysis. The study demonstrates the effectiveness of UAV LiDAR for detailed topographic mapping in complex karst terrains and offers a foundation for future automated classification and temporal change analysis. Full article

Technological development has strongly impacted all processes related to the design, construction, and management of real estate assets. In fact, the introduction of the BIM approach has required the application of three-dimensional survey technologies, and in particular the use of LiDAR instruments, both in their static (TLS—terrestrial laser scanner) and dynamic (iMMS—indoor mobile mapping system) implementations. Operators and developers of LiDAR technologies, for the implementation of scan-to-BIM procedures, initially placed particular care on the 3D surveying accuracy obtainable from such tools. The incorporation of RGB sensors into these instruments has progressively expanded LiDAR-based applications from essential topographic surveying to geospatial applications, where the emphasis is no longer on the accurate three-dimensional reconstruction of buildings but on the capability to create three-dimensional image-based visualizations, such as virtual tours, which allow the recognition of assets located in every area of the buildings. Although much has been written about obtaining the best possible accuracy for extensive asset surveying of large-scale building complexes using iMMS systems, it is now essential to develop and define suitable procedures for controlling such kinds of surveying, targeted at specific geospatial applications. We especially address the design, field acquisition, quality control, and mass data management techniques that might be used in such complex environments. This work aims to contribute by defining the technical specifications for the implementation of geospatial mapping of vast asset survey activities involving significant building sites utilizing iMMS instrumentation. Three-dimensional models can also facilitate virtual tours, enable local measurements inside rooms, and particularly support the subsequent integration of self-locating image-based technologies that can efficiently perform field updates of surveyed databases. Full article

Effective trail management is essential for preventing environmental degradation and promoting sustainable recreational use. This study proposes a GIS-based, explainable machine learning framework for predicting forest trail degradation using exclusively environmental variables, eliminating the need for costly visitor monitoring data that remains unavailable in most operational forest settings. Field surveys conducted in Geumjeongsan, South Korea, classified trail segments as degraded or non-degraded based on physical indicators such as erosion depth, trail width, and soil hardness. Environmental predictors—including elevation, slope, trail slope alignment (TSA), topographic wetness index (TWI), vegetation type, and soil texture—were derived from spatial analysis. Three machine learning algorithms (Binary Logistic Regression, Random Forest, and Gradient Boosting) were systematically compared using confusion matrix metrics and AUC-ROC (Area Under the Receiver Operating Characteristic Curve). Random Forest (RF) was selected for its strong performance (AUC-ROC = 0.812) and seamless integration with SHAP (SHapley Additive exPlanations) for transparent interpretation. Spatial block cross-validation achieved an AUC-ROC of 0.729, confirming robust spatial generalization. SHAP analysis revealed vegetation type as the most significant predictor, with hardwood forests showing higher degradation susceptibility than mixed forests. A susceptibility map generated from the RF model indicated that 40.7% of the study area faces high to very high degradation risk. This environmental-only approach enables proactive trail management across data-limited forest systems globally, providing actionable insights for sustainable trail maintenance without requiring visitor use data. Full article

The automation in image acquisition and processing using UAV drones has the potential to acquire terrain data that can be utilized for the accurate production of 2D and 3D digital data. In this study, the DJI Phantom 4 drone was employed for large-scale topographical mapping, and based on the photogrammetric Structure-from-Motion (SfM) algorithm, drone-derived point clouds were used to generate the terrain DSM, DEM, contours, and the orthomosaic from which the topographical map features were digitized. An evaluation of the horizontal (X, Y) and vertical (Z) coordinates of the UAV drone points and the RTK-GNSS survey data showed that the Z-coordinates had the highest MAE(_X,Y,Z), RMSE_(X,Y,Z) and Accuracy_(X,Y,Z) errors. An integrated georeferencing of the UAV drone imagery using the mobile RTK-GNSS base station improved the 2D and 3D positional accuracies with an average 2D (X, Y) accuracy of <2 mm and height accuracy of −2.324 mm, with an overall 3D accuracy of −4.022 mm. Geometrically, the average difference in the perimeter and areas of the features from the RTK-GNSS and UAV drone topographical maps were −0.26% and −0.23%, respectively. The results achieved the recommended positional accuracy standards for the production of digital geospatial data, demonstrating the cost-effectiveness of low-cost UAV drones for large-scale topographical mapping. Full article

The proliferation of unmanned aerial vehicles has enabled cost-effective topographic surveys to be collected at high frequencies. However, terrain analyses rarely take advantage of the information provided by repeated observations. As a result, the ability to characterize the topographic surface and surface changes resulting from dynamic surface processes is undermined by the accumulation and propagation of uncertainty. Accurate surface model parameterisation benefits all derived local characteristics, such as surface slope and curvature. To address this, several advances in adaptive Kalman filtering were evaluated with respect to surface model coefficient estimation error, and the sensitivity to initial noise statistics was tested. A simple surface with exactly known parameters was simulated for a set of common geomorphological change regimes and survey temporal distributions. The results confirmed that all Kalman filters reduced error relative to a least-squares estimator under static conditions. Only adaptive filters outperformed a least-squares estimator under dynamic conditions, where average error was often reduced by approximately 50%, and up to 80%. However, adaptive Kalman filters exhibited up to a 40% increase in maximum error relative to a least-squares estimator in response to sudden surface changes, returning to lower error within 15–25 epochs. The adaptive Kalman filters were sensitive to the overestimation of measurement noise greater than two orders of magnitude from the true noise, resulting in degraded performance. Adaptive Kalman filters consistently and substantially reduced spatio-temporal coefficient error, which includes an estimate of local vertical displacement. The results demonstrated that adaptive Kalman filters address challenges related to the sensitivity of conventional Kalman filter performance to sub-optimal parameterisation, and they are robust estimators for both terrain analysis and surface change analysis when multiple surveys are available. Therefore, adaptive Kalman filters are well-suited for analyzing the local properties of topographic surfaces in general. Full article

The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. Full article

This study applies surveying and representation techniques to analyze the cavities of the Cumae site, an archeological park located in the Phlegraean Fields in the Campania region, providing a documentary basis for monitoring, maintenance, and enhancement efforts. The process core is the comparative management of the numerical models produced employing technologies such as laser scanning, photogrammetry, and structured light scanning, supported by a georeferenced topographical network. The 3D models produced are used for the extraction of ortho-planes and bidimensional drawings of the various cavities from which to initiate the procedures for redesigning and analyzing the entire artifact. Specific research carried out on the Antro della Sibilla enabled a detailed 3D description of the tuff-carved surfaces, helping the interpretation of the manufacture in the optic of consolidation and musealization interventions. The interdisciplinary approach employed, in which historical–archeological, geological–structural, and diagnostic sciences contributed correlatedly, ensures a comprehensive program of data representation. Full article