A preliminary study on the impact of high-level pond tailwater discharge on beach topography: based on unmanned aerial vehicle LiDAR measurement data

The monitoring of beach topographical changes and recovery processes under typhoon storm influence has primarily relied on traditional techniques that lack high spatial resolution. Therefore, we used an unmanned aerial vehicle light detection and ranging (UAV LiDAR) system to obtain the four time periods of topographic data from Tantou Beach, a sandy beach in Xiangshan County, Zhejiang Province, China, to explore beach topography and geomorphology in response to typhoon events. The UAV LiDAR data in four survey periods showed an overall vertical accuracy of approximately 5 cm. Based on the evaluated four time periods of the UAV LiDAR data, we created four corresponding DEMs for the beach. We calculated the DEM of difference (Dod), which showed that the erosion and siltation on Tantou Beach over different temporal scales had a significant alongshore zonal feature with a broad change range. The tidal level significantly impacted beach erosion and siltation changes. However, the storm surge did not affect the beach area above the spring high-tide level. After storms, siltation occurred above the spring high-tide zone. This study reveals the advantage of UAV LiDAR in monitoring beach changes and provides novel insights into the impacts of typhoon storms on coastal topographic and geomorphological change and recovery processes.

The development and management of green open spaces are essential in overcoming environmental problems such as air pollution and urban warming. 3D modeling and biomass calculation are the example efforts in managing green open spaces. In this study, 3D modeling was carried out on point clouds data acquired by the UAV photogrammetry and UAV LiDAR methods. 3D modeling is done explicitly using the point clouds fitting method. This study uses three fitting methods: the spherical fitting method, the ellipsoid fitting method, and the spherical harmonics fitting method. The spherical harmonics fitting method provides the best results and produces an R2 value between 0.324 to 0.945. In this study, Above-Ground Biomass (AGB) calculations were also carried out from the modeling results using three methods with UAV LiDAR and Photogrammetry data. AGB calculation using UAV LiDAR data gives better results than using photogrammetric data. AGB calculation using UAV LiDAR data gives an accuracy of 78% of the field validation results. However, for visualization purposes with a not-too-wide area, a 3D model of photogrammetric data using the spherical harmonics method can be used.

The fine classification of mangroves plays a crucial role in enhancing our understanding of their structural and functional aspects which has significant implications for biodiversity conservation, carbon sequestration, water quality enhancement, and sustainable development. Accurate classification aids in effective mangrove management, protection, and preservation of coastal ecosystems. Previous studies predominantly relied on passive optical remote sensing images as data sources for mangrove classification, often overlooking the intricate vertical structural complexities of mangrove species. In this study, we address this limitation by incorporating unmanned aerial vehicle-LiDAR (UAV-LiDAR) point cloud 3D data with UAV hyperspectral imagery to perform multivariate classification of mangrove species. Five distinct variable scenarios were employed: band characteristics (S1), vegetation index (S2), texture measures (S3), fused hyperspectral characteristics (S4), and a canopy height model (CHM) combined with UAV hyperspectral characteristics and LiDAR point cloud data (S5). To execute this classification task, an extreme gradient boosting (XGBoost) machine learning algorithm was employed. Our investigation focused on the estuary of the Pinglu Canal, situated within the Maowei Sea of the Beibu Gulf in China. By comparing the classification outcomes of the five variable scenarios, we assessed the unique contributions of each variable to the accurate classification of mangrove species. The findings underscore several key points: (1) The fusion of multiple features in the image scenario led to a higher overall accuracy (OA) compared to models that employed individual features. Specifically, scenario S4 achieved an OA of 88.48% and scenario S5 exhibited an even more impressive OA of 96.78%. These figures surpassed those of the individual feature models where the results were S1 (83.35%), S2 (83.55%), and S3 (71.28%). (2) Combining UAV hyperspectral and LiDAR-derived CHM data yielded improved accuracy in mangrove species classification. This fusion ultimately resulted in an OA of 96.78% and kappa coefficient of 95.96%. (3) Notably, the incorporation of data from individual bands and vegetation indices into texture measures can enhance the accuracy of mangrove species classification. The approach employed in this study—a combination of the XGBoost algorithm and the integration of UAV hyperspectral and CHM features from LiDAR point cloud data—proved to be highly effective and exhibited strong performance in classifying mangrove species. These findings lay a robust foundation for future research efforts focused on mangrove ecosystem services and ecological restoration of mangrove forests.

Traditional forest inventories are based on field surveys of established sample plots, which involve field measurements of individual trees within a sample plot and the selection of proper allometric equations for tree volume calculation. Thus, accurate field measurements and properly selected allometric equations are two crucial factors for providing high-quality tree volumes. One key problem is the difficulty in accurately acquiring tree height data, resulting in high uncertainty in tree volume calculation when the diameter at breast height (DBH) alone is used. This study examined the uncertainty of tree height measurements using different means and the impact of allometric models on tree volume estimation accuracy. Masson pine and eucalyptus plantations in Fujian Province, China, were selected as examples; their tree heights were measured three ways: using an 18-m telescopic pole, UAV Lidar (unmanned aerial vehicle, light detection and ranging) data, and direct measurement of felled trees, with the latest one as a reference. The DBH-based and DBH–height-based allometric equations corresponding to specific tree species were used for the calculations of tree volumes. The results show that (1) tree volumes calculated from the DBH-based models were lower than those from the DBH–height-based models. On average, tree volumes were underestimated by 0.018 m3 and 0.117 m3 for Masson pine and eucalyptus, respectively, while the relative root-mean-squared errors (RMSEr) were 24.04% and 33.90%, respectively, when using the DBH-based model; (2) the tree height extracted from UAV Lidar data was more accurate than that measured using a telescopic pole, because the pole measurement method generally underestimated the tree height, especially when the trees were taller than the length of the pole (18 m in our study); (3) the tree heights measured using different methods greatly impacted the accuracies of tree volumes calculated using the DBH–height model. The telescopic-pole-measured tree heights resulted in a relative error of 9.1–11.8% in tree volume calculations. This research implies that incorporation of UAV Lidar data with DBH field measurements can effectively improve tree volume estimation and could be a new direction for sample plot data collection in the future.

Rivers and riparian areas are vital components of ecosystems, but accurately modeling their terrain presents challenges, especially in detecting the river surface. This paper proposes an integrated approach that combines UAV LiDAR and Single Beam Echo Sounder (SBES) data to construct a Digital Terrain Model (DTM) of river and riparian areas. The objective is to overcome the limitations posed by water, which absorbs near-infrared laser energy, resulting in weak or absent LiDAR returns. Different UAV LiDAR densities were examined to determine the optimal configuration for capturing riparian areas. Evaluation of the results utilized various metrics, including root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean bias error (MBE), and correlation coefficient (CC). Three ground filtering methods were implemented and assessed: morphological filters, adaptive triangulated irregular network (ATIN) filtering, and above-ground level (AGL) filtering. Among the evaluated methods, the DTM constructed using ATIN with an 80-meter flight configuration yielded the most accurate results. It achieved an RMSE of 0.18m, an MSE of 0.03m, an MAE of 0.17m, an MBE of 9.08m, and a CC of 1.00. Comparatively, other methods exhibited higher error values and lower correlation coefficients. The findings highlight the efficacy of ATIN filtering in conjunction with an 80-meter UAV LiDAR flight for obtaining reliable DTMs of river and riparian areas. This approach demonstrates significant improvement in accuracy, particularly in terms of RMSE and MSE. The derived DTM can be a valuable tool for safeguarding and managing these critical ecosystems. In summary, this paper successfully addresses the challenge of modeling river and riparian terrains by integrating UAV LiDAR and SBES data. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a highly accurate DTM. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a precise, high DTM with minimal error. The developed model contributes to protecting and preserving river and riparian ecosystems.

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.

The accurate classification of single tree species in forests is important for assessing species diversity and estimating forest productivity. However, few studies have explored the influence of canopy morphological characteristics on the classification of tree species. Therefore, based on UAV LiDAR and hyperspectral data, in this study, we designed various classification schemes for the main tree species in the study area, i.e., birch, Manchurian ash, larch, Ulmus, and mongolica, in order to explore the effects of different data sources, classifiers, and canopy morphological features on the classification of a single tree species. The results showed that the classification accuracy of a single tree species using multisource remote sensing data was greater than that based on a single data source. The classification results of three different classifiers were compared, and the random forest and support vector machine classifiers exhibited similar classification accuracies, with overall accuracies above 78%. The BP neural network classifier had the lowest classification accuracy of 75.8%. The classification accuracy of all three classifiers for tree species was slightly improved when UAV LiDAR-extracted canopy morphological features were added to the classifier, indicating that the addition of canopy morphological features has a certain relevance for the classification of single tree species.

Forest-canopy closure (FCC) reflects the coverage of the forest tree canopy, which is one of the most important indicators of forest structure and a core parameter in forest resources investigation. In recent years, the rapid development of UAV LiDAR and photogrammetry technology has provided effective support for FCC estimation. However, affected by factors such as different tree species and different stand densities, it is difficult to estimate FCC accurately based on the single-tree canopy-contour method in complex forest regions. Thus, this study proposes a method for estimating FCC accurately using algorithm integration with an optimal window size for treetop detection and an optimal algorithm for crown-boundary extraction using UAV LiDAR data in various scenes. The research results show that: (1) The FCC estimation accuracy was improved using the method proposed in this study. The accuracy of FCC in a camphor pine forest (Pinus sylvestris var. mongolica Litv.) was 89.11%, with an improvement of 6.77–11.25% compared to the results obtained from other combined conditions. The FCC accuracy for white birch (White birch platyphylla Suk) was about 87.53%, with an increase of 3.25–8.42%. (2) The size of the window used for treetop detection is closely related to tree species and stand density. With the same forest-stand density, the treetop-detection window size of camphor pine was larger than that of white birch. The optimal window size of camphor pine was between 5 × 5~11 × 11 (corresponding 2.5~5.5 m), while that of white birch was between 3 × 3~7 × 7 (corresponding 1.5~3.5 m). (3) There are significant differences in the optimal-canopy-outline extraction algorithms for different scenarios. With a medium forest-stand density, the marker-controlled watershed (MCW) algorithm has the best tree-crown extraction effect. The region-growing (RG) method has better extraction results in the sparse areas of camphor pine and the dense areas of white birch. The Voronoi tessellation (VT) algorithm is more suitable for the dense areas of camphor pine and the sparse regions of white birch. The method proposed in this study provides a reference for FCC estimation using high-resolution remote-sensing images in complex forest areas containing various scenes.

Accurately estimating aboveground biomass (AGB) is crucial for assessing carbon storage in forest ecosystems. However, traditional field survey methods are time-consuming, and vegetation indices based on optical remote sensing are prone to saturation effects, potentially underestimating AGB in subtropical forests. To overcome these limitations, we propose an improved approach that combines three-dimensional (3D) forest structure data collected using unmanned aerial vehicle light detection and ranging (UAV LiDAR) technology with ground measurements to apply a binary allometric growth equation for estimating and mapping the spatial distribution of AGB in subtropical forests of China. Additionally, we analyze the influence of terrain factors such as elevation and slope on the distribution of forest biomass. Our results demonstrate a high accuracy in estimating tree height and diameter at breast height (DBH) using LiDAR data, with an R2 of 0.89 for tree height and 0.92 for DBH. In the study area, AGB ranges from 0.22 to 755.19 t/ha, with an average of 121.28 t/ha. High AGB values are mainly distributed in the western and central-southern parts of the study area, while low AGB values are concentrated in the northern and northeastern regions. Furthermore, we observe that AGB in the study area exhibits an increasing trend with altitude, reaching its peak at approximately 1650 m, followed by a gradual decline with further increase in altitude. Forest AGB gradually increases with slope, reaching its peak near 30°. However, AGB decreases within the 30–80° range as the slope increases. This study confirms the effectiveness of using UAV LiDAR for estimating and mapping the spatial distribution of AGB in complex terrains. This method can be widely applied in productivity, carbon sequestration, and biodiversity studies of subtropical forests.

In the country of Georgia, the administrative territories of Surami (Khashuri municipality, Shida Kartli region) are particularly susceptible to the development of landslide processes. Among these areas, the Zindisi district stands out as a focal point for our research due to the occurrence of a significant landslide process in 2007, which remains active and poses periodic threats to residential houses and infrastructure. Zindisi district is characterized by dense forest cover and a high population density. Conducting a detailed landslide survey in such a challenging terrain using standard methods is difficult. Therefore, our research aims to overcome these challenges by employing lidar technology in a similar environment.The research initiative commenced with the acquisition of high-density point cloud data utilizing UAV lidar surveys. A UAV (DJI- The Matrix 300 RTK) equipped with a lidar camera (DJI Zenmuse L-1), was deployed to scan the study area. This approach allowed for the capture of detailed topographical information crucial for understanding the landslide processes. The obtained dataset serves as the foundation for creating a precise Digital Elevation Model (DEM) with a spatial resolution of 1 meter. This DEM enabled the identification of landslide boundaries by leveraging lidar-derived high-resolution topographic information. Linear structures were mapped based on hillshade, aspect, slope, and other thematic maps, providing a comprehensive understanding of the terrain.To validate the accuracy of our results, both aerial photos and on-site field investigations were utilized. The combination of lidar technology, high-resolution topographic data, and thorough validation techniques enhances the reliability of our landslide inventory in the Zindisi district. This research contributes valuable insights for effective land management and mitigation strategies in landslide-prone areas. Furthermore, the approach outlined in this research provides a method for landslide mapping in similar environments and demonstrate the potential of UAV LiDAR technology in enhancing landslide risk management in densely populated and forested regions.

The aim of this study was to evaluate topographic changes along a stretch of coastline in the Municipality of Borghetto Santo Spirito (Region of Liguria, Italy, north-western Mediterranean) by means of a remotely piloted aircraft system coupled with structure from motion and multi-view stereo techniques. This sector was surveyed three times over 5 months in the fall–winter of 2013–2014 (1 November 2013, 4 December 2013, 17 March 2014) to obtain digital elevation models and orthophotos of the beach. Changes in beach topography associated with storm action and human activities were assessed in terms of gain/loss of sediments and shifting of the wet–dry boundary defining the shoreline. Between the first and second surveys, the study area was hit by two storms (10–11 November 2013 and 21–22 November 2013) with waves approaching from the E–NNE, causing a shoreline retreat which, in some sectors, reached 7 m. Between the second and third surveys, by contrast, four storms (25–27 December 2013, 5–6 January 2014, 17–18 January 2014 and 6–10 February 2014) with waves propagating from the SE produced a general advancement of the shoreline (up to ~5 m) by deposition of sediments along some parts of the beach. The data also reflect changes in beach topography due to human activity during the 2013 fall season, when private beach managers quarried ~178 m3 of sediments on the emerged beach near the shoreline to accumulate them landwards. The results show that drones can be used for regular beach monitoring activities, and that they can provide new insights into the processes related to natural and/or human-related topographic beach changes.

Gully erosion, driven by the interplay of natural processes and human activities, results in severe soil degradation and landscape alteration, yet approaches for accurately quantifying erosion triggered by extreme precipitation using multi-source high-resolution remote sensing remain limited. This study first extracted digital surface models (DSM) for the years 2014 and 2024 using Ziyuan-3 and GaoFen-7 satellite stereo imagery, respectively. Subsequently, the DSM was calibrated using high-resolution unmanned aerial vehicle photogrammetry data to enhance elevation accuracy. Based on the corrected DSMs, gully erosion depths from 2014 to 2024 were quantified. Erosion patches were identified through a deep learning framework applied to GaoFen-1 and GaoFen-2 imagery. The analysis further explored the influences of natural processes and anthropogenic activities on elevation changes within the gully erosion watershed. Topographic monitoring in the Sandu River watershed revealed a net elevation loss of 2.6 m over 2014–2024, with erosion depths up to 8 m in some sub-watersheds. Elevation changes are primarily driven by extreme precipitation-induced erosion alongside human activities, resulting in substantial spatial variability in surface lowering across the watershed. This approach provides a refined assessment of the spatial and temporal evolution of gully erosion, offering valuable insights for soil conservation and sustainable land management strategies in the Loess Plateau region.

Improvements in miniaturization and affordability of lidar technology, mainly due to innovation in self-driving cars, means that UAV lidar is now an accessible option for geoscience research. We present applications in which UAV lidar contributes to data collection in ways that would otherwise not be possible in the time frame, budget, and/or with the resolution required. Volcanoes: Lava flow surface texture can provide information on lava flow dynamics and emplacement. The transition between pahoehoe and a’a flow textures can indicate changes in flow rates and flow thickness, and the morphology of ripples in ropey pahoehoe flows can indicate flow direction. Hell’s Half Acre, Idaho, USA, is a basaltic lava flow that was erupted ~5000 y.a. Analysis of UAV lidar data at this lava field shows lava flow surface texture in sufficient resolution to define cm-scale pahoehoe ripples. In addition, larger scale lava features such as channels and inflation/deflation ridges can be mapped which allows us to understand the dynamics of the lava flow emplacement. Vegetation: UAV Lidar can be useful for analysis of vegetation canopy, both in stripping canopy (lidar last return) and in using it for tree height (lidar first return). By combining UAV lidar with other airborne data, e.g. multispectral imaging, we can identify and map tree species at Ft de Soto Park, in Florida, USA. Permafrost: Permafrost thermokarst features can develop rapidly and climate change will cause an increase in these rapid thaw events. With UAV lidar we can strip the vegetation to reveal the underlying ground surface which can then be used to assess and model permafrost processes. UAV surveys are quick and relatively inexpensive (as compared to crewed aviation) and data can be collected in response to a thaw event. We present data from Alaska, USA, at known sites of rapid thermokarst thaw. UAV lidar, both as a stand-alone dataset, and when integrated with other data streams e.g. multispectral and visible imagery, can provide high-resolution data (both spatial and temporal) on a platform that is relatively low-cost and logistically straightforward to deploy.

Accurate characterization of riverbed sediment is crucial for monitoring cross-sectional changes in rivers and modeling water dynamics, especially during large water discharge events. The UAV LiDAR technique, with recent advancements, offers enhanced capabilities for detailed riverbed topography mapping by eliminating surface vegetation. Despite its potential, the adoption of UAV LiDAR for riverbed cross-sectional profiling has faced delays and skepticism in regular practices. In this study, we applied the UAV LiDAR technique to measure the riverbed topography of a relatively wide river in the Ilan plain, northeast Taiwan. Our findings reveal that UAV LiDAR provides significantly more detailed results compared to Airborne LiDAR and surpasses topography measurements obtained through photogrammetry. The accuracy of UAV LiDAR-derived point clouds outperforms photogrammetry, especially when ground control points for the work of photogrammetry are insufficient or poorly distributed. Despite challenges posed by water bodies absorbing LiDAR signals, UAV LiDAR allows the production of complete riverbed topography, offering reliable estimates during dry seasons. Utilizing UAV LiDAR data, we conducted a comprehensive analysis of both cross-sectional and longitudinal riverbed profiles. The longitudinal profiles exhibit wavy frequencies associated with sediment transport processes, opening avenues for further investigation. Additionally, we evaluated Digital Elevation Models (DEMs) of Differencing (DoD) using previously acquired Airborne LiDAR point clouds. The DoD analysis unveiled the substantial magnitude of sediment movement and redistribution following an extreme rainfall event and dam failure, with a height difference exceeding 9m. This analysis, extending along the river's longitudinal profile, serves as a ground-truth field dataset illustrating how extreme rainfall events can trigger large sediment movements, posing potential hazards to the residents near rivers. Our study demonstrates the utility of UAV LiDAR in high-resolution mapping of riverbed sediment topography and provides valuable insights into sediment dynamics under extreme events, contributing to improved monitoring and hazard assessment practices.

Mangrove forests have been suffering with the impact of anthropogenic activities (i.e. fishing, deforestation) in the past years. Imaging data with high spatial and spectral resolution is a tool to assist in the conservation management of these ecosystems, besides allowing the in situ characterization of mangrove trees. Systems onboard UAVs have been improving the applications of remote sensing for this purpose, delivering high resolution products with lower costs in comparison to airborne systems. The aim of this study is to investigate the applications of coupled UAV hyperspectral and LiDAR data for the generation of ultra-high-resolution images of mangrove forests. Here we present the preprocessing steps and preliminary results of the combined data.