Explore the vast range of titles available.

In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image.

In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight low-light underwater target detection network, named PDSC-YOLOv8n. Firstly, we enhance the input images using the improved Pro MSRCR algorithm for data augmentation. Secondly, we replace the traditional convolutions in the backbone and neck networks of YOLOv8n with Ghost and GSConv modules respectively to achieve lightweight network modeling. Additionally, we integrate the improved DCNv3 module into the C2f module of the backbone network to enhance the capability of target feature extraction. Furthermore, we introduce the GAM into the SPPF and incorporate the CBAM attention mechanism into the last layer of the backbone network to enhance the model’s perceptual and generalization capabilities. Finally, we optimize the training process of the model using WIoUv3 as the loss function. The model is successfully deployed on an embedded platform, achieving real-time detection of underwater targets on the embedded platform. We first conduct experiments on the RUOD underwater dataset. Meanwhile, we also utilized the Pascal VOC2012 dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on RUOD dataset were 79.6% and 58.2%, respectively, and the PDSC -YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 86.1% and 60.8%. The number of parameters of the model is reduced by about 15.5%, the detection accuracy is improved by 6.5%. The original YOLOv8n algorithm was 73% and 53.2% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The PDSC-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 78.5% and 57%, respectively. The superior performance of PDSC-YOLOv8n indicates its effectiveness in the field of underwater target detection.

In tree Aboveground Biomass (AGB) estimation, the traditional harvest method is accurate but unsuitable for a large-scale forest. The airborne Light Detection And Ranging (LiDAR) is superior in obtaining the point cloud data of a dense forest and extracting tree heights for AGB estimation. However, the LiDAR has limitations such as high cost, low efficiency, and complicated operations. Alternatively, the overlapping oblique photographs taken by an Unmanned Aerial Vehicle (UAV)-loaded digital camera can also generate point cloud data using the Aerial Triangulation (AT) method. However, limited by the relatively poor penetrating capacity of natural light, the photographs captured by the digital camera on a UAV are more suitable for obtaining the point cloud data of a relatively sparse forest. In this paper, an electric fixed-wing UAV loaded with a digital camera was employed to take oblique photographs of a sparse subalpine coniferous forest in the source region of the Minjiang River. Based on point cloud data obtained from the overlapping photographs, a Digital Terrain Model (DTM) was generated by filtering non-ground points along with the acquisition of a Digital Surface Model (DSM) of Minjiang fir trees by eliminating subalpine shrubs and meadows. Individual tree heights were extracted by overlaying individual tree outlines on Canopy Height Model (CHM) data computed by subtracting the Digital Elevation Model (DEM) from the rasterized DSM. The allometric equation with tree height (H) as the predictor variable was established by fitting measured tree heights with tree AGBs, which were estimated using the allometric equation on H and Diameter at Breast Height (DBH) in sample tree plots. Finally, the AGBs of all of the trees in the test site were determined by inputting extracted individual tree heights into the established allometric equation. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted individual tree heights were 0.92 and 1.77 m, and the R2 and RMSE of the estimated AGBs of individual trees were 0.96 and 54.90 kg. The results demonstrated the feasibility and effectiveness of applying UAV-acquired oblique optical photographs to the tree AGB estimation of sparse subalpine coniferous forests.

Residential green space is an important component of urban green space and one of the major indicators for evaluating the quality of a residential community. Traditional indicators such as the green space ratio only consider the relationship between green space area and total area of the residential community while ignoring the difference in the amount of green space enjoyed by household residents in high-rise and low-rise buildings. Therefore, it is meaningful to estimate household green space and its spatial distribution in residential communities. However, there are frequent difficulties in obtaining specific green space area and household number through ground surveys or consulting with property management units. In this study, taking a composite residential community in Chongqing, China, as the study site, we first employed a five-lens drone to capture its oblique RGB images and generated the DOM (Digital Orthophoto Map). Subsequently, the green space area and distribution in the entire residential community were extracted from the DOM using VDVI (Visible Difference Vegetation Index). The YOLACT (You Only Look At Coefficients) instance segmentation model was used to recognize balconies from the facade images of high-rise buildings to determine their household numbers. Finally, the average green space per household in the entire residential community was calculated to be 67.82 m2, and those in the high-rise and low-rise building zones were 51.28 m2 and 300 m2, respectively. Compared with the green space ratios of 65.5% and 50%, household green space more truly reflected the actual green space occupation in high- and low-rise building zones.

Bamboo forest is a special forest type, and its aboveground biomass (AGB) is a key indicator of its carbon sequestration capacity and ecosystem productivity. Due to its complex canopy structure and particular growth pattern, the AGBs of individual bamboos that were estimated using traditional remotely sensed data are of relatively low accuracy. In recent years, the point cloud data scanned by terrestrial laser scanners (TLS) offer the possibility for more accurate estimations of bamboo AGB. However, bamboo culms tend to have various bending degrees during the growth process, which causes the AGB estimated on culm height (H) to be generally less than the true value. In this paper, taking one sample plot of the Moso bamboo forest in Hutou Village, Chongqing, China as the study site, we employed a TLS to acquire the point cloud data. The layer-wise distance discrimination method was first developed to accurately segment individual bamboos from the dense stand. Next, the diameter at breast height (DBH) and culm length (L) of an individual bamboo were precisely extracted by fitting the cross-section circle and constructing the longitudinal axis of the bamboo culm, respectively. Lastly, the AGBs of the Moso bamboos in the study site were separately calculated using the allometric equations with the DBH and L as predictor variables. As results, the precision of the complete bamboo segmentation was 90.4%; the absolute error (AE) of the extracted DBHs ranged from −1.22 cm to 0.88 cm (R2 = 0.93, RMSE = 0.40 cm); the AE of the extracted Hs varied from –0.77 m to 1.02 m (R2 = 0.91, RMSE = 0.45 m); and the AE of the extracted Ls varied from −1.08 m to 0.77 m (R2 = 0.95, RMSE = 0.23 m). The total estimated AGB of the Moso bamboos in the sample plot increased by 2.85%, from 680.40 kg on H to 696.36 kg on L. These measurements demonstrated the unique benefits of the TLS-acquired point cloud in characterizing the structural parameters of Moso bamboos and estimating their AGBs with high accuracy.

Unmanned aerial vehicles (UAVs) have been employed extensively for remote-sensing missions. However, due to their energy limitations, UAVs have a restricted flight operating time and spatial coverage, which makes remote sensing over huge regions that are out of UAV flight endurance and range challenging. PAD is an autonomous wireless charging station that might significantly increase the flying time of UAVs by recharging them in the air. In this work, we introduce PADs to simplify UAV-based remote sensing over a huge region, and then we explore the UAV route planning problem once PADs have been predeployed throughout a huge remote sensing region. A route planning scheme, named PAD-based remote sensing (PBRS), is proposed to solve the problem. The PBRS scheme first plans the UAV’s round-trip routes based on the location of the PADs and divides the whole target region into multiple PAD-based subregions. Between adjacent subregions, the UAV flight subroute is planned by determining piggyback points to minimize the total time for remote sensing. We demonstrate the effectiveness of the proposed scheme by conducting several sets of simulation experiments based on the digital orthophoto model of Hutou Village in Beibei District, Chongqing, China. The results show that the PBRS scheme can achieve excellent performance in three metrics of remote sensing duration, the number of trips to charging stations, and the data-storage rate in UAV remote-sensing missions over huge regions with predeployed PADs through effective planning of UAVs.

Microglia are progressively activated by inflammation and exhibit phagocytic dysfunction in the pathogenesis of neurodegenerative diseases. Lipid-droplet-accumulating microglia were identified in the aging mouse and human brain; however, little is known about the formation and role of lipid droplets in microglial neuroinflammation of Alzheimer’s disease (AD). Here, we report a striking buildup of lipid droplets accumulation in microglia in the 3xTg mouse brain. Moreover, we observed significant upregulation of PKM2 and sterol regulatory element binding protein 1 (SREBP1) levels, which were predominantly localized in microglia of 3xTg mice. PKM2 dimerization was necessary for SREBP1 activation and lipogenesis of lipid droplet-accumulating microglia. RNA sequencing analysis of microglia isolated from 3xTg mice exhibited transcriptomic changes in lipid metabolism, innate inflammation, and phagocytosis dysfunction; these changes were improved with capsaicin-mediated pharmacological activation of TRPV1 via inhibition of PKM2 dimerization and reduction of SREBP1 activation. Lipid droplet-accumulating microglia exhibited increased mitochondrial injury accompanied by impaired mitophagy, which was abrogated upon of TRPV1 activation. Capsaicin also rescued neuronal loss, tau pathology, and memory impairment in 3xTg mice. Our study suggests that TRPV1-PKM2-SREBP1 axis regulation of microglia lipid metabolism could be a therapeutic approach to alleviate the consequences of AD.

The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored.

Tufa barrages play an important role in fluviatile tufa ecosystems and sedimentary records. Quantifying the height of tufa barrage is significant for understanding the evolution and development of the Holocene tufa barrage systems. However, for submerged tufa barrages, there is no low-cost non-contact method to retrieve barrage height. Generally, it is difficult to recognize small tufa barrages by means of remotely sensed satellite data, but the combination of unmanned aerial vehicles (UAV) and Structure-from-Motion (SfM) photogrammetry makes it possible. In this study, we used a fixed-wing UAV and a consumer-grade camera to acquire images of the submerged tufa barrage in Lying Dragon Lake, Jiuzhaigou National Nature Reserve, China, and estimated the height of the tufa barrage through UAV-based photogrammetric bathymetry. On this foundation, the relationship between barrage height and its spectrum was established through band ratio analysis using UAV-derived geometric bathymetry and digital orthoimages, which provided an alternative strategy to characterize the height of submerged tufa barrages. However, the spectral characteristics of submerged tufa barrages will oscillate with changes in the environmental conditions. In future research, we will consider using a dedicated aquatic multispectral camera to improve the experimentation.

Light Detection and Ranging (LiDAR) has proved to be an effective technology for accurately extracting forest structural parameters. Unmanned Aerial Vehicles (UAVs) are characterized by its flexibility and low cost. Combining the advantages of both technologies, UAV-LiDAR exhibits great potential in the accurate surveying of large forests. However, for forests dominated by deciduous tree species, the accuracy of individual tree detection and height extraction is inevitably impacted by the leaf-on and leaf-off seasons when UAV-LiDAR scans point clouds. In this study, a planted forest of dawn redwood (Metasequoia glyptostroboides Hu & W. C. Cheng) in Ma’anxi Wetland Park of Chongqing, China, was chosen as the study object. The UAV-LiDAR was first leveraged to capture the point clouds of summer and winter seasons. Then, the canopy height models (CHMs) with different spatial resolutions were generated, based on which the tree quantity and individual heights were extracted. The achieved outcomes included the following: (1) The CHMs of the two seasons could be used to obtain the tree quantity, and the accuracy of individual tree detection from the point cloud scanned in the winter was relatively higher than that in the summer. (2) The spatial resolution of CHM impacted the accuracy of individual tree segmentation and height extraction, and the optimum spatial resolution was 0.3 m (approximately 1/10 of the average canopy diameter of the dawn redwoods). Therefore, to obtain more accurate individual tree heights of the deciduous forest, it is better to scan the point cloud using UAV-LiDAR in the leaf-off season and choose the appropriate spatial resolution of the CHM.