Unmanned Aerial Vehicle Photography Research Articles

Pixel-Level Crack Identification for Bridge Concrete Structures Using Unmanned Aerial Vehicle Photography and Deep Learning

Traditional manual inspection technology has the problems of high risk, low efficiency, and being time-consuming in bridge safety management. The unmanned aerial vehicle (UAV)-based detection technology is widely used in bridge structure safety monitoring. However, the existing deep learning-based concrete crack identification method has great limitations in dealing with complex background and tiny cracks in bridge structures. To address these problems, this study designs a crack pixel-level high-performance segmentation model for bridge concrete cracks that is suitable for UAV detection scenarios using machine vision (MV) and deep learning (DL) algorithms. First, considering the high requirements for the computing performance of the MV-based model for UAV-based detection, the ResNet-18-based lightweight convolutional neural network is used to represent the traditional large-scale backbone network of the pyramid scene parsing network (PSPNet) to develop a high-performance crack automatic identification model. Then, considering that bridge concrete cracks have the characteristics of subtle shapes and complex backgrounds, the spatial position self-attention module is inserted into the PSPNet to improve its detection accuracy. A concrete bridge is used for the case study, and a dataset of cracks in bridge concrete structures collected by UAVs is constructed and used for model training. The experimental results show that the loss function of the developed method in the training process results in a smooth decline, and the developed algorithm achieves the evaluation indicators of 0.9008 precision, 0.8750 recall, 0.8820 accuracy, and 0.9012 IOU on the bridge concrete crack dataset, which are significantly higher than other state-of-the-art baseline methods. In addition, four common UAV bridge detection scenarios, including low light, complex crack forms, high background roughness, and complex background scenes, are used to further test the crack detection ability of the developed crack identification model. The experimental results show that the proposed crack identification method can effectively overcome interference and real-size pixel-level segmentation of crack morphology. In addition, it also achieved a detection efficiency of 35.04 FPS, which shows that the real-time detection ability of the method has good applicability in the UAV detection scene.

A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs.

Deformation Patterns and Failure Mechanisms of Soft-Hard-Interbedded Anti-Inclined Layered Rock Slope in Wolong Open-Pit Coal Mine

Since the beginning of spring 2022, successive landslides have occurred in the eastern pit slope of the Wolong Coal Mine in Qipanjing Town, Otog Banner, Inner Mongolia, which has adversely affected the mine’s production safety. This study aims to reveal the deformation patterns and failure mechanisms of landslides. Firstly, this study establishes the stratigraphic structure of the eastern pit slope of the Wolong Coal Mine using extensive field geological surveys combined with unmanned aerial vehicle photography, drilling, and comprehensive physical exploration techniques. Indoor geotechnical tests and microscopic experiments reveal that rock mass typically exhibits the characteristics of expansibility and water sensitivity. Moreover, the mechanical parameters of the rock mass were determined using a combination of the window sampling method, the Geological Strength Index, and the Hoek–Brown strength criterion estimation theory. Finally, this study consolidates the previously mentioned insights and employs FLAC3D (7.0) software to assess the stress–strain characteristics of the excavated slope. The results indicate that the deformation mode of the Wolong open pit coal mine is the toppling failure of soft-hard-interbedded anti-inclined layered rock slopes. The unloading effect and rock expansion-induced softening lead to stress concentration at the slope corners and more substantial deformation, thereby accelerating upper slope deformation. The deformation and destabilization process of landslides is categorized into four stages: the initial deformation stage, the development stage of lateral shear misalignment, the development stage of horizontal tensile-shear damage, and the slip surface development to the preslip stage. This research offers valuable references and engineering insights for future scientific investigations and the prevention of similar slope-related geological hazards.

Depositional model of the Member Deng-2 marginal microbial mound-bank complex of the Dengying Formation in the southwestern Sichuan Basin, SW China: Implications for the Ediacaran microbial mound construction and hydrocarbon exploration

Recent advances in hydrocarbon exploration have been made in the Member Deng-2 marginal microbial mound-bank complex reservoirs of the Dengying Formation in the western Sichuan Basin, SW China, where the depositional process is regarded confusing. The microfacies, construction types, and depositional model of the Member Deng-2 marginal microbial mound-bank complex have been investigated using unmanned aerial vehicle photography, outcrop section investigation, thin section identification, and seismic reflections in the southwestern Sichuan Basin. The microbialite lithologic textures in this region include thrombolite, dendrolite, stromatolite, fenestral stromatolite, spongiostromata stone, oncolite, aggregated grainstone, and botryoidal grapestone. Based on the comprehensive analysis of “depositional fabrics–lithology–microfacies”, an association between a fore mound, mound framework, and back mound subfacies has been proposed based on water depth, current direction, energy level and lithologic assemblages. The microfacies of the mound base, mound core, mound flank, mound cap, and mound flat could be recognized among the mound framework subfacies. Two construction types of marginal microbial mound-bank complex have been determined based on deposition location, mound scale, migration direction, and sedimentary facies association. Type Jinkouhe microbial mound constructions (TJMMCs) develop along the windward margin owing to their proximity to the seaward subfacies fore mound, with a northeastwardly migrated microbial mound on top of the mud mound, exhibiting the characteristics of large-sized mounds and small-sized banks in the surrounding area. Type E’bian microbial mound constructions (TEMMCs) primarily occur on the leeward margin, resulting from the presence of onshore back mound subfacies, with the smaller southwestward migrated microbial mounds existing on a thicker microbial flat. The platform margin microbial mound depositional model can be correlated with certain lateral comparison profile and seismic reflection structures in the 2D seismic section, which can provide references for future worldwide exploration. Microbial mounds with larger buildups and thicker vertical reservoirs are typically targeted on the windward margin, while small-sized microbial mounds and flats with better lateral connections are typically focused on the leeward margin.

Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography

Rapid and accurate estimation of plant phenotypes plays a vital role in effective breeding and management of maize crops. Unmanned aerial vehicle (UAV) platforms are emerging as a valuable tool in the assessment of crop phenotypes, offering a promising avenue to enhance the accuracy and efficiency of phenotypic analysis. This study executed two UAV photography methods to acquire aerial images for the estimation of leaf area index (LAI) and above-ground biomass (AGB) in summer maize. Nadir photography was implemented to obtain data from three sources including multi-spectral (MS), RGB, and thermal infrared (TIR) sensors. Additionally, RGB imaging-based cross-circling oblique (CCO) photography was implemented to obtain accurate 3D point cloud data. The nadir photography data was processed to generate orthomosaic image and extract features including vegetation index, canopy cover, canopy height, canopy temperature, and textural information. Features including canopy occupation volume, plant area index, canopy cover, and canopy height were extracted from the CCO photography-derived point cloud data. Furthermore, a data augmentation method, called linear regression-based feature augmentation (LRFA), was proposed for augmenting features extracted from nadir and CCO photography. The results showed that the introduction of CCO photography improved the accuracy of LAI and AGB estimation during the big trumpet stage and LAI estimation during the milk stage compared to the integrated multi-source nadir photography data. Notably, the LRFA derived features outperformed raw features in the majority of modeling scenarios, with the random forest achieved an average accuracy (R2) improvement of 6.1% for LAI estimation and 3.7% for AGB estimation. This study highlights the significance of combining different photography technologies and feature augmentation method for estimating maize phenotypes, providing novel opportunities for crop growth monitoring in modern agriculture.

Integration and differentiation: comparison of photography behaviors using unmanned aerial vehicle data in China and Europe

The continuous improvement in the design and manufacture of unmanned aerial vehicles (UAVs) has enabled them to assist humans in aerial tasks. At present, UAVs are not only used for environmental data collection, but also used by photographers to take photos. As the popularity of consumer UAVs rises, an increasing number of tourists are transitioning from the traditional perspectives of mobile phone and hand-held camera photography to the aerial perspective of UAV photography. Therefore this study examines UAV photos and related data uploaded by photographers within China and Europe to the UAV forum SkyPixel by using cloud-based data from Microsoft Azure vision, and sentiment, social network and spatial analyses. In addition, this paper also investigates the commonalities and differences between the behaviors and preferences of photographers who utilize UAV photography recreationally. The results indicate that the core content of UAV photography revolves around three major themes: natural landscapes, destination transportation facilities, and the daily behaviors of people. However, photographers in China mainly exhibit neutral emotions, while European photographers display mostly positive emotions. Hot spots are concentrated in mature tourist areas, especially top destinations near waterbodies. This study fosters a new understanding of the emerging group of UAV users.

Older than you think: using U–Pb calcite geochronology to better constrain basin-bounding fault reactivation, Inner Moray Firth Basin, western North Sea

Like many rift basins worldwide, the Inner Moray Firth Basin (IMFB) is bounded by major reactivated fault zones, including the Helmsdale Fault and the Great Glen Fault (GGF). The Jurassic successions exposed onshore close to these faults at Helmsdale and Shandwick preserve folding, calcite veining and minor faulting consistent with sinistral (Helmsdale Fault) and dextral (GGF) transtensional movements. This deformation has been widely attributed to Cenozoic post-rift fault reactivation. Onshore fieldwork and U–Pb calcite geochronology of five vein samples associated with transtensional movements along the Helmsdale Fault and a splay of the GGF show that faulting occurred during the Early Cretaceous ( c. 128–115 Ma, Barremian–Aptian), while the Helmsdale Fault preserves evidence for earlier Late Jurassic sinistral movements ( c. 159 Ma, Oxfordian). This demonstrates that both basin-bounding faults were substantially reactivated during the episodic NW–SE-directed Mesozoic rifting that formed the IMFB. Although there is good evidence for Cenozoic reactivation of the GGF offshore, the extent of such deformation along the north coast of the IMFB remains uncertain. Our findings illustrate the importance of oblique-slip reactivation processes in shaping the evolution of continental rift basins given that this deformation style may not be immediately obvious in interpretations of offshore seismic reflection data. Supplementary Material: Appendix A – orthomosaic model obtained from unmanned aerial vehicle (UAV) photography of the Helmsdale locality (GeoTiff format); Appendix B – orthomosaic model obtained from UAV photography of the Shandwick locality (GeoTiff format); Appendix C – geochronology data; and Appendix D – additional thin section microphotographs of sample HD1 showing repeated cycles of syntaxial grain growth are available at https://doi.org/10.6084/m9.figshare.c.6708518

DESIGN AND IMPLEMENTATION OF THE INTEGRATED PRACTICUM OF GEOMATICS ACCORDING TO ENGINEERING EDUCATION ACCREDITATION

Abstract. Engineering education accreditation is a new engineering education model promoted by the Ministry of Education in China, which aims to cultivate the ability of students to solve complex engineering problems. A course named “integrated practicum of Geomatics” (IPG) was innovatively designed for the undergraduate specialty of Geomatics. This course involves in the current advanced technologies to lay a solid foundation for students to engage in Geomatics through aerial photography technology design, unmanned aerial vehicle photography, control point selection, GNSS-RTK control point survey, photo interpretation and sketch, 3D (DEM, DOM, and DLG) product production, inputting 3D products into the Geodatabase, and GIS application software development. This paper expounds on the objectives, contents, evaluation methods, and continuous improvement of IPG.

Improved YOLOX-X based UAV aerial photography object detection algorithm

Unmanned Aerial Vehicle (UAV) aerial photography object detection has high research significance in the fields of disaster rescue, ecological environmental protection, and military reconnaissance. The larger width of UAV photography introduces background interference into the detection task, whereas the relatively high imaging height of the UAV results in mostly small objects in the aerial images. YOLOX-X operated fast and achieved advanced results on MS COCO of natural scene images, so YOLOX-X was used as the baseline network in this paper. A UAV aerial photography object detection algorithm YOLOX_w with improved YOLOX-X is proposed to handle the characteristics of complex backgrounds and the large number of small objects in UAV aerial photography images. The model’s performance in detecting small objects is first improved by preprocessing the training set with the slicing aided hyper inference (SAHI) algorithm and by data augmentation. Then, a shallow feature map with rich spatial information is introduced into the path aggregation network (PAN), and a detection head is added to detect small objects. Next, the ultra-lightweight subspace attention module (ULSAM) is added to the PAN stage to highlight the target features and weaken the background features, which improves the detection accuracy of the network. Finally, the loss function of the bounding box regression is optimized to further improve network prediction accuracy. Experimental results on the VisDrone dataset demonstrate that the detection accuracy of the proposed YOLOX_w algorithm improved by 8% when compared with the baseline YOLOX-X. Moreover, migration experiments on the DIOR dataset verify the effectiveness and robustness of the improved method.

AI-enabled and multimodal data driven smart health monitoring of wind power systems: A case study

The development of AI has enabled the fault detection of industrial components to be achieved through the combination with deep learning. A detection method combined with deep learning has also emerged for the fault detection of fan blades, such as models based on neural networks using the appearance or sound of the blades. However, the detection model obtained from a single data type often has limitations, such as low accuracy and overfitting. This is also the problem with fan blade detection. In contrast, multimodal data fusion detection models are often more stable. The modality diversity of blade diagnosis is strong, and it can be achieved from multiple modalities such as image, sound, and vibration. To improve the accuracy of fault diagnosis of fan blades, this article proposes a multimodal double-layer detection system (MTDS) based on decision-level and feature-level fusion. The system includes a wind turbine simulation platform and a multimodal detection system. It mainly obtains different modal data of the simulated wind turbine from the image, sound, and vibration signals, including blade images through unmanned aerial vehicle photography, blade vibration signals through electronic vibrators, and blade sound signals through microphones. The highly correlated sound and vibration modal data are fused at the feature level, and a detection model based on the sound and vibration mixed mode is implemented using a sound-vibration-CNN (SV-CNN) proposed in this case. Then, a detection model of the image mode is trained based on the blade image using a Convolution Block Attention Module ResNet (CBAM-ResNet) network. Finally, the detection input of the two modal models is fed into a perceptron to obtain the final prediction result, and the decision-level fusion is implemented to achieve fan blade detection based on multimodal, namely the implementation of MTDS.

Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique.

BackgroundSeagrass beds are essential habitats in coastal ecosystems, providing valuable ecosystem services, but are threatened by various climate change and human activities. Seagrass monitoring by remote sensing have been conducted over past decades using satellite and aerial images, which have low resolution to analyze changes in the composition of different seagrass species in the meadows. Recently, unmanned aerial vehicles (UAVs) have allowed us to obtain much higher resolution images, which is promising in observing fine-scale changes in seagrass species composition. Furthermore, image processing techniques based on deep learning can be applied to the discrimination of seagrass species that were difficult based only on color variation. In this study, we conducted mapping of a multispecific seagrass bed in Saroma-ko Lagoon, Hokkaido, Japan, and compared the accuracy of the three discrimination methods of seagrass bed areas and species composition, i.e., pixel-based classification, object-based classification, and the application of deep neural network.MethodsWe set five benthic classes, two seagrass species (Zostera marina and Z. japonica), brown and green macroalgae, and no vegetation for creating a benthic cover map. High-resolution images by UAV photography enabled us to produce a map at fine scales (<1 cm resolution).ResultsThe application of a deep neural network successfully classified the two seagrass species. The accuracy of seagrass bed classification was the highest (82%) when the deep neural network was applied.ConclusionOur results highlighted that a combination of UAV mapping and deep learning could help monitor the spatial extent of seagrass beds and classify their species composition at very fine scales.

Mapping Grassland Classes Using Unmanned Aerial Vehicle and MODIS NDVI Data for Temperate Grassland in Inner Mongolia, China

Grassland classification is crucial for grassland management. One commonly used method utilizes remote sensing vegetation indices (VIs) to map grassland classes at various scales. However, most grassland classifications were conducted as case studies in a small area due to lack of field data sources. At a small scale, classification is reliable; however, great uncertainty emerges when extended to other areas. In this study, large amounts of field observations (more than 30,000 aerial photos) were obtained using unmanned aerial vehicle photography in Inner Mongolia, China, during the peak period of grassland growth in 2018 and 2019. Then, four machine learning classification algorithms were constructed based on characteristic indices of MODIS NDVI in the growing season to map grassland classes of Inner Mongolia. Finally, the spatial distribution and temporal variation of temperate grassland classes were analyzed. Results showed that: (1) Among all characteristic indices, the maximum, average, and sum of MODIS NDVI from July to September during 2015 to 2019 greatly affected grassland classification. (2) The random forest method exhibited the best performance with overall accuracy and kappa coefficient being 72.17% and 0.62, respectively. (3) Compared with the grassland class mapped in the 1980s, 30.98% of grassland classes have been transformed. Our study provides a technological basis for effective and accurate classification of the temperate steppe class and a theoretical foundation for sustainable development and restoration of the temperate steppe ecosystem.

Denoising of Maritime Unmanned Aerial Vehicles Photography Based on Guidance Correlation Pixel Sampling and Aggregation

In this paper, we proposed a guidance correlation pixel Sampling and aggregation image denoising for maritime Unmanned Aerial Vehicles(UAVs) photography, providing a reliable data base for maritime reconnaissance work. The overall step is mainly composed of two parts, pixel sampling and pixel aggregation. To improve the content correlation of sampled pixels, we propose a guided sampling scheme based on the basic estimated map and extend this algorithm to the restoration of maritime UAVs image. Finally, a UAVs image denoising system is shown. Our experimental results show that the proposed algorithm can effectively remove noise and achieves <inline-formula> <tex-math notation="LaTeX">${32.98}{dB}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${31.83}{dB}$ </tex-math></inline-formula> in Set12 and BSD68 datasets with less and image distortion. In the actual scene, the PSNR of our denoising algorithm has reached <inline-formula> <tex-math notation="LaTeX">${35.33}{dB}$ </tex-math></inline-formula>, meets the basic needs of practical vision and follow-up research.

ИССЛЕДОВАНИЕ РАЗРЕШАЮЩЕЙ СПОСОБНОСТИ ЦИФРОВЫХ ИЗОБРАЖЕНИЙ В РАЗЛИЧНЫХ ДИАПАЗОНАХ СПЕКТРА

This paper presents the results of the study of image resolution changes depending on the radiation wavelength. As a test object for photographing, images of radial mires were used. The shooting was carried out with two digital cameras with fundamentally different matrices, which are used on un-manned aerial vehicles (UAVs). The photography was carried out with preset shutter speeds, which allowed us to determine the change in spectral resolution from the amount of light which is incident on the matrices. As a result of the performed study, the influence of the spectral composition of the light incident on the matrix on the image resolution of was evaluated. The resolution change in the resulting image, from the spectral composition of the light incident on the matrix during exposure, is of practical scientific interest when performing digital UAV photography in order to calculate the vegetation indi-ces of natural and man-made landscapes.

Mapping of Subtidal and Intertidal Seagrass Meadows via Application of the Feature Pyramid Network to Unmanned Aerial Vehicle Orthophotos

Seagrass meadows are one of the blue carbon ecosystems that continue to decline worldwide. Frequent mapping is essential to monitor seagrass meadows for understanding change processes including seasonal variations and influences of meteorological and oceanic events such as typhoons and cyclones. Such mapping approaches may also enhance seagrass blue carbon strategy and management practices. Although unmanned aerial vehicle (UAV) aerial photography has been widely conducted for this purpose, there have been challenges in mapping accuracy, efficiency, and applicability to subtidal water meadows. In this study, a novel method was developed for mapping subtidal and intertidal seagrass meadows to overcome such challenges. Ground truth seagrass orthophotos in four seasons were created from the Futtsu tidal flat of Tokyo Bay, Japan, using vertical and oblique UAV photography. The feature pyramid network (FPN) was first applied for automated seagrass classification by adjusting the spatial resolution and normalization parameters and by considering the combinations of seasonal input data sets. The FPN classification results ensured high performance with the validation metrics of 0.957 overall accuracy (OA), 0.895 precision, 0.942 recall, 0.918 F1-score, and 0.848 IoU, which outperformed the conventional U-Net results. The FPN classification results highlighted seasonal variations in seagrass meadows, exhibiting an extension from winter to summer and demonstrating a decline from summer to autumn. Recovery of the meadows was also detected after the occurrence of Typhoon No. 19 in October 2019, a phenomenon which mainly happened before summer 2020.

Crown Structure Explains the Discrepancy in Leaf Phenology Metrics Derived from Ground- and UAV-Based Observations in a Japanese Cool Temperate Deciduous Forest

Unmanned aerial vehicles (UAV) provide a new platform for monitoring crown-level leaf phenology due to the ability to cover a vast area while offering branch-level image resolution. However, below-crown vegetation, e.g., understory vegetation, subcanopy trees, and the branches of neighboring trees, along with the multi-layered structure of the target crown may significantly reduce the accuracy of UAV-based estimates of crown leaf phenology. To test this hypothesis, we compared UAV-derived crown leaf phenology results against those based on ground observations at the individual tree scale for 19 deciduous broad-leaved species (55 individuals in total) characterized by different crown structures. The mean crown-level green chromatic coordinate derived from UAV images poorly explained inter- and intra-species variations in spring leaf phenology, most probably due to the consistently early leaf emergence in the below-crown vegetation. The start dates for leaf expansion and end dates for leaf falling could be estimated with an accuracy of &lt;1-week when the influence of below-crown vegetation was removed from the UAV images through visual interpretation. However, a large discrepancy between the phenological metrics derived from UAV images and ground observations was still found for the end date of leaf expansion (EOE) and start date of leaf falling (SOF). Bayesian modeling revealed that the discrepancy for EOE increased as crown length and volume increased. The crown structure was not found to contribute to the discrepancy in SOF value. Our study provides evidence that crown structure is a pivotal factor to consider when using UAV photography to reliably estimate crown leaf phenology at the individual tree-scale.

Is it safer at the beach? Spatial and temporal analyses of beachgoer behaviors during the COVID-19 pandemic

Tourism localities worldwide continue to grapple with how best to sustain coastal visitation during the COVID-19 pandemic. Emerging epidemiological science illustrates the risk of disease transmission is lower outdoors than indoors, and exposure is likely lower in outdoor, coastal environments due to dispersion and dilution of respiratory droplets through regular air flow. That said, it remains unclear how beachgoer behavior affects the likelihood of disease transmission. During summer 2020, we analyzed publicly-available beachcam video data and collected unmanned aerial vehicle (UAV) imagery at the recreational beach oceanfront in Virginia Beach, U.S.A. Data were collected over 24 days, documenting tourists' and recreationists’ behaviors related to the public health guidance from the U.S. Centers for Disease Control, Commonwealth of Virginia Department of Public Health and City of Virginia Beach. Specifically, using a sample test area of beach and adjoining boardwalk, we investigated diurnal patterns of beach and boardwalk use, the location and density of use, as well as the presence of face coverings (i.e., masks) on boardwalk users. Results from beachcam analyses indicate a curvilinear trend in beach use, peaking in the mid-afternoon, while boardwalk use remained consistent throughout the day. Beachcam observations were corroborated by UAV photography and spatial analysis, indicating concentrated use of the beach adjoining shoreline above high tide, with onethird of the landward adjacent upper beach vacant. Among boardwalk pedestrians, few (8.7%) were observed wearing facial coverings. These findings point to both indirect and direct strategies coastal managers can implement to communicate when, where, and how to reduce the potential for transmission while accessing beach amenities during the COVID-19 pandemic.

Effects of Differences in Structure from Motion Software on Image Processing of Unmanned Aerial Vehicle Photography and Estimation of Crown Area and Tree Height in Forests

This study examines the effects of differences in structure from motion (SfM) software on image processing of aerial images by unmanned aerial vehicles (UAV) and the resulting estimations of tree height and tree crown area. There were 20 flight conditions for the UAV aerial images, which were a combination of five conditions for flight altitude, two conditions for overlap, and two conditions for side overlap. Images were then processed using three SfM programs (Terra Mapper, PhotoScan, and Pix4Dmapper). The tree height and tree crown area were determined, and the SfM programs were compared based on the estimations. The number of densified point clouds for PhotoScan (160 × 105 to 50 × 105) was large compared to the two other two SfM programs. The estimated values of crown area and tree height by each SfM were compared via Bonferroni multiple comparisons (statistical significance level set at p &lt; 0.05). The estimated values of canopy area showed statistically significant differences (p &lt; 0.05) in 14 flight conditions for Terra Mapper and PhotoScan, 16 flight conditions for Terra Mapper and Pix4Dmapper, and 11 flight conditions for PhotoScan and Pix4Dmappers. In addition, the estimated values of tree height showed statistically significant differences (p &lt; 0.05) in 15 flight conditions for Terra Mapper and PhotoScan, 19 flight conditions for Terra Mapper and Pix4Dmapper, and 20 flight conditions for PhotoScan and Pix4Dmapper. The statistically significant difference (p &lt; 0.05) between the estimated value and measured value of each SfM was confirmed under 18 conditions for Terra Mapper, 20 conditions for PhotoScan, and 13 conditions for Pix4D. Moreover, the RMSE and rRMSE values of the estimated tree height were 5–6 m and 20–28%, respectively. Although the estimation accuracy of any SfM was low, the estimated tree height by Pix4D in many flight conditions had smaller RMSE values than the other software. As statistically significant differences were found between the SfMs in many flight conditions, we conclude that there were differences in the estimates of crown area and tree height depending on the SfM used. In addition, Pix4Dmapper is suitable for estimating forest information, such as tree height, and PhotoScan is suitable for detailed monitoring of disaster areas.

City image perception of Xi&amp;#x02019;an based on unmanned aerial vehicle photography photos

City image perception of Xi&amp;#x02019;an based on unmanned aerial vehicle photography photos

Structural Analysis of a Fault-related Anticline in the Southwestern Gyeonggi Massif, Korea Using an Unmanned Aerial Vehicle and Field Surveys: The Role of Rejoining Splays in a Duplex-like Structure

Kim, I.; Park, S.-I., and Lee, H.-J., 2020. Structural analysis of a fault-related anticline in the southwestern Gyeonggi Massif, Korea using an unmanned aerial vehicle and field surveys: The role of rejoining splays in a duplex-like structure. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 261-270. Coconut Creek (Florida), ISSN 0749-0208.In fold and thrust belts, various contractional structures accommodate horizontal shortening and vertical thickening in response to orogenic events. In this study, it was conducted a structural analysis of the Gomseom anticline, exposed along the coast in a part of a Phanerozoic orogenic belt in the southwestern Gyeonggi Massif, on the southern Korean Peninsula. To combat difficulties related to accessing the outcrop under tidal effects, high-resolution unmanned aerial vehicle (UAV) images alongside field data was used. The UAV photography technique allowed easy macro and mesoscale structural mapping and down-plunge projection, thereby enabling the efficient interpretation of the geometric and kinematic features of the Gomseom anticline. In the core of the Gomseom anticline, fault-bounded sheets are vertically stacked and folded, giving rise to a unique structural geometry analogous to duplexes. This duplex-like structure is bounded by a submerged thrust at the base and tightly folded thrust imbrication at the top and defined by intervening shortcut rejoining splays branching from the latter. Taking the kinematics of the sheet-bounding faults and intra-sheet minor faults into account, it was suggested an evolutionary model of the Gomseom anticline in which repeated fault-related folding and sequential propagation of rejoining splays give rise to the duplex-like structure. In the rejoining splay model, displacement on the basal thrust is transferred to a passive-roof thrust without hard links between them. Our result can be evaluated as a possible alternative duplex model that contrasts pre-existing models such as “Boyer-type”, “connecting splay”, and “fold” duplexes. However, further testing of the model is required.