Aerial Surveys Research Articles

Quantifying Abundance and Mapping Distribution of Loggerhead Turtles in the Mediterranean Sea Using Aerial Surveys: Implications for Conservation

In the Mediterranean, incidental captures in fishing gear contribute to the high mortality of loggerhead turtles (Caretta caretta). Understanding the effects of bycatch is complex and requires robust knowledge of baseline population parameters such as abundance and density, as well as an understanding of animals’ distribution in relation to commercial fishing efforts. Based on data collected during multi-species line transect aerial surveys conducted between 2009 and 2017, we present density and abundance estimates, corrected for availability bias, for a large sector of the central Mediterranean, discuss temporal and spatial patterns and provide Potential Biological Removal (PBR) values for the monitored areas. Sightings data were also used to evaluate the spatial and temporal usage areas. Strong latitudinal and longitudinal gradients in density, abundance and area usage emerged from the analysis, with turtles occurring in higher numbers in the deeper pelagic waters of the Tunisian Plateau, the Ionian Sea and the Gulf of Taranto, irrespective of the season. PBR values derived from this study are likely unsustainable. This paper investigates the implications of commercial fisheries for Mediterranean loggerhead turtles across an area rarely included in sea turtle monitoring and has the potential to be relevant towards informed management and conservation of this species and highlights the necessity of international collaborative efforts in the region.

Modeling the Colatina lineament using geophysical methods: Insights into the geology of ES, Brazil

The study of the primary dikes in a given region is essential for comprehending its geological evolution. This study focuses on the offshore region in the state of Espírito Santo (ES), which encompasses one of the major oil sedimentary basins in the country, namely the Espírito Santo Basin. The study area is considered structurally complex due to the presence of numerous structural features, and the present work focuses on the Colatina Lineament. Despite several decades of oil and rock prospecting studies, the lateral and depth boundaries of the lineament are still unknown. Therefore, this study aims to utilize geophysical methods, including magnetic.and gravity, obtained from aerial and terrestrial surveys, respectively, to characterize the Colatina Lineament structurally. The study generated thematic maps using highlight filters to investigate the structural importance of the zone of influence. Additionally, 2.5D forward modeling was performed from gravity data, revealing numerous geological structures generated by crustal movements. To visualize the geological encompassed by the Colatina Lineament, a 3D inversion of the magnetic data was performed. The outcomes facilitated the visualization and mapping of the dike bundle and confirmed the impact of tectonic processes in the region.

Developing innovative and cost-effective UAS-PPK module for generating high-accuracy digital surface model

Traditional indirect georeferencing requires time-consuming and labor-intensive field surveys to obtain ground control points (GCPs), making it challenging to apply in high-risk areas with limited accessibility. This study proposes a novel and low-cost system for direct georeferencing using unmanned aerial system post-processing kinematics (UAS-PPK), which is less than a quarter of the price of commercially available products. To evaluate the accuracy of the aerial surveys of the custom-built module and digital surface models, we used 15 checkpoints (CPs) and 99 validation points (VPs). The results showed that this UAS-PPK module could deliver high-precision aerial surveys with a root mean square error (RMSE) of less than 4 cm for three dimensions without using control points. After adding one GCP, the RMSE of three dimensions was close to that of traditional aerial survey methods using 12 GCPs, having a vertical accuracy of 2.51 cm. The same 99 VPs were used to evaluate the accuracy of the digital surface model produced using UAS-PPK. The results showed that the accuracy was close to that of traditional aerial survey methods, having an average error of less than 3 cm. We demonstrated the self-made attachable UAS-PPK module to be a reliable and accurate survey tool in geoscience applications.

A Snapshot into the Lives of Elephants: Camera Traps and Conservation in Etosha National Park, Namibia

Knowledge of elephant movement and grouping patterns in the wild is critical for their management and conservation. Much of these data come from GPS collar data and aerial surveys, which have provided invaluable information, but data from these methods are often limited to small groups or entire populations. Effective elephant management requires both generalized and localized methodologies. Here, we propose the expanded use of camera traps in research relating to elephant localized movements and grouping patterns as an additional tool for elephant conservation management. In this study, we use a battery-powered camera trap to provide daily high-resolution data of African savanna elephant (Loxodonta africana) grouping patterns over the course of an entire year. We present findings on the seasonal and diurnal grouping patterns of elephants at a waterhole in the northeast corner of Etosha National Park from July 2016 to June 2017. The frequency of elephant occurrences varied seasonally and diurnally across all group types (solitary male, male, family, and mixed groups), while group sizes did not vary seasonally, except for male groups. Solitary males occurred relatively equally throughout the day, while male and mixed groups occurred the most midday, and family groups occurred the most in the afternoon. Additionally, we measured the reliability of research assistants when collecting group type and group size data from the camera trap images. Intra- and inter-observer reliability was excellent among and across research assistants, highlighting the potential for non-specialist observers to have greater involvement in camera trap data collection. Our results support the use of camera trap data where GPS collars and aerial surveys are not feasible and where higher-resolution data are needed for more localized management. Finally, we discuss our experience with two different types of camera traps to highlight the pros and cons of each approach.

Surveying wildlife and livestock in Uganda with aerial cameras: Deep Learning reduces the workload of human interpretation by over 70%

As the need to accurately monitor key-species populations grows amid increasing pressures on global biodiversity, the counting of large mammals in savannas has traditionally relied on the Systematic-Reconnaissance-Flight (SRF) technique using light aircrafts and human observers. However, this method has limitations, including non-systematic human errors. In recent years, the Oblique-Camera-Count (OCC) approach developed in East Africa has utilized cameras to capture high-resolution imagery replicating aircraft observers’ oblique view. Whilst demonstrating that human observers have missed many animals, OCC relies on labor-intensive human interpretation of thousands of images. This study explores the potential of Deep Learning (DL) to reduce the interpretation workload associated with OCC surveys. Using oblique aerial imagery of 2.1 hectares footprint collected during an SRF-OCC survey of Queen Elizabeth Protected Area in Uganda, a DL model (HerdNet) was trained and evaluated to detect and count 12 wildlife and livestock mammal species. The model’s performance was assessed both at the animal instance-based and image-based levels, achieving accurate detection performance (F1 score of 85%) in positive images (i.e. containing animals) and reducing manual interpretation workload by 74% on a realistic dataset showing less than 10% of positive images. However, it struggled to differentiate visually related species and overestimated animal counts due to false positives generated by landscape items resembling animals. These challenges may be addressed through improved training and verification processes. The results highlight DL’s potential to semi-automate processing of aerial survey wildlife imagery, reducing manual interpretation burden. By incorporating DL models into existing counting standards, future surveys may increase sampling efforts, improve accuracy, and enhance aerial survey safety.

Slope visualisation and stability study using geographic information science

High-precision visualised three-dimensional (3D) slope modelling and stability analysis have benefited from the quick development of big data technology. This research aims to utilise unmanned aerial vehicle survey data to obtain influencing information on slope topography. A high-precision 3D surface model is established, which is then overlaid with 3D geological models and 3D mineral models constructed using geographic information system technology. This approach enables more efficient and accurate 3D visualisation modelling of slopes. Based on the 3D model, the search for the most dangerous sliding surface under rainfall conditions is completed. The average absolute error of sample points in the surface modelling effect verification experiment is less than 0.5 m, and the average relative error is less than 2%. These results indicate that the overall modelling effect of the 3D slope model can meet the requirements of subsequent stability analysis. According to the results of random simulation, the minimum safety factor on the most neutral surfaces is less than 1, which indicates that the slopes at some locations in this site do have the possibility of landslide. The neutral surface where the lowest safety factor appears can be used to pinpoint the position of the most hazardous sliding surface.

Utility of Spectral Filtering to Improve the Reliability of Marine Fauna Detections from Drone-Based Monitoring.

Monitoring marine fauna is essential for mitigating the effects of disturbances in the marine environment, as well as reducing the risk of negative interactions between humans and marine life. Drone-based aerial surveys have become popular for detecting and estimating the abundance of large marine fauna. However, sightability errors, which affect detection reliability, are still apparent. This study tested the utility of spectral filtering for improving the reliability of marine fauna detections from drone-based monitoring. A series of drone-based survey flights were conducted using three identical RGB (red-green-blue channel) cameras with treatments: (i) control (RGB), (ii) spectrally filtered with a narrow 'green' bandpass filter (transmission between 525 and 550 nm), and, (iii) spectrally filtered with a polarising filter. Video data from nine flights comprising dolphin groups were analysed using a machine learning approach, whereby ground-truth detections were manually created and compared to AI-generated detections. The results showed that spectral filtering decreased the reliability of detecting submerged fauna compared to standard unfiltered RGB cameras. Although the majority of visible contrast between a submerged marine animal and surrounding seawater (in our study, sites along coastal beaches in eastern Australia) is known to occur between 515-554 nm, isolating the colour input to an RGB sensor does not improve detection reliability due to a decrease in the signal to noise ratio, which affects the reliability of detections.

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges.

Harvest block aggregation as a driver of intensive moose browsing pressure on hardwood regeneration in a temperate forest

In many regions of the world, browsing pressure by ungulates has increased over the last century, often reaching unsustainable levels. Overbrowsing by ungulates is threatening biodiversity and causing delays in the establishment of forest regeneration. Moose (Alces alces) is considered as an ecological engineer, as it can cause severe impacts on stand structure, composition, quality, and succession through overbrowsing. In conifer-dominated stands, moose browsing pressure has been shown to vary with the distribution of forage and cover patches across the landscape, but such spatial relationships have not been explored in hardwood-dominated landscapes, where intensive forest management can create clustered patches of good-quality forage and cover, ideal conditions for wintering moose. We investigated the influence of local and landscape variables on browsing by North American moose (Alces alces americana) on commercial and non-commercial woody regeneration in an intensively managed forest landscape of northwestern New Brunswick, Canada. We predicted that browsing pressure at the local scale (vegetation sampling plot) would increase with moose winter counts in the surrounding landscape (1 to 3 km radius). We modeled moose counts as a function of environmental features and road density, using a quasi-Poisson regression. According to an aerial survey (25 km2 sampling plots), moose counts increased with the proportion of forage patches (disturbed stands < 20 years old), whereas they decreased with the proportion of mixed mature stands > 80 years old, which offer both forage and cover. We extrapolated this model to a 2770 km2 study area and used predicted counts as an independent variable to assess its influence on browsing pressure. In 720 plots of 4.15 m2, browsing pressure was quantified using occurrence (i.e. proportion of seedlings/saplings with at least one twig browsed during the previous winter) and intensity (i.e. mean proportion of twigs browsed per stem) on commercial and non-commercial tree seedlings and saplings. We explained variation in occurrence and intensity using zero-inflated models with snow precipitation and local vegetation variables as covariates. Browsing pressure increased with predicted moose counts within a 3 km radius, with snow precipitation and with the proportion of coniferous trees in the canopy, whereas it decreased with sapling density. These results highlight the importance of seedlings and saplings in moose winter diet and confirm the attraction of moose toward recent (<20-year-old) harvest blocks during winter. Therefore, we should avoid aggregating harvest blocks to decrease their attractiveness to moose and browsing pressure on natural hardwood regeneration.

Abundance and distribution of the Caspian seals on ice in the Russian part of the Northern Caspian in February — March 2023

The aim of this research was to assess the current abundance of the Caspian seal population on ice in the Russian part of the North Caspian using instrumental methods of aerial survey in the infrared and optical ranges using a manned aircraft and UAV.The main method of the research was a multispectral aerial survey, the essence of which is to survey seal haulouts synchronously in the infrared and optical ranges. In winter, 90% of all mature individuals gather on the ice substrate of the Northern Caspian for reproduction, lactation and mating and spend a significant part of their time outside the water, which greatly facilitates their detection and counting. The aerial survey was carried out in parallel from two types of aircraft — the La‑8 manned amphibious aircraft and unmanned aerial vehicles equipped with high-resolution cameras in optical and infrared modes, which ensure reliable registration of seals on ice.Novelty: the advantage of joint use of a manned aircraft and a UAV equipped with special photo and infrared equipment is noted.As a result of the research, the flights were carried out from February 20 to March 5, 2023 in the daytime in good visibility conditions at an altitude of 200 to 300 m. amounted to 19704 individuals, including 4062 puppies. The animals were located in the southeastern part of the ice cover near the air demarcation line between the Russian Federation and the Republic of Kazakhstan on the ice edge. An increase in the number of seals was recorded due to migrating individuals from Kazakhstan. Breeding of the Caspian seal in winter 2022/2023 proceeded in a satisfactory manner. During the flight, it was found that there were no fresh releases of seals on the coast of Dagestan. Separate old carcasses, partially torn to pieces, were registered only on the shores of the Agrakhan Peninsula.Practical value: the obtained data are needed to assess the current abundance of the Caspian seal population in the Caspian Sea.

Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

Bark beetle outbreaks can have serious consequences on forest ecosystem processes, biodiversity, forest structure and function, and economies. Thus, accurate and timely detection of bark beetle infestations in the early stage (known as green-attack detection) is crucial to mitigate the further impact, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that primarily rely on aerial surveys and field surveys, which can be impractical over vast areas. Existing approaches that exploit RS and ML/DL exhibit substantial diversity due to the wide range of factors involved. This article provides a comprehensive review of past and current advances in green-attack detection from three primary perspectives: bark beetle and host interactions, RS, and ML/DL. In contrast to prior efforts, this review encompasses all RS systems and emphasizes ML/DL methods to investigate their strengths and weaknesses. We parse existing literature based on multi- or hyperspectral analyses and distill their knowledge based on bark beetle species and attack phases with a primary emphasis on early stages of attacks, host trees, study regions, RS platforms and sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices, ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks and architectures. Although DL-based methods and the random forest algorithm showed promising results, highlighting their potential to detect subtle changes across visible, thermal, and short-wave infrared spectral regions, their effectiveness remains limited, and high uncertainties persist due to the subtle distinctions between healthy and attacked trees. To inspire novel solutions to these shortcomings, we delve into the principal challenges and opportunities from different perspectives, enabling a deeper understanding of the current state of research and guiding future research directions.

Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar

Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurements of these data allow for the observation of deforestation or degradation of existing forests, natural forest regeneration, and the implementation of sustainable agricultural practices like agroforestry. Assessments of tree canopy height and crown projected area at a high spatial resolution are also important for monitoring carbon fluxes and assessing tree-based land uses, since forest structures can be highly spatially heterogeneous, especially in agroforestry systems. Very high resolution satellite imagery (less than one meter (1 m) Ground Sample Distance) makes it possible to extract information at the tree level while allowing monitoring at a very large scale. This paper presents the first high-resolution canopy height map concurrently produced for multiple sub-national jurisdictions. Specifically, we produce very high resolution canopy height maps for the states of California and São Paulo, a significant improvement in resolution over the ten meter (10 m) resolution of previous Sentinel / GEDI based worldwide maps of canopy height. The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps. We also introduce a post-processing step using a convolutional network trained on GEDI observations. We evaluate the proposed maps with set-aside validation lidar data as well as by comparing with other remotely sensed maps and field-collected data, and find our model produces an average Mean Absolute Error (MAE) of 2.8 m and Mean Error (ME) of 0.6 m.

Assessment of Leica CityMapper-2 LiDAR Data within Milan’s Digital Twin Project

The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. The Leica CityMapper-2 hybrid sensor has been used for aerial surveys as it can capture precise and high-resolution multiple data (imagery and LiDAR). The surveying activities are completed, and quality checks are in progress. This paper concerns assessing aerial LiDAR data of a significant part of the metropolitan area, particularly evaluating the accuracy, precision, and congruency between strips and the point density estimation. The analysis has been conducted by exploiting a ground control network of GNSS and terrestrial LiDAR measurements created explicitly for this purpose. The vertical component has an accuracy root mean square error (RMSE) of around 5 cm, and a horizontal component of around 12 cm. Meanwhile, the precision RMSE ranges from 2 to 8 cm. These values are suitable for generating products such as DSM/DTM.

Movement dynamics influence population monitoring and adaptive harvest management strategies in migratory birds

Informed population monitoring efforts are essential for sound management of harvested species, and adaptive strategies that provide detailed information to monitoring efforts often require data inputs from complimentary sources. Movement ecology information is seldom directly incorporated into population monitoring or adaptive harvest management strategies, yet can provide valuable information on species distributions, emigration and immigration rates, and aid in determining optimal population monitoring timing. The Rocky Mountain Population (RMP) of Sandhill Cranes is a harvested population subject to a stringent adaptive harvest management framework and an annual aerial survey to estimate population abundance, but movements of Sandhill Cranes during survey windows, and subsequent changes to harvest quotas based on their movement and distribution have not been investigated. We used seven years of GPS tracking data to estimate state-specific emigration and immigration rates, using a Bayesian multi-state capture-recapture model, among states within the RMP distribution to understand how seasonal crane movements may influence optimal aerial survey timing. We then leveraged these transition probabilities in conjunction with aerial survey count data to model how changes in aerial survey timing and movement-informed crane distribution would influence the current RMP Sandhill Crane adaptive harvest management model resulting in estimated changes to harvest allocation among states based on Sandhill Crane movement. We found that Sandhill Crane emigration from northern states began to increase the week of the aerial survey in late September, and continued to increase as autumn migration progressed into October. As expected, immigration to southern states began as emigration from northern states increased. Importantly, little movement among states occurred prior to the current aerial survey design timing. Overall, we found that current survey timing and shortly thereafter (∼1 week) did not greatly influence estimates of Sandhill Crane distribution, and did not greatly influence the harvest reallocation to each state until mid to late October (range of −42–+52 tag allocation change), much later than the current survey design would allow. Using GPS locations, we found that optimal population monitoring efforts could be improved to account for both detection and seasonal movements, while minimally influencing current adaptive harvest management strategies to stakeholders. Linking movement ecology with population monitoring efforts and subsequently adaptive harvest management strategies yields insightful information that can be beneficial for conservation planning, decision-making, and optimal species management of a migratory bird.

The Palamas Archaeological Project. A preliminary report of the 2022 fieldwork conducted by the ongoing Greek–Swedish archaeological field programme in Palamas, region of Karditsa, Thessaly

This paper presents the preliminary results from the 2022 fieldwork of the Palamas Archaeological Project, an ongoing Greek–Swedish collaboration in the region of Karditsa, Thessaly. Working over the course of two separate field seasons, the project team conducted aerial, architectural, fieldwalking, and geophysical surveys at a number of sites within the survey area, including at the important multi-phase fortified settlements at Metamorfosi and Vlochos. Limited excavations were also conducted at the latter site, producing new evidence for the Hellenistic and Early Byzantine phases of the ancient city, including a probable cemetery. The work continues to add to the knowledge of the archaeology of the region, highlighting the long and dynamic history of human habitation in western Thessaly.

Computer vision‐assisted photogrammetry and one‐image 3D modeling in marine mammals

AbstractImage processing using traditional photogrammetric methods is a labor‐intensive process. The collection of photogrammetry images during aerial surveys is expanding rapidly, creating new challenges to analyze images promptly and efficiently, while reducing human error during processing. Computer vision‐assisted photogrammetry, a field of artificial intelligence (AI), can automate image processing, greatly enhancing the efficiency of photogrammetry. Here, we present a practical and efficient program capable of automatically extracting the fine‐scale photogrammetry of East Asian finless porpoises (Neophocaena asiaeorientalis sunameri). Our results indicated that computer vision‐assisted photogrammetry could achieve the same accuracy as traditional photogrammetry, and the results of the comparisons were validated against the direct measurements. Three‐dimensional (3D) models using computer vision‐assisted photogrammetric morphometrics generated trustworthy body volume estimates. We also explored the one image‐based 3D modeling technique, which is less accurate, but still useful when only one image of the animal is available. Although several limitations exist in the current program, improvements could be made to narrow the virtual‐reality gap when more images are available for machine learning and training. We recommend this program for analyzing images of marine mammals possessing a similar morphological contour.

Detection of Pine Wood Nematode Infestation Based on Improved YOLOv5

Pine wilt disease is a destructive forest disease caused by the parasitism of pine wood nematodes inside pine trees. Once infected, pine trees quickly wither and die due to the inability to drain water, hence it is also known as pine tree withering disease. It is mainly spread by pine sawyer beetles, with the characteristics of fast dissemination, rapid onset, and high mortality rate, making it a significant global plant disease. Failure to effectively control the pine wilt disease in a timely manner will result in the massive death of pine trees in a short period of time in forest areas. With the continuous development and popularization of artificial intelligence and UAV (Unmanned Aerial Vehicle) technology, combining various methods can timely detect diseased trees, and performing timely treatment and protection can greatly save the time and personnel costs of biological pest control. While it is also conducive to promoting the work of multiple related disciplines, such as bioengineering and greening engineering. In this paper, we first conducted a large number of aerial surveys on the forest area through UAV, collected relevant data, and preprocessed the obtained data, including data augmentation, cleaning, filtering, deduplication, formatting, etc., to ensure data quality and accuracy. Then, we labeled the data, and made certain improvements to the original YOLOv5. We added a new RRAM (Recurrent Residual Attention Module) to the original network model, which enables the network to timely focus on important information in redundant data, thus improving the network’s performance. Compared with the original YOLOv5, our network has a stronger performance.

Long-term spatiotemporal distribution, abundance, and priority areas for manatees and calves (Trichechus manatus Sirenia: Trichechidae) in Guatemala

Introduction: The Antillean manatee (Trichechus manatus manatus) is an endangered species found throughout the Caribbean, and the coastal waters of Central and northeastern South America. Their low numbers are the result of a variety of human-related pressures. A small population of manatees has been identified in Guatemala; however, their spatial and temporal dynamics remain unclear.&#x0D; Objective: To examine long-term trends in the spatiotemporal distribution and abundance of manatees in Guatemala. This included identification of priority areas for manatees including the presence of calves, assessing whether distribution areas are inside protected areas, and studying the relationship between manatee sightings and human activities.&#x0D; Methods: Nine years of standardized aerial surveys were conducted along the Atlantic coast (1992, 2005-2008, 2010-2011, 2014, 2022). Quantitative approaches to detect priority areas, specifically the Kernel density estimation and the Getis-Ord Gi* statistic, were used in the spatiotemporal analysis. A Spearman rank correlation analysis tested for significant correlations between human activities, coastline topographies, and manatee numbers along coastline segments. Manatee abundance across years, survey sections, and protected areas were also examined.&#x0D; Results: A total of 293 sightings and 518 manatees were observed including 476 adults (92 %) and 42 calves (8 %). Manatees were most frequently observed as solitary individuals (60 %). Most manatee (61 %) and calf (68 %) sightings occurred inside protected areas where several priority areas were identified. The two priority areas were Refugio de Vida Silvestre Bocas del Polochic (Bocas del Polochic) and Refugio de Vida Silvestre Punta de Manabique, which were identified as important manatee habitats in 1992. Bocas del Polochic had the highest manatee abundance of all protected areas (p &lt; 0.05). However, a shift in manatee distribution was recorded in 2014, although the cause is unclear. No annual significant differences in manatee abundance were found over time (p = 1.0), but significant differences in abundance were detected between survey sections and protected areas (p &lt; 0.05). Manatee numbers had positive significant correlations with ecological and human parameters. The highest correlation was between manatees and rivers (p &lt; 0.01), and the weakest correlation was between manatees, motorboats, and fishing nets (p &lt; 0.01).&#x0D; Conclusions: The results indicate that the local manatee population remained relatively stable for over 20 years, although changes in overall distribution were noted. It is unclear if the changes are temporary or permanent. As a sentinel species, manatee distribution shifts can be used as early warnings about the health of the environment and can depict current or potential impacts on individual- and population-level animal health.

Adaptive Acquisition and Visualization of Point Cloud Using Airborne LIDAR and Game Engine

The development of digital twin for smart city applications requires real-time monitoring and mapping of urban environments. This work develops a framework of real-time urban mapping using an airborne light detection and ranging (LIDAR) agent and game engine. In order to improve the accuracy and efficiency of data acquisition and utilization, the framework is focused on the following aspects: (1) an optimal navigation strategy using Deep Q-Network (DQN) reinforcement learning, (2) multi-streamed game engines employed in visualizing data of urban environment and training the deep-learning-enabled data acquisition platform, (3) dynamic mesh used to formulate and analyze the captured point-cloud, and (4) a quantitative error analysis for points generated with our experimental aerial mapping platform, and an accuracy analysis of post-processing. Experimental results show that the proposed DQN-enabled navigation strategy, rendering algorithm, and post-processing could enable a game engine to efficiently generate a highly accurate digital twin of an urban environment.

A wall or a road? A remote sensing-based investigation of fortifications on Rome's eastern frontier

AbstractDuring a pioneering aerial survey of the Near East in the 1920s, Father Antoine Poidebard recorded hundreds of fortified military buildings that traced the eastern frontier of the Roman Empire. Based on their distribution, Poidebard proposed that these forts represented a line of defence against incursions from the east. Utilising declassified images from the CORONA and HEXAGON spy satellite programmes, the authors report on the identification of a further 396 forts widely distributed across the northern Fertile Crescent. The addition of these forts questions Poidebard's defensive frontier thesis and suggests instead that the structures played a role in facilitating the movement of people and goods across the Syrian steppe.