Aerial Surveys Research Articles

MUN-FRL: A Visual-Inertial-LiDAR Dataset for Aerial Autonomous Navigation and Mapping

This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset captured using a multi-sensor payload to promote the global navigation satellite system (GNSS)-denied navigation research. The dataset features flight distances ranging from 300 m to 5 km, collected using a DJI-M600 hexacopter drone and the National Research Council (NRC) Bell412 Advanced Systems Research Aircraft (ASRA). The dataset consists of hardware-synchronized monocular images, inertial measurement unit (IMU) measurements, 3D light detection and ranging (LiDAR) point-clouds, and high-precision real-time kinematic (RTK)-GNSS based ground truth. Nine data sequences were collected as robot operating system (ROS) bags over 100 mins of outdoor environment footage ranging from urban areas, highways, airports, hillsides, prairies, and waterfronts. The dataset was collected to facilitate the development of visual-inertial-LiDAR odometry and mapping algorithms, visual-inertial navigation algorithms, object detection, segmentation, and landing zone detection algorithms based on real-world drone and full-scale helicopter data. All the data sequences contain raw sensor measurements, hardware timestamps, and spatio-temporally aligned ground truth. The intrinsic and extrinsic calibrations of the sensors are also provided, along with raw calibration datasets. A performance summary of state-of-the-art methods applied on the data sequences is also provided.

Automated Camera Pose Generation for High-Resolution 3D Reconstruction of Bridges by Unmanned Aerial Vehicles

This work explores the possibility of automating the aerial survey of bridges to generate high-resolution images necessary for digital damage inspection. High-quality unmanned aerial vehicle (UAV) based 3D reconstruction of bridges is an important step towards autonomous infrastructure inspection. However, the calculation of optimal camera poses remains challenging due to the complex structure of bridges and is therefore often conducted manually. This process is time-consuming and can lead to quality losses. Research in this field to automate this process is yet sparse and often requires high informative models of the bridge as the base for calculations, which are not given widely. Therefore, this paper proposes an automated camera pose calculation method solely based on an easily accessible polygon mesh of the bridge. For safe operation, point cloud data of the environment are used for automated ground detection and obstacle avoidance including vegetation. First, an initial set of camera poses is generated based on a voxelized mesh created in respect to the quality requirements for 3D reconstruction using defined camera specification. Thereafter, camera poses not fulfilling safety distances are removed and specific camera poses are added to increase local coverage quality. Evaluations of three bridges show that for diverse bridge types, near-complete coverage was achieved. Due to the low computational effort of the voxel approach, the runtime was kept to a minimum, even for large bridges. The subsequent algorithm is able to find alternative camera poses even in areas where the optimal pose could not be placed due to obstacles.

Using camera traps and N‐mixture models to estimate population abundance: Model selection really matters

Abstract Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the sampling and statistical methods, sampling and ecological variation, and sample size. We used images of moose (Alces americanus) from camera traps to parameterize N‐mixture models and tested the effect of ecological conditions, the spatial scale of measurement, and the criteria used to define independent detections on estimates of population abundance. We compared the model estimates to those generated empirically with aerial survey data, the standard method for many species of ungulate. We explored the sensitivity of estimates to model choice based on the common statistical criterion of parsimony. The two most parsimonious N‐mixture models (i.e. AICc) were considerably biased, producing implausibly large and considerably imprecise estimates of abundance. Most of the other models produced estimates of moose abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N‐mixture models was not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggested that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N‐mixture model. We recommend using a suite of models to generate predictions of abundance instead of the single top‐ranked model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps provided a broader set of insights into the behaviour of moose and the co‐occurrence of competitors and predators.

Remote Sensing and GIS in Air Pollution Mitigation: A Bibliometric Review of Chhattisgarh, India

Remote sensing and GIS serve as indispensable tools in the ongoing battle against air pollution and in the quest to improve air quality. These technologies, including satellite imagery and aerial mapping, provide a wealth of data pertaining to atmospheric conditions and the presence of pollutants. Through the integration of this data within Geographic Information Systems (GIS) software, researchers and policymakers gain valuable insights into the distribution and concentration of pollutants such as sulphur dioxide, nitrogen oxides, and particulate matter, all of which have significant implications for human health and the environment. The utilization of remote sensing allows for the monitoring of pollutant movement over large geographical areas, enabling the identification of pollution sources and the assessment of their impact on surrounding regions. GIS, on the other hand, facilitates the visualization of this data through the creation of maps and spatial analyses, offering a comprehensive understanding of air pollution patterns and their correlations with demographic characteristics and land use practices. By leveraging these technologies, decision-makers can pinpoint areas with heightened pollution levels, such as urban centers or regions with extensive industrial activity. Armed with this information, targeted mitigation strategies can be devised and implemented to alleviate pollution burdens and safeguard public health. Furthermore, the integrated approach provided by remote sensing and GIS enables the monitoring of the effectiveness of these strategies over time, allowing for adjustments and refinements as needed. In the context of Chhattisgarh, India, where air pollution poses a significant challenge, the application of remote sensing and GIS holds particular promise. By highlighting the advancements and innovations in these fields within the state, we aim to underscore the potential for informed decision-making and proactive measures to combat air pollution and enhance air quality for the benefit of all residents. In this review paper we aim to highlight the advancements in this field that has occurred in controlling air pollution in the state of Chhattisgarh, India.

Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production

Unmanned aerial vehicles (UAVs) have been employed to perform aerial surveys in many industries owing to their versatility, relatively low cost, and efficiency. Ground control points (GCPs) are used for georeferencing to ensure orthophoto geolocation/positioning accuracy. In this study, we investigate the impact of the number and distribution of GCPs on the accuracy of orthophoto production based on images acquired by UAVs. A test site was selected based on regulatory requirements, and several scenarios were developed considering the specifications of the UAVs used in this study. The locations of GCPs were varied to obtain the results. Based on the results obtained for different numbers of GCPs per unit area and distribution of GCPs, it is shown that UAV-based platforms can be more extensively utilized in a range of applications. The findings of this study will significantly impact the development process of GCP automation algorithms and enable a more cost-effective approach when determining target sites for UAV-based orthophoto production.

Harbour porpoise (Phocoena phocoena) in the Wadden Sea World Heritage Site and requirements for trilateral monitoring

The harbour porpoise (Phocoena phocoena) is considered part of the ‘Outstanding Universal Value’ characterising the Wadden Sea World Heritage Site (WS WHS). The Trilateral Wadden Sea Plan aims to preserve the conservation status of the Trilateral Wadden Sea Cooperation Area, encompassing the WS WHS. The plan has specified two conservation targets for the harbour porpoise: (1) viable stocks and a natural reproduction capacity and (2) conservation of habitat quality for its conservation. To assess the current occurrence of the harbour porpoise in the Wadden Sea area, we collated and analysed data from regional and national research projects using telemetry, aerial surveys, strandings and passive acoustic monitoring, obtained over the years 1990–2020. The results illustrate that porpoises occur in both offshore and intertidal waters, showing seasonal movements and changes in local occurrence over time. Some porpoises displayed limited home ranges throughout the year, suggesting a possible residency for some of the animals using the Wadden Sea area. We also showed that methods, frequency and spatial coverage of monitoring activities vary among the countries Denmark, Germany and the Netherlands. We discuss the suitability of the different methods both regarding the challenges of monitoring in the complex Wadden Sea habitat as well as their ability to target the conservation aims of the WHS. We give several recommendations to assess the status of the species to meet the identified conservation aims.

Aerial, Surface, and Subsurface Multimodal Mapping in Coastal Peru

ABSTRACTThis article describes a series of steps to integrate multiple modes of archaeological mapping in arid and agricultural settings. We use the coastal region of Peru as a case study and share our recent field experience at Cerro San Isidro, a multicomponent hill site located in the agriculture-intensive and mid-elevation (about 500 m asl) Moro region of the Nepeña Valley. In June and July 2022, we spent eight weeks deploying a combination of drone aerial imagery, pedestrian GPS reconnaissance, and GPR survey to map the surface and subsurface features at the site and in the adjacent agricultural fields. Our efforts suggest that the ancient settlement extended over an area of at least 50 ha, well beyond the visible surface architecture. Using a multimodal approach to confirming the partial destruction of archaeological vestiges by modern agricultural encroachment is both time-effective and noninvasive. The article offers insights from our experience, including the sequence of field operations, technical troubleshooting, and the collection and integration of datasets. We discuss the methodological potential and implications of this combination of multimodal mapping and its deployment in coastal Peru, a region that, like many others in the world, is increasingly subject to rapid agricultural expansion and other anthropogenic developments.

Aerial Mapping of the Peruvian Chira Valley Banana Production System to Monitor the Expansion of Fusarium Wilt Caused by Tropical Race 4

The recent incursion of Fusarium wilt of banana (FWB) caused by TR4 threatens banana production in the Chira Valley of Peru. To develop a management strategy, we mapped the entire production area from the air. During 12 flights at an altitude of 610 m (2,000 ft), we gathered 133,700 images in a timeframe of 2 weeks and constructed an orthomosaic map of 73,000 ha. This unveiled the complex logistic network across the banana-producing region, comprising 150 km of primary roads, as well as numerous secondary and tertiary unpaved paths through and bordering banana plantations. Moreover, the Poechos reservoir—with a total length of 450 km—feeds the entire Chira Valley irrigation system, which could further exacerbate TR4 expansion. Using georeferenced landscape details (digital terrain model), we determined areas prone to flooding, which is also relevant for disseminating TR4. Analyses of the maps resulted in the identification of many suspect areas for direct sampling and subsequent analyses to study the expansion of TR4. At four locations, we confirmed two cases, and by June 8, 2023, a total of 207 cases were reported. We conclude that TR4 is a serious threat to the entire region, which exports approximately 25% of the global organic bananas. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning

Despite significant progress has been made towards crop yield prediction with remote sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging frequencies on yield prediction, (2) transferability of the models among different genotypes and test sites, and (3) translation of these research developments to crop breeding that benefit farmers. Existing research predominantly provides an on-site perspective, frequently missing the complexities of real-world applications. The objectives of this paper are to investigate the transferability and generalization capabilities of yield prediction models for crop breeding across test sites located in North and South Americas. Toward that goal, we tested different machine learning techniques including Random Forest Regressor (RF), Gradient Boosting Regression (GB), and Deep Neural Networks (DNN) for soybean yield prediction with experiments conducted in different climate and growth conditions. A novel transfer learning approach was proposed for genotype selection and categorizing soybean yield for screening high-yield varieties. Furthermore, we studied the effect of temporal resolution on yield prediction, focusing on the critical development stages and optimal aerial survey frequencies for precise yield prediction using large 31,404 sample data. Results demonstrated that the combined dataset of Argentina and United States representing different climate regimes provided the highest performance with an R2 of 0.76 using RF and GB algorithms. The classification approach was proven to be most useful for crop breeding as demonstrated by accurately identifying the high-yielding genotypes. Increasing temporal sampling of key phenological stages significantly improved yield prediction. Although transfer learning yielded promising outcomes across trials within Argentina the efficacy of transferring models from Argentina to the United States was limited, attributed to significant seasonal and climate variations. This study pioneered the use of transfer learning for model adaptability in real-world breeding scenarios, training and transferring models within the South and North Americas, providing actionable insights and strategies for the breeding community, aiming to facilitate improved decision-making for agricultural productivity.

The current status of the Lena-Olenek population of wild reindeer (<i>Rangifer tarandus</i> L., 1758, Cervidae, Artiodactyla)

The rational use of wild reindeer (Rangifer tarandus L., 1758) based on reliable data on the number, distribution, age-sex composition, and herd size of the population is a challenging problem for hunting management. The purpose of this study was to characterize the current state of the Lena-Olenek tundra wild reindeer (LOWR) population. The counting of LOWR was based on the aerial census conducted from July 10 to 12, 2018; coordinate data from the GLONASS/Argos satellite tracker collars; stock materials from previous aerial surveys in the tundra of Yakutia. From the data obtained, the livestock of the LOWR was estimated to be 83,260. We specified that deer are housed in three distinct groups: broodstock, mixed, and male. The main livestock in the LOWR were females (44.2 %), calves (22.4 %), and juvenile animals 1–2 years old (17.5 %). Over 32 years, there was a decrease in 62.2% in the number of adult males and an increase in the number of fingerlings and juvenile animals. Since the middle of the last century, there has been a gradual increase in the number of LOWR from 21,000 to 95,000 deer in 2009 and a slight decrease to 83,200 deer in the last decade due to the exclusion of Taimyr reindeer migrants. The results of this study can be used to rationalize the use of wild reindeer resources and the allocation of quotas for production. Further census work will make it possible to monitor the state of the population over time.

The viability of seagrass ecosystems for supporting dugong recovery in Kenya

Seagrasses are the primary source of food for dugongs and a good indicator of marine ecosys- tem health. The East African dugong (Dugong dugon) population is listed as critically endan- gered under the IUCN Red List. This study aimed to document the status of seagrass beds and evaluate their potential for supporting dugong recovery in Kenya. A cross-sectional survey was conducted in December 2016 to March 2017, with data gathered through desktop reviews, inter- views, beach surveys and aerial surveys. Seven seagrass species were found at sampled sites, namely Syringodium isoetifolium, Thalassodendron ciliatum, Halophila ovalis, Zostera capensis, Thal- assia hemprichii, Cymodocea serrulata, and Halodule uninervis. Halodule and Halophila seagrass spe- cies are important in the diet of dugongs. Two dugongs were sighted during the aerial survey. The spread of sea urchins, unplanned infrastructure development, Illegal, Unreported and Unregulated fishing, and boat anchors negatively affected seagrass ecosystems and hence dugong distribution in Kenya.

UAV-RGB-image-based aboveground biomass equation for planted forest in semi-arid Inner Mongolia, China

The acquisition of high-resolution aboveground biomass (AGB) data cost-effectively and expeditiously represents a formidable challenge within the domain of current ecosystem surveillance. Plot-based inventory, the conventional approach for estimating and validating remote sensing data, is nonetheless costly and constrained in terms of spatial coverage. The expeditious advancements in unmanned-aerial-vehicle (UAV) technology furnish the potential to devise AGB equations that transcend traditional diameter-height-based equations alongside techniques for quantifying forest structural parameters through standard RGB aerial imagery. Since the canopy diameter (CD) and tree height (H) can be directly ascertained from UAV-derived datasets, biomass equations parameterized by CD and H may be more valuable. In the present investigation, we established AGB equations predicated on data procured from a UAV outfitted with a high-resolution RGB camera, specifically for planted sparsely Pinus sylvestris forest in central Inner Mongolia, China. Utilizing the aerial imagery, we generated the digital terrain model (DTM), digital surface model (DSM) and the digital orthophoto image (DOM). Then, the canopy height model (CHM) was obtained by subtracting DSM from DTM to extract the H and CD of individual trees. This methodology's CD (R2 = 0.85, RMSE = 0.203 m) and H (R2 = 0.77 and RMSE = 0.671 m) obtained closely mirrored the in-situ measurements. Six prospective AGB equations were constructed for the Pinus sylvestris forest, taking H and CD extracted from UAV aerial survey datasets as the dependent variables. The accuracy of the AGB estimation was appraised by employing extant allometric growth equations, which were parameterized using the ground-measured tree diameter at breast height (DBH) and H. The most efficacious biomass equation, predicated on H and CD data extracted from UAV aerial surveys, was delineated as W=2.3442CD∗H0.9057(R2 = 0.731, RMSE = 2.46 kg), thus presenting a convenient tool for estimating the AGB of sparse Pinus sylvestris forests in semi-arid locales.

Computing bias and variance for Pacific sardine (Sardinops sagax) biomass estimated from aerial surveys in California nearshore waters

Pacific sardine (Sardinops sagax) stocks off the U.S. Pacific coast undergo boom and bust cycles modulated by decadal changes in oceanic and atmospheric conditions. During periods of low abundance, Pacific sardine may contract into nearshore waters, making it more challenging to be surveyed by research vessels that cannot operate in the shallowest habitats, 0 to 40.00 m. In response, a new transect-based design and methods were developed to compute bias and variance of biomass estimated from a collaborative aerial survey implemented in nearshore waters. From experimental aerial surveys conducted in 2010 and from 2018 to 2020, two professional aerial fish spotters underestimated school biomass by 10.5% and 12.6%, whereas most of the variability of aerial biomass was due to within-transect variance, with no significant contribution from the among-transect variance. Implementation of the new transect-based design to aerial surveys from 2020 to 2023 in southern and northern California showed that a substantial biomass of Pacific sardine occurred in nearshore waters, representing 69.9% of total biomass estimated from acoustic surveys in spring 2021, and 36.5% in summer 2021. Seasonal biomass estimates were variable within each region, affected by school dynamics during daytime in nearshore waters and the timing of aerial surveys as determined by weather and environmental conditions. Given that nearshore aerial biomass comprised a measurable proportion of total biomass estimates for Pacific sardine during the recent period of low abundance, it has been used to inform acoustic survey catchability, which is no longer assumed to be 1.0 in recent Pacific sardine stock assessments.

Evaluation of drone surveys for ungulates in southwestern rangelands

AbstractDrone platforms are increasingly used for aerial wildlife surveys, but the validity of population counts has not been fully evaluated in all environments. Aerial surveys generally undercount the true population size and one must estimate detection probability (p) to correct for missed individuals. Detection probability for visual observers is influenced by vegetation and terrain characteristics, but the use of thermal cameras as the observer may also introduce additional factors influencing detection probability. We conducted diurnal, thermal‐based drone surveys for ungulates during February–April 2020 in South Texas, USA, on sites with varying degrees of woody cover and terrain. We examined histograms of georeferenced perpendicular distances to determine the effect of habitat on detection probabilities. We also examined precision in population estimates and variation in repeated surveys. Finally, we compared drone population estimates to independent estimates derived from helicopter, spotlight, and trail‐camera surveys. Distributions of perpendicular distances from the transect were affected by habitat characteristics. A flat grassland site had relatively few detections near the transect because thermal (solar) reflectance in the center of the footage obscured detections. A hilly brushy site had a uniform p (1.00) but resulted in a severe undercount due to the inability of the drone to follow contours of hills. The flat brushy site had a lower p (0.63), indicating visibility bias. Two additional flat, brushy sites were surveyed repeatedly (n = 5 and 9 surveys, respectively). All combinations of up to 3 repeated surveys were pooled to meet the recommended minimum number of detections for distance sampling analyses (≥60 detections). Population estimates had acceptable precision (CV ≤ 20%) 80.7% of the time and variation among repeated surveys was acceptable (CV = 9% and 23%, respectively). Drone‐based population estimates were comparable with estimates generated from corrected helicopter, spotlight, and trail‐camera surveys. Overall, diurnal drone surveys can generate population estimates for large ungulates on southwestern rangelands after accounting for visibility bias, but may be limited by terrain and thermal conditions.

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Environmental, behavioral, and design-related factors affect accuracy and precision of beluga abundance estimates from aerial surveys

Abundance estimation of wildlife populations is frequently derived from systematic survey data. Accuracy and precision of estimates, however, depend on the number of replicate surveys, and on adjustments made for animals unavailable to (availability bias), or available but undetected (perception bias) by observers. This study offers a comprehensive analysis of the relative influence of methodological, environmental and behavioral factors on availability bias estimates from photographic and visual aerial surveys of a small cetacean with a highly clumped distribution, the beluga (Delphinapterus leucas). It also estimates the effect of the number of surveys on accuracy and precision of abundance estimates, using 28 replicate visual surveys flown within a 16—29 day window depending on survey year. Availability bias was estimated using detailed dive data from 27 beluga from the St. Lawrence Estuary, Canada, and applied to systematic visual and photographic aerial surveys of this population, flown using various survey platforms. Dive and surface interval durations varied among individuals, and averaged (weighted) 176.6 s (weighted s.e. = 12.6 s) and 51.6 s (weighted s.e. = 4.5 s), respectively. Dive time and instantaneous availability, but not surface time, were affected by local turbidity, seafloor depth, whale behavior (i.e., whether beluga were likely in transit or not), and latent processes that were habitat-specific. Overall, adjustments of availability for these effects remained minor compared to effects from survey design (photographic or visual) and type of platform, and observer search patterns. For instance, mean availability varied from 0.33—0.38 among photographic surveys depending on sightings distribution across the study area, but exceeded 0.40 for all visual surveys. Availability also varied considerably depending on whether observers searched within 0-90° (0.42—0.60) or 170° (0.70—0.80). Simulation-based power analysis indicates a large benefit associated with conducting more than 1 or 2 survey reps, but a declining benefit of conducting > 5—10 survey reps. An increase in sample size from 2, to 5, and 10 reps decreased the CV from 30, to 19 and 13%, respectively, and increased the probability of the abundance estimate being within 15% of true abundance from 0.42, to 0.59 and 0.69 in species like beluga.

The harbour porpoise Phocoena phocoena in the Belgian part of the North Sea: trends in abundance and distribution

The harbour porpoise Phocoena phocoena is currently common in the Belgian part of the North Sea (BPNS), after decades of virtual absence. This article describes the results of aerial surveys to assess its distribution and density in Belgian waters, in concert with the first results of tests with passive acoustic monitoring devices (Porpoise Detectors - PoDs), and a basic analysis of strandings. The strandings data over four decades clearly demonstrate an increase of harbour porpoises in the BPNS, with only few stranded animals between 1970 and 1997 (0 to 6 per year), increasing numbers between 1998 and 2004 (8 to 40 per year), and even higher numbers between 2005 and 2009 (62 to 94 per year). The combined results of aerial surveys, strandings monitoring and tests with passive acoustic monitoring reveal a seasonal pattern, with harbour porpoises being abundant from February to April, and more scarce from May to January. Average densities in 2008 and 2009, as estimated by aerial monitoring covering most of the BPNS (with the exclusion of the nearshore 5 km strip), ranged from 0.05 animals per km², or in total less than 200 animals in an area equivalent to the BPNS in August 2009, to 1.01 animals per km², or in total almost 3,700 animals in an area equivalent to the BPNS during April 2008. In the first quarter of the year porpoises occur throughout the BPNS, including territorial waters (12 mile zone), whereas they are restricted to more offshore and northerly waters later in the year. Erratic invasions in the BPNS however blur general seasonal spatio-temporal patterns, which complicates our understanding of spatial distribution and migration.

Smart Cities and The UAVs Applications. A Review Of The Ongoing Research in The UAVs Unit

Nowadays, drones are one of the most important and valuable sources for acquiring aerial imagery and data related to inspection, monitoring, mapping, smart city organization, disaster monitoring and prevention, and 3D modeling. Drones are a low-cost alternative to aerial and space photography because they provide users with images with high spatial resolution down to centimeters. These images enable the production of accurate 3D models such as Digital Surface and Terrain Models (DTM/DSM), contour lines, vector information, etc. This research provides a brief overview of the studies conducted in the Unmanned Aircraft Unit - Remote Sensing Center - University of Mosul. The programs, sequence of operations, and treatments that were followed on the drone images taken by these aircraft were reviewed to prepare them for use in remote sensing and aerial survey operations. This flows towards the use of this technology in the development of smart cities. Specialized GIS software was also used to analyze and process the aerial images that were taken using the DJI Phantom 4 aircraft to study several areas in the city of Mosul, where several sorties were conducted at different periods and times of time.

Quantifying spatiotemporal variation of nearshore forage fish schools with aerial surveys in Prince William Sound, Alaska

AbstractObjectiveChanges in abundance and distribution of schooling forage fish, such as the Pacific Sand Lance Ammodytes hexapterus and Pacific Herring Clupea pallasii, can be difficult to document using traditional boat‐based methods, especially in the shallow, nearshore habitats frequented by these species. In contrast, nearshore fish schools are easily observed and quantified from aircraft when light and sea conditions are favorable. We used aerial shoreline surveys to assess interannual variability in the distribution and abundance of schooling forage fish in Prince William Sound, Alaska, during the summers of 2010 and 2012–2022.MethodsDuring the surveys, aerial observers classified fish schools by their size, species, and (in some cases) age‐class. All observations were georeferenced along the flight path, converted to estimated surface area (m2) based on school diameter, and standardized by effort (shoreline kilometers surveyed).ResultPacific Herring were widely distributed, and school densities varied annually; there were several spikes in school density of up to 54.38 m2/km interspersed among years of lower average densities (7.73–25.57 m2/km). In contrast, Pacific Sand Lance were usually limited in their distribution to a few predictable locations. School density in these consistent areas varied across years, from a high of 50.98 m2/km in 2010 to a low of 0.15 m2/km in 2017. We validated 88 schools during aerial surveys conducted in 2014–2016 and 2019–2022, of which 76 (86%) were correctly identified to species.ConclusionHere, we provide indices of Pacific Herring and Pacific Sand Lance school density over time in shallow nearshore coastal areas of Prince William Sound, Alaska. These indices were generated from aerial surveys, which offer an effective alternative to boat‐based surveys for tracking forage fish schools when they occur in shallow and nearshore coastal habitats.

Uncovering extensive populations of the ‘threatened’ Black Crowned Cranes in Gambella, Ethiopia

A reliable assessment of the population size of threatened species is crucial for making informed decisions about their conservation and management. However, there is a lack of comprehensive knowledge on the population of the eastern Black Crowned Crane (Balearica pavonina ceciliae), which is globally recognized as a threatened taxon experiencing a declining population trend. Here, we investigated the density, abundance and factors that influence flock size in the wetlands of the Gambella region in Ethiopia. We used line transects to collect data during dry and wet seasons. Population density and abundance were estimated using DISTANCE software, whereas factors influencing crane flock size were evaluated using a generalized linear model. We found that crane density ranged from 444 to 965 birds per sq km in the dry season and from 62 to 105 individuals per sq km in the wet season. We estimated that the population size within the designated survey areas as 7634 during the dry season and 866 during the wet season. Furthermore, for the broader survey region that encompasses permanent wetlands, the estimated population ranged from 9138 to 19,849 individuals in the dry season and from 1272 to 2166 individuals in the wet season. We found that grazing intensity and proximity to woodlands and rivers had significant positive impacts on crane flock size, whereas an increase in wetland water depth and grass height remarkably reduced flock size. As a result, wetland habitat degradation-related problems are not currently a threat to cranes in this region, but are likely to increase in the future due to large-scale agricultural expansion. The crane population size we estimated in Gambella is high, three to four times higher than the estimate from the entirety of Ethiopia. Our research underscores the global significance of Gambella wetlands as an important habitat for cranes. However, to obtain an accurate population estimate in this region, it is imperative to include seasonal wetlands, savannah grasslands, farmlands and the extended wetlands of South Sudan, which are hydrologically connected to the wetlands of Gambella region. To achieve this, comprehensive surveys and estimations should incorporate various techniques, such as aerial surveys, satellite telemetry, and high-resolution satellite imagery.