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For monitoring hard-to-reach thermal facilities, remote surveillance techniques from unmanned aerial vehicles (UAVs) equipped with a thermal infrared camera represent a progressive platform for rapid information collection. This paper presents the first results of such an application of UAVs to study five thermal objects on the Kuril Islands. Four of them (Kipyashchaya Riwer on Iturup Island, Lake Biryuzovoye on Simushir Island, Yankich Island and Yurieva River on Paramushir Island) are associated with active volcanoes. Heat maps with precise geographical reference have been obtained for all sections of gas hydrothermal discharge. For Kipyashchaya River and Yankich Island verification of air thermometry by ground data was performed and real atmospheric attenuation coefficients were obtained for different flight altitudes under observed meteorological conditions. The first data on the parameters of the thermal plume from the hot Yurievа River flowing into the Sea of Okhotsk were obtained.

In Master Plan for Parks and Open Spaces (or Midori-no-Kihonkeikaku), municipalities delineate their future visions, goals, and strategies for the conservation and promotion of green areas. Regular surveys are typically conducted, especially in densely populated urban areas, to assess and monitor these green areas. However, the data collection approaches are not standardized and left to the discretion of each municipality, considering factors such as cost, effort, desired accuracy, and comparability with historical data. Consequently, the data published by various municipalities lack uniformity, complicating comparisons and a comprehensive understanding of the situation. Furthermore, the rapid advancement in image analysis technology has placed municipal survey methods in a transitional phase. This study targeted 20 special status cities, called “ordinance designated cities” in Japan, to ascertain the current state of vegetation cover surveys method in each city. Interviews were conducted with ordinance designated city from 30th May to 2nd June. As of 2024, eight ordinance designated cities utilized aerial photography and nine used satellite imagery to measure vegetation cover, indicating a nearly equal distribution. Additionally, three cities did not measure vegetation cover. Most cities had not altered their survey methods, but those that did primarily cited cost as the driving factor, with many transitioning from aerial photography to satellite imagery. This shift raised concerns about the consistency of past and future data, underscoring the need to develop methods for integrating data with varying resolutions and coverage areas.

Unmanned Aerial Vehicle (UAV) technology in conjunction with aerial photography and aerial Light Detection and Ranging (LiDAR) was used for topographic surveying of the Win 3 Wind Power Project area, a complex mountainous terrain situated in the Huong Hoa district of Quang Tri province, Vietnam with the primary objective to produce a map with a scale of 1:1,000 with a contour interval of 1.0 m, accompanied by 3D point clouds, a Digital Elevation Model (DEM), and an orthomosaic. Employing the UAV LiDAR system of DJI Matrice 300 RTK, we conduct ed survey flights, achieving a high point density laser scanning of 229 points/m2 and capturing ultra-high-resolution imagery at 4.22 cm/pixel. The integration of aerial LiDAR and photographic data results in a rich and detailed information repository regarding the surveyed terrain demonstrating that UAV LiDAR technology is a robust tool for geographic data collection, particularly in challenging terrain conditions in Vietnam. We also evaluated the accuracy of the created topographic map, achieving results that surpass the technical requirements and quality standards of the project. This underscores the potential and effectiveness of UAV LiDAR technology in the field of large-scale geographic data collection and mapping in Vietnam.

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers.

The Roman town of Falerii Novi sits 50km north of Rome on the ancient Via Amerina. According to historical sources, it was founded after the Roman conquest of the nearby Faliscan centre of Falerii Veteres in 241 BC (Polyb. 1.65; Liv. Epit. 20; Eutrop. 2.28; Oros. 4.11; Zon. 8.18). The urban site appears to have persisted at least until the first half of the sixth century AD, with the bishopric of Aquaviva ascribed to the site in AD 465 (Duchesne, 1892). A monastery and church of Santa Maria di Falleri were founded by Cistercians and first mentioned in documents of the eleventh and twelfth centuries, now forming the only standing structure on site other than the ancient circuit walls.

The advancement of UAV technologies has enabled the automated capture of photos and videos, eliminating the need for manual intervention in flight control. (Research purpose) This research aims to conduct a retrospective analysis of the evolution offlight control systems and the development of aerial photography equipment for agricultural land, covering the period from the mid-19th century to present. (Materials and methods) A systematic literature review was conducted using the historical-analytical method. The paper examines original works by both domestic and international authors, including monographs, scientific journals, conference proceedings, museum exhibitions, photographic archives, and open-source software code. (Results and discussion) The paper identifies six distinct phases in the development of aerial photography and flight control systems. The classification is based on key innovations in camera types, control systems, and aircraft designs. Each phase highlights the predominant cameras, control systems, and aircraft utilized for agricultural applications. (Conclusions) Over the past 165 years, notable changes have occurred in aerial photography parameters, including the type of photographic material, image spatial and spectral resolution, camera weight and mounting, shutter types and their mechanisms, inertial control units, integrated GPS/GLONASS receivers, and light sensors. In terms of flight control systems for UAVs, significant developments over the last 106 years include variations in flight control types, the number offlight-stabilizing sensors, obstacle detection systems, size of the flight control units, flight modes, and takeoff/landing techniques, along with interfaces for attachments. It is anticipated that future intellectualization and miniaturization of flight control systems will not only boost UAV performance but also reduce the economic costs associated with the aerial monitoring of agricultural biological assets.

Monitoring biodiversity over time and space is essential for effective conservation of habitats, processes, and dependent organisms. Estimating large abundances of individuals can be challenging (e.g. birds and mammals), demanding efficient and effective methods. The Makgadikgadi Pans in north-eastern Botswana have high concentrations of breeding and feeding flamingos (Phoenicopterus roseus and Phoeniconaias minor), among the world’s most important breeding sites. No published estimates of flamingos exist since 2009, with historical estimates providing limited details on methods. We developed a semi-supervised machine learning method for counting a large feeding concentration of flamingos (2 June 2019) in aerial photographs from northern Sua Pan of the Makgadikgadi Pans. We also analysed rainfall and flooding frequency and extent, using satellite imagery, estimating likely frequency of these flamingo concentrations. Lastly, we reviewed the site’s global importance as flamingo breeding habitat. Our analysis successfully provided an estimate of 372,172 to 689,473 flamingos, with methods producing over 97 % test accuracy. Uncertainty related primarily to data coverage and collection rather than methodology. This estimate underlined the Makgadikgadi Pans’ significance for flamingos, supported by a high frequency of flooding (>5:10 years). Sua Pan ranked in the top ten breeding sites for the lesser and greater flamingos in the world. These methods can be applied to other large concentrations of flamingos and other animals. Our techniques provide considerable promise for tracking flamingo populations to ensure their protection. We provide code, for use in Google Earth Engine.

Seismogenic deformations of Cape Shartlay represent a very young fault system on the northwestern coast of Lake Baikal. Their study is providing an important opportunity to measure earthquake magnitudes, to identify areas where earthquakes are more likely to occur, and to estimate the probability of earthquake occurrence as applied to seismically active Baikal region. In this connection, the present work was aimed at characterizing in detail the structure, displacements, and reconstruction of the rupture propagation model. The study is based on photogrammetric processing and interpretation of the unmanned aerial survey data, as well as on morphostructural analysis of the displacement profiles and georadiolocation (GPR) data. It has been found that seismogenic ruptures of Cape Shartlay formed under prevailing extension conditions during no less than two earthquakes with magnitudes Mw≥7.0, Ms≥7.2. Seismic rupture propagation was primarily northward. The main rupture with displacement amplitude of more than 2 m contributed 39 to 93 % to the total surface displacement depending on the amount of dislocations on the transverse profile. It is shown that the length of a certain rupture increased almost instantaneously, then displacements along some of the ruptures stopped. A significant elongation of ruptures is primarily due to their merging. The present-day seismogenic zone is highly permeable. According to the tectonophysical model of formation of inner structure of the fault zone, the development of the seismogenic rupture system of Cape Shartlay corresponds to the late disjunctive stage. This means that the rupturing process in this segment of the North Baikal fault may not have stopped yet, and the lack of large earthquakes in the instrumental record implies the accumulation of stress in its southern part. The obtained results provide an opportunity to reconstruct the development of large fault zones by studying the displacement profiles and, therefore, to localize more precisely the places where future earthquakes may occur.

Deep learning, which has exhibited considerable potential and effectiveness in forest resource assessment, is vital for comprehending and managing forest resources and ecosystems. However, extensive assessment of forest resources is highly challenging due to the complex and varied nature of forest types sourced from diverse remote sensing platforms, which include images, point clouds, and fusion data. To facilitate further study, we systematically review the current status, applications and prospects of deep learning technologies for different types of forest remote sensing data. After considering more than two hundred forest-related papers published over the past decade, we introduce sensors and devices for forest data acquisition, classify deep learning methods based on their data processing methods and operational principles, and categorize diverse instances of these methods with various forest applications. Moreover, we summarize available datasets related primarily to forest data and examine the global geographic distribution of the relevant literature. Comprehensive insights into the advantages and limitations of each method are described, offering a forward-looking perspective on the trend of applying deep learning technology to forest research. In this paper, we aim to provide an overview of the current trends and challenges of deep learning techniques applied to forest research, creating a comprehensive picture for use as a reference by both academia and industry professionals.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network’s focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.

Currently, unmanned aerial vehicles are widely used with navigation based on data from onboard integrated systems including inertial and satellite sensors. In this case, to solve many target tasks, their preliminary exit to a given point of the flight route along the shortest horizontal trajectory is provided. However, in practice, there may be situations when the information received from navigation satellites may no longer be available, which leads to a decrease in navigation accuracy. Considered a technique for detecting the trajectory of unmanned aerial vehicles under conditions of loss of signals from navigation satellites using the underlying surface images. As a criterion indicating the occurrence of deviations of unmanned aerial vehicles from a specified trajectory, it is proposed to use the change in parallaxes of adjacent pairs of images. Analytical relations describing the functional relationship between changes in image parallaxes and parameters of linear and angular deviations of unmanned aerial vehicles from a specified trajectory. All possible options of these deviations are also considered. The obtained results provide an a priori estimate of the threshold value of parallax changes corresponding to the acceptable level of unmanned aerial vehicles deviations from the specified trajectory by means of modelling. Based on this estimate, it is possible to improve the accuracy of trajectory detection of unmanned aerial vehicles under conditions of loss of signals from navigation satellites.

Calculation of the volume of completed mining works at mining enterprises is one of the important aspects of surveying and geological service. The article is devoted to the study of the feasibility of using the determination of the volume of mining mass based on multicopter surveying, because the use of modern devices and software allows performing tasks in an automated mode, carrying out design and planning of mining production with greater accuracy and with less time and resources. The paper examines methods of geodetic surveys and the possibility of using quadcopters to monitor the process of mineral extraction in open-pit mining operations, and to monitor the correctness of all technological processes. Dump surveying was performed at the enterprise in two ways: tacheometric surveying and using a Phantom 2 quadcopter. As a result of research based on the application of information and computer technologies, polynomial dependences were obtained, characterizing the dependence between the errors of the spatial orientation of the take-off and the time of the quadcopter and the dependence of the angle of its rotation around the vertical axis depending on time. A methodology for determining the expediency of using multicopters in certain conditions with the use of information and computer technologies has been developed. Feasibility is substantiated on the basis of research the use of quadcopters in the field, by comparing the traditional method for estimating the volume of reserves by the method of tacheometric surveying.

Introduction. The use of available satellite images and aerial photography by unmanned aerial vehicles (UAVs) in the tasks of environmental monitoring is challenged by the imperfection of existing tools. Geographic information systems are characterized by insufficient flexibility to automatically work with heterogeneous sources. The latest models based on artificial intelligence in ecology require preliminary data preparation. The article presents the results of designing a software system for environmental monitoring based on machine vision sensor data, which provides data unification while being flexible both in terms of data sources and methods of their analysis.Aim. Creation of a generalized software system for coordinated spatial marking of heterogeneous machine vision data for environmental monitoring tasks.Materials and methods. Software engineering methods, database theory methods, spatial markup methods, image processing methods.Results. A generalized method for unifying data was developed. The method is based on the analysis of existing open data from remote sensing of the Earth, as well as UAV aerial photography and approaches to environmental monitoring. To implement the method, a flexible architecture of the software system was designed, and a data model for a document-oriented DBMS was developed, which allows storing data and scaling the data analysis procedure.Conclusion. The existing sources of data and tools for environmental monitoring were analyzed. A generalized method for unifying machine vision data, an architecture, and a data model was created. The method, architecture, and model were successfully implemented as a software system with a web interface

Remote sensing was used to map the invasion of yellow-flowered legumes on the Central Plateau of New Zealand to inform weed management strategy. The distributions of Cytisus scoparius (broom), Ulex europaeus (gorse) and Lupinus arboreus (tree lupin) were captured with high-resolution RGB photographs of the plants while flowering. The outcomes of herbicide operations to control C. scoparius and U. europaeus over time were also assessed through repeat photography and change mapping. A grid-square sampling tool previously developed by Manaaki Whenua—Landcare Research was used to help transfer data rapidly from photography to maps using manual classification. Artificial intelligence was trialled and ruled out because the number of false positives could not be tolerated. Future actions to protect the natural values and vistas of the Central Plateau from legume invasion were identified. While previous control operations have mostly targeted large, highly visible legume patches, the importance of removing outlying plants to prevent the establishment of new seed banks and slow spread has been underestimated. Outliers not only establish new, large, long-lived seed banks in previously seed-free areas, but they also contribute more to range expansion than larger patches. Our C. scoparius and U. europaeus change mapping confirms and helps to visualise the establishment and expansion of uncontrolled outliers. The power of visualizing weed control strategies through remote sensing has supported recommendations to improve outlier control to achieve long-term, sustainable landscape-scale suppression of invasive legumes.

Aerial photography is becoming an integral part of remote sensing in digital agriculture. The first aerial photographs were taken in the mid-19th century. (Research purpose) The paper aims to retrospectively analyze the evolution of aerial photography equipment for capturing agricultural lands, beginning with the creation of the first aerial photograph up to the present day. (Materials and methods) A historical-analytical approach was employed to examine the existing literature. Within this study, the development of agricultural aerial photography equipment was categorized into four distinct time periods: 1885-1908, 1909-1945, 1946-1979, and from 1980 to the present day. (Results and discussion) In the initial phase of experimental aerial photographic equipment development, significant advancements were achieved, encompassing the emergence of the first photograph, the creation of portable cameras and their adaptation for use with hot air balloons and kites, rockets, and birds. Technological growth in the first half of the 20th century contributed to elevating aerial photography to a versatile tool applied for a wide range of intelligence operations, including agricultural tasks. The evolution of space technologies in the second half of the 20th century resulted in the rapid development of both aerial photography equipment and their carriers. This progress facilitated the use of color aerial photography for the examination of the Earth's surface. The advancements of digital technologies at the end of the 20th century and the beginning of the 21st century facilitated the use of high-resolution digital aerial cameras mounted on various carrier platforms, ranging from unmanned aircraft to artificial Earth satellites. (Conclusions) A retrospective analysis reveals that the development and creation of equipment for aerial photography of agricultural lands unfolded in a sporadic fashion. This progression was closely intertwined with global political, social, and economic situation, as well as the state of technological advancement in related areas. Over the coming decade, the sustained application of aerial photography in agriculture is poised to enhance the efficiency of unmanned aircraft, reduce the production costs associated with aerial photography, and facilitate the widespread adoption of digital remote sensing technology within the agricultural sector.

To determine changes of glacier No. 31 (SU5A15106031), happened in the beginning of the XXI century, satellite images, obtained from the Landsat-7, 8, 9 and Sentinel-2 satellites in different years, were analyzed. As a result, data on the glacier changes were obtained for the period from 2000 to 2022. During this time, the length of the main stream of the glacier decreased by 144 m (6.8%), and the total area of the entire glacier – by 0.089 km2 (9.7%). The rate of retreat of the glacier front varied from 2 to 15 m/year, on average – 6.5 m/year, and the glacier area decreased by on average of 0.004 km2/year. In 2022, the glacier consisted of two separate ice streams, with a total area of 0.823 km2 and a maximum length of 1.955 km. Aerial photography was carried out using a DJI Phantom 4 quadcopter. A high-resolution orthophotoplan (±5 cm), a digital terrain model, a three-dimensional model of the surface of the glacier tongue and adjacent territories, with a total area of 0.25 km2, were made. Radar sounding was performed by the Python-3 georadar in two configurations: at frequencies 50 and 100 MHz. Two cross-sections of the glacier were constructed from the radar data: one was taken at a frequency of 100 MHz, and the second at both – 50 MHz and 100 MHz. The analysis of the radiogram of these profiles made possible to reveal that the larger thickness of the glacier in the study area was measured along the left side of the glacier tongue, the maximum recorded thickness was equal to 66 m

This article is devoted to the study of surface karst manifestations on the territory of inactive salt mines of Solotvyno, Transcarpathian region, Ukraine. The historical development of the salt mine from the moment of the creation of the first mine (the end of the 18th century) to the present is considered. Based on the results of previously published studies, monitoring of karst manifestations from 2010 to 2020 was implemented. The last stage of monitoring in 2021 was the aerial survey of the territory obtained by the Arrow UAV using the Sony QX1 camera. Pix4Dmapper software was used for image processing. The aim of the work is to estimate the scale of destructive geological processes by the method of photogrammetry. During the execution of the work, the methods of processing materials of aerial photography, analysis of developments in the direction of photogrammetry were applied. As a result of the study, the volume-planar characteristics of karst formations on the territory of Solotvyno were calculated using the photogrammetry method. This scientific work is relevant for assessing the scale of the development of degenerative geological processes on the territory of Ukraine, in particular within the Transcarpathian region. The practical significance of the obtained results lies in their application in order to ensure environmental monitoring. The results of the study can be used to predict the impact on economic and infrastructure objects, followed by the development of a plan of specific actions for prevention, localization and work with the consequences of man-made processes in the adjacent territory.

Monitoring of pink salmon (Oncorhynchus gorbuscha) migration to the estuaries and spawning grounds in the rivers of southeastern and southwestern Sakhalin, including the Aniva Bay, was conducted in summer 2022 using a no-tipping technique with unmanned aerial vehicles (UAV). Abilities, advantages and disadvantages of UAV use for such surveys are discussed to provide a scientific basis for improved management of salmon fishery in the Far East of Russia.

The paper shows the possibility of using unmanned aerial vehicles with additional outboard equipment for aerial photography. It is noted that some parameters of the drone and additional suspension equipment are not taken into account by the software when calculating the drone flight time for one battery charge. (Research purpose) To develop an algorithm for calculating the flight time of an unmanned aerial vehicle with installed outboard equipment. (Materials and methods) The technical characteristics of DJI Phantom 4 pro and DJI Matrice 200v2 were studied, as well as the specifications of Parrot Sequoia, MicaSense Altum multispectral cameras mounted on the drone. The existing research results are used to calculate the flight mission time depending on the route length and the battery capacity. (Results and discussion) It is found that the maximum flight time of a drone with additional outboard equipment is reduced due to the additional equipment mass, increased power consumption, the preflight preparation time, the need to return to the take-off point and the necessity to preserve the battery life and save the flight load. The maximum flight time calculated for DJI Phantom 4 pro and DJI Matrice 200v2 with multispectral cameras Parrot Sequoia, MicaSense Altum is 8 minutes and 18 minutes, respectively, with a minimum flight load. A method for calculating the number of batteries for aerial photography with additional outboard equipment is determined. (Conclusions) An algorithm for calculating the flight time of an unmanned aerial vehicle with additional outboard equipment is developed, the parameters ignored by the standard software in the flight time calculation are taken into account.

Agriculture is an area that currently benefits from the use of new technologies and techniques, such as artificial intelligence, to improve production in crop fields. Zacatecas is one of the states producing the most onions in the northeast region of Mexico. Identifying and determining vegetation, soil, and humidity zones could help solve problems such as irrigation demands or excesses, identify spaces with different levels of soil homogeneity, and estimate the yield or health of the crop. This study examines the application of artificial intelligence through the use of deep learning, specifically convolutional neural networks, to identify the patterns that can be found in a crop field, in this case, vegetation, soil, and humidity zones. To extract the mentioned patterns, the K-nearest neighbor algorithm was used to pre-process images taken using unmanned aerial vehicles and form a dataset composed of 3672 images of vegetation, soil, and humidity (1224 for each class). A total of six convolutional neural network models were used to identify and classify the patterns, namely Alexnet, DenseNet, VGG16, SqueezeNet, MobileNetV2, and Res-Net18. Each model was evaluated with the following validation metrics: accuracy, F1-score, precision, and recall. The results showed a variation in performance between 90% and almost 100%. Alexnet obtained the highest metrics with an accuracy of 99.92%, while MobileNetV2 had the lowest accuracy of 90.85%. Other models, such as DenseNet, VGG16, SqueezeNet, and ResNet18, showed an accuracy of between 92.02% and 98.78%. Furthermore, our study highlights the importance of adopting artificial intelligence in agriculture, particularly in the management of onion fields in Zacatecas, Mexico. The findings can help farmers and agronomists make more informed and efficient decisions, which can lead to greater production and sustainability in local agriculture.

Teluk Latak Village is located in Bengkalis District, Bengkalis Regency, Riau. It has 2 hamlets, namely Langgam Muara Hamlet and Simpang Baru Hamlet. Teluk Latak Village is one of the coastal villages in the west of Bengkalis Island which has a flat topography with an area of ​​​​approximately 23 km2. The problem that arises in Teluk Latak Village is that the regional potential of the natural resources in the area has not been explored. To obtain information on the geographical and geological conditions of Teluk Latak Village, aerial mapping is necessary. Therefore, the purpose of this community service activity is to utilize geospatial data through aerial mapping in Teluk Latak Village, Bengkalis District, Bengkalis Regency, Riau. The method of implementation is to conduct aerial mapping using drone equipment to obtain land and vegetation conditions in the Teluk Latak village area. It is hoped that this activity can contribute to village residents and village officials in mapping the potential of Teluk Latak Village in the social and economic fields.

Abstract Background Large-scale species monitoring remains a significant conservation challenge. Given the ongoing biodiversity crisis, the need for reliable and efficient methods has never been greater. Drone-based techniques have much to offer in this regard: they allow access to otherwise unreachable areas and enable the rapid collection of non-invasive field data. Herein, we describe the development of a drone-based method for the estimation of population size in Galápagos marine iguanas, Amblyrhynchus cristatus. As a large-bodied lizard that occurs in open coastal terrain, this endemic species is an ideal candidate for drone surveys. Almost all Amblyrhynchus subspecies are Endangered or Critically Endangered according to the IUCN yet since several colonies are inaccessible by foot, ground- based methods are unable to address the critical need for better census data. In order to establish a drone-based approach to estimate population size of marine iguanas, we surveyed in January 2021 four colonies on three focal islands (San Cristobal, Santa Fe and Espanola) using three techniques: simple counts (the standard method currently used by conservation managers), capture mark-resight (CMR), and drone-based counts. The surveys were performed within a 4-day window under similar ambient conditions. We then compared the approaches in terms of feasibility, outcome and effort. Results The highest population-size estimates were obtained using CMR, and drone-based counts were on average 14% closer to CMR estimates—and 17–35% higher—than those obtained by simple counts. In terms of field-time, drone-surveys can be faster than simple counts, but image analyses were highly time consuming. Conclusion Though CMR likely produces superior estimates, it cannot be performed in most cases due to lack of access and knowledge regarding colonies. Drone-based surveys outperformed ground-based simple counts in terms of outcome and this approach is therefore suitable for use across the range of the species. Moreover, the aerial approach is currently the only credible solution for accessing and surveying marine iguanas at highly remote colonies. The application of citizen science and other aids such as machine learning will alleviate the issue regarding time needed to analyze the images.

The relevance of using unmanned aerial vehicles (UAV) is substantiated in comparison with other methods of aerospace survey. The paper provides examples of tasks requiring the use of UAVs for aerial photography from different heights. It is shown that the introduction of agricultural robots, including UAVs, increases the speed of fi eld work, allows obtaining unique data necessary for the assessment of agricultural territories, crops processing and plant growth stimulation. It is noted that the problem of constructing the movement trajectories of a multirotor UAV for performing agricultural tasks within a minimum time limit remains unresolved. (Research purpose) To reduce the length of the trajectory covering a given area and reduce the flight time of a multirotor UAV, taking into account the analysis of possible obstacles and land plots that are beyond the task scope. (Materials and methods) Geometric methods have been used to calculate the UAV flight trajectory covering a given section, the trajectory of movement in an environment with obstacles to the designated point. Photogrammetry methods have been used for processing aerial photography images when forming an orthophotoplane and a terrain map. (Results and discussion) The trajectory calculated by the developed algorithm proves to meet all the requirements: it is continuous, has a minimum number of turns, it is smoothed, and feasible for a multirotor UAV. (Conclusions) It was determined that according to the proposed algorithm it takes less than 0.05 seconds to calculate the trajectory covering a rectangular section with the sides of 200 by 30 meters. It was found that the trajectory in the fi rst 10,200-square-meter section decreased by 9 percent, and in the second 950,000-square-meter section it reduced by 6 percent, compared with the length of the trajectory built using standard algorithms. The flight time reduced by 32 and 10 percent, respectively. The paper presents the key advantages of using UAV for video shooting such as: guaranteed high resolution of photographic materials and the ability to shoot at a given time, allowing for the crop condition assessment.

The paper shows the possibility of collecting multispectral and RGB data on the crop condition using an unmanned aircraft. The use of several programs for creating a flight mission is likely to lead to the inefficient use of the unmanned aircraft resources. (Research purpose) To calculate the parameters of aerial photography with multispectral and RGB cameras to enable the improved efficiency of unmanned aircraft resources. (Materials and methods) The paper studies the specifi cations of RGB and multispectral cameras installed on the drone. The research uses the results of scientifi c research on the calculation of aerial photography parameters such as longitudinal and transverse overlap, as well as the flight speed of an unmanned aircraft. (Results and discussion) It is established that the calculated values of the longitudinal and transverse overlap for the Zenmuse X4S RGB camera, as well as the unmanned aircraft fl ight speed during the joint aerial photography with a multispectral camera, make it possible to use standard mobile applications to create a flight mission. (Conclusions) It is determined that the obtained values guarantee the creation of high-quality digital maps. The parameters for the flight mission are calculated ensuring the effi cient exploitation of the unmanned aircraft resources, using simultaneously the Zenmuse X4S RGB camera and six multispectral cameras on board the unmanned aircraft.

Abstract In the past century, most eruptions of Steamboat Geyser in Yellowstone National Park's Norris Geyser Basin were mainly clustered in three episodes: 1961–1969, 1982–1984, and ongoing since 2018. These eruptive episodes resulted in extensive disturbance to surrounding trees. To characterize tree response over time as an indicator of geyser activity adjustments to climate variability, aerial and ground images were analyzed to document changes in tree coverage around the geyser since 1954. Radiocarbon dating of silicified tree remnants from within 14 m of the geyser vent was used to examine geyser response to possible variations in decadal to centennial precipitation patterns. We searched for atypical or absent growth rings in cores from live trees in years associated with large geyser eruptions. Photographs indicate that active eruptive phases have adversely affected trees up to 30 m from the vent, primarily in the dominant downwind direction. Radiocarbon dates indicate that the geyser formed before 1878, in contrast to the birthdate reported in historical documents. Further, the ages of the silicified trees cluster within three episodes that are temporally correlated with periods of relative drought in the Yellowstone region during the 15th–17th centuries. The discontinuous growth of trees around the geyser suggests that changes in eruptive patterns occur in response to decadal to multidecadal droughts. This inference is supported by the lack of silicified specimens with more than 20 annual rings and by the existence of atypical or missing rings in live trees during periods of extended geyser activity.

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on “space-sky-earth” remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors.

Urban color, primarily emanating from building façades and roofs, plays a pivotal role in shaping a city’s image and influencing people’s overall impression. Understanding the nuances of color patterns contributes significantly to unraveling the uniqueness and identity of a city. This study introduces a statistical method for the systematic analysis of urban color and macroscopic urban structure. Specifically, we employ drones to collect and extract building roof and façade colors in the main urban area of Hangzhou, mapping these colors to the HSV color space. Subsequently, we establish a random walk model and an origin–destination trip model within the urban transportation network to simulate the movement of people. Our experiments reveal robust correlations between façade and roof values and passing frequency (with the Pearson correlations reaching 0.70). Through a rigorous statistical analysis, we gain insights into the distribution of urban color and the impact of architectural structures on color variations, identifying potential patterns or trends. By integrating color data with architectural structure data, our systematic research method deepens the understanding of the visual features that define cities. Beyond theoretical exploration, this approach offers practical insights for building planning and design. This study not only sheds light on the relationship between architectural structures and urban color but also provides valuable guidance for future urban development initiatives.

Visible-infrared person re-identification (VI-ReID) aims at matching pedestrian images with the same identity between different modalities. Existing methods ignore the problems of detailed information loss and the difficulty in capturing global features during the feature extraction process. To solve these issues, we propose a Transformer-based Feature Compensation Network (TFCNet). Firstly, we design a Hierarchical Feature Aggregation (HFA) module, which recursively aggregates the hierarchical features to help the model preserve detailed information. Secondly, we design the Global Feature Compensation (GFC) module, which exploits Transformer’s ability to capture long-range dependencies in sequences to extract global features. Extensive results show that the rank-1/mAP of our method on the SYSU-MM01 and RegDB datasets reaches 60.87%/58.87% and 91.02%/75.06%, respectively, which is better than most existing excellent methods. Meanwhile, to demonstrate our method‘s transferability, we also conduct related experiments on two aerial photography datasets.

The violence in public areas occurs frequently and video surveillance is of great significance for maintaining public safety.Compared with fixed cameras,unmanned aerial vehicles (UAVs) have surveillance mobility.However,in aerial images,the rapid movement of UAVs as well as the change of posture and height cause the problem of motion blur and large-scale change of target.To solve this problem,an attention spatial-temporal convolutional network (AST-GCN) combining attention mechanism is designed to realize the identification of violent behavior in aerial video.The proposed method is divided into two steps:the key frame detection network completes the initial positioning,and the AST-GCN network completes the behavior identification through the sequence features.Firstly,aiming at video violence localization,a key frame cascade detection network is designed to realize violence key frame detection based on human posture estimation,and preliminarily judge the occurrence time of violence.Secondly,the skeleton information of multiple frames around key frames is extracted from the video sequence,and the skeleton data is pre-processed,including normalization,screening and completion,so as to improve the robustness of different scenes and the partial missing of key nodes.And the skeleton temporal-spatial representation matrix is constructed according to the extracted skeleton information.Finally,AST-GCN network analyzes and identifies multiple frames of human skeleton information,to integrate attention module,improve feature expression ability,and complete the recognition of violent behavior.The method is validated on self-built aerial violence data set,and experimental results show that the AST-GCN can realize the recognition of aerial scene violence,and the recognition accuracy is 86.6%.The proposed method has important engineering value and scientific signifi-cance for the realization of aerial video surveillance and human pose understanding applications.

This paper highlights the importance of remote sensing and GIS techniques applied on aerial photographs for forests spatio-temporal dynamics analysis. An assessment of the changes in the distribution and extension of Kiadi cork oak forest was carried out using historical imagery, covering a period of 35 years. The results indicate that, roadways and building surfaces in Kiadi forest have increased by 9.71 and 3.86% respectively, while the surface initially covered by vegetation decreased by 13.57%, as a result of anthropogenic disturbance. Digital processing of historical aerial photographs proved to be a powerful tool for quantitative analysis of forest dynamics.

Analysis of the success of reforestation after a fire is the most important task of forestry. For these purposes, in the summer of 2021, aerial photography was carried out from a Phantom 4 pro UAV (Unmanned Aerial Vehicle) of a 15-year-old burnt-out (2006) along the north-western border of the Botanical Garden of Petrozavodsk State University (Republic of Karelia). In addition to this, a full-scale survey was carried out at the registration sites. Before the forest fire, rocky Scots pine (Pinus sylvestris L.) trees grew. The work was carried out in order to assess the state of natural renewal after a fire along rocky outcrops by combining the processing of aerial photography data with field surveys. As a result of photogrammetric processing, an orthophotomap with a spatial resolution of 4.6 cm/pix, a height map and three-dimensional point clouds were reconstructed. To analyze the quantitative distribution of the territory according to the species composition, the trees were pinned on the orthophotomap according to the registration areas of field surveys, as a result of which the quantitative distribution of tree species was determined in the ratio of 64 % Scots pine and 23 % of drooping birch (Betula pendula Roth) (other species were not identified). Field surveys showed that after the fire, a viable young stand of mixed composition with a predominance of Scots pine (71% of the total number of tree species) was formed. In addition to Scots pine, drooping birch (22 %), aspen (Populus tremula L.) and gray alder (Alnus incana (L.) Moench) (3.5 % each) participate in the formation of young stands on this burned-out area. These indicators are consistent with the analysis of the orthomosaic of the area (the error is less than 10 %). When comparing the height of plants on three-dimensional clouds of points with field measurements, it was possible to reliably determine only the height of plants for a large generally accepted category (> 1.5 m). The maximum height of Scots pine trees in the study area is 6 m, and the average values varied from 3.5 to 4.5 m, which indicates the success of reforestation after burning on rocky outcrops.

The origins of aerial photography can be traced back to the second half of the 19th century, and periods of World Wars contributed to the development of techniques for developing and disseminating aerial photographs for military purposes. In the literature, we find descriptions of the Polish Military Geographical Institute’s (WIG) use of aerial photography to update topographic maps of nearly 40% of the country’s area at the time. Unfortunately, the fate of the pre-war collection of photographs is difficult to establish. Given this, the aerial photographs from the period of the Second World War (German and Allied) constitute the oldest possible complete and consistent photographic documentation of Poland. The series of German prints (373-GXPRINTS) collected at the National Archives and Records Administration in College Park (NARA, USA) are maintained according to the most liberal policy on access and further use. At the same time, the organisation of the NARA archive and the extent to which the collection has been digitised (over one million images) leave a deficiency, and searching for images requires knowledge, methods and experience.

Regional surveys will have a high demand for coverage. To adequately cover a large area while retaining high resolution, mosaics of the area from a variety of scenes can be created. This paper describes a mosaicking procedure that consists of a series of processing steps used to combine multiple aerial images. These images were taken from CropCam unmanned aerial platform flight missions over the desired area to quickly map a large geographical region. The results of periodic processing can be compared and analyzed to monitor a large area for future research or during an emergency situation in the covered area. Digital imagery captured from the air has proven to be a valuable resource for studying land cover and land use. For this study, airborne digital camera images were chosen because they provide data with a higher spatial resolution for trying to map a small research area. On board the UAV autopilot, images were captured from an elevation of 320 meters using a standard digital camera. When compared to other airborne studies, this technique was less expensive and more cost effective. According to this study, onboard a UAV autopilot, a digital camera serves as a sensor, which can be helpful in planning and developing a limited coverage area after mosaicking

Abstract Background The ability to automatically count animals is important to design appropriate environmental policies and to monitor their populations in relation to biodiversity and maintain balance among species. Out of all living mammals on Earth, 60% are livestock, 36% humans, and only 4% are animals that live in the wild. In a relatively short period, development of human civilization caused a loss of 83% of wildlife and 50% of plants. The rate of species extinction is accelerating. Traditional wildlife surveys provide rough population estimates. However, emerging technologies, such as aerial photography, allow to perform large-scale surveys in a short period of time with high accuracy. In this paper, we propose the use of computer vision, through deep learning (DL) architecture, together with aerial photography and density maps, to count the population of Steller sea lions and African elephants with high precision. Results We have trained two deep learning models, a basic UNet without any feature extractor (Model-1) and another with the EfficientNet-B5 feature extractor (Model-2). We measured the model’s prediction accuracy, using Root Mean Square Error (RMSE) for the predicted and actual animal count. The results showed an RMSE of 1.88 and 0.60 to count Steller sea lions and African elephants, respectively, regardless of complex background, different illumination conditions, heavy overlapping and occlusion of the animals. Conclusions Our proposed solution performed very well in the counting prediction problem, with relatively low training parameters and minimum annotation. The approach adopted, combining DL and density maps, provided better results than state-of-art deep learning models used for counting, indicating that the proposed method has the potential to be used more widely in large-scale wildlife surveying projects and initiatives.

In order to solve the problems of high cost of obtaining the top plan view of the main outline of the building in the 3D modeling scenario,low segmentation accuracy of the aerial photography building,interference on the roof of the building,etc.,a method of accurately segmenting the aerial photography building based on deep residual network is proposed,in which the positions of five points are expressed as heat maps as additional input channels of the network,and good segmentation effect is achieved in the task of accurately segmenting the aerial photography building.Experimental results show that the proposedmethod has higher segmentation accuracy and segmentation efficiency than the traditional semi-automatic segmentation method Grabcut.It has better robustness and anti-interference than DEXTR method.This method can provide high-precision top-view contour map and top-view picture of buildings for 3D reconstruction of buildings,and can also be used in the production process of aerial photography building data sets as an accurate and effective mask annotation tool or semi-automatic contour annotation tool to improve the annotation efficiency of datasets.

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on onion crops and the challenges presented throughout its phenological cycle. Unmanned aerial vehicles (UAVs) and digital image processing were used to monitor the crop and identify patterns such as humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 tons/ha. However, only 25.00 tons/ha were reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being medium-sized. Approximately 20% of the production was lost due to blight and unfavorable weather conditions.

The outcomes of environmental impact assessment (EIA) influence millions of hectares of land and can be a contentious process. A vital aspect of an EIA process is consideration of the accumulation of impacts from multiple activities and stressors through a cumulative effects assessment (CEA). An opportunity exists to improve the rigor and utility of CEA and EIA by incorporating core scientific principles of landscape ecology into EIA. With examples from a Canadian context, we explore realistic hypothetical situations demonstrating how integration of core scientific principles could impact EIA outcomes. First, we demonstrate how changing the spatial extent of EIA boundaries can misrepresent cumulative impacts via the exclusion or inclusion of surrounding natural resource development projects. Second, we use network analysis to show how even a seemingly small, localized development project can disrupt regional habitat connectivity. Lastly, we explore the benefits of using long-term historical remote sensing products. Because these approaches are straightforward to implement using publicly available data, they provide sensible opportunities to improve EIA and enhance the monitoring of natural resource development activities in Canada and elsewhere.

The results of ground-penetrating radar (GPR) studies of permafrost and aerial photography, carried out at key sites in the Sentsa River valley (Oka Plateau, Eastern Sayan Ridge), are presented.For geophysical studies, an OKO-2 GPR completed with an AB-90 shielded antenna unit was used with a maximum sounding depth of up to 20 m and a resolution of 0.5 m. To account for the landscape elevation, the Trimble TS635 tacheometer and the Leiсa DISTO D 510 rangefinder performed hypsometric measurements with a step of 1.0 m. Aerial photography was carried out by a remotely piloted aircraft (RPA) DJI Inspire 1 Pro, equipped with a Zenmuse 3X camera (a resolution of 3840×2160 pixels) with a spatial resolution of 5.7–7.8 cm/pixel (in different years).In the structure of frozen lacustrine-alluvial sediments, three GPR complexes are distinguished, corresponding to the active layer and frozen rocks with different amounts of schlieren, lenses and layers of texture-forming ice. The orthophoto map and tacheometric survey analysis showed that the destruction of frost mounds occurs from the second half of April to the first half of October. The most significant relief change is due to the thawing of icy pulverescent clayey silts. It leads to subsidence blocks in the ledge of the Sentsa River terrace. Lateral river thermoerosion also contributes to the frost mounds destruction.

The Black Wattle (Acacia mearnsii De Wild) is now an integral part of the landscape of the El Kala region. Fifty years after its introduction, this Australian legume occupies more than 3200 ha and represents a major threat to local biodiversity.The present work was undertaken on the one hand, in order to ensure a sustainable management of the natural resources of the region; on the other hand, in order to develop a better understanding of the triggering mechanisms for the spread of the invasive species.The purpose of this study is to assess changes in land cover from 1959 to 2016, but also to examine the appearance, evolution and current distribution of the spots of Acacia mearnsii in two sites, using the tools of the airborne and satellite remote sensing.The results obtained show that on sites 1 (forest of Boumalek, 1536 ha) and 2 (forest of Tonga, 1690 ha), the initial plant formations, without Black Acacia, lost 708 and 581 ha respectively. The cork oak forest with undergrowth has undergone a particularly remarkable retraction (196 ha in Boumalek, 350 ha in Tonga). In almost 30 years (from 1987 to 2016), the area of invaded eucalyptus and cork oak forests increased respectively from 62 ha to 487 ha and from 0.2 to 188 ha in Boumalek, from 30 ha to 730 ha and from 1.6 to 120 ha in Tonga.During the period considered, of almost 60 years, disturbances of natural and especially anthropic origin have been aggravating factors of the invasion, by opening invasion windows and by triggering the emergence of Acacia mearnsii seeds.

Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the state of crops and the formation of optimal management decisions.(Materials and Methods) The authors studied the results of scientifi c research in the fi eld of remote sensing technology using unmanned aerial vehicles and multispectral cameras, as well as the experience of using vegetation indexes to assess the condition of crops in the precision farming system. The limiting factors for the vegetation indexes research were determined: a limited number of monochrome cameras in popular multispectral cameras; key indicators for monitoring crops required by agronomists. After processing aerial photographs from an unmanned aerial vehicle, a high-precision orthophotomap, a digital fi eld model, and maps of vegetation indexes were created.(Results and discussion) More than 150 vegetation indexes were found. Not all of them were created through observation and experimentation. The authors considered broadband vegetation indexes to assess the status of crops in the fi elds. They analyzed the vegetation indexes of soybean and winter wheat crops in the main phases of vegetation.(Conclusions) The authors found that each vegetative index had its own specifi c scope, limiting factors and was used both separately and in combination with other indexes. When calculating the vegetation indexes for practical use, it was recommended to be guided by the technical characteristics of multispectral cameras and took into account the index use eff ectiveness at various vegetation stages.

Finally, the 20th century gave geographers the opportunity to rise above the surface of the earth to observe from a distance. While contemplating the earth from the top was an enchantment for the geographers of the beginning of the century who experienced and expressed it enthusiastically, it took a long time for them to make common use of it in their publications, which is shown, in particular, by a systematic review of the BAGF journal since its first issue. Today, oblique aerial photography is marginalized by more rigorous documents, but the oblique view, which retains the reality of landscapes, can bring much to the geographer, if put in such order as to highlight the spatial organization which is proposed in one single look.

Nature is the most important resource that the textile printing designer relies on to invent and inspire new and contemporary ideas by taking advantage of the rapid development in the fields of art and design. The technological and technological development in imaging has contributed to seeing the structures of living cells, including the exquisite formations in which the creations of the Almighty and Exalted are manifested, which helped in carrying out many research and studies in the biological and medical fields, Likewise, the increasing development in aerial photography and remote sensing techniques helped in exploring the Earth's surface and its various geological characteristics There is no doubt that all of these discoveries in the presence of giant data storage units whose capabilities are increasing day after day have contributed to recording countless numbers of high-quality digital images that record the smallest details that workers in the field of art and design can benefit from in creating many Design ideas in various fields. The research aims to study the mechanism of recording high-resolution images of the surface of the globe, including the creations of the Almighty Creator and the changes that occurred in it due to climatic changes and urban encroachment of human beings and how to benefit from the cosmic and urban systems in inspiring designs suitable for printing textile fabric pendants that contribute to completing the aesthetic vision of the Interior design with furnishing fabrics for the modern home. The research relies on the descriptive analytical approach to study the aesthetics of digital aerial photography of the surface of the globe, as well as the experimental approach to obtain innovative design ideas for printed hanging textile that suit the purpose of modern home furnishing, The researcher has reached many important results, including that digital aerial photography of the Earth’s surface can secrete millions of images and design elements that help the designer to invent renewable and diverse design ideas. The researcher also found that digital aerial imagery of the surface of the globe is variable and varied even if it is for the same spot on the surface of the globe due to climate changes as well as continuous urban sprawl. The researcher recommends in-depth study and analysis of digital aerial photos of the surface of the globe and benefit from them in various fields of art and design and in the field of textile printing design in particular

Precision farming relies on the knowledge of differences in land characteristics, and these enable us to treat any given area with individually applied cultivation technology. This reduces inputs and costs, and has a positive effect on soil properties with the elimination of soil degradation processes. This study examines the possibilities of preventing soil erosion in precision farming using spatial data acquired from remote land sensing. This method provides precise identification of soil erosion processes essential to ensure precision farming, Here, we applied remote land sensing for erosion research and the most important components of the digital relief model for precision farming. This was obtained by processing both aerial photogrammetry and aerial landscape scanning information These methods enabled processing an erosion map and identifying erosion risk sites which require altered cultivation technology to ensure precision farming.

This paper explores the entanglement between photography and aerial military operations during the World Wars, showing how, in warfare, the camera became a technology of power serving a dual purpose: 1) It was a weapon used to map the territory as well as to detect and bomb specific targets, and 2) it constituted a powerful propagandistic medium employed to circulate persuading and aesthetically innovative aerial vistas among civilians. The technological and industrial sophistication that was adapted to the modern aerial battlefield required optical and photo-developments. These technical improvements challenged military activity while also reshaping civilians’ perception and conception of the landscape as well as determining new aesthetic canons. At the core of this article there is the notion of training of the eye – understood as the process, which involved both experts and the general public, of assimilating new photographic vistas from the sky. Using mostly the North American and German frames of reference, and interweaving military technology, visual culture, and landscape studies, this paper analyzes production and reception of “the view from above” mainly through mass-market illustrated magazines, such as the American Life and the German Berliner Illustrirte Zeitung. Developed within the military context, the peculiarity of aerial photography became embroiled with the idea of a cold, hunting, distanced and simultaneously penetrating gaze. However, recent scholarship understands the aerial view differently, due to the latest use of aerial photography for environmental science, and with the purpose of raising public awareness on the devastating ecological impact of industrialization and militarization. The contemporary progression from aerial photography to satellite imagery can in fact be interpreted along two directions: the God's-eye view of surveillance and/or the bird’s-eye view of environmental care.

The need for irrigation water is influenced by soil water content or more precisely by available water (pF 2.5 and pF 4.2). There is a need for technological breakthroughs in using Unmanned Aerial Vehicle (UAV) to identify water content quickly and broadly and accurately. The study was conducted in an area of ±18 hectares in the Sisim Sub Watershed in September 2019 at 09.00 a.m. Aerial photographs were taken at an altitude of 100 m with DJI Phantom Pro 3.0. The number of observation points was 75 points, where 15 points for validation were calculated based on the map scale. Photo processing was made using Agisoft. The Digital Elevation Model (DEMNAS) with 8.2 m resolution was used to compare the red, green and blue bands. The analysis used was Co-Kriging Geo Statistics Analysis, the compilation of algorithms based on the regression equation and ten index formulations. Validation was done by correlation continued with the regression or paired t-test if the parameter relationship was close. The available water measured in the field ranged from 5.16-48.28%. The results showed that the formulation of soil water content could be run on the Red, Green, and Blue bands, Intensity index, TGI index, ExGreen index and DEMNAS with a weak correlation (below 0.5), where TGI had the highest value (r=0.32). A test of t-pairing was not done because of a weak correlation. The highest estimation of pF 4.2 is DEMNAS (r=0.35), and pF 2.5 was on the TGI index (r=0.4).

In Republic of Korea, cracks in concrete structures are considered to be objective structural defects, and the constant maintenance of deteriorating facilities leads to substantial social costs. Thus, it is important to develop technologies that enable economical and efficient building safety inspection. Recently, the application of UAVs and deep learning is attracting attention for efficient safety inspection. However, the currently developed technology has limitations in defining structural cracks that can seriously affect the stability of buildings. This study proposes a method to define structural cracks on the outer wall of a concrete building by merging the orthoimage layer and the structural drawing layer with the UAV and deep learning that were previously applied during a safety inspection. First, we acquired data from UAV-based aerial photography and detected cracks through deep learning. Structural and non-structural cracks were defined using detected crack layer, design drawing layer defined the structural part, and the orthoimage layer was based on UAV images. According to the analysis results, 116 structural parts cracks and 149 non-structural parts cracks were defined out of a total of 265 cracks. In the future, the proposed method is expected to greatly contribute to safety inspections by being able to determine the quality and risk of cracks.

Peatland permafrost landforms, such as palsas and peat plateaus, often represent the most southern lowland permafrost occurrences in the Northern Hemisphere. While peatland permafrost is often found in continental conditions, over a thousand permafrost peatlands were recently identified along the previously understudied coastline of the Labrador Sea in northeastern Canada. The vulnerability of these landscapes to thaw is unknown but is expected to have hydrological and ecological impacts on important caribou habitat, the abundance of culturally relevant berries, and permafrost carbon storage. Using a combination of aerial photography (from 1948, 1985, 1992, 1994, and 2021) and high-resolution satellite imagery (from 2017, 2020, and 2021), we assess multi-decadal areal changes to peatland permafrost landforms at seven peatlands along the Labrador Sea coastline spanning from Red Bay (51.7° N) to north of Hopedale (55.7° N). Analyses reveal declines in permafrost extent of 33%–93% at individual sites, occurring at mean rates of 0.8%–1.5%/a. Permafrost loss was found to occur most rapidly at mixed palsa and peat plateau sites (mean rate of 1.4%/a), followed by palsa sites (mean rate of 1.2%/a) and peat plateau sites (mean rate of 0.9%/a). Patterns of permafrost loss also differed between landform types, with more complete loss of individual landforms at palsa sites and more lateral and internal loss of existing landforms at peat plateau and mixed sites. This widespread degradation of peatland permafrost over the past 28–73 years is attributed to regional warming and peatland greening. Understanding recent change to permafrost peatlands in coastal Labrador is an important step towards predicting future habitat change in northeastern Canada and will inform regional land management in areas dominated by these culturally important landforms.

Program Agisoft Photoscan is a universal tool for generating 3D surface models of surveyed objects from photographic images of these objects. Photoscan is successfully used both for building models of objects and objects of different scales - from miniature archaeological artifacts to large buildings and structures, and for building terrain models based on aerial photography data and generating DEMs and orthomosaics based on these models . Data processing in Photoscan is extremely automated: the operator is entrusted only with the functions of monitoring and managing the operating modes of the program. This article discusses the use of aerial photography from an unmanned aerial vehicle (UAV) when creating digital orthophoto and topographic plans. On the improvement of the applied complexes with the use of UAVs for obtaining APS materials for cartographic purposes. Ease of UAV control, deadlines and quality, timeliness of the data transmitted from them, in our opinion, confirms the feasibility of performing these works on the creation of digital topographic maps and plans.

Las fotografías aéreas realizadas por Julio del Olmo en 1999 nos han permitido conocer de primera mano la existencia de un edificio de probable cronología romana junto al lavajo El Monduengo (Puras, Valladolid). Esta zona se localiza inmediatamente al sur de la villa romana de La Calzadilla, también conocida como villa romana de Almenara de Adaja-Puras, que se encuentra al sur de la provincia de Valladolid. Se ubica en una zona muy cercana a Coca (Segovia), la Cauca prerromana y romana cuya influencia seguramente se hizo sentir en el ritmo de colonización del campo de época romana altoimperial. En este trabajo proponemos la revisión de una de esas imágenes aéreas oblicuas tomadas por Julio del Olmo ya que, a pesar de su perspectiva, consta de gran calidad y además es la única que hasta 2020 nos mostraba los restos soterrados de este edificio. Además, hemos explorado las posibilidades para la teledetección que nos brinda la amplia serie de ortofotografías disponibles para la zona, incluyendo series históricas como USAF Serie B y el Vuelo Interministerial. Para la realización de este estudio, hemos empleado los fotogramas infrarrojos junto a la tradicional ortofotografía PNOA tomada en el espectro visible RGB, para calcular una serie de índices de vegetación y suelo que usan principalmente las bandas Roja e Infrarroja. Este trabajo de teledetección se ha realizado únicamente con imágenes públicas y software open-source (QGIS, R Project, SNAP). El estudio de la documentación gráfica nos ha llevado a plantear una prospección geofísica mediante georradar (Noggin 250 MhZ) para obtener una imagen actual y precisa que nos permita conocer la extensión y morfología de dicho edificio. Los datos obtenidos nos permiten comprender la evolución del yacimiento de Almenara de Adaja-Puras a lo largo de la época romana tardía con posterioridad al siglo III d.C. Además, comparamos esta estructura con algunas otras similares conocidas tanto en el propio yacimiento de La Calzadilla, como en otros del valle del Duero. ABSTRACT: In 1999, the aerial photographs taken by Julio del Olmo of several archaeological sites in the Duero valley allowed us to discover the existence of a new Roman building in the area of El Monduengo lagoon. This area is located immediately south of the well-known Roman villa of La Calzadilla, also known as the villa of Almenara de Adaja-Puras, in the south of the province of Valladolid, very close to Coca, the pre-Roman and Roman city of Cauca, whose influence was felt in the pace of Roman expansionism in countryside during the Early Imperial period. In this paper we propose to revisit of one of these oblique aerial images taken by Julio del Olmo, which, despite its oblique perspective, is of great quality. Moreover, even today, it is also the only image to date that shows us the buried remains of this Roman building. In addition, we have explored the possibilities for remote sensing provided by the wide range of publicly available orthophotographs, including historical series such as USAF Series B and the Interministerial Flight. In order to carry out this remote sensing study, we have used the Near Infrared frames recorded alongside the traditional PNOA orthophotography in the visible RGB spectrum. Using both RG and NIR bands we calculate vegetation and soil indices; these indices employ chiefly the Red and Near Infraed band of the electromagnetic spectrum. This remote sensing work has been carried out only with public images and open-source software (QGIS, R Project, SNAP). The study of the graphic documentation has led us to carry out a geophysical survey using Ground Penetrating Radar (Noggin 250 mhz). This way we obtain an updated and accurate image of the extension and morphological characteristics of the new building. This work is carried out in order to improve our understanding of the evolution of the Almenara de Adaja-Puras site from the end of the Late Iron Age to the Late Roman period. In addition, we compare this building with some other structures of a similar type known both at the La Calzadilla site and in other sectors of the Duero Valley.

The application of remote sensing methods provides useful information that can be used for numerous research. Thus, spatial changes in soil, vegetation, hydrography and such can be analyzed. By analyzing the data obtained by remote sensing methods, high-quality and important data can be obtained for monitoring changes in soil movement caused by landslides. This method provides the possibility of determining the state of the observed space over a longer period of time. Historical aerial imagery has a high level of spatial detail analysis. Comparative analysis of the aerial imagery from the past, recent ones and other surveys can certainly provide information on the trend of ground movement, as well as lead to conclusions for taking specific measures. The present paper gives an example of the analysis of the particular area of the “Umka” landslide based on historical surveys. The “Umka” landslide is located along the right bank of the Sava River near the city of Belgrade, which, with its long-term activity, jeopardizes residential buildings, infrastructure facilities and the population that still lives on it.