Aerial Photographs Research Articles

지난 100여 년 동안 한라산 구상나무 숲의 시공간적 분포변화

Changes in the distribution of Korean fir(Abies koreana) forest in Mt. Hallasan from the early 1900s to the present were analyzed using old maps and aerial photographs. In 1918, the area of Korean fir forest was analyzed as 1,168.4ha, and it has continued to decrease since then. The forest area in 2021 was analyzed as 606.0ha, a decrease of 562.4ha compared to 1918, which is a 48.1% decrease over 100 years. The growth decline of Korean fir forest showed differences depending on the times. From the early 1900s to the early 2000s, the average annual decreasing rate was 0.24% to 0.50%, but it rapidly increased to 1.37% to 1.99% after 2006. Forest changes in Mt. Hallasan showed different trends depending on the region. Over the past 100 years, the forest has expanded in the Bangaeoreum area, while it has decreased in the Jindallaeba, Yeongsil, and Keundurewat areas. In particular, the Jindallaeba area has decreased by 60.1% over the past 100 years, showing the largest reduction in distribution. As shown in these results, over the past 100 years, the Korean fir forest in Mt. Hallasan has shown a continuous overall decline, confirming a clear difference in decreasing trend between the 1900s and 2000s, and showing different trends depending on the region. This difference is interpreted as the fact that various growth decline factors cause different artificial or natural intensity of damage depending on the environment of Korean fir forest distribution area.

Rock glaciers across the United States predominantly accelerate coincident with rise in air temperatures

Despite their extensive global presence and the importance of variations in their speed as an essential climate variable, only about a dozen global time series document long-term changes in the velocity of rock glaciers – large tongue-shaped flows of frozen mountain debris. By analysing historical aerial photographs, we reconstruct here 16 new time series, a type of data that has not previously existed for the North American continent. We observe substantial accelerations, as much as 2–3 fold, in the surface displacement rates of rock glaciers across the mountains of the western contiguous United States over the past six to seven decades, most consistent with strongly increasing air temperatures in that region. Variations between individual time series suggest that different local and internal conditions of the frozen debris bodies modulate this overall climate response. Our observations indicate fundamental long-term environmental changes associated with frozen ground in the study region.

The hawk eye scan: Halyomorpha halys detection relying on aerial tele photos and neural networks

This paper faces the challenge of monitoring the Brown Marmorated Stink Bug (H.halys) (Halyomorpha halys) within orchards, utilizing drones, and computer vision. H.halys is an invasive species originating from East Asia, that is extremely polyphagous and poses a significant threat to various crops. Our first contribution is a drone navigation protocol, which ensures risk-free drone flights in cluttered orchard environments, preserving image quality and avoiding obstacles. We then create a pioneering H.halys dataset consisting of aerial telephotos captured in the field autonomously by the drone. The dataset allows the development and evaluation for the first time of multiple ML models for H.halys detection in the field. We trained YOLOv5, YOLOv8, RetinaNet, and Faster-RCNN models using different learning methodologies, exploiting different percentages of images without the bug, and using different slicing procedures for the images. The Medium YOLOv5 model trained with all images containing a bug detects the largest number of H.halys on the testing set and overall performs the best, while RetinaNet and Faster-RCNN provide the best trade-off between precision and recall. Models vary in their ability to handle occluded H.halys and bug-free images, which are common since the presence of the bug cannot be predicted before capturing a photo. These results show promising potential for automating H.halys monitoring, despite the image complexity and the early dataset stage. Our work marks a significant step towards enhancing smart agriculture practices due to the simplicity of the data acquisition process and the off-the-shelf hardware selection.

EA‐YOLO: An Efficient and Accurate UAV Image Object Detection Algorithm

An improved EA‐YOLO object detection algorithm based on YOLOv5 is proposed to address the issues of drastic changes in target scale, low detection accuracy, and high miss rate in unmanned aerial vehicle aerial photography scenarios. Firstly, a DFE module was proposed to improve the effectiveness of feature extraction and enhance the whole model's ability to learn residual features. Secondly, a CWFF architecture was introduced to enable deeper feature fusion and improve the effectiveness of feature fusion. Finally, in order to solve the traditional algorithm's shortcomings it is difficult to detect small targets. We have designed a novel SDS structure and adopted a strategy of reusing low‐level feature maps to enhance the network's ability to detect small targets, making it more suitable for detecting some small objects in drone images. Experiments in the VisDrone2019 dataset demonstrated that the proposed EA‐YOLOs achieved an average accuracy mAP@0.5 of 39.9%, which is an 8% improvement over YOLOv5s, and mAP@0.5:0.95 of 22.2%, which is 5.2% improvement over the original algorithm. Compared with YOLOv3, YOLOv5l, and YOLOv8s, the mAP@0.5 of EA‐YOLOs improved by 0.9%, 1.8%, and 0.6%, while the GFLOPs decreased by 86.4%, 80.6%, and 26.7%. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Archival Aerial Photographs of Africa: Present Potential and Imagining a Machine-Learning Future

Archival Aerial Photographs of Africa: Present Potential and Imagining a Machine-Learning Future

YOLOv7-P: a lighter and more effective UAV aerial photography object detection algorithm

YOLOv7-P: a lighter and more effective UAV aerial photography object detection algorithm

Land use transformation by urban informal settlements and ecosystem impact

The rapid expansion of informal settlements poses significant challenges to urban ecosystems and their services, a pressing issue that this study addresses by examining its impacts in Addis Ababa. This study delves into the impact of informal settlements on ecosystem services in Addis Ababa, comparing them with formal settlements. Utilizing geographic information systems (GIS) and remote sensing, it employed Landsat imagery, aerial photography, and base maps to track spatiotemporal changes and analyze trends. Through a GIS overlay approach, informal settlements and associated land use changes were identified, while the benefit transfer method assessed ecosystem service values. The findings revealed a significant increase in informal settlements on the city’s outskirts, expanding from 77 ha in 2009 (2% of the total area) to 765.6 ha in 2023 (21% of the total area). Nearly half (48.5%) of these informal settlements were established on previously natural land covers through encroachment. The research highlighted the ongoing conversion of agricultural land, forests, and urban green spaces into informal settlements, particularly on the city’s outskirts, leading to significant encroachment on these areas. This transformation caused an annual loss of $1,665,033.7 in ecosystem services from 2009 to 2023. Significant impacts were identified on food production, climate regulation, and habitat ecosystem services. Overall, the annual loss in ecosystem services due to land use changes amounted to $1,933,320.08. Informal settlements accounted for 86.1% of this total loss, highlighting the urgent need for targeted interventions to mitigate their disproportionate effects on ecosystem services. The study underscores the urgency of addressing the impact of informal settlements on ecosystem services through effective urban planning, sustainable land management practices, prevention of encroachment on natural land covers, and promotion of sustainable ecosystem management and utilization.

Near Pan-Svalbard permafrost cryospheric hazards inventory (SvalCryo)

Cryospheric hazards - in this case, thaw slumps (TS) and thermo-erosion gullies (TEG) - are phenomena typical of permafrost-dominated landscapes. Open datasets informing about their spatial, temporal and size distributions in the Arctic are still uncommon, as opposed to the systematic availability of this information for geomorphic processes in mid- to low- latitudes. To date, only the most populated region of Svalbard was covered with TS and TEG inventories. Here, we extend the respective information to most of the Archipelago, totalling 8491 polygons, out of which 3679 are TSs and 4812 are TEGs. These have been manually mapped from aerial photographs throughout Svalbard across the 14 largest ice-free regions. The SvalCryo inventory is highly relevant as the Arctic environment undergoes alarming changes in response to global warming. The idea behind the two inventories is to support the geoscientific community in the quest to evaluate the environmental response to climate change, by creating a baseline for change monitoring, and ultimately to serve as basis for susceptibility, hazard and risk assessment models.

Investigation of planar sliding deformation and analysis of the damage mechanism of a rocky landslide in Yaoping triggered by highway excavation in Hubei, China

During projects to build roads in China's mountainous areas, which are often characterized by the poor stability of rocky slopes, cases of deformation damage occur frequently. Because of the wide distribution of rocky landslides in the smooth layer, the unavoidable perturbation caused by engineering construction and the seriousness of landslide disasters, the destruction of this type of landslide and the research into prevention of the mechanism cannot be ignored. On 6 February 2023, a rock slide occurred in Yaoping village, Hubei Province, China. Using field investigations, Google satellite images, unmanned aerial vehicle photography, indoor testing and numerical simulations, we analysed the geological situation, deformation characteristics, damage process and triggering mechanism. The results show that the Yaoping landslide is a typical downward plane-sliding rock slide induced by the excavation of a highway. When strain softening occurs, the shear strength and friction coefficient of the slip zone soil are significantly reduced. Excavation caused the factor of safety of the slope to decrease substantially, with a maximum decrease rate of 60.7%. Excavation is considered to be the extrinsic factor that triggered the occurrence of the Yaoping landslide, while the reduction of landslide resistance caused by strain softening of the soil in the slip zone is the intrinsic direct factor that led to the occurrence of this type of landslide. The Yaoping landslide has obvious traction landslide characteristics. The landslide from deformation to destruction has four stages: creep at the leading edge of the landslide; traction deformation in the middle and back of the landslide; slip belt softening; and integral sliding. The prevention and control of this type of landslide requires a larger scope and depth of investigation, in order to identify in advance the possible soft surface in the rock layer, to speculate on the possible formation of landslide destruction mode, and to determine the support scheme and scope according to the geological conditions of the slopes on both sides of the highway. The slip zone soil of the Yaoping landslide was used to obtain the strength index of slip zone soil, which provides data support for the overall deformation characterization of the landslide. The results can provide a reference for the risk management and prevention of rock slides caused by human engineering activities during the excavation of roads.

Application of Siamese neural networks to compare aerial photographs with terrain maps

The work presents the methodology for the development and training of the Siamese neural network for comparing aerial photographs with terrain maps. The proposed approach is aimed at identifying stable and informative features in images, which allows to increase the accuracy and automation of the matching process. The presented method uses two identical networks that are trained in parallel, which ensures a reduction in the gap in characteristics between the compared images. The Siamese neural network efficiently handles images of varying quality and detail, making it ideal for comparing aerial photographs with terrain maps. The developed tool allows for quick analysis and comparison of aerial photographs with terrain maps with high efficiency and accuracy, which contributes to the expansion of the field of application in geoinformation research and engineering applications. Figs.: 3. Refs.: 11.

Multi‐decadal changes in vegetation and land use inferred from different data sources: Two case studies at the regional scale in the Slovenian karst

AbstractFew studies have used aerial imagery or various statistical records to analyse temporal land use and vegetation dynamics over large areas. This study presents an innovative approach that merges national‐scale data to provide temporal and spatial digital information on land use and vegetation cover change at high resolution. The work includes 326 historical aerial photographs from 1957 and 1980, derived digital terrain models, digital orthophotos (from 1994 to 1997), current land use (available from 2006), and forestry databases. Using two case studies in Slovenia, a detailed procedure for data preparation, processing (including orthorectification, on‐screen digitisation, visual photointerpretation), and result generation is presented. The used multiple sources showed great potential for the acquisition, mapping and quantifying selected land use categories. The study provided very important material for land use change assessment and implementation of the national documentary heritage. Although the processing methods were time‐consuming, they proved to be very suitable for delineating polygons of change with a spatial resolution of at least 1 m. The results show that the forest in the Rižana catchment has increased by 30 percentage points in the last 60 years and that in the Unica catchment 2/3 of the forest was damaged by large‐scale disturbances in 2014–2018.

Relief displacement of airborne objects

ABSTRACT The increasing availability of unoccupied aircraft systems (UAS, also referred to as drones) has led to their use in taking vertical aerial photographs at relatively small spatial scales. These photographs can be used to measure the distances between objects appearing in the photographs. However, relief displacement can cause an object above or below ground level to appear at a point in a vertical aerial photograph that is not directly in-line with the object’s actual location, causing a measurement error. A UAS was used in this study as a photographed airborne object because its location and altitude could be controlled. We were interested in predicting the horizontal distance of the UAS’s appearance from the centre of a vertical aerial photograph. Predictions of the location of the photographed UAS’s appearance in vertical aerial photographs over both level and sloped surfaces matched measured appearance distances within 0.06–0.48 m. This study shows that the relief displacement formulas typically used to compute the height of a vertical structure appearing in a vertical aerial photograph can additionally be used to compute the actual location of an airborne object (e.g., a flying UAS, bird, bat) if the object’s altitude is known or can be estimated.

Monitoring of Pests and Diseases by New Energy Aerial Mapping Methods

To expand the application of new energy technology in agriculture and improve the monitoring effect of agricultural pests and diseases, this paper proposes a new energy aerial tracing method to extract the identification indicators of pests and diseases in aerial photography. Then, combined with the occurrence area and occurrence rate of pests and diseases, the calculation and analysis were carried out, and the early warning was carried out according to the point characteristics of pests and diseases. The results show that the monitoring and identification of pests and diseases can improve the identification rate of flying pests, plant pests and underground pests by combining the new energy aerial mapping method, improve the identification accuracy from 74.68% to 88.67%, and carry out early warning, reduce the cost of agricultural loss by 38 million~154 million yuan, and the identification range from 14,500 hectares~16,500 hectares, and reduce the monitoring time by 74.5~87.5%. Therefore, new energy technology can meet the monitoring requirements of agricultural pests and diseases, which is helpful to promote the development of new energy further.

Evaluating pasture cover density mapping: a comparative analysis of Sentinel-2 and Spot-5 multispectral sensor images

Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale.

Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning.

Ocean plastic pollution is one of the global environmental problems of our time. "Rubbish islands" formed in the ocean are increasing every year, damaging the marine ecosystem. In order to effectively address this type of pollution, it is necessary to accurately and quickly identify the sources of plastic entering the ocean, identify where it is accumulating, and track the dynamics of waste movement. To this end, remote sensing methods using satellite imagery and aerial photographs from unmanned aerial vehicles are a reliable source of data. Modern machine learning technologies make it possible to automate the detection of floating plastics. This review presents the main projects and research aimed at solving the "plastic" problem. The main data acquisition techniques and the most effective deep learning algorithms are described, various limitations of working with space images are analyzed, and ways to eliminate such shortcomings are proposed.

Evolution of riparian vegetation in response to anthropogenic effects: The Taleqan River, Iran

AbstractDuring the last decades, human interventions, such as dam construction and land‐use change, have caused changes in geomorphic patterns and riparian zones in many rivers in Iran. This study aimed to assess the spatio‐temporal evolution of riparian vegetation and the spatial area of biogeomorphic feedback windows (BFWs) located in the riparian Salicaceae vegetation patches and their traits responding to human interventions in the Taleqan River during 1990–2022. The river was divided into five reaches using the geomorphic unit system (GUS) method, and the river sensitivity index (SI), location of BFWs, vegetation typology and main cover typology were investigated in the reaches using aerial photographs and satellite images from 1990 to 2022. For this purpose, the main cover typology was classified into grassland, woody vegetation, bank‐attached bars, islands, vegetated islands, river channels and urban areas. The stem diameter and density, degradation level and longitudinal/latitudinal alluvial bars in each of the BFWs were investigated using the field survey. Based on the findings, the lowest values of the SI (braiding index [BI], channel activity [CA] and channel width [CW]) and the highest expansion of BFWs were observed in the upstream reaches of the Taleqan River. Human disturbances in these reaches were less than in the reaches downstream, allowing the expansion of vegetation habitats. The vegetation patches were unstable and scattered in the midstream and downstream, and the width decrease of the river channel was created by land‐use change and channelization. A significant direct relationship was observed between BFW establishment, stem diameter and density, and vegetation height. The BFWs create an opportunity for engineer vegetation to prevent erosion and expand new habitats by creating biogeomorphic accumulation landforms and trapping alluvial bars on the lee side of engineer woody vegetation. This study highlights the interactions between riparian areas and hydrogeomorphic processes within riparian corridors exposed to human disturbances to help better define a comprehensive plan for river management.

Preparing Cadastral Map Databases for Mosul District

Investigates the development and implementation of digital cadastral map databases for Mosul District, Iraq, addressing the transition from traditional paper-based systems to modern geospatial technologies. The research explores the integration of Geographic Information Systems (GIS), remote sensing, and Global Positioning System (GPS) technologies to enhance cadastral mapping processes. Encompass data collection from diverse sources, including high-resolution satellite imagery, aerial photography, and existing cadastral maps. A comprehensive analysis of data types— vector, raster, Triangulated Irregular Network (TIN), and tabular—is conducted to establish an optimal database structure. The study delineates the technical capabilities of GIS in data processing, spatial analysis, and model preparation, emphasizing the importance of data accuracy and consistency. A systematic approach to data entry, including map scanning, georeferencing, and digitization, is presented. The research outlines the creation of a multi-layered geographic database, incorporating both spatial and descriptive data. Protocols for database updating using satellite imagery and field surveys are developed, ensuring the currency and reliability of cadastral information. The results demonstrate significant improvements in data management, spatial analysis capabilities, and information accessibility compared to traditional methods. The study concludes that digital cadastral databases offer enhanced decision-making support for land administration and urban planning in Mosul District. Contributes to the body of knowledge on modernizing cadastral systems in developing regions, providing insights into technological integration, data standardization, and the challenges associated with transitioning to digital cadastral management systems. The findings have implications for policy makers and land administrators seeking to implement similar systems in comparable urban contexts.

Traffic conflict analysis in continuous confluence area of cross-river bridge driven by vehicle trajectory data

Objectives To conduct an in-depth study on the spatial distribution of traffic conflicts in the continuous merging areas of cross-river bridges and ensure public transportation safety. Methods First, we utilized drone aerial photography to collect videos of vehicle movements. Using the YOLOv7 object detection algorithm and the Strong SORT multi-object tracking algorithm, we extracted high-precision vehicle trajectory time-series data. Next, based on the motion characteristics of traffic entities, we proposed using Deceleration Rate (DR) to describe rear-end conflicts and Lane Change Speed (LCS) to describe lane-changing conflicts. Additionally, we employed the K-means clustering method to determine the threshold values for minor, moderate, and severe levels of rear-end and lane-changing conflicts. Finally, based on the obtained trajectory data, the values of traffic conflicts are calculated and their severity is classified. A heat map of the spatial distribution of vehicle conflicts in continuous merging zones is then created to study the spatial distribution patterns of traffic conflicts. Results The threshold values for minor, moderate, and severe levels of rear-end conflicts are determined to be 3.06 m/s2, 5.36 m/s2, and 8.04 m/s2, respectively. For lane-changing conflicts, the thresholds are 1.13 m/s, 2.07 m/s, and 3.45 m/s. The spatial distribution of traffic conflicts exhibits a “first increase, then decrease, and then increase again” trend. Conclusions The study identifies the critical areas of traffic conflicts in the continuous merging zones of cross-river bridges. The research results provide a novel approach for acquiring traffic data in these areas and offer a reliable quantitative method for assessing safety risks on these road segments. This provides a theoretical basis for proposing targeted traffic safety management strategies.

Declining Bank Erosion Rate Driven by Hydrological Alterations of a Small Sub-Alpine River

In the 21st century, climate change and its consequences are getting more serious. The changes in temperature and precipitation alter the run-off conditions, subsequently influencing the channel processes of rivers. The study aims to analyse the hydrological changes in a small, sub-alpine river (Rába/Raab River, Central Europe) and the bank erosional processes (1951–2024). The bank erosion was determined based on topographical maps, aerial photographs, and field (RTK–GPS) surveys. Short (2–3 days) floods were common between 1950 and 1980, and low stages occurred in 65–81% of a year. However, extreme regimes developed in the 21st century, as record-high, flash floods altered with long low stages (91–96% of a year). The bank erosion shows a cyclic temporal pattern, gradually increasing until it reaches a high value (4.1–4.9 m/y), followed by a limited erosional rate (2.2–2.8 m/y). However, the magnitude of the bank erosion is decreasing. This could be explained by (1) the lower transport capacity of the more common low stages and (2) the seasonal shift of the flood waves, which appear in the growing season when the riparian vegetation can more effectively protect the banks from erosion.

Aerial SfM–MVS Visualization of Surface Deformation along Folds during the 2024 Noto Peninsula Earthquake (Mw7.5)

This study aimed to map and analyze the spatial pattern of the surface deformation associated with the 2024 Noto Peninsula earthquake (Mw7.5) using structure-from-motion/multi-view-stereo (SfM–MVS), an advanced photogrammetric technique. The analysis was conducted using digital aerial photographs with a ground pixel dimension of 0.2 m (captured the day after the earthquake). Horizontal locations of GCPs were determined using pre-earthquake data to remove the wide-area horizontal crustal deformation component. The elevations of the GCPs were corrected by incorporating quasi-vertical values derived from a 2.5-dimensional analysis of synthetic aperture radar (SAR) results. In the synclinorium structure area, where no active fault had previously been identified, we observed a 5 km long uplift zone (0.1 to 0.2 km in width), along with multiple scarps that reached a maximum height of 2.2 m. The area and shape of the surface deformation suggested that the induced uplift and surrounding landslides were related to fold structures and their growth. Thus, our study shows the efficacy of SfM–MVS with respect to accurately mapping earthquake-induced deformations, providing crucial data for understanding seismic activity and informing disaster-response strategies.