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The Balearic coastline presents an environmental and biological heterogeneity, which confers great complexity on the marine environment and treasures important biodiversity, both at the level of species and marine communities. The endemic phanerogam of the Mediterranean Sea, Posidonia oceanica, holds a pivotal role in maintaining high biodiversity, warranting protection as stipulated in the Posidonia Decree 25/2018. The purpose of this study is to provide quantitative criteria that will allow the delimitation of areas with Posidonia oceanica for conservation and to aid planning and management of this species, contributing to the reduction of biodiversity loss caused by anthropogenic impacts and global change. Utilizing a comprehensive approach, the study employs photo interpretation of aerial photographs taken at depths between 0 and 5 m, data from Side-Scan Sonar (SSS) campaigns, reprocessing information from the LIFE Posidonia project at depths between 20 and 30 m, and targeted sampling using Remote Operated Vehicles (ROV) and ocular recognition at strategic points. The research not only seeks to assess the present state of the phanerogam but also analyzes its evolution, establishing a technological database for consultation and integrated analysis. This database facilitates effective management by tracking habitat changes, representing a significant contribution to the understanding of the impact of global change on ecosystems through Geographical Information Technologies (TIGs). [ABSTRACT FROM AUTHOR]

Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the one-year drift campaign MOSAiC in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in the description of the surface meltwater volume, necessary to estimate budgets, and used in model parametrization to simulate pond coverage evolution. However, observational data on pond depth is spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains entirely unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air—pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea-ice surface emerging from the increasingly available visual image data recorded from UAVs or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models. [ABSTRACT FROM AUTHOR]

The natural resources of Ethiopian high-altitude ecosystems are commonly perceived as increasingly threatened by devastating land-use practices owing to decreasing lowland resources. Quantified time-series data of the course of land-use cover changes are still needed. Very-high-resolution digital data on the historical landscape over recent decades are needed to determine the impacts of changes in afro-alpine ecosystems. However, digital elevation models (DEMs) and orthomosaics do not exist for most afro-alpine ecosystems of Africa. We processed the only available and oldest historical aerial photographs for Ethiopia and any afro-alpine ecosystem. Here, we provide a DEM and an orthomosaic image for the years 1967 and 1984 for the Bale Mountains in Ethiopia, which comprise the largest afro-alpine ecosystem in Africa. We used 298 historical aerial photographs captured in 1967 and 1984 for generating DEMs and orthomosaics with a structure-from-motion multi-view stereo photogrammetry workflow along an elevation gradient from 977 to 4377 m above sea level (a.s.l.) at very high spatial resolutions of 0.84 m and 0.98 m for the years 1967 and 1984, respectively. The structure-from-motion multi-view stereo photogrammetry workflow, employed with Agisoft Metashape, represents a modern approach that combines computer vision and photogrammetry. This method proves useful for reconstructing DEMs and orthomosaics from historical aerial photographs, with a focus on high spatial resolution. To validate the accuracy of the reconstructed DEMs, ground control points gathered through GPS measurements were used, resulting in root mean square error (RMSE) values of 3.55 m for the year 1967 and 3.44 m for the year 1984. Our datasets can be used by researchers and policymakers for watershed management, as the area provides water for more than 30 million people, landscape management, detailed mapping, and analysis of geological and archaeological features as well as natural resources, analyses of geomorphological processes, and biodiversity research. All the datasets are available online at 10.5281/zenodo.7271617 (Muhammed et al., 2022a) for all the inputs used and at 10.5281/zenodo.7269999 (Muhammed et al., 2022b) for the results obtained (very-high-resolution DEMs and orthomosaics) for both the years 1967 and 1984. [ABSTRACT FROM AUTHOR]

This study reports the combination of remote sensing and ground geophysical techniques to locate an abandoned and hidden municipal solid waste landfill located in a fluvial plain in the French Western Alps. Following earthworks and further floods that eroded into the river bank, wastes made of a mixture of plastic, metal and soil/earth, were uncovered and some of them flowed into the river. The existence of an abandoned landfill, several decades-old, was known, but the knowledge of its exact location was forgotten. Historic aerial photographs back to 1948 allowed delineation in space and time of the location of a platform that was used for landfill operations between around 1973 and 1983. A LiDAR DEM acquired in 2012 allowed was used to locate topographic depressions 0.1 to 0.4 m in depth, notably inside the platform. These depressions are interpreted as resulting from differential compaction originating from the presence of compressible wastes. Geophysical mapping techniques (magnetic and electromagnetic) confirmed the presence of anomalies inside the identified platform. Geophysical imaging techniques (ground-penetrating radar, electrical resistivity tomography) provided a quantitative evaluation of the width and depth of the individual pits. The combination of the different techniques allowed for estimating the first-order volume of waste. The methodology adopted in this work is applicable to detect landfills exhibiting differential compaction and physical contrasts. [ABSTRACT FROM AUTHOR]

Context: The Mediterranean mountains have witnessed the revegetation of former pasture and cultivated fields since the mid-20th century. The process started when the mountains joined a dynamic and competitive market, which led to depopulation, abandoning cultivated fields, and a reduction in extensive livestock farming. Objectives: We investigate changes in land use and land cover (LULC) and landscape structure in a valley of the Central Pyrenees in 1956, 1981 and 2017, and we discuss the implications for land management within a multidisciplinary context. Methods: Aerial photographs are used to map the LULC in 1956, 1981 and 2017, followed by landscape ecology metrics and indices to analyse the structure of the landscape. Results: The results show: (i) a reduction in cultivated area (-42%) and pastures (-63%) in the montane belt between 1956 and 2017, while forests doubled in area; (ii) changes in LULC are more significant in the montane belt than in subalpine and alpine areas; (iii) based on the Kappa index, it was found that the 29% of the area had undergone changes in LULC between 1956 and 2017, with the changes being greater in the first period (1956–1981) than in the second (1981–2017); (iv) the structure and heterogeneity of the landscape shows an increase between 1956 and 1981, and a change in trend from 1981 to 2017, so that the landscape is more homogeneous in 2017 than in 1956. Conclusion: The discussion suggests the need to maintain and recover mosaic landscapes, since their structure is akin to traditional cultural ones, to increase biodiversity and sustainability, and provide ecosystem services to the population. [ABSTRACT FROM AUTHOR]

The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop diseases. The advantages and disadvantages of each model considered during training were taken into account, and the Inception V3 algorithm was incorporated into the application. They can monitor the condition of crops on a daily basis with the help of new technology-applications on gadgets. Aerial photographs used by research institutes and agricultural grain centers do not show the changes that occur in agricultural grains, that is, diseases and pests. Therefore, the method proposed in this paper determines the types of diseases and pests of cereals through a mobile application and suggests ways to deal with them. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education & Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Woody plant encroachment (WPE) is a global trend that occurs in many biomes, including savannas, and accelerates with fire suppression. Since WPE can result in increased storage of soil organic carbon (SOC), fire management, which may include fire suppression, can improve ecosystem carbon (C) sequestration in savannas.At our study site in Kruger National Park, South Africa, we used a long‐term (~70 year) fire experiment to study the drivers and consequences of changes in woody cover (trees and shrubs) on SOC sequestration. We surveyed four fire manipulation treatments, replicated at eight locations within the park: annual high‐intensity burns, triennial high (dry season) and low‐intensity (wet season) burns, and fire exclusion, to capture the range of fire management scenarios under consideration. The changes in woody cover were calculated over a period similar to the experiment's duration (~80 years) using aerial photographs (1944–2018). Soils were analysed to 30 cm depth for SOC and δ13C, under and away from the tree canopy to isolate local‐ and landscape‐level effects of WPE on SOC.The largest increases in woody cover occurred with fire exclusion. We found that plots with higher increases in woody cover also had higher SOC. However, trees were not the only contributor to SOC gains, sustained high inputs of C4‐derived C (grasses), even under canopies in fire suppression plots, contributed significantly to SOC. We observed little difference in SOC sequestration between cooler triennial (wet season) burns and fire suppression.Synthesis. Grass input to soil organic carbon (SOC) remained high across the full range of woody cover created by varying burning regimes. The total SOC stocks stored from tree input only matched grass‐derived SOC stocks after almost 70 years of fire exclusion. Our results point to C4 grasses as a resilient contributor to SOC under altered fire regimes and further challenge the assumption that increasing tree cover, either through afforestation schemes or fire suppression, will result in large gains in C sequestration in savanna soils, even after 70 years. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model's detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model's effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection. [ABSTRACT FROM AUTHOR]

Tree detection and fuel amount and distribution estimation are crucial for the investigation and risk assessment of wildfires. The demand for risk assessment is increasing due to the escalating severity of wildfires. A quick and cost-effective method is required to mitigate foreseeable disasters. In this study, a method for tree detection and fuel amount and distribution prediction using aerial images was proposed for a low-cost and efficient acquisition of fuel information. Three-dimensional (3D) fuel information (height) from light detection and ranging (LiDAR) was matched to two-dimensional (2D) fuel information (crown width) from aerial photographs to establish a statistical prediction model in northeastern South Korea. Quantile regression for 0.05, 0.5, and 0.95 quantiles was performed. Subsequently, an allometric tree model was used to predict the diameter at the breast height. The performance of the prediction model was validated using physically measured data by laser distance meter triangulation and direct measurement from a field survey. The predicted quantile, 0.5, was adequately matched to the measured quantile, 0.5, and most of the measured values lied within the predicted quantiles, 0.05 and 0.95. Therefore, in the developed prediction model, only 2D images were required to predict a few of the 3D fuel details. The proposed method can significantly reduce the cost and duration of data acquisition for the investigation and risk assessment of wildfires. [ABSTRACT FROM AUTHOR]

This research implements GIS in transportation, specifically road maintenance. The system is built by utilizing 2D/3D models from aerial photographs using UAV as a base map. Attribute data such as the type and dimensions of road damage can be obtained by interpreting high-resolution 2D/3D models, which display each road damage, making it easier to measure the dimensions of road damage. The assessment of road conditions is done using the PCI method, which indicates that 51% of the roads fall under the category of people with low incomes to severely damaged category. These roads are prioritized on a map based on their area and cost of maintenance. The projection calculation of the amount of damage is analyzed with one do-nothing scenario, where the roads have not been maintained for ten years. The progression of the damage is observed each year, and the reactive maintenance cost is calculated from 2023 to 2032. The cost and duration are analyzed using three do-something scenarios: optimistic, moderate, and pessimistic. The research results show that the moderate scenario has the lowest cost among the other scenarios and is the most effective scenario, as it produces road conditions with an International Roughness Index (IRI) value of less than 6. This research can assist the government in making informed decisions regarding road maintenance. [ABSTRACT FROM AUTHOR]

During 2012, aerial photographs of the Vaal River, South Africa, showed differences in water colour on either side of the Lethabo Weir. Water upstream of the weir was muddy brown, while water downstream was olive-green. Physical and chemical analyses indicated water quality differences upstream and downstream from the weir; furthermore, microscopic examination of water samples revealed that the colour difference were the result of different types of phytoplankton blooms. The upstream section was dominated by cryptophytes and green algae, while the downstream section experienced a cyanobacterial bloom dominated by Microcystis aeruginosa. The same phenomenon was again observed regularly between 2012 and 2022, and routine monitoring of the phytoplankton assemblage likewise indicated differences in the phytoplankton community composition upstream and downstream of the weir. The upstream section was characterised by various eukaryotic algae; in contrast, frequent and intensive cyanobacteria blooms occurred in the downstream section. Multivariate analyses showed a correlation between the different phytoplankton assemblages and physico-chemical properties of the river water. Many freshwater cyanobacteria and algae taxa are problematic: Microcystis and Dolichospermum produce potent cyanotoxins; flagellated green algae, cryptomonads and dinoflagellates produce substances that cause bad tastes and odours, plus the cells can cause floc disruption during drinking water treatment processes; and certain filamentous green algae and diatoms are known for clogging filters. [ABSTRACT FROM AUTHOR]

The Fagradalsfjall eruption commenced on 19 March 2021 on a ~180-m-long eruptive fissure, following a 23-day dike intrusion. New eruptive fissures opened northeast of the initial eruption site on 5, 6–7, 10, and 13 April 2021. The northernmost eruption occurred on 5 April, approximately 1 km northeast of the initial fissure, with the other fissure openings between this and the initial eruptive vents. Still images from web cameras and time-lapse cameras are available for five of the fissure openings. These data show that the eruptions were preceded by steam emitted from cracks in the exact locations where the eruptions started. The time between the first steam observations and the visual appearance of glowing lava ranged between 15 s and 1.5 min during night observations and from 9 to 23 min during daytime observations. The difference in observation time is likely explained by the different lighting conditions. The eruptive vents are located where the north-easterly oriented dike intersected pre-existing north-south-oriented fractures, inferred to be strike-slip faults. These fractures could be identified on a high-resolution ICEYE interferogram as well as on pre-existing aerial photographs and digital elevation models. This interferogram spanned the first day of the eruption (19–20 March 2021). It not only displays deformation related to the pre-eruptive dike intrusion but also shows lineations in locations where eruptive vent openings occurred later in April 2021. These findings demonstrate how Interferometric Synthetic Aperture Radar Analysis (InSAR) can be used to forecast likely locations of subsequent eruptive vent openings, which is of great importance for hazard assessment and defining exclusion zones during fissure eruptions. [ABSTRACT FROM AUTHOR]

Knowledge of the health of banana trees is critical for farmers in order to profit from banana cultivation. Fusarium wilt and banana blood disease (BBD), two significant diseases that infect banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have caused a decline in crop yield, as they destroy trees, starting sequentially from the pseudostem to the fruit. The entire distribution of BBD and fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using unmanned aerial vehicles (UAVs) and a reliable data field for infected trees. Vegetation and soil indices derived from multispectral aerial photographs, such as the normalized difference vegetation index, the modified chlorophyll absorption ratio index, the normalized difference water index (NDWI), and soil pH, may have to be relied upon to explain the precise location of these two diseases. This study used a random forest algorithm to handle a large dataset consisting of multispectral and spectral models. The results show that the soil indices, soil pH, and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BBD is more extensive than that affected by fusarium if variations in planted banana cultivars are not considered. [ABSTRACT FROM AUTHOR]

The forces that drove the birth of Kwun Tong new town in Hong Kong are not comprehensively understood. Previous historical narratives have magnified the impact of industrialization or laissez-faire liberalism. This article looks at the formation of Kwun Tong new town from the perspective of migration and employment, and reveals that employment acted as a catalyst to resettlement. The production of space theory and investigation of British colonial archival documents, proposal maps, and aerial photographs reveal that new town planning unfolded relative to spatial coding in Ngau Tau Kok and urban practice in Shek Kip Mei, showing historical continuity. The social and political dynamics of immigrants and how they interactively influenced the built-environment and resettlement are highlighted in this article. Under morphological analysis, critical changes in urban form provide other angles from which to understand how the new town was shaped. In addition, morphological analysis provides a spatial dialectics, filling the research gap of town planning in Hong Kong in the 1950s. [ABSTRACT FROM AUTHOR]

The morphoevolution of coastal areas is due to the interactions of multiple continental and marine processes that define a highly dynamic environment. These processes can occur as rapid catastrophic events (e.g., landslides, storms, and coastal land use) or as slower continuous processes (i.e., wave, tidal, and current actions), creating a multi-hazard scenario. Maronti Bay (Ischia Island, Southern Italy) can be classified as a pocket beach that represents an important tourist and environmental area for the island, although it has been historically affected by slope instability, sea cliff recession, and coastal erosion. In this study, the historical morphoevolution of the shoreline was analysed by means of a dataset of aerial photographs and cartographic information available in the literature over a 25-year period. Furthermore, the role of cliff recession and its impact on the beach was also explored, as in recent years, the stability condition of the area was worsened by the occurrence of a remarkable landslide in 2019. The latter was reactivated following a cloudburst on the 26th of November 2022 that affected the whole Island and was analysed with the Dem of Difference technique. It provided an estimate of the mobilised volumes and showed how the erosion and deposition areas were distributed and modified by wave action. The insights from this research can be valuable in developing mitigation strategies and protective measures to safeguard the surrounding environment and ensure the safety of residents and tourists in this multi-hazard environment. [ABSTRACT FROM AUTHOR]

The objective of this study was to devise effective safety management systems for enclosed living modified organism (LMO) fields regulated by the Ministry of Environment (MOE), achieved through an assessment of the impact of LM crops on the surrounding flora. A combination of conventional survey methods and cutting-edge drone-based monitoring systems was employed, with a keen focus on their efficacy. Our investigation spans three distinct zones (forest, non-forest, and enclosed field), involving vegetation surveys, biodiversity index analyses, and drone-powered aerial observations to study topographical shifts. Over time, wild plants adjacent to the enclosed LMO field exhibited stability in terms of species composition. Nevertheless, disparities in growth patterns emerged across various areas. Predominantly, herbs thrived in enclosed and non-forest areas, while trees and shrubs flourished in forested regions. Annual plants predominantly populated the non-forest regions, whereas perennials dominated the forested areas. To this end, drones captured aerial photographs of a 31.65-hectare expanse with 40% coverage overlap, furnishing a real-time vegetation map that transcends the capacities of conventional methods. By combining vegetation surveys, drone-generated vegetation mapping, and dynamic monitoring of topographical changes, our research endeavors to facilitate the formulation of a robust safety management framework for LMO confined fields overseen by the MOE. This holistic approach aspires to prevent ecosystem contamination and establish a resilient, enduring system that averts LMO leakage, thereby safeguarding the environment. [ABSTRACT FROM AUTHOR]

Morphology and hydraulic condition of rivers explain habitat and actual species distribution of fish species. However, detailed ground surveys to obtain such habitat information are generally complicated and costly. Therefore, we aimed to explore the possibility of integrating aerial photographs and image recognition technique as a supplementing approach for field surveys. For this purpose, we focused on one benthic species (Acanthogobius flavimanus) and two migratory species (Nipponocypris temminckii and Plecoglossus altivelis) as representative species. Their distribution in the Kanto region of Japan was obtained from the national census on river environments-riparian zone, while aerial photographs of the corresponding river sections were collected from Geographical Survey Institute of Japan. Then, convolutional neural network (CNN) was applied to model the fish distribution based on the riverine photographs. As per the results from hypothesis tests, CNN was capable of learning relevant attributes of the river channel appearance for distribution of A. flavimanus. The model performance was significantly correlated with the number of the training data for this species. For this species, the relatively dark water surface and wide channel width seem to be possible key factors. At the same time, for the other two species, this modeling approach was not as successful as A. flavimanus, while the model performance did not show significant differences among the three species. Although practical application of this approach is still challenging in terms of available data and model validity, it is worthwhile further exploring its applicability to other riverine species and regions. [ABSTRACT FROM AUTHOR]

The rapid formation of glacial lakes is one of the most conspicuous landscape changes caused by atmospheric warming in glacierised regions. However, relatively little is known about the history and current state of glacial lakes in the High Arctic. This study aims to address this issue by providing the first inventory of glacial lakes in Svalbard, focusing in particular on the post-Little Ice Age evolution of glacial lakes and their typology. To do so, we used aerial photographs and satellite imagery together with archival topographic data from 1936 to 2020. The inventory comprises the development of 566 glacial lakes (146 km2) that were still in direct contact with glaciers during the period 2008–2012. The results show a consistent increase in the total area of glacial lakes from the 1930s to 2020 and suggest an apparent link between climatic and geological factors, and the formation of specific lake dam types: moraine, ice, or bedrock. We also detected 134 glacial lake drainage events that have occurred since the 1930s. This study shows that Svalbard has one of the highest rates of glacial lake development in the world, which is an indicator of the overall dynamics of landscape change in the archipelago in response to climate change. [ABSTRACT FROM AUTHOR]

This study provides the first comprehensive reconstruction of the dynamics of Iceberg Glacier, located on western Axel Heiberg Island, and reveals detailed observations of a complete surge for the first time in the Canadian Arctic. Historical aerial photographs, declassified intelligence satellite photographs, optical satellite imagery and synthetic aperture radar data were used to quantify changes in terminus position, ice velocity and glacier thickness since the 1950s. A surge initiated at the terminus in 1981 and terminated in 2003, suggesting a 22-year active phase. High surface velocities, reaching ~2300 m a−1 in 1991, were accompanied by a maximum terminus advance of >7 km and a large transfer of mass down-glacier, causing significant median trunk-wide surface elevation changes attaining >3 ± 1 m a−1. We suggest that the retreat from a pinning point, flotation of the terminus, the removal of sea-ice from the ice front, and an increase in subglacial meltwater availability from relatively high air temperatures in 1981 likely contributed to surge initiation. The ensuing quiescent period has seen a continual decrease in surface flow rates to an average centreline velocity of 11.5 m a−1 in 2020–21, a gradual steepening of the glacier surface and a > 2.5 km terminus retreat. [ABSTRACT FROM AUTHOR]

This study analyses the long‐term dynamics in the Chauvet glacial and periglacial system (southern French Alps) over seven decades (1948–2020), where several lake outburst floods have been documented since 1930. To accurately describe and explain the complex dynamics of this site, our multidisciplinary approach combines (1) photogrammetry of historical aerial photographs and modern high‐resolution satellite and UAV images, (2) geophysical surveys and (3) geomorphological mapping. We provide evidence for spatial and functional interactions between glacial and periglacial features, especially in the lower sector where different landforms with variable ice‐ and debris‐content and specific dynamics are interplayed. We found the highest thinning rates on ice‐rich terrain located in the central part of the valley bottom, which, together with bedrock morphology, most probably determine the location of the thermokarst. We also documented an overall acceleration of the creeping of the landforms after the 2000s, with a flow direction largely oriented towards the thermokarst depression. The outburst water flowed through a conduit whose successive opening and closure seem to mainly depend on the rate of lateral convergence of left‐ and right‐hand landforms and on the rate of ice melting (and roof collapse) along the conduit walls. Today, the site of Chauvet still represents a potential hazard for the region due to the large water storage capacity (up to 180 000 ± 450 m3) and the development of a predominantly bucket shape in the thermokarst sector. [ABSTRACT FROM AUTHOR]

Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 ± 0.175 (standard deviation) to 0.861 ± 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data. [ABSTRACT FROM AUTHOR]

This paper uses three different representations (aerial photograph, map, photograph) to shed light on how environmental imaginaries of the desert systematically created a "wasteland" that enabled an architecture of exploitation and extraction in which the histories, characteristics, and narratives of Saudi Arabia were replaced. Organized in chronological order, the images were produced in connection with US geologist Karl Saben Twitchell's desire to extract resources from Saudi Arabia through his role as the Saudi King's confidant and US expert. Here, representation and extraction allowed Twitchell and his company, Saudi Arabian Mining Syndicate, to frame the landscape as a "regime of emptiness" that enabled the systematic transformation of the Saudi desert. [ABSTRACT FROM AUTHOR]

Rebels can comply with international law during a conflict by not violating international borders, yet strategic goals may incentivize rebels to violate these borders. When do international borders affect the spatial and temporal distribution of rebel activity in a conflict setting? I theorize rebels have an incentive to refrain from violating borders when doing so will reduce their international legitimacy. When international legitimacy is a less important goal, rebels will be more likely to violate borders. I test this claim in the context of the 1948 War in Mandate Palestine by exploiting a quasi-natural experiment in how the 1947 UN partition line was drawn. Using an original dataset on over 1,000 Palestinian villages from British colonial documents and an atlas of aerial photographs of Palestine, I use a geographic regression discontinuity design to see how the UN partition line affected the location and timing of depopulated villages during the war. The results show that villages in areas that the UN assigned to the future Israeli state as part of the 1947 partition plan were more likely to be conquered before Israel received recognition. These findings have important implications for understanding where and when rebels target areas and shed new light on important dynamics of the 1948 War in Palestine. [ABSTRACT FROM AUTHOR]

Due to the rise in tornadoes in the United States, the built environment continues to suffer damage. Masonry buildings comprise a significant portion of the built heritage environment for most countries and are susceptible to natural hazards. However, research at the intersection of extreme wind loading and structural strengthening often neglects historic and aging infrastructure, particularly unreinforced masonry buildings, leading to their exclusion from the building code. This study aims to identify various factors that affect damages to unreinforced masonry structures during tornado loading. To begin, the structural performance of historic masonry constructions after the Mayfield, KY tornado in 2021 was evaluated, with multimodal data collected on-site, including aerial photographs, street-view imagery, and detailed field notes on observed damages. The on-site data included aerial photographs, street-view imagery, and detailed field notes for the observed damages. This data was supplemented by archival research and analyzed to identify the underlying relationships between building attributes and damage levels. The results indicate that damages in historic buildings were extensive and proportional to the year of construction and the distance from the tornado but not necessarily to the retrofit status. [ABSTRACT FROM AUTHOR]

Notes 1 See C. Dauphin, 2021.'On the Mediaeval and Ottoman Syro-Jordanian Hajj Roads to Mecca: The Pilgrim camps in their Landscapes', PEQ Grant reports, I PEQ i 153.2, 156-59. 2 For instance, J 11/1 with a diameter of 420 m and J 11/2 with a diameter of 215 m in Azraq. "Sacred Landscapes": The Umayyad Syro-Jordanian Hajj Roads to Mecca and their Pilgrim Camps (PEF 2020 Grant) and towards final publication of the Umayyad Syro-Jordanian Hajj Roads to Mecca and their Pilgrim Camps: Mapping "Sacred Landscapes" (PEF 2021 Grant) The only possible location for a camp of pilgrims intending to travel southwest to Mecca would have been between mountain and sea, and with direct access to the road unfurling along the Red Sea coast towards Yanbu and inland to Mecca. [Extracted from the article]

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A good understanding of historical change rates is a key requirement for effective coastal zone management and reliable predictions of shoreline evolution. Historical shoreline erosion for the coast of Guardamar del Segura (Alicante, Spain) is analyzed based on aerial photographs dating from 1930 to 2022 using the Digital Shoreline Analysis System (DSAS). This area is of special interest because the construction of a breakwater in the 1990s, which channels the mouth of the Segura River, has caused a change in coastal behavior. The prediction of future shorelines is conducted up to the year 2040 using two models based on data analysis techniques: the extrapolation of historical data (including the uncertainty of the historical measurements) and the Bruun-type model (considering the effect of sea level rises). The extrapolation of the natural erosion of the area up to 1989 is also compared with the reality, already affected by anthropic actions, in the years 2005 and 2022. The construction of the breakwater has accelerated the erosion along the coast downstream of this infrastructure by about 260%, endangering several houses that are located on the beach itself. The estimation models predict transects with erosions ranging from centimeters (±70 cm) to tens of meters (±30 m). However, both models are often overlapping, which gives a band where the shoreline may be thought to be in the future. The extrapolation of erosion up to 1989, and its subsequent comparison, shows that in most of the study areas, anthropic actions have increased erosion, reaching values of more than 35 m of shoreline loss. The effect of anthropic actions on the coast is also analyzed on the housing on the beach of Babilonia, which has lost around 17% of its built-up area in 40 years. This work demonstrates the importance of historical analysis and predictions before making any significant changes in coastal areas to develop sustainable plans for coastal area management. [ABSTRACT FROM AUTHOR]

Originating in Glacier Park, Montana, the transboundary St. Mary River has provided a focus for international water sharing for a century. It was dammed and diverted in the USA, and more extensively in Alberta, where it supports Canada's centre for agricultural irrigation. Following water withdrawals, the riparian cottonwoods (Populus angustifolia) collapsed, and we assessed the woodland decline downstream from the St. Mary Dam, through eight sets of aerial photographs over seven decades. These revealed 88% woodland loss from 1951 to 1999 (R2 = 0.984), including a steeper decline with the 1980s drought. Following the implementation of an environmental flow regime that increased minimum flows after 1993, the remnant woodlands were stabilized, with a slight recovery by 2022. Analyses of the historical hydrology revealed compound contributions to the woodland mortality. (1) Annual river flows declined with increasing water diversion, and (2) late summer flows were particularly depleted. (3) Dam operations resulted in abrupt stage recessions and irregular spikes. (4) Accompanying climate warming, the spring snowmelt advanced and late summer flows declined. (5) The greatest climatic influence involved multiple‐year clusters with high versus low flows, corresponding with the Pacific decadal oscillation (PDO) from 1920 to 1990 (R2 = 0.365), but less coupled thereafter. This long‐term study demonstrates the coordination between hydroclimatic variation, water management and the fate of riparian woodlands, which reveal ecosystem health. An environmental flow regime provided benefit, and for other regulated rivers in dry ecoregions, we recommend functional flow regimes that provide sufficient minimum flows and avoid abrupt flow recession or irregular spikes. [ABSTRACT FROM AUTHOR]