Search

The purpose of this study is to determine whether hyper spectral images can be used to identify plant diseases and crop pressure from aerial photographs. With extensive research on the prevalent methods used closer to the problem, this study offers a potent strategy for identifying crop distress and illnesses using this efficient imaging technique. To identify the spectral fingerprints of common indications and symptoms of plant diseases and crop strains, this study evaluates the available hyper spectral photo datasets. After that, the data are examined using two learning algorithms--the highly randomized trees and the Random Woodland set of rules--to create predictions that are entirely dependent on the results that are discovered. In the end, a benchmarked set of test statistics is used to assess the prediction accuracy. The results of this study show that hyper spectral photo evaluation has a strong and promising utility for crop stress and disease identification. Hyper spectral light evaluation is a method for identifying plant diseases caused by strain on crops. By gathering and evaluating high-dimensional spectral reflection data from satellite or aircraft structures, details regarding the physiological homes of flowers can be identified. The health of plants, illnesses brought on by stress, and agricultural productivity predictions can all be made using these facts. Hyper spectral recordings can also be used to create actions that reduce agricultural losses and enhance the health of plants that are prone to disease. [ABSTRACT FROM AUTHOR]

In the Eurasian forest-steppe, with increasing aridity, the balance between naturally co-existing forest and grassland patches is expected to shift towards grassland dominance in the long run, although feedback mechanisms and changes in land-use may alter this process. In this study, we compared old and recent aerial photographs of Hungarian forest-steppes to find out whether and how the forest proportion and the number of forest patches change at the decadal time scale. The percentage area covered by forest significantly increased in all study sites. The observed forest encroachment may be a legacy from earlier land-use: due to ceased or reduced grazing pressure, forests are invading grasslands until the potential forest cover allowed by climate and soil is reached. The number of forest patches significantly increased at one site (Fülöpháza), while it decreased at two sites (Bugac and Orgovány) and showed no significant change at the fourth site (Tázlár). This indicates that forest encroachment can happen at least in two different ways: through the emergence of new forest patches in the grassland, and through the extension and coalescence of already existing forest patches. Though the present work revealed increasing tree cover at a decadal time scale, the dynamic process should be monitored in the future to see how the vegetation reacts to further aridification. This could help devise a conservation strategy, as the woody/non-woody balance has a profound influence on basic ecosystem properties. [ABSTRACT FROM AUTHOR]

Aiming to support disaster recovery, we have developed a new method to extract damaged buildings by using machine learning that combines 10 explanatory variables obtained from analysis of aerial photographs and observation data. We used site amplification factors, seismic intensities of foreshock and mainshock, distance from faults, estimated building structures and ages, coverage by blue tarps, texture analysis, and digital surface model differences before and after the earthquake as explanatory variables, in addition to convolutional neural network prediction results based on post-earthquake aerial photographs. The random forest method resulted in an overall accuracy of about 81% and an average F-measure of three classes was about 70%, indicating that it can classify possible damage to buildings more accurately than our previous studies. [ABSTRACT FROM AUTHOR]

The anthropogenic relief transformations of the mining landscape are characterised by high dynamics of changes over time that can be effectively mapped on a large scale using aerial images. The Karviná part of the Ostrava-Karviná mining district, which stands for significant hard coal mining area in the Czech Republic, has been selected to analyse the spatio-temporal development of anthropogenic landforms. Anthropogenic landforms were visually identified from the aerial images from 1947, 1966, 1971, 1985, 1994, 2009, and 2018. Specific anthropogenic landforms were analysed along with their spatio-temporal changes based on obtained vector data. The total extent and number of anthropogenic landforms increased the most during the periods 1947–1966 and 1971–1985. The same anthropogenic landforms occurred on circa 24 hectares in the entire period 1947–2018. The most anthropogenic landforms remained preserved in the last observed period 2009–2018. The multi-temporal analysis of aerial photographs and overlay operations in GIS enabled to map the age of specific landforms and to identify changed or unchanged anthropogenic landforms. This method is suitable for the study of landform dynamics at mining sites and is particularly relevant for the planning challenges in post-mining reclamation. [ABSTRACT FROM AUTHOR]

Williams, H.F.L., 2024. Testing digital elevation model of difference (DoD) estimation of washover fan thickness, Matagorda Peninsula, Texas. Journal of Coastal Research, 40(5), 852–859. Charlotte (North Carolina), ISSN 0749-0208. Washover fans are a fundamental component of coastal sediment budgets. While fans can be relatively easily delineated on air photos to obtain fan area, measurement of fan thickness, which is required for volume estimation, is problematic. Field surveys can be conducted, but fieldwork can be labor-intensive, costly, and time-consuming and have access problems. This study used pre- and poststorm LIDAR digital elevation models (DEMs) to create a digital elevation model of difference (DoD), which was then used to estimate the mean thicknesses of 10 Hurricane Harvey washover fans on Matagorda Peninsula, Texas. The accuracy of the LIDAR-derived fan thicknesses was assessed by comparing the LIDAR results to fan mean thicknesses based on pits excavated into fans. Seven out of 10 LIDAR-based thicknesses underestimated pit-based thicknesses, suggesting the presence of systematic bias in the LIDAR-derived DEMs. Underestimation of fan thicknesses was in the range of 36% to 56%. The source of the suspected bias is uncertain; it is possible that compaction of marsh sediments by the washover fans lowered the fans and reduced their apparent thicknesses. It is concluded that pre- and post-storm LIDAR DEMs can be used to estimate washover fan thickness, but in areas of compactible substrates, such as coastal marshes, steps should be taken to identify, evaluate, and address potential bias in the DEM data. [ABSTRACT FROM AUTHOR]

The rapid development of rural settlements into commuter towns in the hinterland of large cities, coupled with intensive agriculture and climate change, may negatively affect the prosperity of these areas. The presented analysis focused on randomly selected rapidly growing settlements in the Prague metropolitan area, examining their development with a focus on the peripheral parts of such settlements. Settlement margins constitute a specific part of rural settlements as it typically contributes to the rural character by providing the gradual gradient between the settlement and the open landscape. The representation of water-absorbing and non-water-absorbing areas and their suitability for the development of taller vegetation along the margins of three randomly selected suburbanised settlements in the hinterland of Prague are analysed from the perspective of the possibility to maintain the traditional character of the settlement margins. Historical maps, aerial photographs, spatial planning documents, and data on current land use and landscape cover were used for the analysis of the settlements' development over the last approx. 200 years. In order to determine whether an area is water absorbing or not, a manual approach was used for historical maps and a machine learning process for present orthophoto maps. The automated machine learning classification process could be utilised, because the used orthophoto imagery includes a near-infrared portion of the spectrum (i.e. NIR imagery). We have observed the trend of decreasing plot size and of reduction in the water-absorbing surfaces suitable for planting taller vegetation, especially in the twenty-first century residential developments, which disrupts the traditional rural character of these settlements. The utilisation and stabilisation of the current vegetation in the historical areas, together with the creation of the vegetation belts along the margins of the newly developed areas, could improve the environmental quality of these settlements, promote ecosystem services, and increase biodiversity. [ABSTRACT FROM AUTHOR]

Poland as well as other countries keep extensive collections of 20th and 21st-century aerial photos, which are underexploited compared to such other archival materials as satellite imagery. Meanwhile, they offer significant research potential in various areas, including urban development, land use changes, and long-term environmental monitoring. Archival photographs are detailed, often obtained every five to ten years, and feature high resolution, from 20 cm to 1 m. Their overlap can facilitate creating precise digital models that illustrate topography and land cover, which are essential variables in many scientific contexts. However, rapidly transforming these photographs into geographically accurate measurements of the Earth's surface poses challenges. This article explores the obstacles in automating the processing of historical photographs and presents the main scientific research directions associated with these images. Recent advancements in enhancing work˚ows, including the development of modern digital photogrammetry tools, algorithms, and machine learning techniques are also discussed. These developments are crucial for unlocking the full potential of aerial photographs, making them easier accessible and valuable for a broader range of scientific fields. These underutilized photographs are increasingly recognized as vital in various research domains due to technological advancements. Integrating new methods with these historical images offers unprecedented opportunities for scientific discovery and historical understanding, bridging the past with the future through innovative research techniques. [ABSTRACT FROM AUTHOR]

Abstract Background Results of spatial and temporal comparison of malaria hotspots and coldspots could improve the health measures of malaria control and eradication strategies. The study aimed to reveal the spatially and temporally independent correlations between the potentially most effective background variables and the number of autochthonous malaria cases. Methods Relationships between malaria cases and background variables were studied in 2 km × 2 km sized quadrates (10 Central European and 10 African). In addition to the current habitat structure of the African sites, annual precipitation, and annual mean temperature, data of the above parameters detected in the nineteenth and twentieth centuries and currently in the Central European sites were included in the analyses (n = 40). Mann–Whitney tests, Principal Component Analysis, and Generalized Linear Models were used for the examinations. Results In addition to the apparent significant positive correlation of malaria cases with annual rainfall and mean temperature, several correlations were found for habitat parameters. The cover of marshlands in the 19th-century habitat structure of Central European quadrates was considerably the same as in the recent African ones. The extent of rural residential areas was significantly smaller in the 19th-century habitat structure of Central European quadrats than in present-day African ones. According to the revealed correlations, the surface cover of rural residential areas is the main driver of the number of autochthonous malaria cases that we can directly impact. Conclusions The study confirmed with historical comparison that not only the annual rainfall and mean temperature, the cover of marshlands and other habitats with breeding sites, but also the elements of the rural human environment play a significant role in the high number of autochthonous malaria cases, probably through the concentration and enhancing sites for vector mosquitoes. The latter confirms that a rapid urbanization process could reduce malaria cases in the most infected areas of Africa. Until the latter happens, extensive biological control of Anopheles larvae and chemical control (both outdoor and indoor) of their imagoes, further mosquito nets, repellents, and carbon dioxide traps will need to be applied more widely in the most heavily infested areas.

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 study reconstructs 16 new time series for rock glacier flow in North America, marking the first for the continent. Analysis shows 2-3 fold accelerations in surface velocities over the past 60-70 years, linked to rising air temperatures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The currently used standardised precipitation index (SPI) does not allow for a reliable assessment of the impact of drought due to the small and unevenly distributed network of meteorological stations. Hopes for developing methods to assess the impact of droughts are pinned on remote data acquisition and the use of analysis of aerial photographs and satellite scenes. The aim of this study is to assess the occurrence of drought based on the normalised difference vegetation index (NDVI) and SPI at multiple time scales (1-, 3-, and 6-, 9- and 12-month). NDVI values do not simply reflect meteorological drought. However, the spatial co-occurrence of meteorological drought with drought defined on the basis of the NDVI index was demonstrated. The study presents a new approach to identifying drought characterized by SPI and NDVI based on the bivariate choropleth map method, which can indicate the actual places of drought occurrence. The study was carried out for the upper Noteć catchment located in Central Poland. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The NW-SE-trending dextral strike-slip faults on the north side of the Tian Shan, e.g., the Karatau fault, Talas–Fergana fault, Dzhalair–Naiman fault, Aktas fault, Dzhungarian fault, and Chingiz fault, play an important role in accommodating crustal shortening. The classic viewpoint is that these strike-slip faults are an adjustment product caused by the difference in the crustal shortening from west to east. Another viewpoint attributes the dextral strike-slip fault to large-scale sinistral shearing. The Alakol Lake fault is a typical dextral strike-slip fault in the north Tian Shan that has not been reported. It is situated along the northern margin of the Dzhungarian gate, stretching for roughly 150 km from Lake Ebinur to Lake Alakol. Our team utilized aerial photographs, satellite stereoimagery, and field observations to map the spatial distribution of the Alakol Lake fault. Our findings provided evidence supporting the assertion that the fault is a dextral strike-slip fault. In reference to its spatial distribution, the Lake Alakol is situated in a pull-apart basin that lies between two major dextral strike-slip fault faults: the Chingiz and Dzhungarian faults. The Alakol Lake fault serves as a connecting structure for these two faults, resulting in the formation of a mega NW-SE dextral strike-slip fault zone. According to our analysis of the dating samples taken from the alluvial fan, as well as our measurement of the displacement of the riser and gully, it appears that the Alakol Lake fault has a dextral strike-slip rate of 0.8–1.2 mm/a (closer to 1.2 mm/a). The strike-slip rate of the Alakol Lake fault is comparatively higher than that of the Chingiz fault in the northern region (~0.7 mm/a) but slower than that of the Dzhungarian fault in the southern region (3.2–5 mm/a). The Chingiz–Alakol–Dzhungarian fault zone shows a gradual decrease in deformation towards the interior of the Kazakhstan platform. [ABSTRACT FROM AUTHOR]

This article explores a little-known archive of historical aerial photographs curated by the General Directorate of Mapping of the Republic of Türkiye's Ministry of Defense and discusses the historical context of their production by US Navy aerial photography squadrons in the 1950s. While the images themselves enable a technical analysis of the method of their collection, contemporary military manuals, domain-specific magazines and newsletters, and eyewitness accounts of how similar photographs were captured fill out the contexts of their production for cartographic purposes, with information about the aircraft involved, their cameras and camera configurations, and mission characteristics. Continuing sections situate the aerial surveys within the framework of US-led initiatives in mapping NATO territories following World War II. As one example of what must have been many special mapping agreements made between NATO countries at this time, the US cartographic surveys over Türkiye discussed here are an expression of postwar realignments of global power, put to the purposes of containment-based security preparations and infrastructure development, and neatly intertwining American military and commercial interests early in the Cold War. [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 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains 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 the importance of such high-resolution data for new satellite retrievals, 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 uncrewed aerial vehicles (UAVs) or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models. [ABSTRACT FROM AUTHOR]

Copyright of Avances is the property of Instituto de Informacion Cientifica y Tecnologica 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.)

The evolution of map development has been shaped by advancing techniques and technologies. Nevertheless, field and remote mapping with cabinet data analysis remains essential in this process. Geological maps are thematic maps that delineate diverse geological features. These maps undergo updates reflecting changes in the mapped area, technological advancements, and the availability of new data. Herein, a geological assessment example focused on enhancing mapped data using digitally processed historical (legacy) aerial images is presented for a case study in the Dinarides karst area in Croatia. The study area of Bribirske Mostine is covered by the Basic Geological Map of Yugoslavia (BGMY) at a 100,000 scale, which was developed during the 1960s. As the BGMY was developed 60+ years ago, one of its segments is further analyzed and discussed, namely, faults. Moreover, applying modern-day technologies and reinterpretation, its data, scale, presentation, and possible areas of improvement are presented. Georeferenced digital historical geological data (legacy), comprising BGMY, archive field maps, and aerial images from 1959 used in BGMY development, are reviewed. Original faults were digitalized and reinterpreted within the geographic information system with the following conclusions: (i) more accurate data (spatial positioning) on faults can be gained by digitally processing aerial photographs taken 60+ years ago with detailed review and analysis; (ii) simultaneously, new data were acquired (additional fault lines were interpreted); (iii) the map scale can be up-scaled to 1:25,000 for the investigated area of Bribirske Mostine; and (iv) a newly developed map for the Bribirske Mostine study area is presented. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture 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.)

Background: Results of spatial and temporal comparison of malaria hotspots and coldspots could improve the health measures of malaria control and eradication strategies. The study aimed to reveal the spatially and temporally independent correlations between the potentially most effective background variables and the number of autochthonous malaria cases. Methods: Relationships between malaria cases and background variables were studied in 2 km × 2 km sized quadrates (10 Central European and 10 African). In addition to the current habitat structure of the African sites, annual precipitation, and annual mean temperature, data of the above parameters detected in the nineteenth and twentieth centuries and currently in the Central European sites were included in the analyses (n = 40). Mann–Whitney tests, Principal Component Analysis, and Generalized Linear Models were used for the examinations. Results: In addition to the apparent significant positive correlation of malaria cases with annual rainfall and mean temperature, several correlations were found for habitat parameters. The cover of marshlands in the 19th-century habitat structure of Central European quadrates was considerably the same as in the recent African ones. The extent of rural residential areas was significantly smaller in the 19th-century habitat structure of Central European quadrats than in present-day African ones. According to the revealed correlations, the surface cover of rural residential areas is the main driver of the number of autochthonous malaria cases that we can directly impact. Conclusions: The study confirmed with historical comparison that not only the annual rainfall and mean temperature, the cover of marshlands and other habitats with breeding sites, but also the elements of the rural human environment play a significant role in the high number of autochthonous malaria cases, probably through the concentration and enhancing sites for vector mosquitoes. The latter confirms that a rapid urbanization process could reduce malaria cases in the most infected areas of Africa. Until the latter happens, extensive biological control of Anopheles larvae and chemical control (both outdoor and indoor) of their imagoes, further mosquito nets, repellents, and carbon dioxide traps will need to be applied more widely in the most heavily infested areas. [ABSTRACT FROM AUTHOR]

For accurate urban planning, three-dimensional (3D) building models with a high level of detail (LOD) must be developed. However, most large-scale 3D building models are limited to a low LOD of 1–2, as the creation of higher LOD models requires the modeling of detailed building elements such as walls, windows, doors, and roof shapes. This process is currently not automated and is performed manually. In this study, an end-to-end framework for the creation of 3D building models was proposed by integrating multi-source data such as omnidirectional images, building footprints, and aerial photographs. These different data sources were matched with the building ID considering their spatial location. The building element information related to the exterior of the building was extracted, and detailed LOD3 3D building models were created. Experiments were conducted using data from Kobe, Japan, yielding a high accuracy for the intermediate processes, such as an 86.9% accuracy in building matching, an 88.3% pixel-based accuracy in the building element extraction, and an 89.7% accuracy in the roof type classification. Eighty-one LOD3 3D building models were created in 8 h, demonstrating that our method can create 3D building models that adequately represent the exterior information of actual buildings. [ABSTRACT FROM AUTHOR]

Copyright of VAD: Veredes, Arquitectura y Divulgación is the property of Alberto Alonso Oro 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.)

Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls. [ABSTRACT FROM AUTHOR]

Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 106 m3) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 106 m3 of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 105 m3 of that sediment was redeposited inside the study area. About 5.8 × 105 m3 of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems. [ABSTRACT FROM AUTHOR]

Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have opened up new possibilities for efficient and accurate forest plantation monitoring. This study aimed to explore the utility of lightweight drones as a cost-effective and accurate method for mapping plantation characteristics in two 50 ha forest plots in the Nayla Range, Jaipur. By combining aerial photographs collected by the drone with photogrammetry and limited ground survey data, as well as topography and edaphic variables, this study examined the relative contribution of drone-derived plantation canopy information. The results demonstrate the immense potential of lightweight drones and object-oriented image analysis in providing valuable insights for optimizing silvicultural operations and planting trees in complex forest environments. [ABSTRACT FROM AUTHOR]

In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block over Warsaw is considered, featuring 38 photographs acquired in 1986 (Wild RC10, Normal Aviogon II lens, 23 × 23 cm format) with a ground sampling distance (GSD) of 4 cm. Aerial triangulation (AT) and dense image matching (DIM) procedures are considered, analyzing how scanning modalities are important in the massive digitization of analog images for georeferencing and 3D product generation. The achieved results show how consumer-grade scanners, unlike more expensive photogrammetric scanners, do not possess adequate recording quality to ensure high accuracy and geometric precision for geoprocessing purposes. However, consumer-grade scanners can be used for time and cost-efficient applications where a partial loss of data quality is not critical. [ABSTRACT FROM AUTHOR]

This article focuses on the Ottoman Period urban fabric of Silifke, a crucial historical town in Turkey. The aim of this paper is to develop a research framework for the transfer of cultural heritage and cultural continuity in multi-layered historical cities. In this context, investigating the level of sociocultural transition and physical permeability between layers is the main problem of the article. The scope of the study consists of an approximately 1.5 km2 (150 hectares) area within the boundaries of the 3rd Degree archaeological site determined in 1995 by the Adana Cultural and Natural Heritage Conservation Regional Board. In the case area, there are building remains and 58 registered buildings, 40 of which were built during the Ottoman Period. The findings and evaluations were examined in three parts: the periods before the Ottoman Period, the Ottoman Period, and the Republican Period. Analyses were developed independently or overlapped from archive documents such as the Presidential Ottoman Archive (BOA), provincial yearbooks, engravings of travelers, photographs, aerial photographs of different years, current maps, zoning plans, and conservation plans. Then, we made descriptions and inferences about urban change/transformation, which is affected by the political, economic, social, cultural, and environmental factors of the town and will shape the future change/transformation and management of the town. The ultimate goal is to set up a basis for the Silifke town center that will guide future interventions and design and planning policies for cultural continuity. [ABSTRACT FROM AUTHOR]

Mangrove forests have unquestionably high environmental and ecological value. Mangrove trees are believed to have habitat zonation that is controlled mainly by the relative sea level. However, earlier discussions of mangrove habitats have remained limited in terms of their quality and quantity because of a lack of high-resolution spatial information of microtopography and trees. To clarify mangrove habitability over a wide forest area, we compounded mobile laser scanning (MLS) and aerial laser scanning (ALS) LiDAR dataset of the Miyara River mangrove on Ishigaki Island, Okinawa, Japan. The MLS provided sub-canopy data, while the unmanned aerial vehicle ALS data mainly provided a point cloud of the canopy. We corrected point clouds and combined these data. The results indicated that ALS is unable to reconstruct the microtopography of the dense mangrove area well. Moreover, tree species were not identifiable from the ALS data. However, by applying MLS to the mangrove forest, we obtained high-resolution microtopography and tree information inside the forest, although the measurement area was limited to comparison with ALS. By combining ALS and MLS point clouds, 3D point clouds of the forest were well reconstructed. From these point clouds, a high-resolution digital elevation model was created. Subsequently, we segmented trees individually from composite MLS point clouds and identified each tree species. Consequently, the spatial distribution of thousands of mangrove trees was reconstructed at the Miyara River mouth. The spatial distribution of mangrove tree species together with earlier aerial photographs suggests that mangrove species have been segregated in accordance with changes in their elevation and environment over 40 years. Our findings suggest that the distribution of the species changed sensitively along with dynamic variation of the microtopography. [ABSTRACT FROM AUTHOR]

Measurements using drones have enabled significant changes in the inventorying and monitoring of mining areas. Drone-based measurements can be faster and more accurate [Mazurek 2018]. Aerial photographs taken with drones allow the surveying department in mines to accurately represent the photographed terrain and make precise measurements, which can be used, among other things, to calculate the volume of mass. The aim of the article is to present the results of research on the automated process of acquiring and processing photogrammetric data for the purpose of calculating mass volumes. As part of the research, an algorithm based on classical methods and deep learning was developed. In collaboration with the Silesian University of Technology and 3D Format company from Gliwice, the AGH University of Krakow has developed a system for automated volumetric measurements based on low-altitude photogrammetry using non-metric photos and artificial intelligence (AI) algorithms to provide cyclical volume measurement services on the Polish market. The idea of the system is to acquire data automatically, then provide the data in the cloud, maximize measurement automation, and provide results in near real-time. The entire process is to be conducted using software available through the website. The project was divided into several stages. This particular publication focuses on the automation of the measurement of surveying points. [ABSTRACT FROM AUTHOR]

The Bale Mountains National Park (BMNP) in Ethiopia comprises the largest fraction of the Afro-Alpine ecosystem in Africa, which provides vital mountain ecosystem services at local, regional, and global levels. However, the BMNP has been severely threatened by natural and anthropogenic disturbances in recent decades. In particular, landscape alteration due to human activities such as red ash quarrying has become a common practice in the BMNP, which poses a major environmental challenge by severely degrading the Afro-Alpine ecosystem. This study aims to quantify the long-term volumetric changes of two red ash quarry sites in the BMNP using historical aerial photographs and in situ data, and to assess their impact on the Afro-Alpine ecosystem. The Structure-from-Motion multi-view stereo photogrammetry algorithm was used to reconstruct the three-dimensional landscape for the year 1967 and 1984 while spatial interpolation techniques were applied to generate the current digital elevation models for 2023. To quantify the volumetric changes and landscape alteration of the quarry sites, differences in digital elevation models were computed. The result showed that the volume of resources extracted from the BMNP quarry sites increased significantly over the study period from 1984 to 2023 compared with the period from 1967 to 1984. In general, between 1967 and 2023, the total net surface volume of the quarry sites decreased by 503,721 ± 27,970 m3 and 368,523 ± 30,003 m3, respectively. The extent of the excavated area increased by 53,147 m2 and 45,297 m2 for Site 1 and 2, respectively. In terms of habitat loss, major gravel road construction inside the BMNP resulted in the reduction of Afro-Alpine vegetation by 476,860 m2, ericaceous vegetation by 403,806 m2 and Afromontane forest by 493,222 m2 with associated decline in species diversity and density. The excavation and gravel road construction have contributed to the degradation of the Afro-Alpine ecosystem, especially the endemic Lobelia rhynchopetalum on the quarry sites and roads. If excavation continues at the same rate as in the last half century, it can threaten the whole mountain ecosystem of the National Park and beyond, highlighting the importance of preventing these anthropogenic changes and conserving the remaining Afro-Alpine ecosystem. [ABSTRACT FROM AUTHOR]

Seagrasses, rooted aquatic plants growing completely underwater, are extremely important for the coastal ecosystem. They are an important component of the total carbon burial in the ocean, they provide food, shelter, and nursery to many aquatic organisms in coastal ecosystems, and they improve water quality. Due to human activity, seagrass coverage has been rapidly declining, and there is an urgent need to monitor seagrasses consistently. Seagrass coverage has been closely monitored in the Chesapeake Bay since 1970 using air photos and ground samples. These efforts are costly and time-consuming. Many studies have used remote sensing data to identify seagrass bed outlines, but few have mapped seagrass bed density. This study used Sentinel-2 satellite data and machine learning in Google Earth Engine and the Chesapeake Bay Program field data to map seagrass density. We used seagrass density data from the Chincoteague and Sinepuxent Bay to train machine learning algorithms and evaluate their accuracies. Out of the four machine learning models tested (Naive Bayes (NB), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF)), the RF model outperformed the other three models with overall accuracies of 0.874 and Kappa coefficients of 0.777. The SVM and CART models performed similarly and NB performed the poorest. We tested two different approaches to assess the models' accuracy. When we used all the available ground samples to train the models, whereby our analysis showed that model performance was associated with seagrass density class, and that higher seagrass density classes had better consumer accuracy, producer accuracy, and F1 scores. However, the association of model performance with seagrass density class disappeared when using the same training data size for each class. Very sparse and dense seagrass classes had replacedhigherbetter accuracies than the sparse and moderate seagrass density classes. This finding suggests that training data impacts machine learning model performance. The uneven training data size for different classes can result in biased assessment results. Selecting proper training data and machine learning models are equally important when using machine learning and remote sensing data to map seagrass density. In summary, this study demonstrates the potential to map seagrass density using satellite data. [ABSTRACT FROM AUTHOR]

Recent climatic changes have profoundly impacted the urban microclimate, exposing city dwellers to harsh living conditions. One effective approach to mitigating these events involves incorporating more green infrastructure into the cityscape. The ecological services provided by urban vegetation play a crucial role in enhancing the sustainability and livability of cities. However, monitoring urban vegetation and accurately estimating its status pose challenges due to the heterogeneous nature of the urban environment. In response to this, the current study proposes utilizing a remote sensing-based classification framework to enhance data availability, thereby improving practices related to urban vegetation management. The aim of the current research is to explore the spatial pattern of vegetation and enhance the classification of tree species within diverse and complex urban environments. This study combines various remote sensing observations to enhance classification capabilities. High-resolution colored rectified aerial photographs, LiDAR-derived products, and hyperspectral data are merged and analyzed using advanced classifier methods, specifically partial least squares-discriminant analysis (PLS-DA) and object-based image analysis (OBIA). The OBIA method demonstrates an impressive overall accuracy of 95.30%, while the PLS-DA model excels with a remarkable overall accuracy of 100%. The findings validate the efficacy of incorporating OBIA, aerial photographs, LiDAR, and hyperspectral data in improving tree species classification and mapping within the context of PLS-DA. This classification framework holds significant potential for enhancing management practices and tools, thereby optimizing the ecological services provided by urban vegetation and fostering the development of sustainable cities. [ABSTRACT FROM AUTHOR]

The retreat of glaciers is a reality throughout the Andes Mountain range, especially in low-altitude mountains. One of these cases is the loss of the remaining ice mass in Carihuairazo (Tungurahua, Ecuador), which in recent years has experienced a considerable retreat. This research aims to characterize the retreat of this glacier and its implications for the nearby community (Cunucyacu) through the application of a multi-source methodology, which includes the collection of glacier aerial photographs, data from nearby meteorological stations, the use of global climate reanalysis data, interviews with community members, and mountaineers who work and frequent the area. To characterize the glacier's mass evolution, a hydroglaciological model was applied, using input data from meteorological series, and its parameters were calibrated with the photographic record of the glacier's outline. The results show a glacier loss of 99% of its surface in 1956 (0.34 km2) by 2021. The model successfully simulates the glacier area variation over 67 years, revealing a continuous decrease since 1978, with short periods of recovery and equilibrium, where temperature is the variable that best explains the glacier's retreat. However, the model fails to consider the effect of external factors, such as the eruption of the Tungurahua volcano that could enhance the glacier retreat. The Carihuairazo glacier is in a situation of inevitable disappearance, highlighting the vulnerabilities of communities facing this phenomenon as a consequence of climate change. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV), also known as drone is one of the most interesting emerging technologies with a wide range of applications including agriculture, surveillance, security, search and rescue, mapping, farmland surveying, and wildlife conservation, among others. In Nigeria, UAV is gaining popularity in applications such as in social functions for taking both moving and static pictures, military and security, mapping and agriculture. In this work, an UAV was designed, some components produced by 3D printing, coupled and tested with certain level of local content in view. The UAV is a quadcopter equipped with a camera for real-time image capture of farmlands and it can perform autonomous missions by using global positioning system (GPS) waypoints. The weight of the quadcopter was approximately calculated and hence the electrical and mechanical components selected using a standard empirical design table. The thrust to weight ratio was set at 2:1. Autodesk Maya and Inventor software were used to design the frame in two parts: the frame arms on which the propellers are mounted and the central connecting part that links the four arms. The arms are made up of aluminum square pipes while the central connecting part for the arms was printed with a 3D printer (200 x 200 x 200 Wanhao duplicator i3) using PolyEthylene Terephthalate Glycol (PETG) Filament material of 1.75mm standard gauge. Material selection was based on material strength, cost and availability. NCH Debut video and image capture software was used to record live feeds from the UAV onboard camera. Functionality tests for lift, stability, yaw, roll, pitch, loiter, auto landing, return to launch, flight time, altitude/signal, auto mission using GPS waypoints were carried out with the completed UAV on a less windy day to avoid the influence of wind. It was generally observed that the UAV successfully took off the ground, gained stability, flew to over 100m height, captured aerial photographs of the land below it while on flight at the required height and landed safely. Cost saving advantage of 45.28% was achieved when compared to imported equivalent types. [ABSTRACT FROM AUTHOR]

One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and "readability" compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring. [ABSTRACT FROM AUTHOR]

Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year's orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results. [ABSTRACT FROM AUTHOR]

The active rift zones in Iceland provide unique insight into the geodynamic processes of divergent plate boundaries. The geodynamics of Iceland are studied intensively, particularly, by geophysical methods sensitive to active and/or visible structures such as earthquake seismic and Synthetic Aperture Radar observations or aerial photographs. However, older and less active structures, that may exert a strong control on the presently active geodynamics, are often buried beneath recent volcanic or sedimentary deposits and are—due to their passive mode—overseen by the typical geophysical investigations. Aeromagnetic surveys provide spatial information about subsurface magnetization contrasts relating to both active and inactive structures. However, the aeromagnetic data in Iceland were collected in the 1970-80s and are relevant only to large-scale regional rift studies. With the availability of reliable drones and light-weight atomic scalar sensors, high-quality drone magnetic surveys can provide an unprecedented spatial resolution of both active and passive structures of rift systems as compared to conventional airborne surveys. Here, we present the results of a drone-towed magnetic scalar field and scalar gradiometry study of the north-northeast trending Bárðarbunga spreading center to the north of the Vatnajökull ice cap, Iceland. Our results provide new information about the structural complexity of rift zones with evidence of densely-spaced, conjugate and oblique faults throughout the area. Evidence is shown of a hitherto unknown and prominent east-northeast trending fault structure that coincides with the northern tip of the main eruption edifice of the 1797 and 2014-15 Holuhraun volcanic events. We suggest that this pre-existing structure controlled the locus of vertical magma migration during the two Holuhraun events. [ABSTRACT FROM AUTHOR]

In the continental Mediterranean mountains of the Iberian Peninsula is located a landscape characterized by the presence of enclosed land parcels delimited by dry stone walls or vegetation, or by both these elements. This landscape has been included, since 2018, in UNESCO's Representative List of Intangible Cultural Heritage of Humanity under the name of Art of dry stone walling, knowledge and techniques. However, today's territorial dynamics jeopardize the maintenance of this landscape heritage. This work set out to understand their origins, dynamics, and evolution from the 11th century (Middle Ages) to the present using historical documentation from different sources and diachronic cartography from aerial photographs. The fieldwork was designed to identify natural, cultural features and recent dynamics, in particular those related to urbanization changes of the last 70 years. Finally, we delved into the new dynamics of exploitation that were based on extensification and a loss of productive diversity. The results obtained lead us to consider that the loss of this agro-landscape must be prevented, with a reasonable livestock grazing criteria and a rational management of its heritage features. This requires regional, national, and European policies that recognize the enclosed landscape as a heritage ecosystem in which biodiversity and agriculture are closely linked. [ABSTRACT FROM AUTHOR]

In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups (≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires. Tree mortality due to climate change and other disturbances is on the rise. Here, the authors use high-resolution remote sensing data, ground observations and deep learning to quantify individual dead trees and potential drivers across California in the year 2020, encompassing 91.4 million dead trees. [ABSTRACT FROM AUTHOR]

With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed. [ABSTRACT FROM AUTHOR]

Ventilation corridors are critical for regulating the appropriate flow of urban air streams. Details on how they function, especially in a specific location, can be found in local eco-physiographic studies. Such analyses are designed during conceptual work on the conditions and directions of the development of a studied area. Such documentation includes advanced research on the impact of natural conditions on the area's growth and evolution. In Warsaw, Poland, such a study makes it possible to define various natural areas that are essential for the appropriate functioning of the city and increase its inhabitants' well-being. This provides an initial basis for further analysis and motivates researchers to investigate the distribution of natural sites. It inspired the authors to assess the land-use structure in the context of the location of ventilation corridors. Thus, land use and land cover (LULC) modifications were examined in the context of their tendency to aid in the ventilation of the city. The object of the study is the Bródnowski Ventilation Corridor (BVC) – one of several designated for Warsaw in the spatial planning concepts of the 1980s and 1990s. In the research encompassing the years 1982–2020, the authors analysed the land use and roughness determined by the development characteristics and vegetation cover. References were made to current and archival planning documents, aerial photographs, satellite images, and the Topographic Objects Database BDOT10k. The research was prompted by the municipality's intention to urbanize the study area, which will undoubtedly eliminate the ventilation function of the southern part of the corridor and significantly reduce its remaining territory. Analysis of changes in the land cover of the south part of the corridor confirms its marginal importance for the city's ventilation. In this area, ongoing modifications proceed slowly, and urbanization – mainly low-rise and less intensive buildings – is limited. This zone has largely retained and is likely to maintain its favourable features from the point of view of ventilation and air regeneration. [ABSTRACT FROM AUTHOR]

This paper describes a singular group of evidence gathered on a small hill at ca. 900 meters of altitude. The hill is located in mid-eastern Asturias, near the Cantabrian Mountains in the northern part of Iberia. The archaeological remains consist of one prehistoric barrow at the top part which is already included in the Sites and Monuments Record. Moreover, several other tiny elevations surround the main barrow, and around them, a few new enclosures have been identified. Despite their irregular conservation, all of them have small parapets meaning clear archaeological features. Old and new data were gathered thanks to three different techniques used in this research: fieldwork, aerial photographs and LiDAR analysis. Due to the lack of archaeological excavations and geophysics analysis, the chronology suggested here must be, at this point of the research, very broad. Finally, the manuscript explores different kinds of enclosures, from prehistoric and historic times, which can be similar to the evidence identified in el Cuetu. [ABSTRACT FROM AUTHOR]

Contextualization: Direct measurement of biomass in pastures is the most reliable method; however, it is a destructive and wasteful method. Knowledge gap: Recently, techniques using remote sensing to estimate physiological and morphological characteristics in crops have been investigated from their optical properties and reach biomass estimates. It is valuable to evaluate these techniques in perennial forages such as Kikuyu grass (Cenchrus clandestinus). Purpose: This work aimed to develop a methodology to estimate biomass in prairies based on their spectral and morphological characteristics using different sources of remote information. Methodology: A grazing strip of 1870 m² was used, subdivided into 27 subplots, where direct measurements of biomass and undisturbed height (ASD, for its Spanish acronym) were carried out. Aerial photographs were taken with a UAS. A Sentinel 2B satellite image that captured the scene of the study site coinciding with the UAS flight date and the field measurements was selected. A digital elevation model (DEM) was calculated using aerial photogrammetry techniques, which was used to estimate pasture height. The Red-Green-Blue Vegetation Index (RGBVI) was calculated from the orthophotography, and the Normalized Difference Vegetation Index (NDVI) was determined from the satellite image. The height measured in the field was correlated with the estimated height by DEM using the Pearson correlation coefficient. The biomass measured in the field was correlated with the NDVI, RGBVI, and the estimated height through linear regression models. Results and conclusions: A strong correlation (0.64) was found between the directly measured height and the height calculated using the DEM. However, the correlation between NDVI and biomass was low (R2= 0.13), and no relationship was found with the RGBVI index (R2= 0.02). An average relationship (R2=0.42) was found between the height estimated by DEM and the biomass, indicating that this is a promising methodology to replace destructive methods and to provide more accurate information in time and space. [ABSTRACT FROM AUTHOR]