Search Results (50)

This study investigates the morphodynamic evolution of an embayed cobble beach located on a mesotidal cliff coast in northern Spain. La Maruca/Pinquel beach was selected for its distinctive geomorphological setting, perched on a well-sorted cobble substrate and bordered by a slightly elevated (less than 1 m) wave-cut platform. Firstly, the availability of orthophotos and the achievement of field surveys enabled a detailed topographic mapping of morphological features. Sedimentological analyses based on grain size and clast shape revealed characteristics indicative of prolonged low-energy wave conditions. A permanent sharply crested ridge and ephemeral staggered tidal berms define the morphology of the beach. Additional depositional features such as washovers, tabular structures, and lobes are also well developed. Sediment accumulation is most pronounced in the western sector, where overwash lobes migrate landward. A W-to-E gradient in cobble size and the presence of boulders in the lower foreshore can be observed. Secondly, a morphosedimentary model was developed based on the obtained data to interpret the beach’s dynamic behavior under current and projected coastal forcing. Finally, by analyzing orthophotographs spanning a 40-year period (1984–2024), the long-term geomorphological evolution of the beach was documented. The results reveal significant morphological transformations, notably a shoreline retreat of approximately 12 m and a reduction in the cobble-covered surface area, among other findings. Future analyses of sediment transport processes and lithological responses to erosion will be able to offer a deeper understanding of the complex behavior and resilience of pebble beach systems in response to changing environmental conditions. Full article

The waste dump from the abandoned La Soterraña mine, a former mercury extraction site, contains high concentrations of mercury (Hg) and arsenic (As), which pose a significant environmental risk due to direct exposure to the environment. Given the site’s topography and slope, surface runoff has been identified as the primary mechanism for the dispersal of these toxic elements into nearby watercourses. This study quantifies the amount of Hg and As released into fluvial systems through surface runoff from the waste dump. Historical topographic data, Airborne Laser Exploration Survey public data from the National Plan for Aerial Orthophotographs (1st PNOA-LiDAR) of the Spanish Ministry of Transport, Mobility and Urban Agenda, and high-precision photogrammetric drone surveys were utilized, with centimeter-level accuracy achieved using airborne GNSS RTK positioning systems on the drone. The methodology yields reliable results when comparing surfaces generated from topographic data collected with consistent methodologies and standards. Analysis indicates an environmental release exceeding 1000 kg of mercury (Hg) and 12,000 kg of arsenic (As) between 2019 and 2023, based on high spatial resolution data (GSD = 8 cm). These findings highlight a sustained temporal contribution of chemical contaminants, which imposes serious environmental and biological health risks due to persistent exposure to toxic elements. Full article

This study investigates the use of high-resolution RGB aerial imagery from Spain’s National Aerial Orthophotography Plan (PNOA) for archeological feature detection through spectral index analysis and unsupervised clustering. Focusing on the Roman site of Munigua, eight orthophotographs acquired between 2014 and 2024 were analyzed to compute five RGB-based spectral indices: VARI, GLI, ExG, CSI, and BI. These indices were used to detect surface spectral anomalies potentially linked to buried archeological structures. A multi-temporal approach was employed, with Principal Component Analysis (PCA) and K-Means clustering applied independently to each image. This allowed for the identification of temporally persistent anomalies (areas that remained within the same spectral cluster across multiple years), suggesting the presence of underlying anthropogenic features. Despite the lack of near-infrared data, the combination of RGB-based indices and temporal clustering proved effective for non-invasive prospection. The methodology is scalable, repeatable, and relies entirely on open-access datasets, making it suitable for broader applications in heritage monitoring and landscape archeology. The results underscore the potential of RGB imagery and time-series clustering in detecting subtle archeological signals within complex vegetated environments. Full article

In the management of forests, the boundaries of individual units of land containing similar forest resources (e.g., stands) are delineated and used to guide the implementation of management activities. Traditionally, stand boundaries are drawn or digitized by hand; however, work recently has been conducted to automate the process using aerial imagery or airborne light detection and ranging (LiDAR) data as supporting resources. The work described here applies an object-based image analysis (OBIA) process to aerial imagery and to a landform index database. The size and shape of stands in the outcomes of these applications are then adjusted to conform to the desired product of land managers. These products are then intersected as they each contain information of value in the stand delineation process. The intersected database is then adjusted once again to conform to the desired product of land managers. Conformity of the size and shape of the resulting stand boundaries to a reference database drawn subjectively by hand was low to moderate. Specifically, the overall agreement for spatial and thematic (class names) accuracies was 43.0% and 56.8%, respectively. Nevertheless, the process of automating the stand delineation effort remains promising for achieving an efficient and non-subjective characterization of a structurally complex forested environment. Full article

Riverine environmental information includes important data to collect, and the data collection still requires personnel’s field surveys. These on-site tasks still face significant limitations (i.e., hard or danger to entry). In recent years, as one of the efficient approaches for data collection, air-vehicle-based Light Detection and Ranging technologies have already been applied in global environmental research, i.e., land cover classification (LCC) or environmental monitoring. For this study, the authors specifically focused on seven types of LCC (i.e., bamboo, tree, grass, bare ground, water, road, and clutter) that can be parameterized for flood simulation. A validated airborne LiDAR bathymetry system (ALB) and a UAV-borne green LiDAR System (GLS) were applied in this study for cross-platform analysis of LCC. Furthermore, LiDAR data were visualized using high-contrast color scales to improve the accuracy of land cover classification methods through image fusion techniques. If high-resolution aerial imagery is available, then it must be downscaled to match the resolution of low-resolution point clouds. Cross-platform data interchangeability was assessed by comparing the interchangeability, which measures the absolute difference in overall accuracy (OA) or macro-F1 by comparing the cross-platform interchangeability. It is noteworthy that relying solely on aerial photographs is inadequate for achieving precise labeling, particularly under limited sunlight conditions that can lead to misclassification. In such cases, LiDAR plays a crucial role in facilitating target recognition. All the approaches (i.e., low-resolution digital imagery, LiDAR-derived imagery and image fusion) present results of over 0.65 OA and of around 0.6 macro-F1. The authors found that the vegetation (bamboo, tree, grass) and road species have comparatively better performance compared with clutter and bare ground species. Given the stated conditions, differences in the species derived from different years (ALB from year 2017 and GLS from year 2020) are the main reason. Because the identification of clutter species includes all the items except for the relative species in this research, RGB-based features of the clutter species cannot be substituted easily because of the 3-year gap compared with other species. Derived from on-site reconstruction, the bare ground species also has a further color change between ALB and GLS that leads to decreased interchangeability. In the case of individual species, without considering seasons and platforms, image fusion can classify bamboo and trees with higher F1 scores compared to low-resolution digital imagery and LiDAR-derived imagery, which has especially proved the cross-platform interchangeability in the high vegetation types. In recent years, high-resolution photography (UAV), high-precision LiDAR measurement (ALB, GLS), and satellite imagery have been used. LiDAR measurement equipment is expensive, and measurement opportunities are limited. Based on this, it would be desirable if ALB and GLS could be continuously classified by Artificial Intelligence, and in this study, the authors investigated such data interchangeability. A unique and crucial aspect of this study is exploring the interchangeability of land cover classification models across different LiDAR platforms. Full article

Grasslands are one of the most abundant and biodiverse ecosystems in the world. However, in southern European countries, the abandonment of traditional management activities, such as extensive grazing, has caused many semi-natural grasslands to be invaded by shrubs. Therefore, there is a need to characterize semi-natural grasslands to determine their aboveground primary production and livestock-carrying capacity. Nevertheless, current methods lack a realistic identification of vegetation assemblages where grassland biophysical parameters can be accurately retrieved by the inversion of turbid-medium radiative transfer models (RTMs) in fine-grained landscapes. To this end, in this study we proposed a novel framework in which multiple endmember spectral mixture analysis (MESMA) was implemented to realistically identify grassland-dominated pixels from Sentinel-2 imagery in heterogeneous mountain landscapes. Then, the inversion of PROSAIL RTM (coupled PROSPECT and SAIL leaf and canopy models) was implemented separately for retrieving grassland biophysical parameters, including the leaf area index (LAI), fractional vegetation cover (FCOVER), and aboveground biomass (AGB), from grassland-dominated Sentinel-2 pixels while accounting for non-vegetated areas at the subpixel level. The study region was the southern slope of the Cantabrian Mountains (Spain), with a high spatial variability of fine-grained land covers. The MESMA grassland fraction image had a high accuracy based on validation results using centimetric resolution aerial orthophotographs (R² = 0.74, and RMSE = 0.18). The validation with field reference data from several mountain passes of the southern slope of the Cantabrian Mountains featured a high accuracy for LAI (R² = 0.74, and RMSE = 0.56 m²·m⁻²), FCOVER (R² = 0.78 and RMSE = 0.07), and AGB (R² = 0.67, and RMSE = 43.44 g·m⁻²). This study provides a reliable method to accurately identify and estimate grassland biophysical variables in highly diverse landscapes at a regional scale, with important implications for the management and conservation of threatened semi-natural grasslands. Future studies should investigate the PROSAIL inversion over the endmember signatures and subpixel fractions depicted by MESMA to adequately address the parametrization of the underlying background reflectance by using prior information and should also explore the scalability of this approach to other heterogeneous landscapes. Full article

Studying historical rural landscapes beyond their archaeological and cultural significance, as has typically been addressed in previous research, is important in the context of current environmental challenges. Some historical rural landscapes, such as Roman land divisions, have persisted for more than 2000 years and may still contribute to sustainability goals. To assess this topic, the hydraulic and vegetation network of the centuriation northeast of Padua were studied, emphasising their multiple benefits. Their length, distribution, and evolution over time (2008–2022) were vectorised and measured using available digital terrain models and orthophotographs in a geographic information system (GIS). The results revealed a significant decline in the length of water ditches and hedgerows across almost all examined areas, despite their preservation being highlighted in regional and local spatial planning documents. These findings indicate the need for a better understanding of the local dynamics driving such trends and highlight the importance of adopting a more tailored approach to their planning. This study discusses the GIS metrics utilised and, in this way, contributes to landscape monitoring and restoration actions. Finally, a multifunctional approach to the sustainable planning of this area is proposed here—one that integrates the cultural archaeological heritage in question with environmental preservation and contemporary climate adaptation and mitigation strategies. Full article

The image is a particularly valuable data carrier in medical forensic and forensic analyses. One of the analyses, as mentioned above, is to assess whether a graphically captured object is the same object examined in reality. This is a complicated process due to perspective foreshortening, making it difficult to determine the scale and proportion of objects in the frame, as well as the subsequent correct reading of their actual measurements. This paper presented a method for the 3D reconstruction of silhouettes of people recorded in a photo or video, with the aim of identifying these people through subsequent comparative studies. The authors presented an algorithm for dealing with graphic evidence, using the example of the analysis of spatial correlation of the silhouette of the perpetrator of the actual event (recorded via CCTV footage) with the silhouette of the suspect (scanned in 3D in custody). In this paper, the authors posed the thesis that the isometric (devoid of perspective foreshortening) display mode that 3D platforms offer, and the animation of the figure to the desired identical poses, provides the possibility of not only obtaining linear measurements of the person but also of orthophotographic visualization of body proportions, allowing their comparison with another silhouette, which is difficult to achieve in perspective view of the studied image. Full article

Thermal refuges are becoming increasingly influential for dictating the population status and spatial distribution of cold-water stenotherm salmonids in the mid- to southern extent of their range. The global climate is predicted to continue to warm, and therefore, the overall thermal suitability of freshwater habitats for stream salmonids is predicted to decline in concert. However, stream and river thermal heterogeneity will offer considerable resiliency for these populations. Thermal refuges are formed by many physical processes; common natural refuges include cold tributary plumes, groundwater springs, alcoves, and hyporheic upwellings. However, many anthropogenically formed refuges (such as stratified reservoirs or cold-water tailrace outflows) also exist in hydropower-regulated rivers. The significance of these refuges to stream salmonids depends on their size and temperature differential, but also other habitat characteristics such as their depth, flow velocity, Froude number, and many biotic factors within the refuges. Modern technologies such as drone-mounted thermal infrared cameras and other remote sensing techniques allow for the efficient identification of such refuges, and inexpensive options include the identification of refuges during ice cover using orthophotographs. Behavioural thermoregulation, i.e., salmonids aggregating in cold-water refuges, can be either facultative or obligate and the timing of these events is governed by life stage, species, and population-specific physiologically regulated cumulative thresholds that are inherently related to the recent thermal history, or hysteresis, of each individual. Salmonids appear to have an excellent spatial cognition for locating and relocating cold-water refuges, and their spatial distribution is largely affected by the availability of the cold-water refuges during the warm-water period in many thermally stressed rivers. Gregarious behaviour is the norm for salmonid fishes within the thermal refuges; however, the size/microhabitat hierarchy appears to dictate the within-refuge distribution at the micro-scale. There continues to be a great impetus for protecting—and in carefully determined cases creating—cold-water refuges in the future. A thorough understanding of what a “goldilocks” refuge is for various salmonids and their different life stages will be imperative as cold-water restoration is gaining popularity. Finally, disentangling the roles of the climate-induced and landscape activity-induced warming potential of fluvial freshwater will be important to ensure continued environmentally responsible landscape activities in future waterscapes. Full article

In this paper, we present a deep learning processing flow aimed at Advanced Driving Assistance Systems (ADASs) for urban road users. We use a fine analysis of the optical setup of a fisheye camera and present a detailed procedure to obtain Global Navigation Satellite System (GNSS) coordinates along with the speed of the moving objects. The camera to world transform incorporates the lens distortion function. YOLOv4, re-trained with ortho-photographic fisheye images, provides road user detection. All the information extracted from the image by our system represents a small payload and can easily be broadcast to the road users. The results show that our system is able to properly classify and localize the detected objects in real time, even in low-light-illumination conditions. For an effective observation area of 20 m × 50 m, the error of the localization is in the order of one meter. Although an estimation of the velocities of the detected objects is carried out by offline processing with the FlowNet2 algorithm, the accuracy is quite good, with an error below one meter per second for urban speed range (0 to 15 m/s). Moreover, the almost ortho-photographic configuration of the imaging system ensures that the anonymity of all street users is guaranteed. Full article

Colombia has hundreds of historical shipwrecks, but systematic research on this topic is scarce, which makes locating wreck sites problematic. Colombia is home to the Caribbean archipelago of San Andres, Old Providence, and Santa Catalina. Its complex environmental conditions make it a “ship trap”. On 2 February 1894, the USS Kearsarge ran aground on Roncador Cay, one of the Archipelago’s islets, and the location of the wreck site remains uncertain. Due to its role in the American Civil War, the Kearsarge is important naval heritage. Based on historical and cartographic records, orthophotographs, Landsat images, and light-detection-and-ranging (LiDAR) data, this study aimed to estimate the location of the wreck site in a Geographic Information System (GIS). Court-martial records, particularly nautical data and astronomical coordinates, were reviewed, including a study from 1894 indicating the wreck’s location on a map without coordinates. Nautical charts were also analyzed to find the Kearsarge wreck symbol. To identify the wreck site’s ordnance, logbooks and information on previous salvage efforts were examined. The analysis of nautical charts revealed a few shipwrecks, but not the Kearsarge. Historical and remote-sensing data were processed in a GIS, along with the most recent nautical chart of Roncador Cay from 2017, to obtain a possible geographical location. This resulted in coordinates, which were used to detect features associated with the USS Kearsarge in the processed data. Although the wreck was not detected, the data helped to estimate the approximate coordinates for where the wreck could be located, quantifying our degree of uncertainty. Full article

Remote sensing is a relevant method to map inaccessible areas, such as intertidal mudflats. However, image classification is challenging due to spectral similarity between microphytobenthos and oyster reefs. Because these elements are strongly related to local geomorphic features, including biogenic structures, a new mapping method has been developed to overcome the current obstacles. This method is based on unmanned aerial vehicles (UAV), RGB, and multispectral (four bands: green, red, red-edge, and near-infrared) surveys that combine high spatial resolution (e.g., 5 cm pixel), geomorphic mapping, and machine learning random forest (RF) classification. A mudflat on the Atlantic coast of France (Marennes-Oléron bay) was surveyed based on this method and by using the structure from motion (SfM) photogrammetric approach to produce orthophotographs and digital surface models (DSM). Eight classes of mudflat surface based on indexes, such as NDVI and spectral bands normalised to NIR, were identified either on the whole image (i.e., standard RF classification) or after segmentation into five geomorphic units mapped from DSM (i.e., geomorphic-based RF classification). The classification accuracy was higher with the geomorphic-based RF classification (93.12%) than with the standard RF classification (73.45%), showing the added value of combining topographic and radiometric data to map soft-bottom intertidal areas and the user-friendly potential of this method in applications to other ecosystems, such as wetlands or peatlands. Full article

This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems. Full article

Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have compared and combined two of the most common data sources for updating urban tree inventories: Airborne Laser Scanning (ALS) point clouds combined with aerial orthophotographs, and street-level imagery from Google Street View (GSV). Depending on the data source, different methodologies were used to identify the trees. In the first stage, the use of individual tree detection techniques in ALS point clouds was compared with the detection of objects (trees) on street level images using computer vision (CV) techniques. In both cases, a high success rate or recall (number of true positive with respect to all detectable trees) was obtained, where between 85.07% and 86.42% of the trees were well-identified, although many false positives (FPs) or trees that did not exist or that had been confused with other objects were always identified. In order to reduce these errors or FPs, a second stage was designed, where FP debugging was performed through two methodologies: (a) based on the automatic checking of all possible trees with street level images, and (b) through a machine learning binary classification model trained with spectral data from orthophotographs. After this second stage, the recall decreased to about 75% (between 71.43 and 78.18 depending on the procedure used) but most of the false positives were eliminated. The results obtained with both data sources were robust and accurate. We can conclude that the results obtained with the different methodologies are very similar, where the main difference resides in the access to the starting information. While the use of street-level images only allows for the detection of trees growing in trafficable streets and is a source of information that is usually paid for, the use of ALS and aerial orthophotographs allows for the location of trees anywhere in the city, including public and private parks and gardens, and in many countries, these data are freely available. Full article

The dehesa is an anthropic complex ecosystem typical of some areas of Spain and Portugal, with a key role in soil and biodiversity conservation and in the search for a balance between production, conservation and ecosystem services. For this reason, it is essential to have tools that allow its characterization, as well as to monitor and support decision-making to improve its sustainability. A multipurpose and scalable tool has been developed and validated, which combines several low-cost technologies, computer vision methods and RGB aerial orthophotographs using open data sources and which allows for automated agroforestry inventories, identifying and quantifying units with important ecological significance such as: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water. The development has been carried out from images of the national aerial photogrammetry plan of Spain belonging to 32 dehesa farms, representative of the existing variability in terms of density of trees, shrub species and the presence of other ecological elements. First, the process of obtaining and identifying areas of interest was automated using WMS services and shapefile metadata. Then, image analysis techniques were used to detect the different ecological units. Finally, a classification was developed according to the OBIA approach, which stores the results in standardized files for Geographic Information Systems. The results show that a stable solution has been achieved for the automatic and accurate identification of ecological units in dehesa territories. The scalability and generalization to all the dehesa territories, as well as the possibility of segmenting the area occupied by trees and other ecological units opens up a great opportunity to improve the construction of models for interpreting satellite images. Full article