Search Results (92)

Bagheria, located on the northern coast of Sicily, is home to one of the Mediterranean’s most remarkable ensembles of Baroque villas, constructed between the 17th and 18th centuries by the aristocracy of Palermo. Originally situated within a highly structured rural landscape of citrus groves, gardens, and visual axes, these monumental residences have undergone substantial degradation due to uncontrolled urban expansion throughout the 20th century. This study presents a diachronic spatial analysis of Bagheria’s territorial transformation from 1850 to 2018, integrating historical cartography, aerial photography, satellite imagery, and Geographic Information System (GIS) tools. A total of 33 villas were identified, georeferenced, and assessed based on their spatial integrity, architectural condition, and relationship with the evolving urban fabric. The results reveal a progressive marginalization of the villa system, with many heritage assets now embedded within dense residential development, severed from their original landscape context and deprived of their formal gardens and visual prominence. Comparative insights drawn from analogous Mediterranean heritage landscapes, such as Ortigia (Siracusa), the Appian Way (Rome), and Athens, highlight the urgency of adopting integrated conservation frameworks that reconcile urban development with cultural and ecological continuity. As a strategic response, the study proposes the creation of a thematic cultural route, La città delle ville, to enhance the visibility, accessibility, and socio-economic relevance of Bagheria’s heritage system. This initiative, supported by adaptive reuse policies, smart heritage technologies, and participatory planning, offers a replicable model for sustainable territorial regeneration and heritage-led urban resilience. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article

Lossless image compression has been studied and widely applied, particularly in medicine, space exploration, aerial photography, and satellite communication. In this study, we proposed a low-complexity lossless compression for image (LOCO-I) predictor based on the joint photographic expert group–lossless standard (JPEG-LS). We analyzed the nature of the LOCO-I predictor and offered possible solutions. The improved LOCO-I outperformed LOCO-I by a reduction of 2.26% in entropy for the full image size and reductions of 2.70, 2.81, and 2.89% for 32 × 32, 16 × 16, and 8 × 8 block-based compression, respectively. In addition, we suggested vertical/horizontal flip for block-based compression, which requires extra bits to record and decreases the entropy. Compared with other state-of-the-art (SOTA) lossless image compression predictors, the proposed method has low computation complexity as it is multiplication- and division-free. The model is also better suited for hardware implementation. As the predictor exploits no inter-block relation, it enables parallel processing and random access if encoded by fix-length coding (FLC). Full article

When rivers impinge on the steep bluffs of valley walls, dynamic changes stem from a combination of fluvial and mass wasting processes. This study identifies the geomorphic changes, drivers, and timing of a landslide adjacent to the Apalachicola River at Alum Bluff, the tallest natural geological exposure in Florida at ~40 m, comprising horizontal sediments of mixed lithology. We used hydrographic surveys from 1960 and 2010, two sets of LiDAR from 2007 and 2018, historical aerial, drone, and ground photography, and satellite imagery to interpret changes at this bluff and river bottom. Evidence of slope failure includes a recessed upper section with concave scarps and debris fans in the lower section with subaqueous features including two occlusions and a small island exposed from the channel bottom at lower water levels. Aerial photos and satellite images indicate that the failure occurred in at least two phases in early 2013 and 2015. The loss in volume in the 11-year interval, dominantly from the upper portion of the bluff, was ~72,750 m³ and was offset by gains of ~14,760 m³ at the lower portion of the bluff, suggesting that nearly 80% of the material traveled into the river, causing changes in riverbed morphology from the runout. Despite being along a cutbank and next to the scour pool of a large meandering river, this failure was not driven by floods and the associated lateral erosion, but instead by rainfall in noncohesive sediments at the upper portion of the bluff. This medium-magnitude landslide is now the second documented landslide in Florida. Full article

The Liaohe River Estuary Wetland, located in Panjin City, plays a critical role in reducing pollution loads, maintaining biodiversity, and ensuring ecological security in China’s coastal regions, contributing significantly to the implementation of the land–sea coordination strategy. As key components of ecological restoration projects, monitoring and evaluating restoration effectiveness provide a reliable basis for decision-making and ecosystem management. This study established an innovative three-dimensional integrated monitoring and evaluation system combining satellite imagery, UAV aerial photography, and field sampling surveys, addressing the technical gaps in multi-scale and multi-dimensional dynamic ecological monitoring. Through systematic monitoring and the assessment of key indicators, including water environment, soil environment, biodiversity, water conservation capacity, and carbon sequestration capacity, we comprehensively evaluated the enhancement effects of ecological restoration projects on regional ecosystem structure, quality, and service functions. The findings demonstrated that the satellite–airborne–ground integrated monitoring technology significantly improved water quality and soil properties, enhanced soil–water conservation capabilities, and increased biodiversity indices and carbon sequestration potential. These results validate the scientific validity of ecological protection measures and the comprehensive benefits of restoration outcomes. The primary contributions of this research lie in the following: developing a novel monitoring framework that provides critical data support for decision-making, project acceptance, effectiveness evaluation, and adaptive management in ecological restoration; establishing transferable methodologies applicable not only to the Liaohe River Estuary wetlands, but also to similar ecosystems globally, showcasing broad applicability in ecological governance. Full article

Drones are useful tools for performing tasks that are difficult for humans. Thus, they are being increasingly utilized in various fields. In smart construction, a range of methods, including robots and drones, has been proposed to inspect facilities and other similar structures. Global navigation satellite system (GNSS) shadowing can occur when large bridge substructures, which are difficult for humans to access, are inspected using drones because GNSS is a major component in drone operation. This study develops a path planning algorithm to address areas with GNSS shadowing. The operation mode of the drone is classified into waypoint selection based on the photography point algorithm (WPS-PPA) and GNSS non-shadowing area algorithm (WPS-GNSA). Both algorithms are experimentally compared for flight performance in the GNSS shadowing area. A field experiment was conducted by varying the distance between the drone and the bridge substructure and by comparing the success of the flights. In successful flights, the GNSS reception of WPS-GNSA reached 1.4 times that of WPS-PPA. Furthermore, even in failed flights, compared to the WPS-PPA algorithm, the WPS-GNSA algorithm continued flight until the GNSS signal further deteriorated. Accordingly, WPS-GNSA is more favorable than WPS-PPA for inspecting bridge substructures under GNSS signal shadowing. Full article

At the turn of the 20th century, Christian and Catholic churches in Western nations established numerous mission hospitals in non-European regions. In China, mission hospitals represent a significant category of modern architectural heritage, symbolising advancements in healthcare and medical education while also serving as historical artifacts of early cultural interactions between China and the West. With ongoing developments in medical technology, these mission hospital structures no longer meet contemporary healthcare demands; many have been repurposed or temporarily abandoned. Preserving and effectively repurposing mission hospital heritage has thus emerged as a critical issue. In the present study, the Shantung Christian University Union Medical College was examined as a case study in addressing this challenge. The site retains the original Outpatient Building, Inpatient Building, Medical Teaching Building, and other architectural heritage and has preserved the original mixed Chinese and Western architectural styles. A combination of historical research, field investigation, and historic layering was adopted in the present study, drawing primarily on data from historical maps, satellite images from different periods, aerial photography from drones, architectural drawings, and other relevant historical data. Through case studies, methods for characterising and identifying the landscape and architectural heritage of mission hospitals were explored. Principles for the preservation and regeneration of the heritage of church hospitals were also proposed, with a view to providing a reference for the study and preservation of this type of heritage. 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

Unmanned aerial vehicle (UAV) imaging provides the ability to obtain high-resolution images at a lower cost than satellite imagery and aerial photography. However, multiple UAV images need to be mosaicked to obtain images of large areas, and the resulting UAV multispectral image mosaics typically contain seam lines. To address this problem, we applied irradiance, vignette, and bidirectional reflectance distribution function (BRDF) filters and performed field work using a DJI Mavic 3 Multispectral (M3M) camera to collect data. We installed a calibrated reference tarp (CRT) in the center of the collection area and conducted three types of flights (BRDF, vignette, and validation) to measure the irradiance, radiance, and reflectance—which are essential for irradiance correction—using a custom reflectance box (ROX). A vignette filter was generated from the vignette parameter, and the anisotropy factor (ANIF) was calculated by measuring the radiance at the nadir, following which the BRDF model parameters were calculated. The calibration approaches were divided into the following categories: a vignette-only process, which solely applied vignette and irradiance corrections, and the full process, which included irradiance, vignette, and BRDF. The accuracy was verified through a validation flight. The radiance uncertainty at the seam line ranged from 3.00 to 5.26% in the 80% lap mode when using nine images around the CRT, and from 4.06 to 6.93% in the 50% lap mode when using all images with the CRT. The term ‘lap’ in ‘lap mode’ refers to both overlap and sidelap. The images that were subjected to the vignette-only process had a radiance difference of 4.48–6.98%, while that of the full process images was 1.44–2.40%, indicating that the seam lines were difficult to find with the naked eye and that the process was successful. Full article

Accurate crop yield estimation is critical to successful agricultural operations. Current crop growth models often overlook the spatial and geographic components of the lands, leading to suboptimal yield estimates. To address this issue, assimilation of satellite vegetation products into these models can account for spatial variations in the land and improve estimation accuracy. In this paper, the AquaCrop model, a water-driven crop growth model, was selected for recalibration and assimilation of satellite-derived biophysical products due to its simplicity and lack of computational complexity. To this end, field samples of soil (sampled before cultivation) and crop features were collected during the growing season of silage maize. Digital hemisphere photography (DHP) and destructive sampling methods were used for measuring fraction vegetation cover (fCover) and biomass in Qaleh-Now County, southern Tehran, in 2019. Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. Here, we propose the use of an optimization water cycle algorithm (WCA) instead of a PSO algorithm as an assimilation method for the parameter calibration of AquaCrop. This study also focused on using both fCover and biomass state variables simultaneously in the model, as opposed to only the fCover, and found that using both variables led to significantly higher calibration accuracy. The WCA method outperformed the PSO method in AquaCrop’s calibration, leading to more accurate results on maize yield estimates. It has enhanced results, decreasing RMSE values by 3.8 and 4.7 ton/ha, RRMSE by 6.4% and 10%, and increasing R² by 0.17 and 0.35 for model calibration and validation, respectively. These results suggest that assimilating satellite-derived data and optimizing the calibration process through WCA can significantly improve the accuracy of crop yield estimations in water-driven crop growth models, highlighting the potential of this approach for precision agriculture. Full article

Timely identification of problems in electrical distribution networks is crucial to preventing major failures, reducing costs, and ensuring a reliable power supply. This paper presents a monitoring, inspection, and early warning system designed specifically for electrical networks, utilizing satellite imagery to complement traditional inspections. The system uses spectral indices derived from satellite images to monitor environmental factors such as humidity, vegetation, snow cover, and burned areas, offering a comprehensive view of the grid’s surroundings. Collected daily, this information detects changes that may pose risks to power lines and infrastructure. The system also allows users to include custom indices, ensuring flexibility in various environmental and network contexts. An integrated AI model estimates vegetation height from Sentinel-2 images, identifying potential risk areas where vegetation could threaten power lines. One key advantage of the system is the reduced reliance on costly, frequent manual inspections, lowering operational expenses compared to other methods like aerial photography or LiDAR scanners. Additionally, it provides early alerts to grid operators when potential issues are detected, enabling timely intervention and proactive maintenance. This improves network efficiency and reliability by enhancing the response to critical situations and facilitating preventive risk management. Full article

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems. Full article

In applications such as satellite remote sensing and aerial photography, imaging equipment must capture brightness information of different ground scenes within a restricted dynamic range. Due to camera sensor limitations, captured images can represent only a portion of such information, which results in lower resolution and lower dynamic range compared with real scenes. Image super resolution (SR) and multiple-exposure image fusion (MEF) are commonly employed technologies to address these issues. Nonetheless, these two problems are often researched in separate directions. In this paper, we propose MEFSR-GAN: an end-to-end framework based on generative adversarial networks that simultaneously combines super-resolution and multiple-exposure fusion. MEFSR-GAN includes a generator and two discriminators. The generator network consists of two parallel sub-networks for under-exposure and over-exposure, each containing a feature extraction block (FEB), a super-resolution block (SRB), and several multiple-exposure feedback blocks (MEFBs). It processes low-resolution under- and over-exposed images to produce high-resolution high dynamic range (HDR) images. These images are evaluated by two discriminator networks, driving the generator to generate realistic high-resolution HDR outputs through multi-goal training. Extensive qualitative and quantitative experiments were conducted on the SICE dataset, yielding a PSNR of 24.821 and an SSIM of 0.896 for 2× upscaling. These results demonstrate that MEFSR-GAN outperforms existing methods in terms of both visual effects and objective evaluation metrics, thereby establishing itself as a state-of-the-art technology. Full article

A common practice used by the Germans and collaborators in World War II, as part of the Holocaust, was to use existing Jewish cemeteries as places for mass burial. Research was completed at the Old Jewish Cemetery in Riga, Latvia, the Livas Jewish Cemetery in Liepaja, Latvia, and the Zaliakalnis Jewish Cemetery in Kaunas, Lithuania. The Old Jewish Cemetery in Riga was adjacent to the Riga Ghetto and was used to bury individuals murdered in the ghetto. In Kaunas, an area of the Zaliakalnis Jewish Cemetery is devoid of grave stones, and literature sources and testimony indicate that this area was used for the mass burial of Jews from the Kaunas Ghetto and other mass killings. In Liepaja, the local Jewish Heritage Foundation believes that there are mass graves within the Livas Cemetery. Methodologies for this research include the use of a pulseEkko Pro 500-megahertz ground-penetrating radar (GPR) system. Electrical resistivity tomography (ERT) data were collected through a linear array of electrodes coupled to a direct current (DC) resistivity transmitter and receiver. Analysis of aerial photography and satellite images was also employed at each location. ERT and GPR data indicate three separate trench anomalies in the Old Jewish Cemetery in Riga. The presence of these anomalies corroborates Holocaust survivor testimony that bodies were buried in mass graves in that area. In the Zaliakalnis Jewish Cemetery in Kaunas, ERT and GPR data indicate an anomaly in the western part of the cemetery, and ERT data further indicate two other possible mass graves. In Liepaja, preliminary GPR analysis indicates an anomaly in a cleared section of the cemetery. Based on the presence of geophysical anomalies in all three cemeteries, which correlate with literature sources and Holocaust survivor testimony, there is a high probability that mass graves are present at each site. Future research directions include expanding the search areas in each cemetery, additional literature and testimony-based research, and the addition of other geophysical methodologies. Full article

The observation of the phytoplankton distribution with a high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to hydraulic phenomena. Photography from UAVs, which has an excellent temporal and spatial resolution, is an effective method to obtain water quality information comprehensively. In this study, we attempted to develop a method for estimating the chlorophyll concentration from aerial images using machine learning that considers brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis. The reflectance of harmful algae bloom (HAB) was different from that of phytoplankton seen under normal conditions; so, the images containing HAB were the causes of error in the estimation of the chlorophyll concentration. First, the images when the bloom occurred were extracted by the discrimination with machine learning. Then, the other images were used for the regression of the concentration. Finally, the coefficient of determination between the estimated chlorophyll concentration when no bloom occurred by the image analysis and the observed value reached 0.84. The proposed method enables the detailed depiction of the spatial distribution of the chlorophyll concentration, which contributes to the improvement in water quality management in reservoirs. Full article