The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity.

Detecting faults in solar photovoltaic modules (PVM) is crucial for enhancing their longevity, power output, and overall reliability. Visual anomalies such as soiling, partial shading, cell damage, and glass breakage pose significant challenges for fault identification, particularly in harsh environmental conditions. Therefore, it is essential to maintain healthy PV systems with extended lifecycles and optimal performance through the quick and efficient detection of faults. This work introduces a comprehensive approach that encompasses dataset creation, preprocessing, and PV fault classification utilizing the EfficientNet B0 model. Processed RGB images serve as input for the model, enabling the classification of visual faults in PVM. The performance evaluation of the proposed deep neural network model includes metrics such as classification accuracy, F1 score, specificity, and recall. The results highlight the exceptional performance of the proposed model, achieving a classification accuracy of 97.24% for visual fault identification in PV modules. Moreover, the study underscores the model's robustness and efficacy through a comparative analysis with other classification techniques reported in the literature.

Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.

Background . The paper is devoted to the analysis of vertical displacements based on remote sensing data as an identifier of hazardous engineering-geological processes in areas with underground infrastructure. The study was carried out on the example of the of the tunnel between Demiivska and Lybidska stations of the Kyiv subway. In December 2023, processes of uneven compaction and vibration creep of the soil massif around the tunnel lining were detected, and there was a risk of loss of stability of the tunnel structures and an emergency. Methods . This study employs the Differential Interferometric Synthetic Aperture Radar (D-InSAR) method which allows monitoring of soil surface deformations through phase change analysis among radar images. The correction procedures were applied to mitigate noise in processed images caused by temporal and geometric decorrelation, atmospheric disturbances, and other noise interferences. Correction and filtering method, specifically Canny and Sobel linear filters, were used to improve accuracy. Their application to processed satellite images enhances the contours of recorded vertical displacements and reduces geometric distortion noise, preserving the structural integrity of the images. According to our calculations, effective anomaly detection in images of urbanized areas requires a minimum threshold of 25 % in image contrast and clarity. The filters' application for highlighting significant intensity changes achieved a 28 % increase in clarity, indicating high processing effectiveness for further analysis of displacement maps and other parameters related to vertical shifts. Results . Abnormal zones of vertical displacements were identified within the study area based on vertical displacement data. During the 2022-2023 observation period, these zones shifted toward the metro tunnel axis. Vertical displacements directly above the area of subsidence near the 'Rozetka' store were detected during the fifth observation period, October-December 2023, coinciding with the tunnel closure for repairs. Overall, displacement values shifted from negative in 2022 to positive in 2023, suggesting that displacements may have served as an early indicator of underground structure deformation activation. The use of filters allowed for more precise identification of displacement dynamics and localization of deformation zones throughout the observation periods. In the final period, the anomalous zone aligned with the location of tunnel deformations and recorded surface subsidence. Conclusions . Using the example of the distillation tunnel section, we demonstrate the possibility of using the analysis of vertical surface displacements performed by D-InSAR together with a combination of Kenny and Sobel filters to track vertical surface displacements, which is important for monitoring the condition of underground facilities and preventing possible accidents. This study lays the foundation for further development of methodological approaches to the analysis of potential deformations of underground structures based on surface dynamics (vertical displacements). Further improvement of the methodology will help to ensure the accuracy and reliability of data in the context of monitoring underground structures.

Heavy metal stress can lead to morphological and physiological variations in crops. We aimed to distinguish heavy metal stress levels based on the variations of morphological and physiological parameters from radiative transfer and statistical models. Sentinel-2 satellite images and in situ measured data were collected from heavy metal-contaminated soils of rice growing areas in Zhuzhou City, Hunan Province, China. The chlorophyll content (chlorophyll a + chlorophyll b, Cab) and leaf area index (LAI) were calculated using a PROSAIL radiative transfer model and the multilayer perceptron algorithm. A two-dimensional feature space was established from Cab-LAI. Furthermore, a normalized heavy metal stress index (HMSI) from the established Cab-LAI theoretical triangular model was explored to distinguish heavy metal stress levels in rice. The results indicated that (i) the PROSAIL and artificial neural network algorithm were successful at deriving physiological parameters with high estimation accuracy. Pearson's correlation coefficient between the predicted and measured Cab was 0.85; (ii) the correlation between the measured concentration of cadmium in the soil and the HMSI was 0.84, indicating that it is a good indicator of rice damage caused by heavy metal stress, with the maximum HMSI occurring in rice subjected to high pollution; and (iii) high pollution occurred on both sides of the Xiangjiang River, whereas moderate pollution mainly existed around the heavily polluted areas. Areas with non-pollution and mild pollution were distributed over most of the study area. Combining rice Cab with LAI is a feasible method to determine the distribution of rice heavy metal stress levels over a large area.

The Karnak Temples complex, a monumental site dating back to approximately 1970 BC, faces significant preservation challenges due to a confluence of mechanical, environmental, and anthropogenic factors impacting its stone blocks. This study provides a comprehensive evaluation of the deterioration affecting the northeast corner of the complex, revealing that the primary forms of damage include split cracking and fracturing. Seismic activities have induced out-of-plane displacements, fractures, and chipping, while flooding has worsened structural instability through uplift and prolonged water exposure. Soil liquefaction and fluctuating groundwater levels have exacerbated the misalignment and embedding of stone blocks. Thermal stress and wind erosion have caused microstructural decay and surface degradation and contaminated water sources have led to salt weathering and chemical alterations. Multi-temporal satellite imagery has revealed the influence of vegetation, particularly invasive plant species, on physical and biochemical damage to the stone. This study utilized in situ assessments to document damage patterns and employed satellite imagery to assess environmental impacts, providing a multi-proxy approach to understanding the current state of the stone blocks. This analysis highlights the urgent need for a multi-faceted conservation strategy. Recommendations include constructing elevated platforms from durable materials to reduce soil and water contact, implementing non-invasive cleaning and consolidation techniques, and developing effective water management and contamination prevention measures. Restoration should focus on repairing severely affected blocks with historically accurate materials and establishing an open museum setting will enhance public engagement. Long-term preservation will benefit from regular monitoring using 3D scanning and a preventive conservation schedule. Future research should explore non-destructive testing and interdisciplinary collaboration to refine conservation strategies and ensure the sustained protection of this invaluable historical heritage.

An extensive discoloration (yellowing, browning), and defoliation (leaf loss) were observed in Slovak forests during the summer of 2022. These phenomena are attributed to the combination of very low atmospheric precipitation and extremely high air temperatures from June to early August. In this study, the deterioration of forest health was analysed by comparing the image classification of Sentinel-2 satellite data from the year of intense drought occur-rence, 2022, with that from a referenced year without drought occurrence, 2020. The results indicated that in 2022, the proportion of heavily damaged stands with defoliation exceeding 50% doubled, reaching 19.3% (417,000 ha), and an area of 223,000 ha experienced an increase in defoliation by 30% or more. The damage exhibited an uneven spatial distribution, with the most significant impact observed in the western and southern parts of central Slovakia, as well as partially in the southern part of eastern Slovakia. Further GIS analyses revealed that forests growing on slopes with southern aspects suffered more severe damage than with northern exposures. However, the difference between the most damaged forests with south-southeast exposure (12.2%) and the least damaged ones with north-northwest exposure (8.2%) was only 4%. The level of damage gradually decreased with increasing altitude. Nevertheless, compared to previous studies, the damage was significantly manifested even in the fourth forest vegetation zone, up to an elevation of approximately 800 m. Regarding soil texture, which influences the water regime, the damage gradually decreased with decreasing sand content, ranging from sandy soils (17.5%) to clayey soils (6.6%).

Freeze-thaw cycles significantly impact construction by altering soil properties and stability, which can lead to delays and increased costs. While soil-stabilizing additives are vital for addressing these issues, stabilized soils remain susceptible to volume changes and structural alterations, ultimately reducing their strength after repeated freeze-thaw cycles. This study aims to introduce a different approach by employing magnesium chloride (MgCl2) as an antifreeze and soil stabilizer additive to enhance the freeze-thaw resilience of clay soils. We investigated the efficiency of MgCl2 solutions at concentrations of 4%, 9%, and 14% on soil by conducting tests such as Atterberg limits, standard proctor compaction, unconfined compression, and freeze-thaw cycles under extreme cold conditions (-10 degrees C and -20 degrees C), alongside microstructural analysis with SEM, XRD, and FTIR. The results showed that MgCl2 reduces the soil's liquid limit and plasticity index while enhancing its compressive strength and durability. Specifically, soil treated with a 14% MgCl2 solution maintained its volume and strength at -20 degrees C, with similar positive outcomes observed for samples treated with 14% and 9% MgCl2 solutions at -10 degrees C. This underlines MgCl2's potential to enhance soil stability during initial stabilization and, most importantly, preserve it under cyclic freeze-thaw stresses, offering a solution to improve construction practices in cold environments.

Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R-CV(2) and RMSECV of 0.59 and 82 kg m(-3), respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe.

Permanently shadowed regions (PSRs) at the poles of the Moon are potential reservoirs of trapped volatile species, including water ice. Knowledge of the distribution and abundance of water ice at the poles provides key scientific background for understanding the evolution of volatiles in the Earth-Moon system and for human exploration efforts. The Lunar Reconnaissance Orbiter Camera (LROC) acquired images of the terrain within PSRs to search for indications of water ice. In addition, the LRO Miniature Radio-Frequency (Mini-RF) instrument acquired S-band radar observations to further characterize these regions. Specifically, the m-chi decomposition was used to assess the distribution of materials within and around PSRs based on the type of backscatter. Double-bounce backscatter is indicative of water ice, but could also be produced by randomly distributed blocks at the wavelength scale. To ascertain whether these signatures are due to water ice or blocks, we quantified the abundance of detectable blocks in areas with double-bounce backscatter using the LROC Narrow Angle Camera (NAC). Block populations were measured for a suite of craters with different ages, sizes, and radar characteristics. For fresh craters, a correlation between block size, block density and double-bounce backscatter was found. Within PSRs exhibiting double-bounce backscatter, no blocks were found. Additionally, no albedo variations were observed at PSRs, in contrast to observations of PSRs on Mercury. While the possibility of water ice in some lunar craters still exists, these results indicate that they are likely small-scale, and that the observed radar anomalies at PSR-bearing craters are most likely due to the presence of wavelength-scale blocks.