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Since the 1970s, extensive halite extraction in Maceió, Brazil, has resulted in significant geological risks, including ground collapses, sinkholes, and infrastructure damage. These risks became particularly evident in 2018, following an earthquake, which prompted the cessation of mining activities in 2019. This study investigates subsidence deformation resulting from these mining operations, focusing on the collapse of Mine 18 on 10 December 2023. We utilized the Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPS-InSAR) technique to analyze a dataset of 145 Sentinel-1A images acquired between June 2019 and April 2024. Our approach enabled the analysis of cumulative displacement, the loss of amplitude stability, the evolution of amplitude time series, and the amplitude change matrix of targets near Mine 18. The study introduces an innovative QPS-InSAR approach that integrates phase and amplitude information using amplitude time series to assess the lifecycle of radar scattering targets throughout the monitoring period. This method allows for effective change detection following sudden events, enabling the identification of affected areas. Our findings indicate a maximum cumulative displacement of −1750 mm, with significant amplitude changes detected between late November and early December 2023, coinciding with the mine collapse. This research provides a comprehensive assessment of deformation trends and ground stability in the affected mining areas, providing valuable insights for future monitoring and risk mitigation efforts. [ABSTRACT FROM AUTHOR]

Understanding geometric and biophysical characteristics is essential for determining grapevine vigor and improving input management and automation in viticulture. This study compares point cloud data obtained from a Terrestrial Laser Scanner (TLS) and various UAV sensors including multispectral, panchromatic, Thermal Infrared (TIR), RGB, and LiDAR data, to estimate geometric parameters of grapevines. Descriptive statistics, linear correlations, significance using the F-test of overall significance, and box plots were used for analysis. The results indicate that 3D point clouds from these sensors can accurately estimate maximum grapevine height, projected area, and volume, though with varying degrees of accuracy. The TLS data showed the highest correlation with grapevine height (r = 0.95, p < 0.001; R2 = 0.90; RMSE = 0.027 m), while point cloud data from panchromatic, RGB, and multispectral sensors also performed well, closely matching TLS and measured values (r > 0.83, p < 0.001; R2 > 0.70; RMSE < 0.084 m). In contrast, TIR point cloud data performed poorly in estimating grapevine height (r = 0.76, p < 0.001; R2 = 0.58; RMSE = 0.147 m) and projected area (r = 0.82, p < 0.001; R2 = 0.66; RMSE = 0.165 m). The greater variability observed in projected area and volume from UAV sensors is related to the low point density associated with spatial resolution. These findings are valuable for both researchers and winegrowers, as they support the optimization of TLS and UAV sensors for precision viticulture, providing a basis for further research and helping farmers select appropriate technologies for crop monitoring. [ABSTRACT FROM AUTHOR]

Understanding and accurately predicting stomatal conductance in almond orchards is critical for effective water-management strategies, especially under challenging climatic conditions. In this study, machine-learning (ML) regression models trained on multispectral (MSP) and thermal infrared (TIR) data acquired from unmanned aerial vehicles (UAVs) are used to address this challenge. Through an analysis of spectral indices calculated from UAV-based data and feature-selection methods, this study investigates the predictive performance of three ML models (extra trees, ET; stochastic gradient descent, SGD; and extreme gradient boosting, XGBoost) in predicting stomatal conductance. The results show that the XGBoost model trained with both MSP and TIR data had the best performance (R2 = 0.87) and highlight the importance of integrating surface-temperature information in addition to other spectral indices to improve prediction accuracy, up to 11% more when compared to the use of only MSP data. Key features, such as the green–red vegetation index, chlorophyll red-edge index, and the ratio between canopy temperature and air temperature (Tc-Ta), prove to be relevant features for model performance and highlight their importance for the assessment of water stress dynamics. Furthermore, the implementation of Shapley additive explanations (SHAP) values facilitates the interpretation of model decisions and provides valuable insights into the contributions of the features. This study contributes to the advancement of precision agriculture by providing a novel approach for stomatal conductance prediction in almond orchards, supporting efforts towards sustainable water management in changing environmental conditions. [ABSTRACT FROM AUTHOR]

In the capitalist rush to attract more visitors, cities are committing significant resources to heritage conservation, driven by the substantial economic benefits generated by the tourism industry. However, less famous or less well-resourced cities, often with smaller populations, also known as intermediary cities, find it difficult to allocate funds to protect their most significant heritage sites. In this conservation context, intermediary cities, often on the periphery or 'at the margins', can fill the gaps and needs of urbanism through a better strategic understanding of the challenges of global touristification, thus this research provides urban planning tools for local governments with limited resources to preserve their architectural heritage through remote sensing, for its advantages in terms of lower economic cost, as a valuable monitoring tool to effectively identify high-vulnerability sites that require priority attention in the conservation of architectural heritage. In other words, it allows for a reduction in the territory of those areas located 'at the margins' in terms of urban planning and management, by approaching the territorial, urban, architectural and tourism problems from a transdisciplinary perspective in the preservation of the architectural heritage. This study explores the application of optical (Sentinel-2) using neural networks for classifying the land cover and radar (Sentinel-1 and PAZ) satellite images to obtain the ground motion as a geotechnical risk study, together with geospatial data, for the monitoring of architectural heritage in intermediate cities. Focusing on the districts of Bragança and Guarda in Portugal, the approach allows the direct identification of vulnerable architectural heritage, identifying 9 highly-vulnerable areas using PAZ data and 7 areas using Sentinel-1 data. Furthermore, this work provides an understanding of the potential and limitations of these technologies in heritage preservation because compares the processing results of freely accessible medium-resolution Sentinel-1 radar imagery with the high-resolution radar images from the innovative PAZ satellite. [ABSTRACT FROM AUTHOR]

Remote sensing, particularly satellite-based, can play a valuable role in monitoring areas prone to geohazards. The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards. This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data, with a particular emphasis on: (1) detecting mining subsidence, where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar (InSAR) technology. The results showed that the Efficient Channel Attention (ECA) U-Net model performed better than the U-Net (baseline) model in terms of Mean Intersection over Union (MIoU) and Intersection over Union (IoU) indicators; (2) monitoring water conservancy and hydropower engineering. The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets (SBAS) InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side. The dam body also showed obvious deformation with a velocity exceeding 60 mm/a; (3) the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation. The overall outcome of these methods showed that the use of Artificial Intelligence (AI) techniques in combination with InSAR data leads to more efficient analysis and interpretation, resulting in improved accuracy and prompt identification of potential hazards; and (4) finally, this study also presents a method for detecting landslides in mountainous regions, using optical imagery. The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods, this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades. [ABSTRACT FROM AUTHOR]