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P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

In practical radar applications, the diversity of wideband transmission signals presents a significant challenge for radar target recognition systems. Traditional electromagnetic (EM) simulation methods often require the point-by-point sampling of target echo signals under specific radiation sources, and this process must be repeated for different transmission signals. This results in extensive EM computations and low computational efficiency. Additionally, the physical attribute information of the target is often obscured within the simulated echoes, complicating feature extraction. To address these issues, we propose a novel method for the efficient and rapid modeling and analysis of complex target time-domain echoes based on a forward scattering center model. This method constructs an impulse response model of the target under unit impulse signal excitation, representing the inherent scattering properties of the target and remaining unaffected by changes in the radiation source. Subsequently, this model is convolved with arbitrary wideband input signals to quickly reconstruct the wideband echo signals of the target. Numerical examples validate the accuracy and effectiveness of the proposed model. This method offers several advantages: firstly, it provides high computational efficiency by quickly convolving with the input signals, thereby avoiding the extensive EM calculations required by traditional methods due to changes in the radiation source. Secondly, it achieves significant data compression; the proposed echo model only requires the model parameters to be calculated at the central frequency point, enabling the rapid reconstruction of target echo data across a wideband range and effectively reducing storage requirements. Finally, it presents clear and complete physical attributes by employing a "cause-to-effect" forward-modeling approach, where the model parameters fully represent the inherent physical properties of the target. This effectively addresses the issue of physical interpretability in optical region radar target recognition. [ABSTRACT FROM AUTHOR]

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies. [ABSTRACT FROM AUTHOR]

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent. [ABSTRACT FROM AUTHOR]

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. [ABSTRACT FROM AUTHOR]

The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very-high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation IceBridge over the 2011–2019 period to investigate our hypothesis that vertical heterogeneities in firn (i.e. ice layers) cause vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1 m is strongly related to the vertical heterogeneity of a firn profile and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles over a depth range of ∼ 4 m. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011–2013 covering an area of ∼ 350 815 km 2 , followed by firn replenishment over the years 2014–2019 spanning an area of ∼ 667 725 km 2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM. [ABSTRACT FROM AUTHOR]

Global marine gravity anomaly models are predominantly recovered from along-track radar altimeter data. While remarkable advancements has been achieved in gravity anomaly modelling, the quality of gravity anomaly model remains constrained by the absence of across-track geoid gradients and the reduction of radar altimeter data, particularly in coastal and high-latitudes regions. ICESat-2 laser altimetry operates three-pair laser beams with a small footprint and near-polar orbit, enabling the determination of across-track geoid gradients and providing more valid observations in certain regions. The ICESat-2 altimeter data processing method is presented including the determination of across-track geoid gradients and the combination of along/across-track geoid gradients. A new global marine gravity model, SDUST2022GRA, is recovered from radar and laser altimeter data using different method for determining each altimeter data error. The accuracy and spatial resolution of SDUST2022GRA is assessed by published global gravity anomaly models (DTU17, V32.1, NSOAS22) and available shipborne gravity measurements. The accuracy of SDUST2022GRA is 4.43 mGal on a global scale, which is at least 0.22 mGal better than that of others models. Moreover, in local coastal and high-latitude regions, SDUST2022GRA achieves an accuracy improvement of 0.16–0.24 mGal compared to others models. The spatial resolution of SDUST2022GRA is approximately 20 km in a certain region, slightly better superior others models. These assessments suggests that SDUST2022GRA is a reliable global marine gravity anomaly model. By comparing SDUST2022GRA with incorporating ICESat-2 and SDUST2021GRA without ICESat-2, the percentage contribution of ICESat-2 to the improvement of gravity anomaly model accuracy is 13 % in the global ocean region, and it is increasing with an proportion of ICESat-2 altimeter data in high-latitude and coastal regions. The SDUST2022GRA are freely available at the site of https://doi.org/10.5281/zenodo.8337387 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar's simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Identifying and extracting urban features such as buildings and producing accurate information about the location of features from remotely sensed images is important in updating maps, spatial databases, urban planning, and meeting the needs of urban services. Supervised methods of producing training data were widely applied in deriving accurate information from such images. However, those approaches require human intervention and are therefore time-consuming, and associated with classification accuracy limitations. This study presents a novel approach for extracting buildings from remotely sensed images by automating training data selection steps for supervised classification. To prepare training samples intelligently and automatically, a knowledge-based integration of radar and optical images was used. First, a general height constraint was applied to a digital surface model (DSM) obtained from radar images to divide the image pixels into two parts. Then, by applying a local height filter in each part, pixels representing buildings were extracted. Considering that the buildings are not the only features with significant heights, another constraint defined as coherence constraint was considered. Then, histogram of the optical image pixels corresponding to the filtered radar DSM pixels extracted from the previous steps was used to select sample training pixels. By considering pixels within the maximum range of the histogram, the final building samples for Support Vector Machine (SVM) classification were derived and applied for building extraction. The output showed that it sufficiently and reliably improved the existing supervised classification limitation to extract building. The performance of the proposed method was evaluated over different image datasets regardless of input image type and region. It achieved an overall average accuracy of 92% and a kappa coefficient of 0.83. [ABSTRACT FROM AUTHOR]

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information on urban built-up areas is significant for managing urban growth and development. Earth Observation (EO) data are valuable sources for meeting this demand. However, the extraction of urban built-up areas from EO data is challenging due to the limitations of EO data sources. To overcome this challenge, this study follows an approach that assesses the performance of optical (Sentinel-2), radar (Sentinel-1) and fused (Sentinel-1 and Sentinel-2) data to extract urban built-up areas using machine learning algorithms including Random Forest (RF), K-Nearest Neighbors (KNN) and KDTree KNN. The results were statistically analyzed by considering the Overall Accuracy (OA) and kappa coefficient. In addition, 15 cm GSD (Ground Sample Distance) aerial photography of the study area was used to validate the results. According to the results, Sentinel-2 produced better representation and accuracy of urban built-up areas than Sentinel-1 and even the fused image. Regarding to machine learning algorithms classification performance, RF performed better in both OA and Kappa coefficient along all datasets. The research findings can have significant implications for various domains, such as urban planning, land use management and open avenues for further comparisons of different EO data sources and machine learning algorithms for built-up areas extraction. [ABSTRACT FROM AUTHOR]

This study assesses the relative performance of Sentinel-1 and -2 and their combination with topographic information for plow agricultural land soil salinity mapping. A learning database made of 255 soil samples' electrical conductivity (EC) along with corresponding radar (R), optical (O), and topographic (T) information derived from Sentinel-2 (S2), Sentinel-1 (S1), and the SRTM digital elevation model, respectively, was used to train four machine learning models (Decision tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB). Each model was separately trained/validated for four scenarios based on four combinations of R, O, and T (R, O, R+O, R+O+T), with and without feature selection. The Recursive Feature Elimination with k-fold cross validation (RFEcv 10-fold) and the Variance Inflation Factor (VIF) were used for the feature selection process to minimize multicollinearity by selecting the most relevant features. The most reliable salinity estimates are obtained for the R+O+T scenario, considering the feature selection process, with R2 of 0.73, 0.74, 0.75, and 0.76 for DT, GB, RF, and XGB, respectively. Conversely, models based on R information led to unreliable soil salinity estimates due to the saturation of the C-band signal in plowed lands. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments. [ABSTRACT FROM AUTHOR]

A technique for estimating the velocity of radio meteors is proposed. The presented method is based on the analysis of the phase of the Fourier spectra of complex amplitudes of signals reflected from meteor trails. The method is very fast compared to the Fresnel transform method, works at low signal-to-noise ratios and is automatic. Approaches for increasing the signal-to-noise ratio and criteria for rejecting the obtained estimates are proposed. An error of 1–10% was achieved, depending on the signal-to-noise ratio (from −5 dB) and the velocity of the meteor. Comparison of meteoroid velocities from Kazan Federal University meteor radar measurements with optical observations showed good agreement of estimates with a slight underestimation of 2–8%, presumably due to the neglect of deceleration. [ABSTRACT FROM AUTHOR]

Multisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle. [ABSTRACT FROM AUTHOR]

The aim of this study was to verify the possibility of obtaining vital sign information using a laser and radar sensor in a manner that is non-invasive and painless for test animals. A dataset was obtained from respiratory movement of anaesthetized male F344 rats, signals of laser and radar sensors were recorded simultaneously with vital data acquired with an integrated multiple-channel intraoperative monitor. In addition, respiratory movements were also video recorded, and used as reference data of respiration rate (RR; ref-RR). Reference data for heart rate (HR; ref-HR) were obtained from the R wave of electrocardiogram data for each epoch. Signals recorded from the radar sensor (I- and Q-signals) were input to a computer, and HR (radar-HR) and RR (radar-RR) were estimated using the frequency analysis method. Among the six positions where respiratory movements were measured by the laser sensor, the number of peak counts matched the visual counts of respiratory movements in the video records. The respiratory movements were significantly the greatest over the most caudal rib in the dorsal (p < 0.001). The average radar-RR and ref-RR values showed correspondence (ref-RR, 69 ± 6.2 breaths/min; radar-RR, 68 ± 5.7 breaths/min (p = 0.04–1.00); equivalence ratio, 86%). The radar-HR data showed slight variability; however, there was 80% homology compared with the ref-HR values (ref-HR, 336 ± 19.6 beats/min; radar-HR, 348 ± 34.1 (p = 0.10–0.95)). Although comparison of the data under noradrenaline administration failed to track drug-induced changes in some cases, the HR and RR data of anesthetized rats measured from the radar sensor system showed comparable accuracy to other conventional methods. [ABSTRACT FROM AUTHOR]

Various deformation events and weathering processes are among the obstacles to exploration of natural resources, especially mineral ones. This study addressed these obstacles by proposing solutions through the utilization of multiple multispectral, radar, and magnetic datasets. The study region, situated in a region between two blocks associated with the Arabian–Nubian Shield, is known for the presence of these obstacles. Also, the region is characterized by the conflicting nature of these blocks in relation to occurrences of gold mineralization. Various optical and multi-frequency radar datasets were used for detailed geological/mineralogical discrimination and surface lineaments mapping, respectively. Numerous composite enhancement, processing, and classification methods were applied to aeromagnetic and multispectral data to derive as much information as possible from these data. The results of the classification of minerals, the separation of rock units, and the identification of deep-seated geological structures were combined to produce a map for predicting gold prospectivity by generating a random forest model using 10 controlling factors and the previously defined zones of gold mineralization. A three-dimensional model was generated to show the locations of the expected gold mineralization potential areas with regard to depth in the investigated region. This model figured out the strong relation between gold potentialities related to their suggested pathways, particularly WNW- and N-trending shear zones. Our model indicates that the region is promising for mining activities and presents a new approach for mapping pathways of gold-mineralized–altered zones that can be applied worldwide. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R2: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R2: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests. [ABSTRACT FROM AUTHOR]

The geological environment damage caused by coal mining subsidence has become an important factor affecting the sustainable development of mining areas. Reconstruction of the Coal Mining Subsidence Field (CMSF) is the key to preventing geological disasters, and the needs of CMSF reconstruction cannot be met by solely relying on a single remote sensing technology. The combination of Unmanned Aerial Vehicle (UAV) and Synthetic Aperture Radar (SAR) has complementary advantages; however, the data fusion strategy by refining the SAR deformation field through UAV still needs to be updated constantly. This paper proposed a Prior Weighting (PW) method based on Satellite Aerial (SA) heterogeneous remote sensing. The method can be used to fuse SAR and UAV Light Detection and Ranging (LiDAR) data for ground subsidence parameter inversion. Firstly, the subsidence boundary of Differential Interferometric SAR (DInSAR) combined with the large gradient subsidence of Pixel Offset Tracking (POT) was developed to initialize the SAR preliminary CMSF. Secondly, the SAR preliminary CMSF was refined by UAV LiDAR data; the weights of SAR and UAV LiDAR data are 0.4 and 0.6 iteratively. After the data fusion, the subsidence field was reconstructed. The results showed that the overall CMSF accuracy improved from ±144 mm to ±51 mm. The relative errors of the surface subsidence factor and main influence angle tangent calculated by the physical model and in situ measured data are 1.3% and 1.7%. It shows that the proposed SAR/UAV fusion method has significant advantages in the reconstruction of CMSF, and the PW method contributes to the prevention and control of mining subsidence. [ABSTRACT FROM AUTHOR]

We study the interrelations between salt karst and landscape evolution at the Ze'elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze'elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze'elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze'elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment. [ABSTRACT FROM AUTHOR]

The total mass flux due to meteoric input is not well constrained and estimates vary greatly depending on the measurement technique used. The source of this discrepancy remains an open question in the field. Previous studies investigating the discrepancy by directly comparing mass estimates made using two techniques have been limited by extremely small sample sizes. This work presents a set of 166 meteors observed simultaneously by the MAARSY radar (53.5 MHz) and two nearby optical cameras. Independent masses are estimated using observations from both systems and compared against each other. The resulting mass estimates using both methods agree to within a factor of three on average. The results show two dominant trends: better agreement as meteoroid velocity increases and underestimation of the radar mass for the largest meteoroids observed (>10 mg). These trends had not been quantified by previous studies limited by very small sample sizes, and could help to explain the historic discrepancy between mass estimates by different systems. The general agreement between the radar and photometric masses indicates that both methods perform well independently, and can reliably be applied to radar or optical observations without restriction of simultaneous observations by two systems. [ABSTRACT FROM AUTHOR]

The assessment of morphological mapping capacity of X-band radars is performed by comparing radar-derived shoreline estimates with in-situ data measured from DGPS-RTK surveys in Cala Millor beach (October 22–24, 2018). Radar estimates are obtained by applying an automatic method based on digital image processing, threshold segmentation and first-order derivative filters on X-Band radar variance images. The study site is covered by the endemic Posidonia oceanica seagrass meadow at the seabed. It represents a challenge on the estimation of local shoreline. Results reveal that X-band radar estimate the shoreline location with a mean bias of 4.66 m, a relative error of 2.95% and a RMSE of 14.07 m. Also, the beach morphology is assessed by the identification of surrounding rocky outcrops and a submerged sandbar. Since the obtained radar estimation errors are comparable with the reported by previous studies but using video-cameras, results reveal a potential complementary use of both marine radars and optical cameras to provide higher-resolution and more accurate shoreline measurements in a broader area. • An automated shoreline detection method for nearshore areas using X-Band marine radar is proposed. • We combine threshold segmentation, edge detection, digital processing and statistical properties of the surrounding. • Shoreline location is detected with a mean bias of 4.66 m, a relative error of 2.95% and a RMSE of 14.07 m. • We suggest the complementary use of marine radars and optical camera for shoreline measurements. [ABSTRACT FROM AUTHOR]

The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In order to solve this problem, a depth image estimation method based on a two-dimensional (2D) Kaniadakis entropy thresholding method is proposed which transforms a weak signal extraction problem into a denoising problem for point cloud data. The characteristics of signal peak aggregation in the data and the spatio-temporal correlation features between target image elements in the point cloud-intensity data are exploited. Through adequate simulations and outdoor target-imaging experiments under different signal-to-background ratios (SBRs), the effectiveness of the method under low signal-to-background ratio conditions is demonstrated. When the SBR is 0.025, the proposed method reaches a target recovery rate of 91.7%, which is better than the existing typical methods, such as the Peak-picking method, Cross-Correlation method, and the sparse Poisson intensity reconstruction algorithm (SPIRAL), which achieve a target recovery rate of 15.7%, 7.0%, and 18.4%, respectively. Additionally, comparing with the SPIRAL, the reconstruction recovery ratio is improved by 73.3%. The proposed method greatly improves the integrity of the target under high-background-noise environments and finally provides a basis for feature extraction and target recognition. [ABSTRACT FROM AUTHOR]

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model's accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4). [ABSTRACT FROM AUTHOR]

Forecasting landslide inundation upon catastrophic failure is crucial for reducing casualties, yet it remains a long‐standing challenge owing to the complex nature of landslides. Recent global studies indicate that catastrophic hillslope failures are commonly preceded by a period of precursory creep, motivating a novel scheme to foresee their hazard. Here, we showcase an approach to hindcast landslide inundation by linking satellite‐captured precursory displacements to modeling of consequent granular‐fluid flows. We present its application to the 2021 Chunchi, Ecuador landslide, which failed catastrophically and evolved into a mobile debris flow after four months of precursory creep, destroying 68 homes along its lengthy flow path. Underpinned by uncertainty quantification and in situ validations, we highlight the feasibility and potential of forecasting landslide inundation damage using observable precursors. This forecast approach is broadly applicable for flow hazards initiated from geomaterial failures. Plain Language Summary: One of the most effective approaches to reduce landslide damage, is somehow getting to know in advance where the target landslide is about to occur and how large the damage area will be when it occurs. Here, we show a possible solution of using satellite‐observed precursory motion to find and quantify the landslide source, and then input this information into a granular‐flow model to estimate its potential damage area when evolving into a debris flow. This seamlessly integrated method could allow to effectively inform hazard reduction, as large catastrophic landslides have been widely observed to manifest precursory destabilization weeks to months before the final failure. As a representative example, we applied this approach to the 2021 Chunchi, Ecuador landslide event and found it highly effective for predicting landslide inundation based on both model uncertainty quantification and field validations. Key Points: Satellite radar and optical observations uncover precursory landslide motion to infer source area and volumeWe propose an approach to forecast landslide inundation through seamless integration of precursory motion and granular‐flow modelingUncertainty quantification and in situ validations corroborate the effectiveness of this forecast approach [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of computing a preliminary orbit of a celestial body from one topocentric position vector P 1 and a very short arc (VSA) of optical observations A 2 . Using the conservation laws of the two-body dynamics, we write the problem as a system of 8 polynomial equations in 6 unknowns. We prove that this system is generically consistent, namely, for a generic choice of the data P 1 , A 2 , it always admits solutions in the complex field, even when P 1 , A 2 do not correspond to the same celestial body. The consistency of the system is shown by deriving a univariate polynomial v of degree 8 in the unknown topocentric distance at the mean epoch of the observations of the VSA. Through Gröbner bases theory, we also show that the degree of v is minimum among the degrees of all the univariate polynomials solving this problem. Even though we can find solutions to our problem for a generic choice of P 1 , A 2 , most of these solutions are meaningless. In fact, acceptable solutions must be real and have to fulfill other constraints, including compatibility with Keplerian dynamics. We also propose a way to select or discard solutions taking into account the uncertainty in the data, if present. The proposed orbit determination method is relevant for different purposes, e.g., the computation of a preliminary orbit of an Earth satellite with radar and optical observations, the detection of maneuvres of an Earth satellite, and the recovery of asteroids which are lost due to a planetary close encounter. We conclude by showing some numerical tests in the case of asteroids undergoing a close encounter with the Earth. [ABSTRACT FROM AUTHOR]

Optical correlation, interferometry, and field investigation of laterally offset features were undertaken to analyze the kinematics of the 2020 Mw 6.6 Masbate earthquake. Ground displacement fields show a peak left-lateral offset of 0.6 m corresponding to Mw 6.6 geodetic moment magnitude, with an average 0.4 m left-lateral slip. The slip distributions also indicate a single asperity located ∼200 m SE of the centroid. Post-seismic deformation estimates from interferometry highlight an at least 0.14 m left-lateral offset consistent with a Mw 6.2 post-seismic moment magnitude. The total and post-seismic slip distributions coincide with each other, with both peaks adjacent to the centroid. Slip measurements and the ∼28.2 –41 km rupture length estimates from field and remote sensing datasets characterize the Masbate segment as capable of producing long ruptures with significant offsets despite the presence of interseismic creep. Post-seismic interferograms resolved the rupture far better than optical correlation, which was degraded due to high-amplitude noise from sensor and environmental sources. Nevertheless, this review of the 2020 Mw 6.6 Masbate earthquake provides a comprehensive slip measurement of the surface rupture and demonstrates the presence of two transtensional basins in the Masbate province, revealing new insights into the seismic hazard and seismotectonic setting of the central Philippines. [ABSTRACT FROM AUTHOR]

Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory estimation, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot be effectively achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range Light Detection and Ranging (LiDAR) data, which enables the measurement of individual tree height in forest plots with high precision. Besides, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and Synthetic Aperture Radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining basal area of individual tree within plots using UAV LiDAR data, this study calculated weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at large spatial scale, we also developed a machine learning-based mixed-effects model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 m and 13.3 m with standard deviations of 2.9 m and 3.3 m, respectively. The accuracy of mapped products was validated utilizing LiDAR and field measurement data. The correlation coefficient (푟) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while RMSE ranged from 2.6 to 4.1 m and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024). [ABSTRACT FROM AUTHOR]

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The current mature Simultaneous Localisation And Mapping (SLAM) algorithms, when applied to tunnel scenarios with point cloud degradation and poor lighting conditions, often lead to a sharp increase in the estimated attitude error of the unmanned aerial vehicle (UAV), or even prevent the UAV from moving autonomously due to severe feature degradation. To address the above problems, the authors propose a SLAM algorithm based on factor graph optimisation, Iterative Closest Point and Normal Distributions Transform algorithms. A front‐end point cloud registration module and a back‐end construction algorithm based on filtering and graph optimisation are designed. To verify the effectiveness of the proposed algorithm, experiments are conducted on KITTI dataset and real tunnel scenes, and compared with LiDAR Odometry and Mapping (LOAM) and lightweight and ground optimised (LeGO)‐LOAM algorithms. The results show that the average processing time of the proposed method is about 75 ms, which can meet the real‐time requirements of autonomous aerial vehicles. Compared with LOAM and LeGO‐LOAM in the real tunnel experiment, the proposed method shows the tunnel 3D map construction. [ABSTRACT FROM AUTHOR]

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430 m), average time consumption (from 30 to 22 s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways. [ABSTRACT FROM AUTHOR]

Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests. [ABSTRACT FROM AUTHOR]

The achievements of high driving performance and error minimization of autonomous vehicles (AVs) in harsh weather are the biggest challenges for the society of autonomous research area. AVs are mainly driven by the sensor fusion technology of light detection and ranging (LiDAR), radio detection and ranging (RADAR), and camera sensors. In harsh weather such as rain, storm, law lighting, snowfall, and vapor, the detection performances of all the sensors are obstructed. The camera imaging for object detection systems is highly affected by different types of noise in adverse weather conditions and its performance is very anxious for error-free AV driving. This article proposes the prediction-based adaptive fusion alignment (AFA) algorithm of the robust path and object tracking systems with the deep convolutional neural networking (D-CNN) model for detection accuracy improvement, calculative error reduction, and overall driving error minimization of AVs in harsh weather conditions. RADAR and LiDAR are not deep learning (DL) based yet. The D-CNN model of DL algorithms for camera image processing and the segmentation process of object classification is used for actual object detection and localization. The AV-simulated driving accuracy in harsh weather is significantly increased with the proposed AFA and D-CNN algorithms. [ABSTRACT FROM AUTHOR]

The integration of multi-modal data allows for a more accurate representation of the ground characteristics. For a comprehensive interpretation of remote sensing data, existing multi-modal data fusion research mainly focuses on the joint utilization of 3D Light Detection and Ranging (LiDAR) and 2D Hyperspectral Image (HSI) data. However, existing algorithms do not pay much attention to the interaction of high-level semantic information between different modal data before fusion. This paper proposes a novel multi-modal data fusion deep learning network with the Cross-Modal Self-Attentive Feature Fusion Transformer (SAFFT). The framework employs a multi-head self-attention layer to fuse various attention information from multiple heads, effectively enhancing advanced feature information from different modalities for comprehensive integration. Experimental results on the Houston 2013 dataset demonstrate the effectiveness of the proposed method, which achieves an overall accuracy (OA) of 94.3757% in classifying 15 semantic classes. [ABSTRACT FROM AUTHOR]

Paleoseismological research for a slowly deforming intraplate fault can provide essential information for understanding not only the spatiotemporal characteristics of past earthquakes but also seismic behavior in the case of long recurrence intervals. To reveal the paleoseismological properties and faulting processes of the intraplate fault, the Geumwang Fault Zone in the central Korean Peninsula, we conducted comprehensive paleoseismological investigations along the fault zone, incorporating geomorphological mapping with airborne light detection and ranging (LiDAR), electrical resistivity tomography (ERT), borehole drilling, trench excavation, optically stimulated luminescence (OSL) dating, and microstructural analysis. Along NE-SW-striking lineaments of the Geumwang Fault Zone, surface deformation is weakly recognized by LiDAR imagery in a damage zone along the northern section of the fault zone (Suha site). Results of ERT and borehole logging at the Suha site suggest a localized zone of low resistivity and unconformity level separation in sedimentary layers, respectively. A trench section excavated along the ERT traverse and borehole sites exposes a fault contact between granite and unconsolidated Quaternary strata comprising boulders (47 ± 3 ka), clayey sand (24 ± 2 ka), pebbly cobbles, coarse sand, and artificial layers from bottom to top. The < 5-cm-wide slip zone is oriented N09°E/85°NW and cuts the granite to the west and the boulder layer to the east. This slip zone that covered by the clayey sand stratum records an apparent vertical offset of ∼1.5 m and has sub-horizontal striations indicating dextral movement. Microstructures at the contact between the granite and the boulder layer support the occurrence of seismic slip propagation along the contact and include injected sedimentary materials, clay-clast aggregates, and fresh, open fractures in quartz and feldspar grains in the boulder layer. The slip zone consists of a < 4.5-cm-wide zone of cataclasite and a < 5-mm-wide principal slip zone (PSZ). Microstructures in the slip zone and sediments near the slip zone include seismic-slip indicators of pressurized gouge materials and fluid injection within PSZ, and deformed sediments. These reveal that the slip zone underwent repeated seismic slip events during uplift to the surface. Our paleoseismological analyses with microstructures show that the boulder layer was cut by strike-slip faulting with a minor vertical component between 47–24 ka, following which the overlying sediments were deposited along the exposed fault scarp as incision fill. The results show that microstructural observations can provide key information on the deformation of unconsolidated sediments and on the nature and timing of seismic faulting. [ABSTRACT FROM AUTHOR]

Advanced technologies such as light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have revolutionized the detection of subtle surface deformation and the generation of high-resolution digital elevation models, overcoming the challenges posed by low tectonic activity and climatic surface erosion on fault-generated landscapes. This study presents a new record of paleoearthquake surface rupture along a section of the central Gongju Fault, transecting the central part of the Korean Peninsula, by analyzing geomorphic, stratigraphic, and structural features. We identified a NE-SW-striking, prominent fault-generated landform derived from LiDAR analysis and surface ruptures showing a vertical offset of < 15 cm by trench excavation. We also constrained the depositional ages to ∼94 ka using optically stimulated luminescence (OSL). Our comprehensive findings suggest that the seismic activity along the main trace of the Gongju Fault resulted in a distributed deformation within the fault zone, likely from multiple seismic events rather than a single occurrence. Structural features of the surface ruptures exposed on the trench wall show systematic changes in slip zone geometry, influenced by the pre-existing fault geometry, the imposed regional tectonic stress, and the physical properties of unconsolidated materials. This study enhances our understanding of seismic activity and fault dynamics in the central part of the Korean Peninsula, highlighting the significant influence of geological and climatic factors over tens of thousands of years in the intraplate regions with similar tectonic and climate settings. [ABSTRACT FROM AUTHOR]

Large mammalian herbivores exert strong top‐down control on plants, which in turn influence most ecological processes. Accordingly, the decline, displacement, or extinction of wild large herbivores in African savannas is expected to alter the physical structure of vegetation, the diversity of plant communities, and downstream ecosystem functions. However, herbivore impacts on vegetation comprise both direct and indirect effects and often depend on herbivore body size and plant type. Understanding how herbivores affect savanna vegetation requires disaggregating the effects of different herbivores and the responses of different plants, as well as accounting for both the structural complexity and composition of plant assemblages. We combined high‐resolution Light Detection and Ranging (LiDAR) with field measurements from size‐selective herbivore exclosures in Kenya to determine how herbivores affect the diversity and physical structure of vegetation, how these impacts vary with body size and plant type, and whether there are predictable associations between plant diversity and structural complexity. Herbivores generally reduced the diversity and abundance of both overstory and understory plants, though the magnitude of these impacts varied substantially as a function of body size and plant type: only megaherbivores (elephants and giraffes) affected tree cover, whereas medium‐ and small‐bodied herbivores had stronger effects on herbaceous diversity and abundance. We also found evidence that herbivores altered the strength and direction of interactions between trees and herbaceous plants, with signatures of facilitation in the presence of herbivores and of competition in their absence. While megaherbivores uniquely affected tree structure, medium‐ and small‐bodied species had stronger (and complementary) effects on metrics of herbaceous vegetation structure. Plant structural responses to herbivore exclusion were species‐specific: of five dominant tree species, just three exhibited significant individual morphological variation across exclosure treatments, and the size class of herbivores responsible for these effects varied across species. Irrespective of exclosure treatment, more species‐rich plant communities were more structurally complex. We conclude that the diversity and architecture of savanna vegetation depend on consumptive and nonconsumptive plant–herbivore interactions; the roles of herbivore diversity, body size, and plant traits in mediating those interactions; and a positive feedback between plant diversity and structural complexity. [ABSTRACT FROM AUTHOR]