Egyptian Journal of Remote Sensing and Space Sciences最新文献

Mangroves are known for their carbon storage capacity, yet they are under immense pressure from human activities. This study assessed anthropogenic disturbances on mangroves’ aboveground biomass (AGB) in northern Borneo, Malaysia, using airborne light detection and ranging (LiDAR) data. Three global or pantropical allometries were compared in the development of an AGB estimation model by regressing LiDAR metrics against the AGB. The best model predicted AGB from Saenger and Snedaker allometry with an R² of 0.85 and a root mean square error (RMSE) of 14.59 Mg/ha (relative RMSE: 7.24 %). The high-resolution AGB map revealed a natural AGB gradient in intact mangroves from the coast to the interior. However, only a weak correlation between the distance from shoreline and AGB in disturbed mangroves was found. The LiDAR estimated AGBs were 196.36 Mg/ha and 157.27 Mg/ha for intact mangroves and disturbed mangroves, respectively. Relatively high AGB areas were abundant in the intact mangroves but scarce in the disturbed mangroves. The LiDAR-based AGB assessment is accurate and high-resolution, supporting carbon stock conservation and sustainable management activities under climate change mitigation programs such as REDD + .

This paper presents the process of conducting the structural analysis of AlAinSat-1 CubeSat through a numerical solution using Siemens NX. AlAinSat-1 is a 3U remote-sensing CubeSat carrying two earth observation payloads. The CubeSat is scheduled for launch on SpaceX Falcon 9 rocket. To ensure the success of the mission and its ability to withstand the launch environment, several scenarios should be analyzed. For AlAinSat-1 model the finite element analysis (FEA) method is used, and four types of structural analyses are considered: modal, quasi-static, buckling, and random vibration analyses. The workflow cycle includes idealizing, meshing, assembling, applying connections and boundary conditions, and eventually running the simulation utilizing the Siemens Nastran solver. The simulation results of all analysis types indicate that the model can safely withstand the loads exerted during launch. Also, the numerical results of the Command and Data Handling Subsystem (CDHS) module of AlAinSat-1 are experimentally validated through a vibration test conducted using an LV8 shaker system. The module successfully passed the test based on the test success criteria provided by the launcher.

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.

For landslide prevention and control, it is essential to establish a landslide susceptibility prediction framework that can explain the model’s decision-making process. Wushan County, Chongqing was selected as the study area, and seventeen landslide conditioning factors were initially chosen for this investigation. GeoDetector was used to remove noise factors and reduce the latitude of the data. The research investigates the use of three machine learning methods for assessing landslide susceptibility: SVM, RF, and XGBoost, and finally explains the decision mechanism of the model by SHAP-PDP. The results indicate that XGBoost has better evaluation results than RF and SVM. And XGBoost uncertainty is lower. The integrated interpretation framework based on SHAP-PDP can evaluate and interpret landslide susceptibility models both globally and locally, which is of great practical significance for the application of machine learning in landslide prediction.

Mineral identification plays a vital role in understanding the diversity and past habitability of the Martian surface. Mineral mapping by the traditional manual method is time-consuming and the unavailability of ground truth data limited the research on building supervised learning models. To address this issue an augmentation process is already proposed in the literature that generates training data replicating the spectra in the MICA (Minerals Identified in CRISM Analysis) spectral library while preserving absorption signatures and introducing variability. This study introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture for mineral identification using the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) hyperspectral data. MICAnet is inspired by the Inception-v3 and InceptionResNet-v1 architectures, but it is tailored with 1-dimensional convolutions for processing the spectra at the pixel level of a hyperspectral image. To the best of the authors’ knowledge, this is the first DCNN architecture solely dedicated to mineral identification on the Martian surface. The model is evaluated by its matching with a TRDR (Targeted Reduced Data Record) dataset obtained using a hierarchical Bayesian model. The results demonstrate an impressive f-score of at least .77 among different mineral groups in the MICA library, which is on par with or better than the unsupervised models previously applied to this objective.

The high spatial resolution data presents a problem when it comes to mapping and identifying distinct tree species based on the characteristics of their canopies. The deep learning Semantic Segmentation approach based on U-Network (U-Net.) artificial intelligence model that we provide here can recognize, and map Azadirachta indica trees canopy cover. This method trains its model by making use of image chips and labels of the item being segmented. The new testing images processed for multiple stages of pixel level of convolution and pooling operations. The sampling methods allow increase to make complete to make the recognized object on the image. The model’s ability to identify items based on canopy shape, structure, and pixel data makes it very useful for mapping and recognizing a single tree species as well as several tree species. The model validation results indicated an accuracy of 84–89 percent, which is regarded to be rather good. Based on ground census data, the overall accuracy of identification is 89 percent, F1 score 0.91–0.94, while the complete tree canopy validation (Intersection to Union) for canopy matching area is 0.79–0.89. The method has the potential to be utilised for identification, mapping of tree canopy. The approach has the potential to be used for important research initiatives i.e tree censuses and the identification and mapping of crop plant identification. The deep learning model used as inferences for automatization of the identification of the tree species helps to resolve identification and mapping based complex problems in agro-forestry allied fields.

In this paper, bathymetry retrieval is combined with the Depth Invariant Index (DII) substrate cluster to acquire more accurate water depth. DIIs are calculated through the selected samples that are in bright and dark pixels firstly. Then, substrates are clustered with DIIs by using the K-MEANS cluster algorithm. Last, in-situ data and Genetic Algorithm (GA) are applied to solve the models’ parameters of the Stumpf model and the Legleiter model. The feasibility of this method is investigated in the Xia Shan Reservoir, Shandong Province, China. The experimental results show that (1) When there are various bottom types in the study area, the substrates cluster before bathymetry retrieval can significantly improve the retrieval accuracy. For example, in the without cluster case, the $R^2$ values are both around 0.72 in the GF-2 image and the $R^2$ values are both 0.53 in the Sentienl-2 image, and the minimum RMSE and RRMSE values are 1.09 m and 19.36 % respectively. When substrates are clustered into two clusters and three clusters, R² values have all increased and RMSE and RRMSE values decreased. (2) Clustering substrates into more clusters may not necessarily improve retrieval accuracy. For our research area, it’s better to divide the substrate into two clusters. For the two clusters case, the bathymetry result using the Legleiter model has a higher retrieval accuracy, which RMSE is 0.76 m, R² is 0.9 and RRMSE is 11.76 %. Compared with the three clusters case, the bathymetry retrieval accuracy of the two clusters case improves more obviously.

The GRACE and GRACE Follow-On (GFO) missions, led by American and German teams, along with the European mission Swarm, have revolutionized the study of Earth's dynamic gravity field through precise measurements. Our objective is to fill the data GRACE gaps and the gap between GRACE and GFO missions using Swarm data, focusing on Africa. We utilized data from GRACE processing centers (CSR, GFZ, and JPL), Swarm data from the Czech Academy of Sciences (ASU) and the International Combination Service for Time-variable Gravity (COST-G), QF, as well as IGG data. Both frequency and space domains were examined, evaluating Potential Degree Variances (PDV), harmonic coefficients, Terrestrial Water Storage (TWS), gravity anomaly, and potential/geoid using GRACE, GFO, and Swarm. Results indicated agreement among processing centers for potential degree variances, gravity anomaly, and geoid undulation. However, discrepancies were observed in harmonic coefficients and TWS. To address this, we employed parametric least square adjustment to estimate new Swarm-modified coefficients, selecting Swarm ASU and GRACE/GFO CSR data. Comparison of Singular Spectrum Analysis method (SSA), IGG, and Swarm-modified SHCs during the data gap period exhibited correlation coefficients exceeding 0.86. Overall, the new coefficients significantly improved agreement between original GRACE coefficients and modified coefficients in all aspects.

This systematic review examines development of techniques used in lunar crater mapping between 1965 and 2022. Using the Web of Science and Google Scholar databases, the systematic review adhered to specific criteria that focus on post-1965 research articles in English. Through using Boolean operations and guided by the PRISMA Framework, the search yielded 20 pertinent articles. The findings reveal that from 1965 to 1980, techniques like radar and infrared mapping were used, alongside the Lunar Radar Sounder for subsurface studies and terrain mapping to analyse surface roughness and topography. Contour maps helped in understanding lunar magnetic fields. Between 1981 and 2000, lunar mapping evolved to include gamma-ray spectrometry for elemental analysis, electron reflection studies for crustal magnetic field analysis, cratering records for comparative planetology, lander-rover systems for resource exploration and laser ranging for asteroid studies. From 2001 to 2022, advancements included automatic crater detection algorithms, comprehensive lunar characteristic reviews from recent missions and remote sensing for detailed crater analysis. High-resolution data provided views into crater composition and morphology and aid in small crater cataloguing and depth-to-diameter measurements mainly at the Lunar South Pole. The discussion section highlights those initial telescopic observations gave way to quantitative studies during the Space Age. Modern developments include rovers, high-resolution cameras and advanced algorithms for geological analysis. Calibration methods (e.g., the Robotic Lunar Observatory ROLO model, GIRO (Global Space-based Inter-Calibration System), and radiance calibration) have also been critical. This technological evolution has enhanced understanding of the Moon and its role in the solar system.

Field-based high-resolution carbonate facies mapping is often challenging due to the limited accessible exposure, high-degree of heterogeneity, and lack of distinct natural characteristics between different lithofacies. To mitigate this issue, we proposed a novel approach by integrating multispectral remote sensing, advanced image processing techniques, and supervised classification to perform high-resolution carbonate lithofacies mapping and utilized the extensive Mesozoic carbonate in Saudi Arabia as an example. For this study, the Tuwaiq Mountain Formation (TMF) was selected not only because of its wide aerial distribution but also its importance as conventional and unconventional hydrocarbon reservoirs in the subsurface. Our proposed method was able to map and delineate different members (T₁, T₂, T₃) and key lithofacies in the TMF. In addition, based on the spectral characteristics, the middle member of TMF (T₂) can be further subdivided into two subunits (T_2-a of higher reflectance & T_2-b of lower reflectance). These findings are further corroborated by detailed microfacies analysis, which validates the presence of two sub-members of T₂ (T_2-a: Spiculitic foraminiferal wackestone and T_2-b: Coralline floatstone facies). This resulted in a revised and accurate lithofacies map that made significant modifications over older maps. The overall accuracy of TMF lithofacies is 93.4 % with a kappa coefficient of 0.88. This study demonstrates that multispectral remote sensing approach are effective at distinguishing different carbonate units and providing high-resolution carbonate facies maps. The proposed approach should be applicable to other carbonate outcrops globally and could help in improving carbonate lithofacies mapping where the outcrops are not accessible.