Search Results (393)

Forest aboveground biomass (AGB) is a key indicator of forest productivity and carbon sequestration, yet many remote sensing AGB models overlook spatial autocorrelation in plot observations and model residuals. This study proposes a hybrid framework that combines a CNN-Transformer (Convolutional Neural Network-Transformer) model with geostatistical Kriging of residuals to improve regional AGB mapping in Lishui City, Zhejiang Province, China. Using 398 forest plots and multi-source predictors derived from Sentinel-2 imagery, ALOS-2 PALSAR-2 SAR data, and ALOS 12.5 m DEM, relevant variables were screened using Random Forest importance ranking. The most influential predictors included Sentinel-2 Band 8 and Band 12, EVI, PC1, mean77, HH/HV, ARVI, NDVI, RVI, and elevation. Ten-fold cross-validation showed that the CNN-Transformer-CK model had the highest accuracy in predicting forest AGB, with a validation R² of 0.72 and RMSE of 12.18 t/ha, followed by the CNN-Transformer model (R² = 0.69, RMSE = 12.22 t/ha) and RF (R² = 0.59 and RMSE = 14.31 t/ha). The proposed approach supports wall-to-wall AGB mapping for forest management and conservation planning. Full article

Land subsidence poses a persistent challenge to Tianjin, a major coastal city in China, with implications for urban infrastructure and sustainable development. This study examines the spatiotemporal evolution of ground subsidence in Tianjin from 2003 to 2024 using multi-source SAR observations from Envisat ASAR (C-band), ALOS PALSAR (L-band), and Sentinel-1 (C-band). Surface deformation was derived using SBAS-InSAR with atmospheric phase correction. Due to limitations in data availability, SAR observations are temporally discontinuous; therefore, the long-term subsidence evolution was reconstructed by integrating multi-sensor deformation rates through a model-based time-series fitting approach. The results show pronounced subsidence during 2003–2010 in inland districts such as Wuqing, Beichen, Jinnan, and Jinghai, with maximum rates exceeding 50 mm/yr. After 2017, regional subsidence rates generally declined, while localized deformation became increasingly concentrated in coastal reclamation areas of the Binhai New Area, particularly around Dongjiang Port and Fuzhuang. Spatial and temporal patterns of subsidence exhibit clear correspondence with changes in groundwater use intensity and phases of urban construction and land reclamation. These observations suggest a transition in dominant subsidence controls over time. The results provide a long-term observational perspective on subsidence evolution in Tianjin and offer a geospatial basis for land-use planning and infrastructure risk assessment in coastal cities. Full article

Recurrent flooding poses a persistent and growing threat to West African watersheds facing rapid urbanization and climate change. Despite advances in machine learning and geospatial datasets, urban planning and flood prevention often rely on limited datasets and traditional analysis. This study addresses this research gap in the Bonoumin-Palmeraie watershed (Abidjan, Côte d’Ivoire) by developing an integrated approach leveraging remote sensing, Geographic Information Systems (GIS), and the Random Forest algorithm to assess and map flood susceptibility. Twelve conditioning factors related to topography, hydrology, land use, and climate were derived from Sentinel-1, ALOS PALSAR, and multi-source earth observation datasets. Historical flood extents were mapped in Google Earth Engine to train the Random Forest model in a Google Colab environment. The model demonstrated high discriminatory power, yielding an Area Under the Curve of 0.94 and Overall Accuracy of 0.83. Drainage density, rainfall, and altitude were identified as the primary explanatory drivers. The resulting flood susceptibility map indicates that 39% of the watershed exhibits medium to very high susceptibility, with critical hotspots in the neighborhoods of Palmeraie, Attoban, Akouedo, Djorogobité, and Riviera-Sogefiha. While limited by the exclusion of certain anthropogenic variables and ground truth constraints, the study provides a reproducible, data-driven framework for flood risk assessment in tropical urban environments. These findings offer essential scientific support for urban planners and decision-makers to enhance territorial planning and sustainable flood management in Abidjan. Full article

Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin (242.7 km², 1823–3140 m elevation), Eastern Anatolia, Turkey. Three automatic weather stations spanning 872 m elevation gradient provided meteorological forcing, while MODIS MOD10A2 8-day composite products supplied operational snow cover observations validated against Landsat-5/7 (30 m resolution, 87.3% agreement, Kappa = 0.73) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band). Uncalibrated model performance was modest (R² = 0.384, volumetric difference = 29.78%), demonstrating necessity of site-specific calibration. Systematic adjustment of snowmelt and rainfall runoff coefficients yielded excellent calibrated performance for 2009 melt season: R² = 0.8606, correlation coefficient R = 0.927, Nash–Sutcliffe efficiency = 0.854, and volumetric difference = 3.35%. Enhanced temperature lapse rate (0.75 °C/100 m vs. standard 0.65 °C/100 m) reflected severe continental climate. Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors (R² = 0.881). Results confirm SRM’s operational feasibility for seasonal forecasting and flood warning in data-scarce snow-dominated basins, with modest requirements (daily temperature, precipitation, and satellite snow cover) aligning with operational monitoring capabilities. The methodology provides a transferable framework for regional water resource management in climatically vulnerable mountain environments where snowmelt supports agriculture, hydropower, and municipal supply. Full article

The spaceborne full-polarimetric (FP) synthetic aperture radar (SAR) is an advanced sensor for high-resolution Earth observation. However, FP data acquired by such a system are prone to distortions induced by ionospheric Faraday rotation (FR). From the perspective of exploiting these distortions, this enables the estimation of the ionospheric FR angle (FRA), and consequently the total electron content, across most global regions (including the extensive ocean areas) using spaceborne FP SAR measurements. The accuracy of FRA estimation, however, is highly sensitive to noise interference. This study addresses denoising in FRA retrieval based on the Bickel–Bates estimator, with a specific focus on noise reduction methods built upon the adaptive Goldstein filter (AGF) that was originally designed for radar interferometric processing. For the first time, three signal-to-noise ratio (SNR)-based AGFs suitable for FRA estimation are investigated. A key feature of these filters is that their SNRs are all defined using the amplitude of the Bickel–Bates estimator signal rather than the FRA estimates themselves. Accordingly, these AGFs are applied to the estimator signal instead of the estimated FRAs. Two of the three AGFs are developed by adopting the mathematical forms of SNRs and filter parameters consistent with the existing SNR-based AGFs for interferogram. The third AGF is newly proposed by utilizing more general mathematical forms of SNR and filter parameter that differ from the first two. Specifically, its SNR definition aligns with that widely used in image processing, and its filter parameter is derived as a function of the defined SNR plus an additionally introduced adjustable factor. The three SNR-based AGFs tailored for FRA estimation are tested and evaluated against existing AGF variants and classical image denoising methods using three sets of FP SAR Datasets acquired by the L-band ALOS PALSAR sensor, encompassing an ocean-only scene, a plain land–ocean combined scene, and a more complex land–ocean combined scene. Experimental results demonstrate that all three filters can effectively mitigate noise, with the newly proposed AGF achieving the best performance among all denoising methods included in the comparison. Full article

This study utilizes Digital Elevation Models (DEMs) with different spatial resolutions (SRTM 90 m, ASTER DEM 30 m, and ALOS PALSAR 12.5 m), Landsat-8 satellite imagery, and the Bouguer WGM-12 gravity model to analyze morphotectonic features in the Upper Guajira region of Colombia, a desert area in northern South America, area that is composed by low-relief serranías of Cabo de la Vela, Carpintero, Cosinas, Simarua, Jarara, and Macuira. Three DEMs were used to extract and map morphotectonic lineaments, drainage networks, and morphological features. Lineaments were characterised by azimuth frequency, length, density, lithological distributions, and geological timeframes, with support from a digitized geological map from the Colombian Geological Service (SGC). The analysis of the east–west (E-W) Cuisa fault, using the Riedel shear model, suggests a transtensional/transpressional tectonic regime influenced by the Caribbean and South American plates, characterised by NE-SW and E-W fault orientations. Lineaments were grouped into five geochronological categories based on the geological map, revealing a shift from NE-SW to E-W orientations from the Cretaceous period onward, reflecting the ongoing movement of the Caribbean plate. Folds and faults from this tectonic activity were enhanced using Landsat-8 band combinations. The WGM-12 model was separated into regional and residual signals, with the latter highlighting the serranías subregions. Residual gravity analysis revealed significant negative anomalies, suggesting lower-density lithologies surrounded by higher-density blocks. This pattern aligns with the regional geological framework and may reflect a crustal root or terrain dragging linked to the tectonic processes that shaped the serranías. Derivative residual gravity data also revealed lineaments oriented NE–SW, whose distribution extends beyond the morphometric boundaries of the subregions. The study found a strong correlation between structural and drainage patterns, demonstrating structural control over geomorphology. This study establishes a solid morphotectonic and geophysical framework for the Upper Guajira region, demonstrating how multi-resolution DEM analysis combined with gravity data can resolve regional deformation patterns, crustal architecture, and tectonic development along the Caribbean–South American plate boundary. Full article

Methane emissions from rice paddies account for over 11% of global atmospheric CH₄, making water management practices such as Alternate Wetting and Drying (AWD) critical for climate change mitigation. Remote sensing offers an objective approach to monitoring AWD implementation and improving greenhouse gas estimation accuracy. This study investigates the backscattering mechanisms of L-band SAR for inundation/non-inundation classification in paddy fields using full-polarimetric ALOS-2 PALSAR-2 data. Field surveys and satellite observations were conducted in Ryugasaki (Ibaraki) and Sekikawa (Niigata), Japan, collecting 1360 ground samples during the 2024 growing season. Freeman–Durden decomposition was applied, and relationships with plant height and water level were analyzed. The results indicate that plant height strongly influences backscatter, with backscattering contributions from the surface decreasing beyond 70 cm, reducing classification accuracy. Random forest models can classify inundated and non-inundated fields with up to 88% accuracy when plant height is below 70 cm. However, when using this method, it is necessary to know the plant height. Volume scattering proved robust to incidence angle and observation direction, suggesting its potential for phenological monitoring. These findings highlight the effectiveness of L-band SAR for water management monitoring and the need for integrating crop height estimation and regional adaptation to enhance classification performance. Full article

Monitoring the stability of hydraulic structures such as dams and reservoirs in seismically active regions is essential for ensuring their safety and operational reliability. This study presents a comprehensive geospatial approach combining lineament analysis and geodynamic zoning to assess the structural stability of the Voroshilov and Priyut reservoirs located in the Almaty region, Kazakhstan. A regional lineament map was generated using ASTER GDEM data, while ALOS PALSAR data were used for detailed local analysis. Lineaments were extracted and analyzed through automated processing in PCI Geomatica. Lineament density maps and azimuthal rose diagrams were constructed to identify zones of tectonic weakness and assess regional structural patterns. Integration of lineament density, GPS velocity fields, InSAR deformation data, and probabilistic seismic hazard maps enabled the development of a detailed geodynamic zoning model. Results show that the studied sites are located within zones of low local geodynamic activity, with lineament densities of 0.8–1.2 km/km², significantly lower than regional averages of 3–4 km/km². GPS velocities in the area do not exceed 4 mm/year, and InSAR analysis indicates minimal surface deformation (<5 mm/year). Despite this apparent local stability, the 2024 Voroshilov Dam failure highlights the cumulative effect of regional seismic stresses (PGA up to 0.9 g) and localized filtration along fracture zones as critical risk factors. The proposed geodynamic zoning correctly identified the site as structurally stable under normal conditions but indicates that even low-activity zones are vulnerable under cumulative seismic loading. This demonstrates that an integrated approach combining remote sensing, geodetic, and seismic data can provide quantitative assessments for dam safety, predict potential high-risk zones, and support preventive monitoring in tectonically active regions. Full article

Synthetic Aperture Radar (SAR) has been widely applied for rice monitoring, especially in cloud-prone areas, due to its ability to penetrate clouds. However, only limited methods were developed to monitor separately irrigated rice and rainfed rice ecosystems. This study demonstrated the use of multi-temporal polarimetric dual-polarization (dual-pol) SAR (Sentinel-1B and ALOS PALSAR-2) data to monitor and discriminate the irrigated and favorable rainfed rice ecosystems in the province of Iloilo, Philippines. Key polarimetric parameters derived from H–A–α and model-based dual-pol decomposition were analyzed to characterize the rice phenology of both ecosystems. Segmented regression was performed to detect breakpoints corresponding to changes in rice phenology within each ecosystem and used to identify the parameters to use for classification. Based on the results, Sentinel-1B polarimetric parameters (entropy, anisotropy, and alpha) can capture the phenological dynamics, whereas ALOS2 polarimetric parameters were more sensitive to water conditions, as reflected in span and volume scattering. Furthermore, irrigated rice exhibited more stable and predictable scattering patterns than favorable rainfed rice. Using the Random Forest classifier, various combinations of backscatter and polarimetric parameters from Sentinel-1B and ALOS2 were tested to discriminate between the two ecosystems. The highest classification accuracy (81.81% overall accuracy; Kappa = 0.6345) was achieved using the combined backscatter (S1B VH, ALOS2 HH, and HV) and polarimetric parameters from both sensors. The results demonstrated that polarimetric parameters effectively capture phenological stages and associated scattering mechanisms, with the integration of Sentinel-1B and ALOS2 data improving the discrimination of irrigated and favorable rainfed rice systems. Full article

By altering aquifer storage capacity, groundwater level (GWL) plays a critical role in driving surface deformation, including ground subsidence and uplift. Groundwater depletion can induce sinkholes or subsidence, whereas recharge can cause surface uplift. These processes pose significant risks to soft grounds composed of soft alluvial sediments, emphasizing the importance of regular monitoring. In this study, we applied the small baseline subset (SBAS) technique to conduct a time-series analysis of surface deformation in Gimhae City, South Korea, where a continuous GWL increase was observed. Seasonal trend decomposition using the Loess (STL) method was employed to isolate the long-term GWL trend by removing seasonal variability. Multi-frequency synthetic aperture radar datasets, including ALOS PALSAR, COSMO-SkyMed, and Sentinel-1, revealed a cumulative surface uplift of approximately 9.2 cm, primarily concentrated along the deepest GWL contour line and confined between two lineament structures. The decomposed velocities from Sentinel-1 highlighted the predominance of vertical displacement over horizontal movement. Time-series analyses consistently showed uplift patterns, whereas correlation analysis demonstrated a strong relationship (R² > 0.75) between surface deformation and GWL changes from 2013 to 2021. These results suggest a significant link between surface uplift and the rising GWL in Gimhae City, providing insights into the hydrogeological processes that influence ground deformation. Furthermore, a time lag between the GWL changes and surface displacement was identified, providing valuable insights into the dynamics of groundwater-related surface deformation. Full article

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping. Full article

The heavy mineral-rich wadi deposits sourced from various wadis close to Gabal Homret Waggat in the central eastern Desert of Egypt are being analyzed to assess their genesis and paleoenvironment. This study integrates remote sensing (ALOS/PALSAR DEM and ASTER imagery), mineralogical, and geochemical analyses (XRF and SEM-EDX). Remote sensing analysis (ASTER and ALOS/PALSAR) delineated three main watersheds and identified granitic plutons as the primary source rocks. Mineralogical analysis revealed a diverse heavy mineral assemblage, including zircon, rutile, ilmenite, magnetite, staurolite, and sillimanite, indicative of a provenance dominated by granitic and metamorphic rocks. Grain size analysis shows that the samples range from very platykurtic to extremely leptokurtic (Kg: 0.598–5.350 φ), indicating deposition in predominantly fluvial environments. Geochemical data show enrichment in SiO₂, Al₂O₃, K₂O, and Na₂O, indicating a felsic (granitic) source with low Chemical Index of Alteration (CIA: 41.89–51.83) and Plagioclase Index of Alteration (PIA: 37.97–52.78) values, and indicating that the source rocks show low to moderate chemical weathering. Tectonic discrimination diagrams suggest that the source rocks were formed in a continental island arc or active continental margin, consistent with the Arabian–Nubian Shield. The presence of economically valuable minerals like zircon and rare-earth-element-bearing monazite and columbite highlights the significant resource potential of these placer deposits. Full article

Mangroves are critical blue carbon ecosystems, yet accurately estimating their aboveground carbon (AGC) stocks remains challenging due to structural complexity and spectral saturation in dense canopies. This study aims to develop a scalable AGC estimation framework by integrating high-resolution canopy height (CH) data from UAV-LiDAR with multi-source satellite features from Sentinel-1, Sentinel-2, and ALOS PALSAR-2. Using the Maowei Sea mangrove zone in Guangxi, China, as a case study, we extracted structural, spectral, and textural features and applied Random Forest regression with Recursive Feature Elimination (RFE) to optimize feature combinations. Results show that incorporating UAV-derived CH significantly improves model accuracy (R² = 0.75, RMSE = 14.18 Mg C ha⁻¹), outperforming satellite-only approaches. CH was identified as the most important predictor, effectively mitigating saturation effects in high-biomass stands. The estimated total AGC in the study area was 88,363.73 Mg, with a mean density of 53.01 Mg C ha⁻¹. This study highlights the advantages of cross-scale UAV–satellite data fusion for accurate, regionally scalable AGC mapping, offering a practical tool for blue carbon monitoring and coastal ecosystem management under global change. Full article

Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task, but its effectiveness is limited by persistent cloud cover in tropical regions. A Synthetic Aperture Radar (SAR) offers a cloud-independent alternative for burned area mapping. This study investigates the performance of a Stacking Ensemble Neural Network (SENN) model using polarimetric features derived from both C-band (Sentinel 1) and L-band (Advanced Land Observing Satellite—Phased Array L-band Synthetic Aperture Radar (ALOS-2/PALSAR-2)) data. The analysis covers three representative sites in Indonesia: peatland areas in (1) Rokan Hilir, (2) Merauke, and non-peatland areas in (3) Bima and Dompu. Validation is conducted using high-resolution PlanetScope imagery(Planet Labs PBC—San Francisco, California, United States). The results show that the SENN model consistently outperforms conventional artificial neural network (ANN) approaches across most evaluation metrics. L-band SAR data yields a superior performance to the C-band, particularly in peatland areas, with overall accuracy reaching 93–96% and precision between 92 and 100%. The method achieves 76% accuracy and 89% recall in non-peatland regions. Performance is lower in dry, hilly savanna landscapes. These findings demonstrate the effectiveness of the SENN, especially with L-band SAR, in improving burned area detection across diverse land types, supporting more reliable fire monitoring efforts in Indonesia. Full article

Water body detection in synthetic aperture radar (SAR) imagery plays a critical role in applications such as disaster response, water resource management, and environmental monitoring. However, it remains challenging due to complex background interference in SAR images. To address this issue, a bi-encoder and hybrid feature fuse network (BiEHFFNet) is proposed for achieving accurate water body detection. First, a bi-encoder structure based on ResNet and Swin Transformer is used to jointly extract local spatial details and global contextual information, enhancing feature representation in complex scenarios. Additionally, the convolutional block attention module (CBAM) is employed to suppress irrelevant information of the output features of each ResNet stage. Second, a cross-attention-based hybrid feature fusion (CABHFF) module is designed to interactively integrate local and global features through cross-attention, followed by channel attention to achieve effective hybrid feature fusion, thus improving the model’s ability to capture water structures. Third, a multi-scale content-aware upsampling (MSCAU) module is designed by integrating atrous spatial pyramid pooling (ASPP) with the Content-Aware ReAssembly of FEatures (CARAFE), aiming to enhance multi-scale contextual learning while alleviating feature distortion caused by upsampling. Finally, a composite loss function combining Dice loss and Active Contour loss is used to provide stronger boundary supervision. Experiments conducted on the ALOS PALSAR dataset demonstrate that the proposed BiEHFFNet outperforms existing methods across multiple evaluation metrics, achieving more accurate water body detection. Full article