Search Results (415)

This study presents an integrated framework for offshore wind resource assessment and wind farm micrositing in the Northern Aegean Sea of Türkiye by combining machine learning-assisted measure–correlate–predict (MCP) modelling, long-term reanalysis data and computational fluid dynamics (CFD). One year of measurements from a 41 m meteorological mast on Küçük Ada, offshore Aliağa, İzmir, was analyzed together with a 21-year ECMWF Reanalysis v5 (ERA5) dataset. The measurements indicated a mean annual wind speed of 8.07 m/s, a wind shear exponent of 0.049, a Weibull shape parameter of 2.06 and a persistent northeasterly wind regime. Long-term conditions were reconstructed using 64 meteorological and cyclic predictors derived from four ERA5 grid points and their bilinear interpolation to the mast location. Five H₂O algorithm families were evaluated using randomized grid searches and 12-fold temporal cross-validation. Distributed Random Forest provided the best performance for 100 m wind speed, with RMSE = 1.969 m/s, MAE = 1.504 m/s, bias = −0.027 m/s and an out-of-fold Pearson correlation coefficient of r = 0.884. TreeSHAP analysis was applied to interpret predictor contributions. High-resolution WindSim simulations with 28.75 million cells, ALOS PALSAR topography and CORINE land-cover data supported turbine micrositing. The proposed 1.43 GW wind farm yielded 5494.5 GWh/year after wake losses, with a capacity factor of 43.9% and an overall wake loss of 5.2%. The framework provides a robust basis for offshore wind development in Türkiye. Full article

Landslide susceptibility mapping identifies terrain prone to slope failure under comparable environmental and triggering conditions. Grid-based machine-learning outputs can be spatially fragmented and difficult to translate into slope-scale mitigation. We developed a two-stage grid-to-slope-unit framework for rainfall-conditioned susceptibility assessment in Pingyuan County, Guangdong Province, China. Elevation, slope angle, slope aspect, normalized difference vegetation index, and rainfall were derived from an ALOS-PALSAR digital elevation model, Sentinel-2 imagery, and rainfall-station observations. A Random Forest model was trained at grid scale using 28 pre-2024 landslides and 28 pseudo-absence samples. Grid-scale scores and the five conditioning factors were then summarized within 4786 slope units and used in a second Random Forest model. An inventory of 359 landslides mapped after the June 2024 rainfall event was used for event-conditioned validation. High and very-high susceptibility classes occupied 21.10% of the modeled area and contained 299 validation landslides, yielding a capture rate of 83.3%. The very-high class contained 64.62% of validation landslides within 9.10% of the area. The framework produced geomorphically coherent terrain units for regional screening and field investigation, while detailed stability assessment remains necessary for individual sites. Full article

This study introduces a multisource, multiscale approach to delineate structural lineaments and assess mineralization controls in Wadi El-Markh, Central Eastern Desert, Egypt. Four datasets (ALOS PALSAR DEM, NISAR L-band SAR, BIOMASS P-band SAR, and aeromagnetic data) were processed using tailored workflows to extract lineaments at complementary depths, from the surface to deep crustal levels. The AP-DEM workflow employed multi-azimuth hill-shading and directional Prewitt filtering. NISAR data were speckle-denoised before directional filtering of the HH and HV polarizations. BIOMASS data underwent polarimetric decomposition followed by directional filtering. Aeromagnetic data were reduced to the pole, analyzed using power-spectrum for depth estimation and regional–residual separation, and then processed using a CET grid-analysis workflow for lineaments extraction and generation of a Contact Occurrence Density (COD) map. Directional analysis revealed sensor-dependent biases: NISAR HH favored an E-W trend, whereas HV yielded a more isotropic distribution; BIOMASS surface scattering highlighted E-W and N-S trends, whereas volume scattering emphasized NW-SE and NE-SW trends. Aeromagnetic data identified both shallow- and deep-seated structures, with a dominant NW-SE trend. GIS-based fuzzy overlay produced a consensus structural complexity map. Validation against 15 known mining sites showed spatial coincidence rates ranging from 60% (AP-DEM and NISAR HH) to 86.7% (BIOMASS volume scattering and fuzzy overlay) for mining sites falling within high-structural-complexity zones. The results establish a reliable framework for identifying structurally controlled mineralization targets in arid terrains. Full article

Flash floods represent an extreme hydro-geomorphological hazard in the arid and semi-arid regions in northwestern Saudi Arabia, presenting major risks to significantly expanding urban regions, infrastructure, and sustainable development projects of Saudi Vision 2030. The present study assesses the flood susceptibility of the Wadi Al-Disha catchment (4212.81 km²) using a high-resolution 12.5 m spatial resolution ALOS-PALSAR digital elevation model (DEM). A dual-model approach, combining the scale-dependent Morphometric Ranking Method and the scale-independent El-Shamy approach, was applied over district 18 sub-catchments to investigate, analyze, and calculate 15 hydro-morphometric effective parameters. The Ranking Method model assigned sub-catchment SC-18 to the high-flood susceptibility zone, SC-4 as a low-flood susceptibility unit, and the rest of the 15 sub-catchments as moderate risk, with SC-9, the largest sub-catchment, scoring highest among the moderate rank, just below the high-susceptibility rank threshold. On the other hand, the El-Shamy Approach model defined SC-3 and SC-8 as high-flood susceptibility classes, while the remaining 16 sub-catchments reflect moderate-flood susceptibility classes. This proposed assessment is a physically-based susceptibility evaluation extracted from terrain morphometry; it does not consider socio-economic exposure or hydrological (rainfall–runoff) information, which we identify explicitly as scope boundaries below. Comparative validation with a regional study states that a bifurcation ratio indicates that tectonic rifting impacts bifurcation parameter values ($Rb>4.0$), forcing the El-Shamy Approach model to underestimate susceptibility in structurally complex, high-relief landscapes. To underestimate the impact of these flood susceptibilities and secure sustainable infrastructure in the very close Prince Mohammed bin Salman Royal Reserve, the study suggests designing targeted dams in rapid-velocity headwaters and creating an early warning system on the plateaus upstream. Full article

Accurate historical estimates of forest carbon stocks are essential for greenhouse gas inventories and REDD+ Measurement, Reporting and Verification (MRV) systems, particularly in remote and persistently cloudy regions where field inventories and optical remote sensing are limited. This study presents a retrospective mapping framework with potential relevance for Tier-3 forest carbon estimation of forest volume and carbon stocks in the temperate forests of southern Chile using historical ALOS PALSAR L-band SAR data integrated with Chile’s Continuous National Forest Inventory (CNFI). Three pilot zones in Los Lagos, Aysén, and Magallanes were analysed, covering approximately 42,000 km² of native forests dominated by Lenga, Coihue de Magallanes, Siempreverde, Roble–Raulí–Coihue, Coihue–Raulí–Tepa, and Alerce forest types. Annual 25 m ALOS PALSAR mosaics were processed to derive HH and HV backscatter, HH/HV ratio, and Radar Forest Degradation Index (RFDI) layers, which were used as predictors in k-nearest neighbours (k-NN) models calibrated with inventory plots projected to the 2010 reference year. Model performance varied substantially among forest types and pilot zones, with test r² values ranging from 0.12 to 0.90 and RMSE values between approximately 100 and 300 m³·ha⁻¹; the highest r² values were associated with forest types represented by relatively small samples and should therefore be interpreted cautiously. m³·ha⁻¹ Stratification by altitude and restriction to moderate volume ranges improved predictive performance in several cases, highlighting the influence of ecological gradients and SAR signal saturation at high levels of biomass. Despite substantial pixel-level uncertainty, the methodology reproduced broad regional patterns of forest structure and carbon distribution. Results demonstrate the potential of combining historical ALOS PALSAR archives with national forest inventories to support spatially explicit historical carbon estimation in data-limited forest regions. Full article

Torrential flows, a broad category of rapid hydrogeomorphic processes that in the Colombian Andes includes debris flows, mudflows, and hyperconcentrated flows, pose a major hazard in tropical mountain regions. This study used two complementary binary classification models to examine geomorphometric conditioning and antecedent rainfall triggering of torrential flow occurrence. A 12.5 m ALOS PALSAR DEM and 42 years of daily rainfall data (1981–2023) from IDEAM rain gauges and CHIRPS v2 were analyzed in a GIS-based regional framework. Antecedent rainfall variables were aggregated at watershed scale using zonal statistics. The conditioning dataset comprised 642 watersheds (321 with documented events and 321 controls). Gradient boosting ranked first in the preliminary grouped holdout comparison, whereas the uncalibrated random forest achieved the highest mean score under spatial leave-one-province-out validation and was selected as the final conditioning model (mean ROC-AUC = 0.747 ± 0.052). Basin scale and relief were the leading morphometric associations. In the rainfall trigger model, previous day IDEAM mean rainfall and previous day IDEAM maximum rainfall were the two leading permutation importance predictors, followed by monthly CHIRPS rainfall; the 90-day IDEAM maximum accumulation ranked fourth. This ordering indicates that immediate rainfall dominated the fitted model, while longer antecedent wetness retained a secondary contribution. The results support watershed prioritization and regional hazard assessment; because operational rainfall thresholds were not derived, they should not be treated as a ready-to-use early-warning model. Full article

The southern Red Sea coastal zone of Jizan Province in Saudi Arabia is increasingly exposed to seismic hazards, soil erosion, flash flooding, and tectonically shaped landscape instability, raising challenges for sustainable development. This paper integrates high-resolution 12.5 m ALOS PALSAR digital elevation model data, morphometric analyses, geomorphic interpretations, and the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion vulnerability and relative tectonic activity along 24 sub-basins. A total of 22 morphometric parameters were investigated, analyzed, and integrated into a weighted compound ranking key for prioritizing erosion-prone sub-basins. Geomorphic interpretation was assessed using the hypsometric integral, valley-floor width-to-height ratio, and basin shape, which were processed in the Relative Tectonic Activity (RTA) model. The results recognize sub-basins 8, 7, 22, 18, 23, 13, 3, and 9 as the highest-priority zones for soil conservation, while hypsometric integral values (0.04–0.48) reveal mature landscapes with geomorphic reactivation. High spatial correlation among morphometric prioritization, RTA interpretation, and the RUSLE model reveals that drainage characteristics, relief, lithology, and tectonic signatures indicate a significant spatial association with the soil erosion framework. The proposed model presents a reliable baseline key for sub-basin prioritization and climate-resilient mega-structure planning in data-poor settings, directly supporting SDG 9 and SDG 13. Full article

Accurate canopy-height mapping is essential for forest carbon monitoring, yet spatially continuous estimates over northern forests remain limited by sparse LiDAR sampling and unreliable optical data. We present a probabilistic deep learning framework that integrates seasonal Landsat optical, Sentinel-1 C-band SAR, and yearly ALOS-PALSAR-2 L-band SAR composites with GEDI training targets to produce 30 m canopy-height and pixel-level uncertainty maps over Ontario, Canada. A ResUNet ensemble is trained with a Laplace negative log-likelihood (NLL) loss; aleatoric and epistemic uncertainty are captured through a mixture of Laplace distributions, calibrated post hoc by Platt scaling and validated against airborne LiDAR (ALS P98). Under matched architecture and inputs, replacing Gaussian with Laplace NLL improved $R^2$ by 18.6%, reduced RMSE by 13.7%, and cut systematic bias from 1.43 to 0.14 m. The primary model reached $R^2=0.70$, RMSE = 3.65 m, and bias = 0.23 m, exceeding GEDI’s own footprint-level agreement with the independent ALS reference within Ontario’s managed forest zone. Seasonal compositing raised $R^2$ by 12.9% over summer-only inputs, and SAR-only configurations outperformed four global products in every forest class. The framework also delivers a calibrated, pixel-level uncertainty layer that reflects local structural difficulty beyond canopy height and can serve as a confidence layer for downstream carbon-stock and forest-management applications. Full article

Under the combined impacts of climate change and intensified human activities in coastal zones, mangrove ecosystems are increasingly exposed to degradation, fragmentation, and declines in ecological functions. It is therefore important to systematically examine the progress and evolution of the research hotspots in remote sensing monitoring of mangroves. In this study, 942 publications on mangrove remote sensing monitoring from 2000 to 2025 were retrieved from the China National Knowledge Infrastructure (CNKI) and the Web of Science Core Collection, comprising 485 CNKI records and 457 Web of Science records. CiteSpace 6.4.R2 was used to conduct bibliometric and knowledge-mapping analyses of publication trends, geographic distribution, author collaboration networks, keyword co-occurrence, keyword cluster timelines, and burst keywords. The results show that research on mangrove remote sensing monitoring generally increased over time, with marked growth after 2015. Research topics gradually shifted from early studies on mangrove distribution mapping, land-use change, and image classification to multi-source remote sensing applications, change detection, biomass estimation, blue carbon assessment, and machine learning- and deep learning-based methods. Author collaboration networks provide a descriptive overview of collaboration patterns and suggest that cross-team and cross-regional collaboration still needs to be strengthened. Keyword co-occurrence, cluster timeline, and burst keyword results indicate that remote sensing monitoring, machine learning, deep learning, random forest, support vector machine, object-based image analysis, ALOS PALSAR, ALOS-2 PALSAR-2, blue carbon, carbon stock, aboveground biomass, ecosystem services, and forest degradation are important themes in this field. Future research should further strengthen multi-source remote sensing data integration, cross-regional validation of intelligent algorithms, degradation monitoring indicator systems, uncertainty assessment, and long-term time-series analysis. These efforts will improve the accuracy, comparability, and management applicability of mangrove remote sensing monitoring and provide scientific support for coastal ecological conservation, mangrove restoration, and blue carbon governance. Full article

Atmospheric artifacts can obscure tectonic deformation in interferometric synthetic aperture radar (InSAR) observations, while conventional correction methods often depend on external atmospheric data. This study proposes a supervised residual-learning framework that directly predicts noise components in unwrapped InSAR maps rather than reconstructing deformation signals. Physically informed synthetic datasets were generated by combining Okada and Mogi deformation models with topography-correlated tropospheric delays, spatially correlated turbulent noise, and long-wavelength ramps. A network-depth sensitivity analysis identified a 20-layer denoising convolutional neural network as the optimal balance between accuracy and model complexity. Tests on independent synthetic datasets showed that the model reliably distinguished deformation from noise when the signal-to-noise ratio exceeded approximately 10⁻¹, whereas performance degraded under extremely noise-dominated conditions. The framework was further evaluated using ALOS/PALSAR and ALOS-2/PALSAR-2 observations of post-eruptive deformation at Mt. Ontake, Japan, and coseismic deformation associated with the 2009 L’Aquila earthquake, Italy. Compared with uncorrected, GACOS-corrected, and linear-corrected results, the CNN correction reduced topography-correlated and long-wavelength artifacts, improved temporal consistency, and generally achieved closer agreement with GNSS observations. These results suggest that residual noise learning provides an efficient approach for automatic atmospheric correction of unwrapped InSAR observations, with the potential for transferability across different deformation-source settings. Full article

Forest cover change alters land–atmosphere exchanges of energy, water, and carbon, thereby influencing local and regional climate. This study assessed climatic patterns associated with afforestation in Khyber Pakhtunkhwa, Pakistan, from 2003 to 2023 using remote sensing data and Google Earth Engine. Land surface temperature (LST) was treated as the primary response variable, while evapotranspiration (ET) was analyzed as a secondary response variable. Air temperature; precipitation; vegetation indices, including the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI); and elevation were used as supporting variables to interpret the broader climatic and biophysical responses of afforestation. MODIS land-cover, LST, ET, and vegetation-index products, together with climate research unit (CRU) climate data and ALOS-PALSAR DEM, were used to evaluate spatiotemporal trends and variable relationships. The results showed that mean LST increased by 0.520 ± 0.070 °C across KP during 2003–2023; however, areas classified as forest gain showed a localized cooling pattern of 0.490 ± 0.050 °C during the 2013–2023 forest-cover transition assessment window. Afforested areas also exhibited increased ET, whereas forest-loss areas showed reduced ET and higher LST. Specifically, ET increased by 0.013 ± 0.002 mm/8-day in afforested areas, whereas forest-loss areas showed a decline of 0.005 ± 0.001 mm/8-day. CRU-derived regional air temperature showed an increasing tendency of 0.310 ± 0.050 °C, whereas precipitation showed only a weak and statistically non-significant regional tendency; therefore, precipitation was used only as background climatic context. The NDVI and the EVI were negatively correlated with daytime LST, and elevation showed a strong negative relationship with LST. Overall, the findings indicate that forest-cover gain was associated with localized surface cooling patterns and improved vegetation–climate regulation indicators in the study area. Full article

Deterministic empirical formulas often fail to capture the epistemic uncertainties of dam failure mechanisms, leading to biased risk estimations. To address this, we propose the Probabilistic Hydro-Risk Forecasting System (PHRFS), a stochastic framework integrating Latin Hypercube Sampling (LHS) with HEC-RAS 2D hydrodynamic modeling. Applied to the Honghu-Erji Reservoir (Inner Mongolia), an ensemble of 60 stratified scenarios was generated based on a multi-model envelope of breach parameters and simulated over a $12.5\mathrm{m}$ ALOS PALSAR DEM using the Full Momentum Shallow Water Equations with the Eulerian–Lagrangian method (SWE-ELM). The SWE-ELM simulations reveal a heavy-tailed distribution of peak discharge (mean $2.65\times {10}^4{\mathrm{m}}^3/\mathrm{s}$; max $5.85\times {10}^4{\mathrm{m}}^3/\mathrm{s}$), significantly exceeding deterministic estimates. Sensitivity analysis identifies a physical dichotomy: breach depth ($D_b$) primarily controls flood magnitude ($r\approx 0.7$), whereas formation time ($t_f$) governs the arrival timeline. This temporal uncertainty propagates downstream, creating a distinct longitudinal gradient in SWE-ELM-derived warning time—ranging from rapid onset ($33.0\pm 19.3\min$) in proximal zones to a substantial lead time ($33.1\pm 4.2\mathrm{h}$) in the distal Hailar District. Consequently, a moderate coupling ($r=0.65$) emerges between economic loss and loss of life, while their spatial patterns remain strongly differentiated by warning time and exposure. Upstream settlements face high mortality risks due to insufficient evacuation windows, whereas the downstream urban center faces high economic exposure (∼1.57 billion CNY) but limited life loss (∼12.15 persons). These findings provide a scientific basis for differentiating emergency strategies, shifting from immediate life-saving in upstream reaches to asset protection in downstream areas. Full article

Rice cultivation is a major source of methane (CH₄) emission in the agricultural sector, with a significantly higher global warming potential than carbon dioxide. Accurate and scalable quantification of CH₄ from rice paddies is essential for carbon accounting. This study presents an automated framework for estimating rice CH₄ emissions from irrigated paddies in the central plain of Thailand, integrating multi-sensor Synthetic Aperture Radar (SAR) observations with the IPCC methodology. The framework combines Sentinel-1 C-band SAR time series for phenological detection, ALOS-2 PALSAR-2 L-band full-polarimetric SAR for water regime classification, and IPCC water-scaling factors corresponding to Continuous Flooding, Single Drainage, or Multiple Drainage regimes. Evaluated across five stratified holdout sets, the phenology detection algorithm achieved planting and harvesting date Mean Absolute Errors of 6.1 ± 1.4 and 8.3 ± 1.7 days, with a 97.0% ± 2.7% operational detection rate. Water regime classification employed rice growth stage-specific Support Vector Machine classifiers with Radial Basis Function kernels (SVM-RBF), achieving per-stage test Balanced Accuracy ranging from 0.59 to 0.89. End-to-end integration using a four-track counterfactual decomposition yielded a full-pipeline mean absolute error of 18.5 ± 4.5 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$ (21.4% of the mean ground-based CH₄ calculation) and a mean bias of 3.5 ± 5.8 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$. Water level classification was confirmed as the dominant algorithmic uncertainty source, while the IPCC Tier 1 emission factor structural range (−32% to +48% of the default) exceeded all algorithmic errors combined. The proposed framework provides a spatially explicit approach for integrating multi-frequency SAR data into IPCC-compliant methane estimation, supporting Monitoring, Reporting, and Verification applications. Full article

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

Achieving scalable monitoring of Alternate Wetting and Drying (AWD) for methane mitigation in rice cultivation depends on establishing field benchmarks for drainage behavior and demonstrating that satellite observations can reliably detect corresponding changes in water status. We analyzed about two million high-frequency in situ water-level observations from hundreds of sensors deployed in rice fields across the Philippines and Japan to quantify drainage duration from near-surface conditions to 15 cm below the soil surface and to test the sensitivity of open-access PALSAR-2 dual-polarization L-band SAR to vertical water-level variations. Across 564 drainage events, the median drainage duration was 19.0 h, and only 0.9% of events exceeded 240 h, indicating that drainage happens generally within a day. Seasonal differences were evident in Pangasinan, while small Chiba and Cagayan samples suggested exploratory longer-duration patterns; multiple drainage events occurred in 48.0% of Philippine dry-season fields but only 21.6% of wet-season fields. PALSAR-2 data showed a statistical significance in detecting inundation at Mid crop growth stage with cross-polarization band, but the significant overlap induces challenges in operational applications. These results provide empirical benchmarks for AWD-related drainage dynamics while showing that dual-polarization PALSAR-2 alone is unlikely to support robust field-scale monitoring of rice-field water status. Full article