Search Results (380)

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

Ravine forests are key to conserving biodiversity and maintaining ecosystem processes in fragmented landscapes. Here, we evaluated the phenology of plant species in the Los Nogales Nature Sanctuary (Lo Barnechea, Chile) using Sentinel-2 images (2019–2024) and the Alos Palsar DEM (12.5 m). We calculated the Normalized Difference Vegetation Index (NDVI), the Topographic Position Index (TPI), and Diurnal Anisotropic Heat (DAH) to assess vegetation dynamics across different topographic and thermal gradients. Generalized Additive Models (GAM) revealed that tree species exhibited more stable, regular seasonal NDVI trajectories, while shrubs showed moderate fluctuations, and herbaceous species displayed high interannual variability, likely reflecting sensitivity to climatic events. Spatial analysis indicated that trees predominated on steep slopes and higher elevations, herbs were concentrated in low-lying, moisture-retaining areas, and shrubs were more common in areas with higher thermal load. These findings highlight the significant role of terrain and temperature in shaping plant phenology and distribution, underscoring the utility of remote sensing and topographic indices for monitoring ecological processes in complex mountainous environments. Full article

Accurate estimation of forest aboveground carbon stock (AGC) is critical for climate change mitigation and ecological management. This study develops a high-resolution AGC estimation workflow for the Pinglu Canal basin, integrating Sentinel-2, Sentinel-1, ALOS PALSAR, and SRTM data with field survey measurements. Feature selection via Recursive Feature Elimination and modeling with a Random Forest algorithm—optimized through hyperparameter tuning—yielded high predictive accuracy under the ALL data combination (R² = 0.818, RMSE = 11.126 tC/ha), enabling the generation of a 10 m-resolution AGC map. The total AGC in 2024 was estimated at 2.26 × 10⁶ tC. To evaluate human-induced changes, we established a baseline scenario based on historical AGC trends (2002–2021) and climate data. Comparisons revealed that afforestation and vegetation restoration during canal construction led to higher AGC values than projected under natural conditions. This positive deviation highlights the effectiveness of targeted ecological interventions in mitigating carbon loss and promoting forest recovery. Our results demonstrate a cost-effective, scalable method for AGC mapping using freely accessible remote sensing data and machine learning. The findings also provide insights into balancing large-scale infrastructure development with ecosystem conservation. Full article

Estimating aboveground biomass (AGB) is crucial for understanding the carbon cycle in terrestrial ecosystems, particularly within the context of climate change. Therefore, it is essential to research and compare different methods of AGB estimation to achieve acceptable accuracy. This study modelled AGB in temperate forests of central Mexico using active and passive remote sensing data combined with machine learning techniques (Random Forest and XGBoost) and compared the estimations against a traditional method, such as linear regression. The main goal was to evaluate the performance of machine learning techniques against linear regression in AGB estimation and then validate against an independent forest inventory database. The models obtained acceptable performance in all cases, but the machine learning algorithm Random Forest outperformed (R²_cv = 0.54; RMSE_cv = 19.17) the regression method (R²_cv = 0.41; RMSE_cv = 25.76). The variables that made significant contributions, in both Random Forest and XGBoost modelling, were NDVI, kNDVI (Landsat OLI sensor), and the HV polarisation from ALOS-Palsar. For validation, the Machine learning ensemble had a higher Spearman correlation (r = 0.68) than the linear regression (r = 0.50). These findings highlight the potential of integrating machine learning techniques with remote sensing data to improve the reliability of AGB estimation in temperate forests. Full article

Groundwater serves as a vital resource for sustainable water supply, particularly in semi-arid regions where surface water availability is limited. This study explores groundwater potential zones in the East Desert, Qift–Qena, Egypt, using a multidisciplinary approach that integrates remote sensing (RS), geographic information systems (GIS), geostatistics, and field validation with water wells to develop a comprehensive groundwater potential mapping framework. Sentinel-2 imagery, ALOS PALSAR DEM, and SMAP datasets were utilized to derive critical thematic layers, including land use/land cover, vegetation indices, soil moisture, drainage density, slope, and elevation. The results of the groundwater potentiality map of the study area from RS reveal four distinct zones: low, moderate, high, and very high. The analysis indicates a notable spatial variability in groundwater potential, with “high” (34.1%) and “low” (33.8%) potential zones dominating the landscape, while “very high” potential areas (4.8%) are relatively scarce. The limited extent of “very high” potential zones, predominantly concentrated along the Nile River valley, underscores the river’s critical role as the primary source of groundwater recharge. Moderate potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and they account for 27.3%. These layers were combined with geostatistical analysis of data from 310 groundwater wells, which provided information on static water level (SWL) and total dissolved solids (TDS). GIS was employed to assign weights to the thematic layers based on their influence on groundwater recharge and facilitated the spatial integration and visualization of the results. Geostatistical interpolation methods ensured the reliable mapping of subsurface parameters. The assessment utilizing pre-existing well data revealed a significant concordance between the delineated potential zones and the actual availability of groundwater resources. The findings of this study could significantly improve groundwater management in semi-arid/arid zones, offering a strategic response to water scarcity challenges. Full article

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies. Full article

The elevated precision of data regarding the Earth’s surface, facilitated by the enhanced interoperability among various GNSSs (Global Navigation Satellite Systems), enables the classification of land use and land cover (LULC) via satellites equipped with optical sensors, such as Sentinel-2 of the Copernicus program, which is crucial for land use management and environmental planning. Likewise, data from SAR satellites, such Copernicus’ Sentinel-1 and Jaxa’s ALOS PALSAR, provide diverse environmental investigations, allowing different types of spatial information to be analysed thanks to the particular features of analysis based on radar. Nonetheless, in optical satellites, the relatively low resolution of Sentinel-2 satellites may impede the precision of supervised AI classifiers, crucial for ongoing land use monitoring, especially during the training phase, which can be expensive due to the requirement for advanced technology and extensive training datasets. This project aims to develop an AI classifier utilising high-resolution training data and the resilient architecture of ResNet, in conjunction with the Remote Sensing Image Classification Benchmark (RSI-CB128). ResNet, noted for its deep residual learning capabilities, significantly enhances the classifier’s proficiency in identifying intricate patterns and features from high-resolution images. A test dataset derived from Sentinel-2 raster images is utilised to evaluate the effectiveness of the neural network (NN). Our goals are to thoroughly assess and confirm the efficacy of an AI classifier utilised on high-resolution Sentinel-2 photos. The findings indicate substantial enhancements compared to current classification methods, such as U-Net, Vision Transformer (ViT), and OBIA, underscoring ResNet’s transformative capacity to elevate the precision of land use classification. Full article

This study analyzes the potential of optical and radar satellite data to monitor faba bean (Vicia faba L.) phenology over six years (2016–2021) in southwestern France. Using Sentinel-1, Sentinel-2, and Landsat-8 data, temporal variations in NDVI and radar backscatter coefficients (γ⁰_VV, γ⁰_VH, and γ⁰_VH/VV) are examined to assess crop growth, detect anomalies, and evaluate the impact of climatic conditions and sowing strategies. The results show that NDVI and the radar ratio (γ⁰_VH/VV) were suited to monitor faba bean phenology, with distinct growth phases observed annually. NDVI provides a clear seasonal pattern but is affected by cloud cover, while radar backscatter offers continuous monitoring, making their combination highly beneficial. The signal γ⁰_VH/VV exhibits well-marked correlations with NDVI (r = 0.81) and LAI (r = 0.83), particularly in orbit 30, which provides greater sensitivity to vegetation changes. The analysis of individual fields (inter-field approach) reveals variations in sowing strategies, with both autumn and spring plantings detected. Fields sown in autumn show early NDVI (and γ⁰_VH/VV) increases, while spring-sown fields display delayed growth patterns. This study also highlights the impact of climatic factors, such as precipitation and temperature, on inter-annual variability. Moreover, faba beans used as an intercropping species exhibit a shorter and more intense growth cycle, with a rapid NDVI (and γ⁰_VH/VV) increase and an earlier end of the vegetative cycle compared to standard rotations. Double logistic modeling successfully reconstructs temporal trends, achieving high accuracy (r > 0.95 and rRMSE < 9% for γ⁰_VH/VV signals and r > 0.89 and rRMSE < 15% for NDVI). These double logistic functions are capable of reproducing the differences in phenological development observed between fields and years, providing a reference set of functions that can be used to monitor the phenological development of faba beans in real time. Future applications could extend this methodology to other crops and explore alternative radar systems for improved monitoring (such as TerraSAR-X, Cosmos-SkyMed, ALOS-2/PALSAR, NISAR, ROSE-L…). Full article

This study develops an optimized approach for small-scale forest area extraction in mountainous regions by integrating Landsat multispectral and ALOS PALSAR-2 radar data through threshold-based classification methods. The threshold fusion method proposed in this study achieves innovations in three key aspects: First, by integrating Landsat NDVI with PALSAR-2 polarization characteristics, it effectively addresses omission errors caused by cloud interference and terrain shadows. Second, the adoption of a decision-level (rather than feature-level) fusion strategy significantly reduces computational complexity. Finally, the incorporation of terrain correction (slope > 20° and aspect 60–120°) enhances classification accuracy, providing a reliable technical solution for small-scale forest monitoring. The results indicate that (1) the combination of Landsat multispectral remote sensing data and PALSAR-2 radar remote sensing data achieved the highest classification accuracy, with an overall forest classification accuracy of 97.62% in 2015 and 96.97% in 2022. The overall classification accuracy of Landsat multispectral remote sensing data alone was 93%, and that of PALSAR radar data alone was 85%, which is significantly lower than the results obtained using the combined data for forest classification. (2) Between 2015 and 2023, the forest area of Helan Mountain experienced certain fluctuations, primarily influenced by ecological and natural factors as well as variations in the accuracy of remote sensing data. In conclusion, the method proposed in this study enables more precise estimation of the forest area in the Helan Mountain region of Ningxia. This not only meets the management needs for forest resources in Helan Mountain but also provides valuable reference for forest area extraction in mountainous regions of Northwest China. Full article

Mapping urban pluvial flooding (UPF) in data-scarce regions poses significant challenges, particularly when drainage systems are inadequate or outdated. These limitations hinder effective flood mitigation and risk assessment. This study proposes an innovative approach to address these challenges by integrating deep learning (DL) models with traditional methods. First, deep convolutional generative adversarial networks (DCGANs) were employed to enhance drainage network data generation. Second, deep recurrent neural networks (DRNNs) and multi-criteria decision analysis (MCDA) methods were implemented to assess UPF. The study compared the performance of these approaches, highlighting the potential of DL models in providing more accurate and robust flood mapping outcomes. The methodology was applied to Lahore, Pakistan—a rapidly urbanizing and data-scarce region frequently impacted by UPF during monsoons. High-resolution ALOS PALSAR DEM data were utilized to extract natural drainage networks, while synthetic datasets generated by GANs addressed the lack of historical flood data. Results demonstrated the superiority of DL-based approaches over traditional MCDA methods, showcasing their potential for broader applicability in similar regions worldwide. This research emphasizes the role of DL models in advancing urban flood mapping, providing valuable insights for urban planners and policymakers to mitigate flooding risks and improve resilience in vulnerable regions. Full article

Forested wetlands in temperate mountain ecosystems play a critical role in carbon sequestration and biodiversity maintenance, yet their accurate delineation remains challenging due to spectral similarity with forests and anthropogenic interference. Here, we present an optimized two-stage Random Forest framework integrating 2019–2022 growing season datasets from Sentinel-1 C-SAR, ALOS-2 L-PALSAR, Sentinel-2 MSI, and Landsat-8 TIRS with environmental covariates. The methodology first applied NDBI thresholding (NDBI > 0.12) to exclude 94% of urban/agricultural areas through spectral masking, then implemented an optimized Random Forest classifier (ntree = 1200, mtry = 28) with 10-fold cross-validation, leveraging 42 features including L-band HV backscatter (feature importance = 47), Sentinel-2 SWIR (Band12; importance = 57), and land surface temperature gradients. This study pioneers a 10 m resolution forest swamp map in the Changbai Mountain wetlands, achieving 87.18% overall accuracy (Kappa = 0.84) with strong predictive performance (AUC = 0.89). Forest swamps showed robust classification metrics (PA = 80.37%, UA = 86.87%), driven by L-band SAR’s superior discriminative power (p < 0.05). Quantitative assessment demonstrated that L-band SAR increased classification accuracy in canopy penetration scenarios by 4.2% compared to optical-only approaches, while thermal-IR features reduced confusion with forests. Forested swamps occupied 229.95 km² (9% of protected areas), predominantly in transitional ecotones (720–850 m elevation) between herbaceous wetlands and forest. This study establishes that multi-sensor fusion enables operational wetland monitoring in topographically complex regions, providing a transferable framework for temperate mountain ecosystems. The dataset advances precision conservation strategies for these climate-sensitive habitats, supporting sustainable development goals targets for wetland protection through enhanced machine learning interpretability and anthropogenic interference mitigation. Full article

The exploration of gold and copper is essential for the sustainable development of mining worldwide and in Brazil, given the dependency on copper imports. This study aims to reassess and identify promising areas for sulfide prospecting in southern Brazil, with technologies, including radar images (ALOS PALSAR) and software (PCI Geomatics CATALYST Professional Complete, version 2023, QGIS 3.38.1 (Grenoble), Spring 5.5.6, and Orient 3.20.0), for the extraction and processing of tectonic lineaments. The comparative analysis between these linear structures and the drainage networks allows for the assessment of alluvial gold minerals and disseminated copper in andesites, as observed in the abandoned Seival mines. The methods employed include the extraction of tectonic lineaments and the evaluation of mineral occurrences in the Hilário (volcanogenic) and Arroio dos Nobres (sedimentary) formations of the Bom Jardim Group (Neoproterozoic) and their drainage networks. As a result, this article concludes that the main tectonic alignment directions for copper and gold occurrences disseminated in andesites are predominantly E–W, N–S, N 5° W, N 58° W, N 62° E, and N 23° E, and for alluvial gold N–S and N 45° W. These results are crucial for reassessing abandoned mining areas and identifying the primary mineral orientations in rocks and the predominant orientation of alluvial deposits, serving as structural controls for discovering new mineral occurrences. It is concluded that geotechnologies have expanded the possibilities for study, enabling a more detailed analysis of tectonic lineaments and drainage systems and providing a valuable prospective guide for gold and copper mineral exploration. Full article

Regional mapping of Above Ground Biomass Density (AGBD) using Remote Sensing data has shown high accuracy but lacks replicability at a global scale. In contrast, global models capture AGBD variability across biomes but struggle with biome-specific accuracy. To address this gap, we develop and assess the performance of a Deep Learning model for mapping AGBD at 10-m resolution using multi-source satellite data (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI) across four biomes: Mediterranean, taiga (boreal forests), tropical rainforests, and semi-arid savannas. The model is trained and validated separately for each biome, yielding four regional models with normalized RMSEs of 0.43–0.67 and correlation coefficients (r) of 0.61–0.77 against forest inventories. We compare predictions from these models to a benchmark dataset and to a model trained on all four biomes combined. The regional models consistently outperform both, achieving better metrics than the benchmark. Additionally, an analysis of prediction drivers reveals biome-specific differences, reinforcing the importance of per-biome mapping approaches. This study highlights the advantages and limitations of regional against global modeling, creating the basis for biome-specific, replicable, scalable and multi-temporal AGBD mapping. Full article

Accurate forest monitoring is critical for achieving the objectives of the European Green Deal. While national forest inventories provide consistent information on the state of forests, their temporal frequency is inadequate for monitoring fast-growing species with 15-year rotations when inventories are conducted every 10 years. However, Earth observation (EO) satellite systems can be used to address this challenge. Remote sensing satellites enable the continuous acquisition of land cover data with high temporal frequency (annually or shorter), at a spatial resolution of 10-30 m per pixel. This study focused on northern Spain, a highly productive forest region. This study aimed to improve models for predicting forest variables in forest plantations in northern Spain by integrating optical (Sentinel-2) and imaging radar (Sentinel-1, ALOS-2 PALSAR-2 and TanDEM-X) datasets supported by climatic and terrain variables. Five popular machine learning algorithms were compared, namely kNN, LightGBM, Random Forest, MLR, and XGBoost. The study findings show an improvement in R² from 0.24 when only Sentinel-2 data are used with MultiLinear Regression to 0.49 when XGboost is used with multi-source EO data. It can be concluded that the combination of multi-source datasets, regardless of the model used, significantly enhances model performance, with TanDEM-X data standing out for their remarkable ability to provide valuable radar information on forest height and volume, particularly in a complex terrain such as northern Spain. Full article