Search Results (7,182)

This study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrain of western Yunnan. A machine learning workflow was developed on Google Earth Engine by combining Dynamic World land cover data with official fire records, utilizing a logistic regression-based feature selection strategy and an enhanced SNIC segmentation GEOBIA framework. The performance of four classifiers (RF, SVM, KNN, CART) in burn detection was evaluated through a comparative analysis of their spectral–spatial discrimination capabilities. The results indicated that the RF classifier achieved the highest performance, with an overall accuracy of 96.32% and a Kappa coefficient of 0.951. Spatial analysis further revealed that regions at medium altitudes (800–1600 m) and moderate slopes (15–25°) are more prone to forest fires. This study demonstrates a robust approach for generating accurate large-scale forest fire maps and provides valuable insights for effective fire management in complex terrain areas. Full article

The COllaborative Carbon Column Observing Network has become a reliable source of high-quality ground-based remote sensing network data that provide column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄), and carbon monoxide (XCO). The fiducial reference measurements of these gases from the COCCON complement the TCCON and NDACC-IRWG data. This study shows the application of COCCON data for the validation of existing greenhouse gas satellite products. This study includes the validation of XCH₄ and XCO products from the European Copernicus Sentinel-5 Precursor (S5P) mission, XCO₂ products from the American Orbiting Carbon Observatory-2 (OCO-2) mission, and XCO₂ and XCH₄ products from the Japanese Greenhouse gases Observing SATellite (GOSAT). A total of 27 datasets contributed to this study; some of these were collected in the framework of campaign activities and covered only a short time period. In addition, several permanent stations provided long-term observations. The random uncertainties in the validation results, specifically for S5P with a lot of coincidences pairs, are found to be similar to the comparison with the TCCON. The comparison results of OCO-2 land nadir and land glint observation modes to the COCCON on a global scale, despite limited coincidences, are very promising. The stations can, therefore, expand on the coverage of the already existing ground-based reference remote sensing sites from the TCCON and the NDACC network. The COCCON data can be used for future satellite and model validation studies and carbon cycle studies. Full article

Tropical evergreen forests represent the richest biodiversity in terrestrial ecosystems, and the fine spatial-temporal resolution mapping of these forests is essential for the study and conservation of this vital natural resource. The current methods for mapping tropical evergreen forests frequently exhibit coarse spatial resolution and lengthy production cycles. This can be attributed to the inherent challenges associated with monitoring diverse surface changes and the persistence of cloudy, rainy conditions in the tropics. We propose a novel approach to automatically map annual 10 m tropical evergreen forest covers from 2017 to 2023 with the Sentinel-2 Dynamic World dataset in the biodiversity-rich and conservation-sensitive Central African Republic (CAR). The Copernicus Global Land Cover Layers (CGLC) and Global Forest Change (GFC) products were used first to track stable evergreen forest samples. Then, initial evergreen forest cover maps were generated by determining the threshold of evergreen forest cover for each of the yearly median forest cover probability maps. From 2017 to 2023, the annual modified 10 m tropical evergreen forest cover maps were finally produced from the initial evergreen forest cover maps and NEFI (Non-Evergreen Forest Index) images with the estimated thresholds. The results produced by the proposed method achieved an overall accuracy of >94.10% and a Cohen’s Kappa of >87.63% across all years (F1-Score > 94.05%), which represents a significant improvement over the performance of previous methods, including the CGLC evergreen forest cover maps and yearly median forest cover probability maps based on Sentinel-2 Dynamic World. Our findings demonstrate that the proposed method provides detailed spatial characteristics of evergreen forests and time-series change in the Central African Republic, with substantial consistency across all years. Full article

Forest attributes, such as standing stock, diameter at breast height (DBH), tree height, and basal area, are critical for effective forest management; yet, traditional estimation methods remain labor-intensive and often lack the spatial detail required for contemporary decision-making. This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data and rigorously collected ground truth measurements to produce accurate, national-scale maps of forest attributes in Romania. To ensure the reliability of the model predictions, extensive field campaigns were conducted across representative Romanian forests. During these campaigns, detailed measurements were recorded for every tree within selected plots. For each tree, DBH was measured directly, and tree heights were obtained either by direct measurement—using hypsometers or clinometers—or, when direct measurements were not feasible, by applying well-established DBH—height allometric relationships that have been calibrated for the local forest types. This comprehensive approach to ground data collection, supplemented by an independent dataset from Brasov County collected using the same protocols, allowed for robust training and validation of the machine learning models. This study evaluates the performance of three machine learning algorithms—Random Forest (RF), Classification and Regression Trees (CART), and the Gradient Boosting Tree Algorithm (GBTA)—in predicting the forest attributes from Sentinel-2 satellite imagery. While Random Forest consistently delivered high R² values and low root mean square errors (RMSE) across all attributes, GBTA showed particular strength in predicting standing stock, and CART excelled in basal area estimation but was less reliable for other attributes. A sensitivity analysis across multiple spatial resolutions revealed that the performance of all algorithms varied significantly with changes in resolution, emphasizing the importance of selecting an appropriate scale for accurate forest mapping. By focusing on both the methodological advancements in machine learning applications and the rigorous, detailed empirical forest data collection, this study provides a clear solution to the problem of obtaining reliable, spatially detailed forest attribute maps. Full article

A uniformly distributed global ground network is essential for the accurate determination of GNSS orbit and clock parameters. However, achieving an ideal ground network is often difficult. When limited to a sparse or regional network of ground stations, the integration of LEO satellites can substantially enhance the accuracy of GNSS Precise Orbit Determination (POD). In practical processing, discontinuities with complicated gaps can occur in LEO attitude quaternions, particularly when working with a restricted observation network. This hampers the accuracy of determining GNSS/LEO integrated orbits. To address this, an investigation was conducted using data from seven LEO satellites, including those from Sentinel-3, GRACE-FO, and Swarm, to evaluate integrated POD performance under sparse or regional station conditions. Particular focus was placed on addressing attitude discontinuities. Four scenarios were analyzed, encompassing both continuous data availability and one-, two-, and three-hour interruptions after one hour of continuous data availability. The results showed that the proposed quaternion rotation matrix interpolation method is reliable for the integrated POD of GNSSs and LEOs with strict attitude control. Full article

National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approaches help address this issue by reducing dependence on fully annotated images and leveraging unlabeled data. However, their potential for large-scale LC mapping needs further investigation. This study explored the use of NFI data with deep learning and weakly supervised and self-supervised methods. Using Sentinel-2 images and the Portuguese NFI, which covers other LC types beyond forest, as sparse labels, we performed weakly supervised semantic segmentation with a convolutional neural network to create an updated and spatially continuous national LC map. Additionally, we investigated the potential of self-supervised learning by pretraining a masked autoencoder on 65,000 Sentinel-2 image chips and then fine-tuning the model with NFI-derived sparse labels. The weakly supervised baseline achieved a validation accuracy of 69.60%, surpassing Random Forest (67.90%). The self-supervised model achieved 71.29%, performing on par with the baseline using half the training data. The results demonstrated that integrating both learning approaches enabled successful countrywide LC mapping with limited training data. Full article

Background/Objectives: Cervical cancer primarily disseminates through the lymphatic system, with the metastatic involvement of pelvic and para-aortic lymph nodes significantly impacting prognosis and treatment decisions. Sentinel lymph node (SLN) mapping is critical in guiding surgical management. However, resource-limited settings often lack advanced detection tools like indocyanine green (ICG). This study evaluated the feasibility and effectiveness of SLN biopsy using alternative techniques in a high-risk population with a high prevalence of large tumours. Methods: This prospective, observational study included 42 patients with FIGO 2018 stage IA1–IIA1 cervical cancer treated between November 2019 and April 2024. SLN mapping was performed using methylene blue alone or combined with a technetium-99m radiotracer. Detection rates, sensitivity, and false-negative rates were analysed. Additional endpoints included tracer technique comparisons, SLN localization patterns, and factors influencing detection success. Results: SLNs were identified in 78.6% of cases, with bilateral detection in 57.1%. The combined technique yielded higher detection rates (93.3% overall, 80% bilateral) compared to methylene blue alone (70.4% overall, 40.7% bilateral, p < 0.05). The sensitivity and negative predictive values were 70% and 93.87%, respectively. Larger tumours (>4 cm), deep stromal invasion, and prior conization negatively impacted detection rates. False-negative SLNs were associated with larger tumours and positive lymphovascular space invasion. Conclusions: SLN biopsy is feasible in resource-limited settings, with improved detection rates using combined tracer techniques. However, sensitivity remains suboptimal due to a steep learning curve and challenges in high-risk patients. Until a high detection accuracy is achieved, SLN mapping should complement, rather than replace, pelvic lymphadenectomy in high-risk cases. Full article

Paddy rice mapping is critical for food security and environmental management, yet existing methods face challenges such as cloud obstruction in optical data and speckle noise in synthetic aperture radar (SAR). To address these limitations, this study introduces PRICOS, a novel paddy rice index that systematically combines time series Sentinel-2 optical features (NDVI for bare soil/peak growth, MNDWI for the submerged stages) and Sentinel-1 SAR backscatter (VH polarization for structural dynamics). PRICOS automates key phenological stage detection through harmonic fitting and dynamic thresholding, requiring only 10–20 samples per region to define rice growth cycles. Validated across six agroclimatic regions, PRICOS achieved overall accuracy (OA) and F1 scores of 0.90–0.98, outperforming existing indices like SPRI (OA: 0.79–0.95) and TWDTW (OA: 0.85–0.92). By integrating multi-sensor data with minimal sample dependency, PRICOS provides a robust, adaptable solution for large-scale paddy rice mapping, advancing precision agriculture and climate change mitigation efforts. Full article

Accurate digital soil organic carbon mapping is of great significance for regulating the global carbon cycle and addressing climate change. With the advent of the remote sensing big data era, multi-source and multi-temporal remote sensing techniques have been extensively applied in Earth observation. However, how to fully mine multi-source remote sensing time-series data for high-accuracy digital SOC mapping remains a key challenge. To address this challenge, this study introduced a new idea for mining multi-source remote sensing time-series data. We used 413 topsoil organic carbon samples from southern Xinjiang, China, as an example. By mining multi-source (Sentinel-1/2) remote sensing time-series data from 2017 to 2023, we revealed the temporal variation pattern of the correlation between Sentinel-1/2 time-series data and SOC, thereby identifying the optimal time window for monitoring SOC using Sentinel-1/2 data. By integrating environmental covariates and a super ensemble model, we achieved high-accuracy mapping of SOC in Southern Xinjiang, China. The results showed the following aspects: (1) The optimal time windows for monitoring SOC using Sentinel-1/2 data were July–September and July–August, respectively; (2) the modeling accuracy using multi-source sensor data integrated with environmental covariates was superior to using single-source sensor data integrated with environmental covariates alone. In the optimal model based on multi-source data, the cumulative contribution rate of Sentinel-2 data is 51.71% higher than that of Sentinel-1 data; (3) the stacking super ensemble model’s predictive performance outperformed the weight average and simple average ensemble models. Therefore, mining the optimal time windows of multi-source remote sensing data and environmental covariates, driven a super ensemble model, represents a high-accuracy strategy for digital SOC mapping. Full article

Lymphadenectomy for vulvar carcinoma is characterized by many complications. Studies have demonstrated the diagnostic accuracy of sentinel lymph node biopsy (SLNB) as a valid alternative to lymphadenectomy in the early stages of vulvar squamous cell carcinoma (VSCC). Objective: To evaluate the feasibility, safety, and accuracy, as well as the oncological outcomes of SLNB following scar injection; in addition, to assess the role of a repeat sentinel node procedure in patients with local vulvar recurrence after primary treatment. Materials and Methods: A systematic computerized search of the literature was performed in the main electronic databases (MEDLINE, EMBASE, Web of Science, Pub Med, and Cochrane Library) from 2010 to August 2024. Only scientific publications in English were included. Risk of bias assessment was performed. Results: Five articles were included in the study: four retrospective and one prospective observational studies. All patients’ characteristics, including type of surgery, postoperative morbidities, adjuvant therapy, and recurrence, as well as SLN detection and oncological outcomes, have been reported. Four studies compared the scar-injection group (cases) with the tumor-injection group (controls); only one study described the SLNB after vulvar recurrence (second procedure), comparing it with SLNB during primary vulvar surgery (first procedure). Conclusions: SLNB is a feasible and safe option in patients who have had previous excision of the vulvar tumor and in patients with a recurrence of VSCC who are not able or willing to undergo lymphadenectomy. Moreover, it accurately reflects the nodal status in these patients. Full article

Airborne LiDAR (ALS) data have been extensively utilized for aboveground biomass (AGB) estimation; however, the high acquisition costs make it challenging to attain wall-to-wall estimation across large regions. Some studies have leveraged ALS data as intermediate variables to amplify sample sizes, thereby reducing costs and enhancing sample representativeness and model accuracy, but the cost issue remains in larger-scale estimations. Satellite LiDAR data, offering a broader dataset that can be acquired quickly with lower costs, can serve as an alternative intermediate variable for sample expansion. In this study, we employed a three-stage up-scaling approach to estimate forest AGB and introduced a method for quantifying estimation uncertainty. Based on the established three-stage general-hierarchical-model-based estimation inference (3sGHMB), an RK-3sGHMB inference method is proposed to make use of the regression-kriging (RK) method, and then it is compared with conventional model-based inference (CMB), general hierarchical model-based inference (GHMB), and improved general hierarchical model-based inference (RK-GHMB) to estimate forest AGB and uncertainty at both the pixel and forest farm levels. This study was carried out by integrating plot data, sampled ALS data, wall-to-wall Sentinel-2A data, and airborne P-SAR data. The results show that the accuracy of CMB (Radj2 = 0.37, RMSE = 33.95 t/ha, EA = 63.28%) is lower than that of GHMB (Radj2 = 0.38, RMSE = 33.72 t/ha, EA = 63.53%), while it is higher than that of 3sGHMB (Radj2 = 0.27, RMSE = 36.58 t/ha, EA = 60.43%). Notably, RK-GHMB (Radj2 = 0.60, RMSE= 27.07 t/ha, EA = 70.72%) and RK-3sGHMB (Radj2 = 0.55, RMSE = 28.55 t/ha, EA = 69.13%) demonstrate significant accuracy enhancements compared to GHMB and 3sGHMB. For population AGB estimation, the precision of the proposed RK-3sGHMB (p = 94.44%) is the highest, providing that there are sufficient sample sizes in the third stage, followed by RK-GHMB (p = 93.32%) with sufficient sample sizes in the second stage, GHMB (p = 90.88%), 3sGHMB (p = 88.91%), and CMB (p = 87.96%). Further analysis reveals that the three-stage model, considering spatial correlation at the third stage, can improve estimation accuracy, but the prerequisite is that the sample size in the third stage must be sufficient. For large-scale estimation, the RK-3sGHMB model proposed herein offers certain advantages. Full article

Monitoring and analyzing reservoir landslides are essential for predicting and mitigating geohazards, which are crucial for maintaining sustainability and supporting socio-economic development in reservoir areas. High spatiotemporal resolution is vital for effective reservoir landslide monitoring and analysis. For this purpose, we improved the resolution of the differential interferometric synthetic aperture radar (DInSAR) technique by fusing two-path deformation results from an overlapping Sentinel-1 area. First, we summarized the mathematical ratio relationship between deformation from the two paths. Second, time-series linear interpolation and time-reference difference removal were applied to the two separate deformation results of time-series DInSAR. Third, a ratio algorithm was adopted to fuse the deformation of the two paths into one integrated time-series result. The standard deviations of the deformation before and after fusion were similar, confirming the accuracy of the fusion results and feasibility of the method. From the integrated deformation, we analyzed the hydraulic impact, mechanisms, and physical processes associated with four reservoir landslides in the Three Gorges Reservoir area of China, accounting for rainfall and water-level data. The comprehensive analysis presented herein provides new insights on the hydraulic mechanisms of reservoir landslides and verifies the efficacy of this new integrated method for landslide investigation and monitoring. Full article

From 2020 to 2022, hydrological extremes such as severe floods and droughts occurred successively in Jiujiang city, Poyang Lake Basin, posing a threat to regional water quality safety. The chlorophyll-a (Chl-a) concentration is a key indicator of river eutrophication. Until now, there has been a lack of empirical research exploring the Chl-a trend in inland water in Jiujiang in the context of hydrological extremes. In this study, Sentinel-2 satellite remote sensing data sourced from the Google Earth Engine (GEE) cloud platform, along with hourly water quality data collected from monitoring stations in Jiujiang city, Jiangxi Province, China, are utilized to develop a quantitative inversion model for the Chl-a concentration. The Chl-a concentrations for various inland water types were estimated for each quarter from 2020 to 2022, and the spatiotemporal distribution was analyzed. The main findings are as follows: (1) the quantitative inversion model for the Chl-a concentration was validated via in situ measurements, with a coefficient of determination of 0.563; (2) the spatial estimates of the Chl-a concentration revealed a slight increasing trend, increasing by 0.1193 μg/L from 2020 to 2022, closely aligning with the monitoring-station data; (3) an extreme drought in 2022 led to less water in inland water bodies, and consequently, the Chl-a concentration displayed a significant upward trend, especially in Poyang Lake, where the mean Chl-a concentration increased by approximately 1 μg/L from Q1 to Q2 in 2022. These findings revealed the seasonal changes in the Chl-a concentrations in inland waters in the context of extreme hydrological events, thus providing valuable information for the sustainable management of water quality in Jiujiang city. Full article

Analyzing the spatial and temporal trends in cropping patterns and intensity on a larger scale is essential for implementing timely policy decisions and strategies in response to climate change and variability. By converting cropping intensity estimates, we can compute net and gross production values, indirectly indicating food security status in the study region. This study compared the utility of optical (MOD13Q1) and SAR (Sentinel 1A) datasets for determining cropping patterns and associated intensity estimates across multiple agricultural seasons from 2019 to 2023, with spatial resolutions of 250 m and 20 m, respectively. The analysis revealed that the highest and lowest gross cropped areas using Sentinel 1A data were 55.85 lakh hectares (2022–2023) and 52.88 lakh hectares (2019–2020), respectively. For MODIS data, the highest and lowest gross cropped areas were 62.07 lakh hectares (2022–2023) and 56.87 lakh hectares (2019–2020). Similarly, the highest and lowest net sown areas using Sentinel 1A data were 43.71 lakh hectares (2022–2023) and 41.76 lakh hectares (2019–2020), and for MODIS data, the values were 48.81 lakh hectares (2022–2023) and 46.39 lakh hectares (2019–2020), respectively. Regardless of the datasets used, the highest gross and net cropped areas were reported in Tiruvannamalai district and the lowest in Kanchipuram district. Thiruvarur district reported the highest cropping intensity, while Sivagangai district had the lowest. Among all seasons, the rabi season accounted for the maximum area, followed by the kharif and summer seasons. The study concluded that single cropping (51%) was the dominant cropping pattern in Tamil Nadu, followed by double cropping (31%) and triple cropping (17%) in both datasets. Sentinel 1A data showed better performance in estimating gross and net cropped areas than optical data, with deviations ranging from 7.02% to 11.01%, regardless of the year and cropping estimates derived. The results indicated that the spatial resolution of the datasets was not a significant factor in determining cropping patterns and intensity on a larger scale. However, this may differ for smaller study areas. Full article

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features. Full article