Search Results (3,493)

Understanding the distribution of forest aboveground biomass (AGB) is pivotal for carbon monitoring. Field-based inventorying is time-consuming and costly for large-area AGB estimations. The integration of multimodal remote sensing (RS) observations with single-year, field-based forest inventory analysis (FIA) data has the potential to improve the efficiency of large-scale AGB modeling and carbon monitoring initiatives. Our main objective was to systematically compare the AGB prediction accuracies of machine learning algorithms (e.g., random forest (RF) and support vector machine (SVM)) with those of conventional statistical methods (e.g., multiple linear regression (MLR)) using multimodal RS variables as predictors. We implemented a method combining AGB estimates of actual FIA subplot locations with airborne LiDAR, National Agriculture Imagery Program (NAIP) aerial imagery, and Sentinel-2 satellite images for model training, validation, and testing. The hyperparameter-tuned RF model produced a root mean square error (RMSE) of 27.19 Mgha⁻¹ and an R² of 0.41, which outperformed the evaluation metrics of SVM and MLR models. Among the 28 most important explanatory variables used to build the best RF model, 68% of variables were derived from the LiDAR height data. The hyperparameter-tuned linear SVM model exhibited an R² of 0.10 and an RMSE of 32.17 Mgha⁻¹. Additionally, we developed an MLR using eight explanatory variables, which yielded an RMSE of 22.59 Mgha⁻¹ and an R² of 0.22. The linear ensemble model, which was developed using the predictions of all three models, yielded an R² of 0.79. Our results suggested that more field data are required to better generalize the ensemble model. Overall, our findings highlight the importance of variable selection methods, the hyperparameter tuning of ML algorithms, and the integration of multimodal RS data in improving large-area AGB prediction models. Full article

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

High-Andean wetlands are critical ecosystems for water regulation and carbon storage. This study analyzes the impact of climate variability on vegetation dynamics in eight wetlands located in the Estero Ortiga sub-basin, within the Los Nogales Nature Sanctuary (Metropolitan Region, Chile), between 2017 and 2024. We used time series of NDVI and NDCI indices derived from Sentinel-2 imagery (January 2017 to September 2024), along with monthly temperature and precipitation data from ERA5-Land (January 2016 to September 2024). Trends were assessed through linear regression, and vegetation–climate relationships were analyzed using Pearson correlations with a one-year lag. Results show a progressive decline in vegetation cover (slope: −2.04 × 10⁻⁵ NDVI) and chlorophyll content (slope: −1.15 × 10⁻⁵ NDCI), with strong positive correlations between annual precipitation and vegetation indices in the subsequent summer (R = 0.83–0.88 for NDVI; R = 0.84–0.90 for NDCI). Annual NDVI reclassification highlighted a reduction in healthy vegetation cover from 2020 onward. This research provides novel evidence linking climate trends and vegetation health in high-Andean wetlands, reinforcing the utility of satellite-based indicators for conservation monitoring. Full article

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring. Full article

The detection of seafloor signal photons in various topographies is challenging. Previous research has divided photons into clusters based solely on their density, which is closely related to the settings of the empirical parameters. Inappropriate parameters may mistakenly identify the water column noise photons as seafloor photons. To overcome these limitations, this study introduces a novel slope iterative adaptive filter (SIAF) method that innovatively integrates ICESat-2 ATL03 photon data with Sentinel-2-derived topographic slopes. Inspired by satellite-derived bathymetry, we extracted topographic slopes from multispectral images as auxiliary information to guide the photon extraction. The initial slope estimation was derived from the multispectral images, and the optimal slope direction was determined iteratively, using the detected signal photons in each step. The average and maximum overall accuracies of SIAF were 93.43% and 95.7%, respectively. The validation of the extraction results with sonar data indicated that the SIAF achieved an average root mean square error (RMSE) of 0.49 m. Crucially, the SIAF resolves critical shortcomings of prior techniques: (1) it avoids the isotropic assumption of density-based methods, (2) it mitigates AVEBM’s vulnerability to noise in steep-slope regions, and (3) it enables robust automation without manual parameter tuning. Consequently, SIAF proved to be an efficient approach for the automatic mapping of water depths in shallow-water zones. Full article

Urologic oncological surgery increasingly makes use of robotic systems to realize precise and minimally invasive resections, convent to shorter hospital stays and faster recovery times. The dexterity gains enabled through procedures such as robot-assisted (RA) prostatectomy have helped realize significant advancements in recent years. Complementing these effects via the used of hybrid tracers that illuminate surgical targets, i.e., cancerous tissue, has helped advance the surgical decision making via enhanced visualization. A well-known example is Indocyanine green (ICG)-Technetium-99m (^99mTc)-nanocolloid, a hybrid extension of the radiopharmaceutical ^99mTc-nanocolloid. These hybrid tracers provide a direct link between preoperative imaging roadmaps and intraoperative target identification, and improve efficiency, accuracy, and confidence of the urologist in procedures such as sentinel lymph node biopsy (SLNB). Receptor-targeted hybrid tracer analogues, for e.g., prostate specific membrane antigen (PSMA), are also being explored as an extension of the ongoing efforts that use radiotracers such as ^99mTc-PSMA-I&S. Together, these efforts jointly pave the way for novel techniques in intraoperative lesion localization in other urological malignancies. This narrative review discusses the potential use of hybrid tracers in robotic oncological urology, including different imaging techniques and their applications for tumor localization for prostate, bladder, and kidney cancer. Full article

The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry’s management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user’s, producer’s, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R² = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization. Full article

Global food security is increasingly challenged by climate change and the availability of arable land. This situation calls for improved crop monitoring and management strategies. Rice is a staple food for nearly half of the world’s population and a significant source of calories. Accurately identifying rice varieties is crucial for maintaining varietal purity, planning agricultural activities, and enhancing genetic improvement strategies. This study evaluates the effectiveness of machine learning algorithms to identify the most effective approach to predicting rice varieties, using multitemporal Sentinel-2 images in the Marismas del Guadalquivir of Sevilla, Spain. Spectral reflectance data were collected from ten Sentinel-2 bands, which include visible, red-edge, near-infrared, and shortwave infrared regions, at two key phenological stages: tillering and reproduction. The models were trained on pixel-level data from the growing seasons of 2021 and 2024, and they were evaluated using a test set from 2022. Four classifiers were compared: random forest, XGBoost, K-nearest neighbors, and logistic regression. Performance was assessed based on accuracy, precision, recall, specificity and F1 score. Non-linear models outperformed linear ones. The highest performance was achieved with the Random Forest classifier during the reproduction phase, reaching an exceptional accuracy of 0.94 using all bands or only the most informative subset (red edge, NIR, and SWIR). This classifier also maintained excellent accuracy (0.93 and 0.92) during the initial tillering phase. This fact demonstrates that it is possible to perform reliable varietal mapping in the early stages of the growing season. Full article

The harmonized multi-sensor satellite data assists users by providing seamless analysis-ready data with enhanced temporal resolution. The Harmonized Landsat Sentinel (HLS) product has gained popularity due to the seamless integration of Landsat OLI and Sentinel-2 MSI, achieving a temporal resolution of 2.8 to 3.5 days. However, applications that require monitoring intervals of less than three days or cloudy data can limit the usage of HLS data. Gaofen-1 (GF-1) Wide Field of View (WFV) data provides the capacity further to enhance the data availability by harmonization with HLS. In this study, GF-1/WFV data is harmonized with HLS by employing deep learning-based conditional Generative Adversarial Networks (cGANs). The harmonized WFV data with HLS provides an average temporal resolution of 1.5 days (ranging from 1.2 to 1.7 days), whereas the temporal resolution of HLS varies from 2.8 to 3.5 days. This enhanced temporal resolution will benefit applications that require frequent monitoring. Various processes are employed in HLS to achieve seamless products from the Operational Land Imager (OLI) and Multispectral Imager (MSI). This study applies 6S atmospheric correction to obtain GF-1/WFV surface reflectance data, employs MFC cloud masking, resamples the data to 30 m, and performs geographical correction using AROP relative to HLS data, to align preprocessing with HLS workflows. Harmonization is achieved without using BRDF normalization and bandpass adjustment like in the HLS workflows; instead, cGAN learns cross-sensor reflectance mapping by utilizing a U-Net generator and a patchGAN discriminator. The harmonized GF-1/WFV data were compared to the reference HLS data using various quality indices, including SSIM, MBE, and RMSD, across 126 cloud-free validation tiles covering various land covers and seasons. Band-wise scatter plots, histograms, and visual image color quality were compared. All these indices, including the Sobel filter, histograms, and visual comparisons, indicated that the proposed method has effectively reduced the spectral discrepancies between the GF-1/WFV and HLS data. Full article

Sentinel lymph node biopsy (SLNB) has transformed the management of cutaneous melanoma, emerging as a cornerstone in evaluating regional lymphatic spread while minimizing surgical morbidity. From its theoretical foundation laid by Cabanas to its refinement and clinical validation through landmark trials, SLNB has evolved into a standard of care with significant prognostic value. This review traces the historical trajectory of SLNB, analyzes current guidelines and controversies and explores future directions. Novel imaging technologies, such as indocyanine green fluorescence and augmented reality-assisted mapping, promise to enhance accuracy and reduce invasiveness. Furthermore, the advent of effective systemic therapies and neoadjuvant protocols is reshaping the therapeutic landscape, potentially redefining the role of SLNB in melanoma management. As precision medicine advances, SLNB remains an essential procedure, with its utility continually redefined by technological innovation and evolving oncologic strategies. Full article

The exploitation of mineral resources often necessitates groundwater drainage, which may impact surrounding ecosystems, particularly vegetation. In this study, the effects of passive drainage in the Kornica-Popówka chalk mine in eastern Poland were analyzed using Sentinel-2 satellite images and the NDVI vegetation index. Groundwater monitoring wells were used to delineate the extent of the depression cone, representing areas of potentially altered hydrological conditions. NDVI values were analyzed across multiple time points between 2023 and 2024 to assess the condition of vegetation both inside and outside the depression cone. The results indicate no significant difference in NDVI values during the 2023–2024 study period for this specific chalk mine case between areas affected and unaffected by the depression cone, suggesting that vegetation in this region is not experiencing stress due to lowered groundwater levels. This outcome highlights the influence of other environmental factors, such as rainfall and land use, and suggests that the local geological structure allows plants to maintain sufficient access to water despite hydrological alterations. This study confirms the utility of integrating remote sensing with hydrogeological data in environmental monitoring and underlines the need for continued observation to assess long-term trends in vegetation response to mining-related groundwater changes. Full article

Background: Sentinel lymph node biopsy (SLNB) has traditionally been used to stage the axilla in early-stage breast cancer. However, its utility in women over 70 with hormone receptor-positive tumors and negative axillary imaging is increasingly questioned due to limited therapeutic benefit and potential complications. Objectives. To assess the feasibility of omitting SLNB in women aged 70 and older with clinically node-negative, luminal-type breast cancer. Methods: A retrospective analysis was conducted on women aged 70 and above with histologically confirmed invasive breast cancer, negative axillary imaging, and surgery between January 2021 and December 2024. Eligible patients were selected based on normal axillary ultrasound findings. All underwent SLNB. We examined demographics, clinical characteristics, surgical outcomes, and oncological variables such as recurrence and mortality. Results: A total of 149 women underwent surgery, with a mean age of 77.2 (5.24) years. SLNB was positive in 23.5% of cases, but only 6.7% required axillary dissection. Sensitivity and specificity of SLNB declined notably after age 76. No axillary or breast recurrences were reported. Most patients (89.9%) received hormonal therapy, while 11.4% had chemotherapy and 17.5% axillary radiotherapy. Outpatient management was feasible in 87.9% of cases, and no clinically significant lymphedema was observed. Conclusions: Omitting SLNB in women ≥70 years with luminal breast cancer and negative axillary imaging appears safe and does not compromise oncological outcomes. This strategy minimizes surgical risks and enhances quality of life, supporting a more tailored and less invasive approach to axillary management in older patients. Full article

The upper Mapocho River basin, located in central Chile, has been affected by numerous landslides in the past, which may become more frequent due to a projected increase in intense precipitation events in the context of climate change. Against this background, this study aimed to analyze the ground deformation associated with an active landslide area in the Yerba Loca basin using the SBAS–DInSAR technique with PAZ and Sentinel-1 images acquired during two time periods, 2019–2021 and 2018–2022, respectively. Using PAZ imagery, the estimated vertical displacement velocity (subsidence) was as high as 9.6 mm/year between 2019 and 2021 in the area affected by the Yerba Loca multirotational slide in August 2018. Analysis of Sentinel-1 images indicated a vertical displacement velocity reaching −94 mm/year between 2018 and 2022 in the Yerba Loca landslide, suggesting continued activity in this area. It, therefore, may collapse again soon, affecting tourism services and the local ecosystem. By focusing on a mountainous region, this study demonstrates the usefulness of radar imagery for investigating landslides in remote or hard-to-reach areas, such as the mountain sector of central Chile. Full article

Maricá, located on the eastern coast of Rio de Janeiro, experiences rapid urban growth driven by infrastructure and economic development. This study presents the first high-resolution projection of Maricá’s urban expansion (2030–2040), integrating oil industry impacts and protected area constraints. Using Sentinel-2 MSI data (10–20 m resolution) classified via Random Forest on Google Earth Engine (90% accuracy) and a Dinamica EGO Cellular Automata model (5 × 5 Moore neighborhood, calibrated on 2015–2020 transitions), results indicate 18.4% urban growth by 2030 (129 km²), expanding to 151 km² (+38.5% total) by 2040, with 72% replacing pastures. This supports sustainable urban management strategies. Full article