Search Results (7,799)

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

Despite their crucial biological role as metabolites, reactive oxygen and reactive nitrogen species (ROS and RNS) can have a negative effect on organisms when their cellular contents overwhelm the normal equilibrium provided by antioxidant defenses. Important biomolecules, such as lipids, proteins, and nucleic acids (i.e., DNA), can be damaged by their oxidative effects, resulting in malfunction or a shorter lifespan of cells and, eventually, of the whole organism. Oxidative stress can be defined as the consequence of an imbalance of pro-oxidants and antioxidants due to external stress sources (e.g., exposure to xenobiotics, UV radiation, or thermic stress). It can be evaluated by monitoring specific biomarkers to determine the state of health of breathing organisms. Assessments of ROS, RNS, specific degenerative oxidative reaction products, and antioxidant system efficiency (antioxidant enzyme activities and antioxidant compound contents) have been extensively performed for this purpose. A wide variety of analytical methods for measuring these biomarkers exist in the literature; most of these methods involve indirect determination via spectrophotometric and spectrofluorometric techniques. This review reports a collection of studies from the last decade regarding contaminant-induced oxidative stress in insects, with a brief description of the analytical methods utilized. Full article

Various fusion methods of optical satellite images have been proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method to simultaneously generate a full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM) to the NDVI of Sentinel-2 (S2) and PlanetScope (PS), using images from 2019 to 2021 of rice paddy and heterogeneous cabbage fields in Korea. Before fusion, S2 was processed with the maximum NDVI composite (MNC) and the spatiotemporal gap-filling technique to minimize cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM, with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to the PS NDVI. Additionally, SSFIT takes four seconds to process data in the field area, while ESTARFM requires a relatively long processing time of five minutes. In some images where ESTARFM was applied, outliers originating from S2 were still present, and heterogeneous NDVI distributions were also observed. This spatiotemporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis. Full article

The intensification of urban heat in high-density cities has raised growing concerns for public health, infrastructural resilience, and environmental sustainability. As large-scale, multi-functional open spaces, sports stadiums play an underexplored role in shaping urban thermal patterns. This study investigates the spatial and temporal thermal characteristics of eight representative stadiums in central Shanghai and the Pudong New Area from 2018 to 2023. A dual-framework approach is proposed: the Stadium-based Urban Island Regulation (SUIR) model conceptualizes stadiums as active cooling agents across micro to macro spatial scales, while the Multi-source Thermal Cognition System (MTCS) integrates multi-sensor satellite data—Landsat, MODIS, Sentinel-1/2—with anthropogenic and ecological indicators to diagnose surface temperature dynamics. Remote sensing fusion and machine learning analyses reveal clear intra-stadium thermal heterogeneity: track zones consistently recorded the highest land surface temperatures (up to 37.5 °C), while grass fields exhibited strong cooling effects (as low as 29.8 °C). Buffer analysis shows that cooling effects were most pronounced within 300–500 m, varying with local morphology. A spatial diffusion model further demonstrates that stadiums with large, vegetated buffers or proximity to water bodies exert a broader regional cooling influence. Correlation and Random Forest regression analyses identify the building volume (r = 0.81), NDVI (r = −0.53), nighttime light intensity, and traffic density as key thermal drivers. These findings offer new insight into the role of stadiums in urban heat mitigation and provide practical implications for scale-sensitive, climate-adaptive urban planning strategies. Full article

Understanding vegetation dynamics and climate variability in the vicinity of Internally Displaced Person (IDP) camps is critical due to the high dependency of displaced populations on local natural resources. This study investigates vegetation cover changes and long-term climate variability around the Sortony IDP camp in Darfur, Sudan, using satellite and climate data spanning 1980 to 2024. High-resolution imagery from PlanetScope and Sentinel–2 Level 2A was used to assess vegetation cover changes from 2015 to 2024, while precipitation, temperature, and drought trends were analyzed over 44 years (1980–2024). Vegetation changes were quantified using the Normalized Difference Vegetation Index (NDVI), and drought conditions were assessed through the Standardized Precipitation Evapotranspiration Index (SPEI) at 6-, 9-, and 12-month timescales. Future precipitation predictions were modeled using the Autoregressive Integrated Moving Average (ARIMA) model. The results revealed a substantial increase in vegetative cover: the dense vegetation class increased by 3.50%, moderate vegetation by 17.33%, and low vegetation by 30.22%. In contrast, sparse and non-vegetated areas declined by 4.55% and 46.51%, respectively. The SPEI analysis indicated a marked reduction in drought frequency and severity after 2015, following a period of prolonged drought from 2000 to 2014. Forecasts suggest continued increases in rainfall through 2034, which may further support vegetation regrowth. These findings underscore the complex interplay between climatic factors and human activity in conflict-affected landscapes. The observed vegetation recovery highlights the region’s potential for ecological resilience, reinforcing the urgent need for sustainable land-use planning and climate-adaptive management strategies in humanitarian and post-conflict settings such as Darfur. Full article

Cotton is a crucial economic crop, and timely and accurate acquisition of its spatial distribution information is of great significance for yield prediction, as well as for the formulation and adjustment of agricultural policies. To accurately and efficiently extract cotton cultivation areas at a large scale, in this study, we focused on the Santun River Irrigation District in Xinjiang as the research area. Utilizing Sentinel-2 satellite imagery from 2019 to 2024, four cotton extraction models—U-Net, SegNet, DeepLabV3+, and CBAM-UNet—were constructed. The models were evaluated using metrics, including the mean intersection over union (mIoU), precision, recall, F1-score, and over accuracy (OA), to assess the models’ performances in cotton extraction. The results demonstrate that the CBAM-UNet model achieved the highest accuracy, with an mIoU, precision, recall, F1-score, and OA of 84.02%, 88.99%, 94.75%, 91.78%, and 95.56%, respectively. The absolute error of the extracted cotton areas from 2019 to 2024 ranged between 923.69 and 1445.46 hm², with absolute percentage errors of less than 10%. The coefficient of determination (R²) between the extracted results and statistical data was 0.9817, indicating the best fit. The findings of this study provide technical support for rapid cotton identification and extraction in large- and medium-sized irrigation districts. Full article

The integration of low Earth orbit (LEO) satellite constellations into the Global Navigation Satellite System (GNSS) has emerged as a prominent research focus, as LEO satellites can significantly enhance the precision of GNSS positioning, navigation, and timing (PNT) services. In the design of LEO navigation constellations, the development of an efficient broadcast ephemeris model is critical for delivering high-accuracy navigation solutions. This study extends the conventional 16-parameter Keplerian broadcast ephemeris model by proposing enhanced 18-, 20-, 22-, and 24-parameter models, ensuring compatibility with existing GNSS ephemeris standards. The performance of these models was evaluated using precise science orbit from five satellites at varying altitudes, ranging from 320 km to 1336 km. By analyzing fitting errors, Signal-in-Space Range Error (SISRE), and Message Size Bits (MSB) across different fitting arc durations and parameter counts, the optimal model configuration was identified. The results demonstrate that the 22-parameter model, which was constructed by augmenting the standard 16-parameter ephemeris with $(\dot{a}$, $\dot{n}$, $Crs3$, $Crc3$, $Crs1$, $Crc1$) delivers the best balance of accuracy and efficiency. With a fitting arc length of 20 min, the SISRE for the GRACE-A (320 km), GRACE-C (475 km), Sentinel-2A (786 km), HY-2A (966 km), and Sentinel-6A (1336 km) satellites were measured at 8.88 cm, 6.21 cm, 2.87 cm, 2.11 cm, and 0.75 cm, respectively. Meanwhile, the corresponding MSB remained compact at 501, 490, 491, 487, and 476 bits. These findings confirm that the proposed 22-parameter broadcast ephemeris model meets the stringent accuracy requirements for next-generation LEO-augmented GNSSs, paving the way for enhanced global navigation services. Full article

The rapid and accurate assessment of regional soil salinity is crucial for effective salinization management. This study proposes an enhanced remote sensing inversion method by integrating both driving and response environmental variables to address lag effects and incomplete factor consideration in existing models. The Yellow River Delta, a coastal saline–alkaline region, was selected as the study area, where soil salinity-sensitive spectral parameters were derived from Sentinel-2 MSI imagery. Six environmental variables, including precipitation, distance from the sea, and soil moisture, were analyzed. Four scenarios were constructed: (1) using only spectral parameters; (2) spectral parameters with driving variables; (3) spectral parameters with response variables; and (4) combining both types. Four modeling methods were employed to assess inversion accuracy. The results show that incorporating either driving or response variables improved accuracy, with validation R² increasing by up to 0.149 and RMSE decreasing by up to 0.097 when both were used. The suitable model, integrating soil moisture, distance from the sea, and chlorophyll content, achieved a calibration R² of 0.813 and validation R² of 0.722. These findings demonstrate that combining both driving and response variables enhances model performance and provides valuable insights for soil salinization management. Full article

Urban greening distribution critically impacts residents’ quality of life and environmental sustainability. While the Green View Index (GVI), derived from street view imagery, is widely adopted for urban green space assessment, its limitation lies in the inability to capture non-street-area vegetation. Remote sensing imagery, conversely, provides full-coverage urban vegetation data. This study focuses on Beijing’s Third Ring Road area, employing DeepLabv3+ to calculate a street-view-based GVI as a predictor. Correlations between the GVI and Sentinel-2 spectral bands, along with two vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), were analyzed under varying buffer radius. Regression and classification models were subsequently developed for GVI prediction. The optimal classifier was then applied to estimate green perception levels in non-street zones. The results demonstrated that (1) at a 25 m buffer radius, the near-infrared band, NDVI, and FVC exhibited the highest correlations with the GVI, reaching 0.553, 0.75, and 0.752, respectively. (2) Among the five machine learning regression models evaluated, the random forest algorithm demonstrated superior performance in GVI estimation, achieving a coefficient of determination (R²) of 0.787, with a root mean square error (RMSE) of 0.063 and a mean absolute error (MAE) of 0.045. (3) When evaluating categorical perception levels of urban greenery, the Extremely Randomized Trees classifier (Extra Trees) demonstrated superior performance in green vision perception level estimation, achieving an accuracy (ACC) score of 0.652. (4) The green perception level in non-road areas within Beijing’s Third Ring Road is 56.8%, which is considered relatively poor. Moreover, the green perception level within the Second Ring Road is even lower than that in the area between the Second and Third Ring roads. This study is expected to provide valuable insights and references for the adjustment and optimization of green perception distribution in Beijing, thereby supporting more informed urban planning and the development of sustainable, human-centered green spaces across the city. Full article

Limited surveillance and laboratory testing for non-influenza viruses remains a challenge in Uganda. The World Health Organization (WHO) designated National Influenza Center (NIC) tested samples from patients with influenza-like illness (ILI) and severe acute respiratory infections (SARIs) during August 2022–February 2023. We leveraged the influenza sentinel surveillance system to detect other respiratory viruses (ORVs). Samples were tested using the US Centers for Disease Control and Prevention (CDC) influenza and SARS-CoV-2 multiplex and the FTD^TM Respiratory Pathogens 21 assays using real-time reverse transcription polymerase chain reaction (RT-qPCR). A total of 687 (ILI = 471 (68.6%) and SARI = 216 (31.4%) samples were tested. The median age was 2 years (IQR: 1–25) for ILI and 6 years (IQR: 1–18) for SARI case definitions (p-value = 0.045). One or more respiratory pathogens were detected in 38.7% (n = 266) of all samples; 33 (12.4%) were selected for metagenomics sequencing and 8 (3%) for SARS-CoV-2 targeted sequencing. Respiratory pathogens were detected by sequencing in 23 of 33 (69.7%) samples. Our study provides insight into the usefulness of this surveillance system in conducting virological testing for other viruses and provides tools and evidence to monitor patterns and characteristics of viruses causing ILI/SARI, which will guide public health decisions and interventions in Uganda. Full article

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

Mapping Soil Organic Carbon (SOC) at a regional scale is essential for assessing soil health and supporting sustainable land management. This study evaluates the potential of using Sentinel-2 imagery and regional calibration to predict SOC in salt-affected agricultural lands in Portugal while also assessing the influence of soil properties, such as texture and salinity, on SOC prediction. A per-pixel mosaicking approach was set to analyze the relationship of spectral reflectance indices linked to bare soil conditions with SOC. SOC prediction models were developed using linear regression (LR) and Partial Least Squares Regression (PLSR). Among the tested approaches, the combination of the maximum Bare Soil Index (maxBSI) with LR produced the most accurate SOC predictions, achieving moderate prediction performance (R² = 0.52; RMSE = 0.16%; LCCC = 70%). This approach slightly outperformed the application of the 90th percentile of bare soil pixels (R90 reflectance) and the median approaches with PLSR. Notably, our findings indicate that soil salinity did not significantly affect SOC predictions within the observed salinity range of EC_e between 1.2 and 10.4 dS m⁻¹ in topsoil. However, further case studies are needed to validate this observation across diverse agricultural conditions. In contrast, soil texture and moisture content emerged as the dominant factors influencing soil reflectance. The combination of per-pixel mosaicking and regional calibration provides a practical, scalable, and cost-effective method for generating SOC maps using open access satellite imagery. To support wider adoption and improve model generalizability, future studies should incorporate a larger number of fields with a wider range of soil properties, crop types, and management practices. Full article

The agricultural sector plays a vital role in achieving the second Sustainable Development Goal: “Zero Hunger”. To ensure food security, agriculture must remain resilient and productive. In Indonesia, a major rice-producing country, the conversion of agricultural land for non-agricultural uses poses a serious threat to food availability. Accurate and timely mapping of paddy rice is therefore crucial. This study proposes a phenology-based mapping approach using a Convolutional Neural Network-Random Forest (CNN-RF) Hybrid model with multi-temporal Sentinel-2 and Landsat-8 imagery. Image processing and analysis were conducted using the Google Earth Engine platform. Raw spectral bands and four vegetation indices—NDVI, EVI, LSWI, and RGVI—were extracted as input features for classification. The CNN-RF Hybrid classifier demonstrated strong performance, achieving an overall accuracy of 0.950 and a Cohen’s Kappa coefficient of 0.893. These results confirm the effectiveness of the proposed method for mapping paddy rice in Indramayu Regency, West Java, using medium-resolution optical remote sensing data. The integration of phenological characteristics and deep learning significantly enhances classification accuracy. This research supports efforts to monitor and preserve paddy rice cultivation areas amid increasing land use pressures, contributing to national food security and sustainable agricultural practices. Full article

As an important input parameter of the ecological network, the accuracy and detail with which forest cover is extracted directly constrain the accuracy of forest ecological network construction. The development of medium- and high-resolution remote sensing technology has provided an opportunity to obtain accurate and high-resolution forest coverage data. As forests have diverse contours and complex scenes on remote sensing images, a model of them will be disturbed by the natural distribution characteristics of complex forests, which in turn will affect the extraction accuracy. In this study, we first constructed a rather large, complex, diverse, and scene-rich forest extraction dataset based on Sentinel-2 multispectral images, comprising 20,962 labeled images with a spatial resolution of 10 m, in a manually and accurately labeled manner. At the same time, this paper proposes the Dynamic Large Kernel Segformer and conducts forest extraction experiments in Liaoning Province, China. We then used forest coverage as an input parameter and classified the forest landscape patterns in the study area using a landscape spatial pattern characterization method, based on which a forest ecological network was constructed. The results show that the Dynamic Large Kernel Segformer obtains 80.58% IoU, 89.29% precision, 88.63% recall, and a 88.96% F1 Score in extraction accuracy, which is 4.02% higher than that of the Segformer network, and achieves large-scale forest extraction in the study area. The forest area in Liaoning Province increased during the 5-year period from 2019 to 2023. With respect to the overall spatial pattern change, the Core area of Liaoning Province saw an increase in 2019–2023, and the overall quality of the forest landscape improved. Finally, we constructed the forest ecological network for Liaoning Province in 2023, which consists of ecological sources, ecological nodes, and ecological corridors based on circuit theory. This method can be used to extract large areas of forest based on remote sensing images, which is helpful for constructing forest ecological networks and achieving coordinated regional, ecological, and economic development. Full article

The Tibetan Plateau’s lakes, serving as critical water towers for over two billion people, exhibit divergent responses to climate change that remain poorly quantified. This study develops a deep learning framework integrating Synthetic Aperture Radar (SAR) altimetry from Sentinel-3A with bias-corrected CMIP6 (Coupled Model Intercomparison Project Phase 6) climate projections under Shared Socioeconomic Pathways (SSP) scenarios (SSP2-4.5 and SSP5-8.5, adjusted via quantile mapping) to predict lake-level changes across eight Tibetan Plateau (TP) lakes. Using a Feed-Forward Neural Network (FFNN) optimized via Bayesian optimization using the Optuna framework, we achieve robust water level projections (mean validation R² = 0.861) and attribute drivers through Shapley Additive exPlanations (SHAP) analysis. Results reveal a stark north–south divergence: glacier-fed northern lakes like Migriggyangzham will rise by 13.18 ± 0.56 m under SSP5-8.5 due to meltwater inputs (temperature SHAP value = 0.41), consistent with the early (melt-dominated) phase of the IPCC’s ‘peak water’ framework. In comparison, evaporation-dominated southern lakes such as Langacuo face irreversible desiccation (−4.96 ± 0.68 m by 2100) as evaporative demand surpasses precipitation gains. Transitional western lakes exhibit “peak water” inflection points (e.g., Lumajang Dong’s 2060 maximum) signaling cryospheric buffer loss. These projections, validated through rigorous quantile mapping and rolling-window cross-validation, provide the first process-aware assessment of TP Lake vulnerabilities, informing adaptation strategies under the Sustainable Development Goals (SDGs) for water security (SDG 6) and climate action (SDG 13). The methodological framework establishes a transferable paradigm for monitoring high-altitude freshwater systems globally. Full article