Search Results (1,301)

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

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning. Full article

Urban livability and sustainable development remain major global challenges, yet the interplay between urban planning layouts and actual human activities has not been sufficiently examined. This study investigates this relationship in Beijing by integrating multi-source spatiotemporal data, including point of interest (POI), Land Use Cover Change (LUCC), remote sensing data, and the railway network. Defining urban functional units as “street + railway network”, we analyze the spatial–temporal evolution within the 6th Ring Road over the past four decades and propose targeted strategies for the urban functional layout. The results reveal the following: (1) The evolution of Beijing’s urban functions can be divided into four stages (1980–1990, 1990–2005, 2005–2015, and 2015–2020), with continuous population growth (+142%) driving the over-concentration of functions in central districts. (2) Between 2010 and 2020, the POI densities of medical services (+133.6%) and transport services (+130.48%) increased most rapidly, subsequently stimulating the expansion of other urban functions. (3) High-density functional facilities and construction land (+179.10%) have expanded significantly within the 6th Ring Road, while green space (cropland, forestland and grassland) has decreased by 86.97%, resulting in a severe imbalance among land use types. To address these issues, we recommend the following: redistributing high-intensity functions to sub-centers such as Tongzhou and Xiongan New Area to alleviate population pressure, expanding high-capacity rail transit to reinforce 30–50 km commuting links between the core and periphery, and establishing ecological corridors to connect green wedges, thereby enhancing carbon sequestration and environmental quality. This integrated framework offers transferable insights for other megacities, providing guidance for sustainable functional planning that aligns human activity patterns with urban spatial structures. Full article

Fuel Moisture Content (FMC) is a critical indicator for assessing forest fire risk and formulating early warning strategies, as its spatiotemporal dynamics directly influence the accuracy of fire danger rating. To improve the accuracy of forest FMC estimation, this study proposes an innovative deep learning method integrating multi-source remote sensing data. By combining the global feature extraction capability of the Transformer architecture with the local temporal modeling advantages of Gated Recurrent Units (GRU) (referred to as the Transformer-GRU model), a high-precision FMC estimation framework is established. The study focuses on forested areas in California, USA, utilizing ground-measured FMC data alongside multi-source remote sensing datasets from MODIS, Sentinel-1, and Sentinel-2. A systematic comparison was conducted among Transformer-GRU model, standalone Transformer models, single GRU models, and two classical machine learning models (Random Forest, RF, and Support Vector Regression, SVR). Additionally, forward feature selection was employed to evaluate the performance of different models and feature combinations. The results demonstrate that (1) All models effectively utilize the derived features from multi-source remote sensing data, confirming the significant enhancement of multi-source data fusion for forest FMC estimation; (2) The Transformer-GRU model outperforms other models in capturing the nonlinear relationship between FMC and remote sensing data, achieving superior estimation accuracy (R² = 0.79, MAE = 8.70%, RMSE = 11.44%, rRMSE = 12.60%); (3) The spatiotemporal distribution patterns of forest FMC in California generated by the Transformer-GRU model align well with regional geographic characteristics and climatic variability, while exhibiting a strong relationship with historical wildfire occurrences. The proposed Transformer-GRU model provides a novel approach for high-precision FMC estimation, offering reliable technical support for dynamic forest fire risk early warning and resource management. Full article

The issue of abandoned cropland poses a significant threat to national food security and the sustainable use of land resources, highlighting the urgent need for an efficient and interpretable remote sensing identification framework. This study integrates three authoritative land cover datasets—the European Space Agency WorldCover (ESA), the Environmental Systems Research Institute Land Cover (ESRI), and the China Resource and Environment Data Cloud Platform (CRLC). Multi-source remote sensing features were extracted using the Google Earth Engine platform, and high-quality training samples were constructed by randomly selecting sample points based on these features in ArcGIS. A recursive feature cross-validation method is employed to eliminate redundant variables, thereby optimizing the feature structure without compromising classification accuracy. In terms of model construction, a multi-objective optimization strategy combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and eXtreme Gradient Boosting (XGBoost) is proposed. By incorporating a pruning mechanism, computational efficiency is significantly improved—accelerating the identification speed by up to 75%—while maintaining model accuracy (OA: 0.9817; Kappa: 0.9633; F1-score: 0.9817; recall: 0.9866). For result interpretation, the SHapley Additive exPlanations (SHAP) method is used to evaluate global feature importance, revealing that variables such as SAVG, B3_p25, Road, DEM, and Population contribute most significantly to the identification of abandoned cropland. Meanwhile, the Local Interpretable Model-Agnostic Explanations (LIME) method is applied to conduct local interpretability analysis on typical samples. The results show that, while some samples share consistent dominant features with the global results, others exhibit stronger local influences from features such as slope and SAVG. The combination of SHAP and LIME for global–local interpretability provides insight into the heterogeneous drivers of cropland abandonment and enhances the transparency of the classification model. This study presents a practical, scalable framework for the rapid identification and management of abandoned cropland, balancing precision, interpretability, and efficiency. Full article

As critical infrastructure for flood control and disaster mitigation, the completeness of a dam spatial database directly impacts regional emergency disaster response. However, existing dam data in some developing countries suffer from severe gaps and outdated information, particularly concerning small- and medium-sized dams, hindering rapid response during disasters. There is an urgent need to improve the physical dam database and implement dynamic monitoring. Yet, current remote sensing identification methods face limitations, including a lack of diverse dam samples, limited analysis of geographical factors, and low efficiency in full-image processing, making it difficult to efficiently enhance dam databases. To address these issues, this study proposes a dam extraction framework integrating comprehensive geographical factor analysis with deep learning detection, validated in Sindh Province, Pakistan. Firstly, multiple geographical factors were fused using the Random Forest algorithm to generate a dam existence probability map. High-probability candidate areas were delineated using dynamic threshold segmentation (precision: 0.90, recall: 0.76, AUC: 0.86). Subsequently, OpenStreetMap (OSM) water body data excluded non-dam potential areas, further narrowing the candidate areas. Finally, a dam image dataset was constructed to train a dam identification model based on YOLOv11, achieving an mAP50 of 0.85. This trained model was then applied to high-resolution remote sensing imagery of the candidate areas for precise identification. Ultimately, 16 previously unrecorded small and medium-sized dams were identified in Sindh Province, enhancing its dam location database. Experiments demonstrate that this method, through the synergistic optimization of geographical constraints and deep learning, significantly improves the efficiency and reliability of dam identification. It provides high-precision data support for dam disaster emergency response and water resource management, exhibiting strong practical utility and regional scalability. Full article

Land use/land cover (LULC) data serve as a critical information source for understanding the complex interactions between human activities and global environmental change. The subtropical karst region, characterized by fragmented terrain, spectral confusion, topographic shadowing, and frequent cloud cover, represents one of the most challenging natural scenes for remote sensing classification. This study reviews the evolution of multi-source data acquisition (optical, SAR, LiDAR, UAV) and preprocessing strategies tailored for subtropical regions. It evaluates the applicability and limitations of various methodological frameworks, ranging from traditional approaches and GEOBIA to machine learning and deep learning. The importance of uncertainty modeling and robust accuracy assessment systems is emphasized. The study identifies four major bottlenecks: scarcity of high-quality samples, lack of scale awareness, poor model generalization, and insufficient integration of geoscientific knowledge. It suggests that future breakthroughs lie in developing remote sensing intelligent models that are driven by few samples, integrate multi-modal data, and possess strong geoscientific interpretability. The findings provide a theoretical reference for LULC information extraction and ecological monitoring in heterogeneous geomorphic regions. Full article

Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R² = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R² = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R² > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

Crop distribution represents crucial information in agriculture, playing a key role in ensuring food security and promoting sustainable agricultural development. However, existing methods for crop distribution primarily focus on modeling temporal dependencies while overlooking the interactions and dependencies among different remote sensing features, thus failing to fully exploit the rich information contained in multisource satellite imagery. To address this issue, we propose a deep learning-based method named HyperVTCN, which comprises two key components: the ModernTCN block and the TiVDA attention mechanism. HyperVTCN effectively captures temporal dependencies and uncovers intrinsic correlations among features, thereby enabling more comprehensive data utilization. Compared to other state-of-the-art models, it shows improved performance, with overall accuracy (OA) improving by approximately 2–3%, Kappa improving by 3–4.5%, and Macro-F1 improving by about 2–3%. Additionally, ablation experiments suggest that both the attention mechanism(Time-Feature Dual Attention, TiVDA) and the targeted loss optimization strategy contribute to performance improvements. Finally, experiments were conducted to investigate HyperVTCN’s cross-feature and cross-temporal modeling. The results indicate that this joint modeling strategy is effective. This approach has shown potential in enhancing model performance and offers a viable solution for crop classification tasks. Full article

In regional traditional landscape studies where continuous literature and physical relics are scarce, image-based materials serve as a crucial medium for reconstructing historical spatial structures. This study focuses on the sandbar polder landscapes in the Yangzhong area, located in the lower Yangtze River. By integrating historical maps, military cartographic surveys, CORONA satellite imagery, and modern remote sensing data, this study developed a multi-source image interpretation framework to reconstruct the traditional dike–water–field–settlement spatial structure. Employing image recognition and morphological analysis, the study extracted features such as dikes, water systems, and settlements, revealing their adaptation mechanisms to microtopography and associated ecological functions, including multi-level irrigation and drainage, hydrological buffering, and flood prevention. The results demonstrate that traditional sandbar polder landscapes exhibit a high degree of experiential adaptation, and their spatial organization offers valuable insights for future green infrastructure planning. The study confirms the applicability of image-based interpretation methods for historical landscape reconstruction and provides a practical path for the activation and translation of traditional landscape units in contemporary urban–rural governance. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

Accurately estimating aboveground carbon storage (AGC) of urban vegetation remains a major challenge, due to the heterogeneity and vertical complexity of urban environments, where traditional forest-based remote sensing models often perform poorly. This study integrates multimodal remote sensing data and incorporates two three-dimensional structural features—mean tree height ($H_{mean}$) and canopy cover ratio (CCR)—in addition to conventional spectral and textural variables. To minimize redundancy, the Boruta algorithm was applied for feature selection, and four machine learning models (SVR, RF, XGBoost, and CatBoost) were evaluated. Results demonstrate that under multimodal data fusion, three-dimensional features emerge as the dominant predictors, with XGBoost using Boruta-selected variables achieving the highest accuracy (R² = 0.701, RMSE = 0.894 tC/400 m²). Spatial mapping of AGC revealed a “high-aggregation, low-dispersion” pattern, with the model performing best in large, continuous green spaces, while accuracy declined in fragmented or small-scale vegetation patches. Overall, this study highlights the potential of machine learning with multi-source variable inputs for fine-scale urban AGC estimation, emphasizes the importance of three-dimensional vegetation indicators, and provides practical insights for urban carbon assessment and green infrastructure planning. Full article

Forest fires pose severe ecological, climatic, and socio-economic threats, destroying habitats and emitting greenhouse gases. Early and timely warning is particularly challenging because fires often originate from small-scale, low-temperature ignition sources. Traditional monitoring approaches primarily rely on single-source satellite imagery and empirical threshold algorithms, and most forest fire monitoring tasks remain human-driven. Existing frameworks have yet to effectively integrate multiple data sources and detection algorithms, lacking the capability to provide continuous, automated, and generalizable fire monitoring across diverse fire scenarios. To address these challenges, this study first improves multiple monitoring algorithms for forest fire detection, including a statistically enhanced automatic thresholding method; data augmentation to expand the U-Net deep learning dataset; and the application of a freeze–unfreeze transfer learning strategy to the U-Net transfer model. Multiple algorithms are systematically evaluated across varying fire scales, showing that the improved automatic threshold method achieves the best performance on GF-4 imagery with an F-score of 0.915 (95% CI: 0.8725–0.9524), while the U-Net deep learning algorithm yields the highest F-score of 0.921 (95% CI: 0.8537–0.9739) on Landsat 8 imagery. All methods demonstrate robust performance and generalizability across diverse scenarios. Second, data-driven scheduling technology is developed to automatically initiate preprocessing and fire detection tasks, significantly reducing fire discovery time. Finally, an integrated framework of multi-source remote sensing data, advanced detection algorithms, and a user-friendly visualization interface is proposed. This framework enables all-weather, fully automated forest fire monitoring and early warning, facilitating dynamic tracking of fire evolution and precise fire line localization through the cross-application of heterogeneous data sources. The framework’s effectiveness and practicality are validated through wildfire cases in two regions of Yunnan Province, offering scalable technical support for improving early detection of and rapid response to forest fires. Full article

Carbon stock plays a crucial role in regulating atmospheric carbon dioxide concentrations and represents a vital ecological function for mitigating climate change and supporting long-term environmental sustainability. Jiangsu Province, a typical region experiencing rapid urbanization and land-use transformation in eastern China, serves as a representative case for regional-scale carbon assessment. This study employs the InVEST model, integrated with multi-source remote sensing data, a random forest algorithm, and a control variable approach, to simulate the spatiotemporal dynamics of carbon stock in Jiangsu Province under a set of climate, productivity, and population scenarios. Three scenario groups were designed to isolate the individual effects of climate change, gross primary productivity, and population density from 2020 to 2060, enabling a clearer understanding of the dominant drivers. The results indicate that the coupled model estimates Jiangsu’s 2020 carbon stock at 1.52 × 10⁹ t C, slightly below the 1.82 × 10⁹ t C estimated by the standalone InVEST model, with the coupled results closer to previous estimates. Compared with InVEST alone, the integrated model significantly improves numerical accuracy and spatial resolution, allowing for finer-scale pattern recognition. By 2060, carbon stock is projected to decline by approximately 24.4% across all scenarios. Among the features, climate change exerts the most significant influence, with an elasticity coefficient range of −37.76–1.01, followed by productivity, while population density has minimal impact. These findings underscore the dominant role of climate drivers and highlight that model integration improves both predictive accuracy and spatial detail, offering a more robust basis for scenario-based assessment. The proposed approach provides valuable insights for supporting sustainable carbon management, real-time monitoring, and provincial-scale decarbonization planning. Full article