Search Results (7,349)

The Gonghe–Yushu Expressway (GYE) traverses the degrading permafrost region of the Qinghai–Xizang Plateau, where climate warming has resulted in widespread water ponding, posing significant engineering challenges. However, the spatiotemporal dynamics of this water accumulation and its impacts on permafrost embankment stability remain inadequately understood. This study integrates high-resolution unmanned aerial vehicle (UAV) remote sensing with ground-penetrating radar (GPR) to characterize the spatial patterns of water ponding and to quantify the spatial distribution, seasonal dynamics, and hydrothermal effects of roadside water on permafrost sections of the GYE. UAV-derived point cloud models, optical 3D models, and thermal infrared imagery reveal that approximately one-third of the 228 km study section of GYE exhibits water accumulation, predominantly occurring near the embankment toe in flat terrain or poorly drained areas. Seasonal monitoring showed a nearly 90% reduction in waterlogged areas from summer to winter, closely corresponding to climatic variations. Statistical analysis demonstrated significantly higher embankment distress rates in waterlogged areas (14.3%) compared to non-waterlogged areas (5.7%), indicating a strong correlation between surface water and accelerated permafrost degradation. Thermal analysis confirmed that waterlogged zones act as persistent heat sources, intensifying permafrost thaw and consequent embankment instability. GPR surveys identified notable subsurface disturbances beneath waterlogged sections, including a significant lowering of the permafrost table under the embankment and evidence of soil loosening due to hydrothermal erosion. These findings provide valuable insights into the spatiotemporal evolution of water accumulation along transportation corridors and inform the development of climate-adaptive strategies to mitigate water-induced risks in degrading permafrost regions. 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

Cannabis is a strictly regulated plant in China, and its illegal cultivation presents significant challenges for social governance. Traditional manual patrol methods suffer from low coverage efficiency, while satellite imagery struggles to identify illicit plantations due to its limited spatial resolution, particularly for sparsely distributed and concealed cultivation. UAV remote sensing technology, with its high resolution and mobility, provides a promising solution for cannabis monitoring. However, existing detection methods still face challenges in terms of accuracy and robustness, particularly due to varying target scales, severe occlusion, and background interference. In this paper, we propose AMS-YOLO, a cannabis detection model tailored for UAV imagery. The model incorporates an asymmetric backbone network to improve texture perception by directing the model’s focus towards directional information. Additionally, it features a multi-scale fusion neck structure, incorporating partial convolution mechanisms to effectively improve cannabis detection in small target and complex background scenarios. To evaluate the model’s performance, we constructed a cannabis remote sensing dataset consisting of 1972 images. Experimental results show that AMS-YOLO achieves an mAP of 90.7% while maintaining efficient inference speed, outperforming existing state-of-the-art detection algorithms. This method demonstrates strong adaptability and practicality in complex environments, offering robust technical support for monitoring illegal cannabis cultivation. Full article

Remote sensing offers an effective alternative for estimating mangrove carbon stocks by analyzing the relationship between satellite pixel values and field-based carbon measurements. This research was carried out in the mangrove forests of western Bali, Indonesia, encompassing three areas situated in a non-conservation mangrove forest area. This study assessed 32 remote sensing vegetation indices derived from Sentinel-2 satellite imagery to identify the optimal model for quantifying the above-ground carbon (Cag) content in mangrove ecosystems. Field data were collected using stratified random sampling and were used to develop regression models linking the Cag with vegetation indices. The Simple Ratio (SR) index exhibited the highest correlation (r = 0.847, R² = 0.707), while the Three Index Vegetation Above-Ground Carbon (TrIVCag) model, combining the SR, Specific Leaf Area Vegetation Index (SLAVI), and Transformed Ratio Vegetation Index (TRVI) indices, achieved the best performance (r = 0.870, R² = 0.728). The model validation results confirmed the reliability of the TrIVCag model, as indicated by a correlation of 0.852 between the model estimates and measured Cag values from independent field data. In 2023, the mangrove area in western Bali (excluding West Bali National Park) was estimated at 376.85 ha, with a total above-ground carbon stock of 34,994.55 tonC/ha. Region A had the highest average Cag at 98.97 tonC/ha, followed by Regions B (66.58 tonC/ha) and C (86.98 tonC/ha). This model offers a practical and scalable approach to carbon monitoring and is expected to play a valuable role in supporting blue carbon conservation efforts and contributing to climate change mitigation. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article

This research presents a fully automated two-step method for segmenting rice seedlings and assessing their health by integrating spectral, morphological, and textural features. Driven by the global need for increased food production, the proposed method enhances monitoring and control in agricultural processes. Seedling locations are first identified by the excess green minus excess red index, which enables automated point-prompt inputs for the segment anything model to achieve precise segmentation and masking. Morphological features are extracted from the generated masks, while spectral and textural features are derived from corresponding red–green–blue imagery. Health assessment is conducted through anomaly detection using a one-class support vector machine, which identifies seedlings exhibiting abnormal morphology or spectral signatures suggesting stress. The proposed method is validated by visual inspection and Silhouette score, confirming effective separation of anomalies. For segmentation, the proposed method achieved mean dice scores ranging from 72.6 to 94.7. For plant health assessment, silhouette scores ranged from 0.31 to 0.44 across both datasets and various growth stages. Applied across three consecutive rice growth stages, the framework facilitates temporal monitoring of seedling health. The findings highlight the potential of advanced segmentation and anomaly detection techniques to support timely interventions, such as pruning or replacing unhealthy seedlings, to optimize crop yield. Full article

The article examines the role of Geographic Information Systems (GIS) as a tool for environmental management and for the planning and development of renewable energy sources (RES). Based on a review of the literature, it is demonstrated that GIS support key managerial functions, including planning, monitoring, decision-making, and communication, by enabling comprehensive spatial analysis and the integration of environmental data. The study emphasizes the importance of GIS in facilitating a systemic and interdisciplinary approach to environmental governance. The paper examines how GIS can help with environmental management, specifically in locating high-risk areas and strategically placing energy investments. Examining GIS’s organizational, technological, and legal facets, it emphasizes how it is increasingly collaborating with cutting-edge decision-support technologies like artificial intelligence (AI), the Internet of Things (IoT), remote sensing, and big data. The analysis emphasizes how GIS help achieve sustainable development’s objectives and tasks. Full article

Offshore drilling platforms leak hundreds of thousands of tons of oil every year causing immeasurable damage to the marine environment, therefore it is important to be able to monitor for oil leakage. A hyperspectral camera, as an advanced device integrating spectral technology and imaging technology, can keenly capture the differences in spectral reflectance of different types of oil and seawater. This study presents the design of a hyperspectral camera covering the 400 nm–900 nm spectral band (90 bands total) and establishes a monitoring system comprising a high-precision inertial navigation system, a stabilization system, and a data acquisition system. Furthermore, this study conducted a field flight experiment using a Cessna aircraft, acquiring hyperspectral data with a one m spatial resolution of a drilling platform around the South China sea at 3000 m altitude, which effectively delineated the spectral characteristics of the oil spill area. The detection system developed in this study provides a robust means for oil spill monitoring on drilling platforms in remote sensing of the marine environment. Full article

Water surface temperature (WST) is an important environmental variable, and its monitoring is essential for understanding and mitigating the impacts of climate change and human activities. For this, satellite remote sensing is particularly useful in providing WST data, especially in cases when in situ monitoring is limited or absent, as is often the case in small inland water bodies. In this study, the approach of retrieving the historical WST data from Landsat-5 Thematic Mapper (TM) was tested by analysing different cases across various water bodies in Lithuania, including two small inland lakes, an artificial reservoir, the Curonian Lagoon, and the coastal waters of the southeastern Baltic Sea. Our results demonstrate that WST can be accurately estimated from single-band Landsat-5 TM images, achieving an R² of around 0.9 in comparison with both in situ (with RMSE of 1.35–1.73 °C) and with MODIS satellite (RMSE of 1.11–1.23 °C) water temperature data, thus enabling analysis of water temperature variations in small-sized lakes and other water bodies, and contributing to the reliable monitoring of WST trends. Full article

The evolution of remote sensing technology has led to significant improvements in high-resolution and hyperspectral image acquisition, enhancing applications like environmental monitoring and disaster assessment. However, the high dimensionality, nonlinearity, and heterogeneity of these images pose challenges for intelligent interpretation. While deep learning models (e.g., CNN) require balancing efficiency and parameter optimization, meta-heuristic algorithms establish self-organizing, parallelized search mechanisms capable of achieving asymptotic approximation towards the global optimum of parameters without requiring gradient information. In this paper, we first propose an improved Gannet Optimization Algorithm (GOA), named TEGOA, which uses the T-distribution perturbation and elite retention to address CNN’s parameter dependency. The experiment on CEC2017 shows that TEGOA has a better performance on composition functions. Hence, it is suitable for solving complex optimization problems. Then, we propose a classification model TEGOA-CNN, which combines TEGOA with CNN to increase the accuracy and efficiency of remote sensing sence classification. The experiments of TEGOA-CNN on two well-known datasets, UCM and AID, showed a higher performance in classification accuracy of remote sensing images. Particularly, TEGOA-CNN achieves 100% classification accuracy on 10 out of the 21 surface categories of UCM. 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

Karst rocky desertification on the Qinghai–Tibet Plateau poses a severe threat to the region’s fragile ecosystem. Accordingly, the rapid and accurate delineation of plateau karst landforms is essential for monitoring ecological degradation and guiding restoration strategies. However, automatic recognition of these landforms in remote sensing imagery is hindered by challenges such as blurred boundaries, fragmented targets, and poor intra-region consistency. To address these issues, we propose the Region Affinity Propagation Network (RAP-Net). This framework enhances intra-region consistency, edge sensitivity, and multi-scale context fusion through its core modules: Region Affinity Propagation (RAP), High-Frequency Multi-Scale Attention (HFMSA), and Global–Local Cross Attention (GLCA). In addition, we constructed the Plateau Karst Landform Dataset (PKLD), a high-resolution remote sensing dataset specifically tailored for this task, which provides a standardized benchmark for future studies. On the PKLD, RAP-Net surpasses eight state-of-the-art methods, achieving 3.69–10.31% higher IoU and 3.88–14.28% higher Recall, thereby demonstrating significant improvements in boundary delineation and structural completeness. Moreover, in a cross-regional generalization test on the Mount Genyen area, RAP-Net—trained solely on PKLD without fine-tuning—achieved 2.38% and 1.94% higher IoU and F1-scores, respectively, than the Swin Transformer, confirming its robustness and generalizability in complex, unseen environments. Full article

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization. Full article

Chlorophyll is crucial for crop photosynthesis and useful for monitoring crop growth and predicting yield. Its content can be indicated by SPAD meter readings. However, SPAD-based monitoring of rice is time- and labor-intensive, whereas remote sensing offers non-destructive, rapid, real-time solutions. Compared with mainstream unmanned aerial vehicle, emerging phenotyping robots can carry multiple sensors and acquire higher-resolution data. Nevertheless, the feasibility of estimating rice SPAD using multi-sensor data obtained by phenotyping robots remains unknown, and whether the integration of machine learning algorithms can improve the accuracy of rice SPAD monitoring also requires investigation. This study utilizes phenotyping robots to acquire multispectral and RGB images of rice across multiple growth stages, while simultaneously collecting SPAD values. Subsequently, four machine learning algorithms—random forest, partial least squares regression, extreme gradient boosting, and boosted regression trees—are employed to construct SPAD monitoring models with different features. The random forest model combining vegetation indices, color indices, and texture features achieved the highest accuracy (R² = 0.83, RMSE = 1.593). In summary, integrating phenotyping robot-derived multi-sensor data with machine learning enables high-precision, efficient, and non-destructive rice SPAD estimation, providing technical and theoretical support for rice phenotyping and precision cultivation. Full article

The East Dongting Lake Wetland, an internationally vital reserve, faces growing ecological threats, necessitating enhanced predictive research on its landscape dynamics. Using the PLUS model and Markov chain method, this study analyzes landscape changes (2010–2022) and simulates 2030 patterns under two scenarios. The key findings reveal the following: (1) poplar plantations plummeted from 28.65% to 2.79% due to restoration policies (e.g., tree removal), while grasslands surged from 21.43% to 59.64%; mudflats and water bodies fluctuated naturally. (2) Natural drivers dominated changes—precipitation and elevation influenced water bodies and grasslands the most, whereas road proximity primarily affected poplar plantations. (3) The PLUS model proved effective for small-scale wetland predictions. (4) Simulations showed divergent 2030 outcomes: under natural development, poplar plantations would rebound to 57.86 km², whereas ecological regulation—restricting plantations and expanding grasslands to 882.70 km²—better supported biodiversity. This study underscores policy-driven restoration success and the PLUS model’s utility for local-scale simulations, offering actionable insights for Dongting Lake’s management and a methodological framework for wetland conservation. Full article