Climate change is rapidly altering hydrological systems through changes in precipitation patterns,increase the rate of glacier retreat rates,altered snow dynamics,and groundwater stress.Although remote sensing has bee...Climate change is rapidly altering hydrological systems through changes in precipitation patterns,increase the rate of glacier retreat rates,altered snow dynamics,and groundwater stress.Although remote sensing has been extensively deployed in hydrological research,existing reviews typically focus on a single hydrological variable or on particular satellite missions.The review synthesizes remote sensing technologies to monitor climate-related hydrological variations across various components of the water cycle.It is a systematic examination of major satellite missions,sensor technologies,and analytical methods used to monitor precipitation,soil moisture,snow cover,surface water processes,and groundwater variability.The review will employ a structured literature review methodology,focusing on recent peer-reviewed articles that apply optical,microwave,radar,and gravimetric remote sensing methods for hydrological monitoring under changing climatic conditions.It has paid specific attention to the provision of the comparative capabilities,spatial-temporal resolutions,and practical applications of key satellite missions,such as Landsat,Sentinel,MODIS(Moderate Resolution Imaging Spectroradiometer),GPM(Global Precipitation Measurement),and GRACE(Gravity Recovery and Climate Experiment).Moreover,to illustrate the use of remote sensing in detecting glacier retreat,drought formation,and coastal groundwater salinization,regional case studies are selected and analyzed.The review identifies new opportunities to use multi-sensor data,machine learning,and high-resolution monitoring to enhance hydrological analyses.This study is useful in practice by synthesizing existing technological opportunities and research trends to enhance climate-responsive water resource monitoring and by outlining future research directions in remote sensing-based hydrological analysis.展开更多
Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundr...Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.展开更多
Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited ...Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited by the need for wound exposure and by increased pain.Our preliminary study demonstrated the innovative application of remote NMES(rNMES)to the skeletal muscle of the distal calf,which showed the potential to accelerate wound healing in remote areas.rNMES was effective in human clinical trials in our previous work,although the underlying mechanisms remain unclear.As rNMES is often used to stimulate muscle contraction in long-term bedridden patients,we analyzed data from the Gene Expression Omnibus(GEO)database and found that exercise promotes midkine(MDK)expression in muscle.MDK is a small secreted heparin-binding protein that interacts with multiple cell surface receptors to promote growth.In the present study,we found that MDK significantly enhanced macrophage efferocytosis in a low-density lipoprotein receptor-related protein 1(LRP1)-dependent manner.Our findings demonstrate that rNMES upregulates MDK expression in skeletal muscles through the AMPK-ERK axis,facilitating its delivery to wounds through the circulatory system and promoting LRP1-mediated efferocytosis of apoptotic cells,thereby expediting wound healing.展开更多
This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and desi...This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format.Subsequently,a remote data transmission link using 4G signals and data processing methods is introduced.The advantages of remote diagnosis are analyzed,and common methods such as correlation analysis,fault diagnosis,and fault prediction are explained in detail.Then,challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed,along with development prospects in technological innovation,algorithm optimization,and application promotion.This research provides ideas for remote analysis and diagnosis based on TCMS data,contributing to the safe and efficient operation of rail vehicles.展开更多
Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satell...Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.展开更多
The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remot...The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remote sensing image,characterized by complex content and well-defined structures,are particularly vulnerable to malicious attacks and information leakage.To address this issue,the author proposes an encryption method based on the enhanced single-neuron dynamical system(ESNDS).ESNDS generates highquality pseudo-random sequences with complex dynamics and intense sensitivity to initial conditions,which drive a structure of multi-stage cipher comprising permutation,ring-wise diffusion,and mask perturbation.Using representative GF-2 Panchromatic and Multispectral Scanner(PMS)urban scenes,the author conducts systematic evaluations in terms of inter-pixel correlation,information entropy,histogram uniformity,and number of pixel change rate(NPCR)/unified average changing intensity(UACI).The results demonstrate that the proposed scheme effectively resists statistical analysis,differential attacks,and known-plaintext attacks while maintaining competitive computational efficiency for high-resolution urban image.In addition,the cipher is lightweight and hardware-friendly,integrates readily with on-board and ground processing,and thus offers tangible engineering utility for real-time,large-volume remote-sensing data protection.展开更多
Organosilicone thin films were prepared through plasma polymerization(pp)in a plasma enhance chemical vapour deposition(PECVD)system,utilizing hexamethyldisilazane(HMDSN)as a monomer precursor,at varying distances(25 ...Organosilicone thin films were prepared through plasma polymerization(pp)in a plasma enhance chemical vapour deposition(PECVD)system,utilizing hexamethyldisilazane(HMDSN)as a monomer precursor,at varying distances(25 mm,35 mm,45 mm,55 mm,and 65 mm)from the plasma source to the substrate.Research has examined how the distance between the substrate and plasma source impacts the properties of thin films,including their thickness,surface morphology,and photoluminescence(PL).It was discovered that as the distance increased,both film thickness and PL intensity also increased.Additionally,the film was observed to be more uniform and smoother when deposited 45 mm below the plasma source.展开更多
This review summarizes studies of hydrothermal alteration minerals at the Qiucun gold deposit in southeastern China and focuses on characterization and mapping of the deposit using hyperspectral remote sensing.The dep...This review summarizes studies of hydrothermal alteration minerals at the Qiucun gold deposit in southeastern China and focuses on characterization and mapping of the deposit using hyperspectral remote sensing.The deposit exhibits multistage fluid-rock interaction,as evidenced by systematic alteration assemblages,including silicification,sericitization by white micas,the development of argillaceous clays,variable chloritization,and locally significant carbonate alteration.We describe the genetic importance of such mineral groups and emphasize their diagnostic Visible and Near-Infrared to Short-Wave Infrared(VNIR-SWIR)spectral signatures,especially Al-OH,Mg-OH/Fe-OH,and CO3 absorption bands,which make it possible to distinguish between minerals,not to mention the fact that,in some instances,compositional trends may be predicted.This review’s methodological advances are discussed beginning with data collection at satellite,airborne,and ground levels,proceeding to processing procedures,such as atmospheric and topographic correction,and culminating in spectral analysis,including continuum removal,spectral matching,and unmixing/classification techniques.An integrated study of hyperspectral findings reveals that alteration minerals develop spatially coherent zones that are strongly controlled by fault/fracture structures and host-rock reactivity,producing proximal silicification/sericitization cores and larger silicified/larcenies of argillaceous rocks owing to diverse apex coverings of carbonate.This should be combined with petrography and geochemistry to address overprinting,mixed pixels,and surface weathering,and to couple mineral maps with ore-forming processes.The review finds that hyperspectral remote sensing offers a solid modeling platform for the deposit-scale alteration at Qiucun and other hydrothermal gold systems,and outlines the directions for future research to integrate quantitatively and more threedimensional alteration characterization.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due...Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.展开更多
Shallow landslides are strongly controlled by near-surface lithological variability,yet conventional geological maps are often too generalized to support accurate susceptibility assessment in complex terrains.This rev...Shallow landslides are strongly controlled by near-surface lithological variability,yet conventional geological maps are often too generalized to support accurate susceptibility assessment in complex terrains.This review synthesizes recent advances in remote sensing–based lithological mapping and evaluates their integration into landslide susceptibility modeling.Evidence from the literature indicates that remote sensing-derived lithological products,particularly those incorporating mineralogical information and higher spatial resolution,consistently outperform traditional geological maps in improving model accuracy and spatial detail,especially in heterogeneous environments.However,key challenges remain,including scale mismatches between surface observations and subsurface controls,limited ground validation,uncertainty propagation,and restricted model transferability across regions.The review identifies multi-sensor data fusion and explainable machine learning as the most promising directions for advancing lithological discrimination and model reliability.Future progress depends on integrating remote sensing with process-based understanding,improving validation strategies,and standardizing uncertainty reporting.These developments are essential for enabling more robust,scalable,and operationally relevant landslide susceptibility assessments in complex terrains.Lastly,we describe the directions of research that focus on multi-sensor fusion,explainable machine learning,UAV(Unmanned Aerial Vehicle)-enabled validation,and standardized uncertainty reporting that can help articulate landslide susceptibility assessment,making them even more robust and operationally significant.展开更多
As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy...As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.展开更多
Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable meas...Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.展开更多
Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targ...Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).展开更多
Urban Heat Islands(UHI)are a significant environmental challenge in rapidly urbanizing cities,exacerbated by climate change and urbanization.The UHI effect causes the high temperatures of urban regions,causing high en...Urban Heat Islands(UHI)are a significant environmental challenge in rapidly urbanizing cities,exacerbated by climate change and urbanization.The UHI effect causes the high temperatures of urban regions,causing high energy consumption,health hazards,and degradation of the environment.Remote sensing technology has found it invaluable to monitor and control UHI because it has been used to give spatially continuous data of land surface temperatures,vegetation,and urban morphology.This review paper summarizes the recent innovations in remote sensing techniques of UHI monitoring,empirical evidence of the UHI trends in various climates,and mitigation and adaptation strategies based on remote sensing.Also,it determines the gaps in the existing research,namely the data integration,mixed-pixel issues,and the socio-political barriers,and points out the emerging technologies that suggest potential solutions.The article ends by suggesting an all-encompassing model of urban heat resilience comprising remote sensing,urban planning,and fair policy formulation in tackling the increasing UHI issues amid global warming.展开更多
Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisiti...Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.展开更多
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim...High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.展开更多
Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strate...Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strategies.However,traditional ground-based surveys are limited in spatial coverage and efficiency,hindering effective forest management.To overcome these limitations,this study developed an integrated assessment framework that couples ground-based modeling with remote sensing inversion to achieve large-scale site quality mapping.Field investigations on Pingtan Island,Fujian Province,China,were used to establish a ground-based evaluation model.Soil fertility was quantified using Principal Component Analysis(PCA),and principal components were classified into discrete fertility grades through K-means clustering.These grades,together with topographic variables,were incorporated into a site quality classification model constructed using Quantification Theory I.The point-based model was subsequently extrapolated using Landsat 9 imagery to generate a spatially continuous site quality map.Spatial autocorrelation(Moran’s Ⅰ)and LISA clustering were further employed to interpret spatial patterns.Results indicate that coastal sandy soils in the study area are generally nutrient-poor,with tree growth primarily constrained by total nitrogen,organic matter,available phosphorus,and total phosphorus.The five most influential site factors,ranked by importance,are soil fertility,distance from the coastline,aspect,slope gradient,and elevation.Optimal conditions for C.equisetifolia growth include fertile soil,location>1000 m from the coastline,south-facing or semi-sunny slopes,slope gradients<15°,and elevations between 10-100 m.Only 11.94%of the area was classified as high-quality(Grade I),while 61.74%fell into moderate or poor grades(Grades Ⅲ and Ⅳ),indicating that most plantations are located on suboptimal sites.This study provides scientific support for improving the precision and sustainability of coastal shelterbelt planning and management,offering practical insights for afforestation strategies,forest restoration,and ecological forestry development in coastal zones.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Spared regions of the damaged central nervous system undergo dynamic remodelling and exhibit a remarkable potential for therapeutic exploitation1.Lesion-remote astrocytes(LRAs),which interact with viable neurons and g...Spared regions of the damaged central nervous system undergo dynamic remodelling and exhibit a remarkable potential for therapeutic exploitation1.Lesion-remote astrocytes(LRAs),which interact with viable neurons and glia,undergo reactive transformations whose molecular and functional properties are poorly understood2.Here,using multiple transcriptional profiling methods,we investigated LRAs from spared regions of mouse spinal cord following traumatic spinal cord injury.展开更多
基金funded by the Inner Mongolia Autonomous Region Science and Technology Plan Project(No 2025YFHH0250).
文摘Climate change is rapidly altering hydrological systems through changes in precipitation patterns,increase the rate of glacier retreat rates,altered snow dynamics,and groundwater stress.Although remote sensing has been extensively deployed in hydrological research,existing reviews typically focus on a single hydrological variable or on particular satellite missions.The review synthesizes remote sensing technologies to monitor climate-related hydrological variations across various components of the water cycle.It is a systematic examination of major satellite missions,sensor technologies,and analytical methods used to monitor precipitation,soil moisture,snow cover,surface water processes,and groundwater variability.The review will employ a structured literature review methodology,focusing on recent peer-reviewed articles that apply optical,microwave,radar,and gravimetric remote sensing methods for hydrological monitoring under changing climatic conditions.It has paid specific attention to the provision of the comparative capabilities,spatial-temporal resolutions,and practical applications of key satellite missions,such as Landsat,Sentinel,MODIS(Moderate Resolution Imaging Spectroradiometer),GPM(Global Precipitation Measurement),and GRACE(Gravity Recovery and Climate Experiment).Moreover,to illustrate the use of remote sensing in detecting glacier retreat,drought formation,and coastal groundwater salinization,regional case studies are selected and analyzed.The review identifies new opportunities to use multi-sensor data,machine learning,and high-resolution monitoring to enhance hydrological analyses.This study is useful in practice by synthesizing existing technological opportunities and research trends to enhance climate-responsive water resource monitoring and by outlining future research directions in remote sensing-based hydrological analysis.
文摘Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.
基金supported by the National Natural Science Foundation of China(Grant No.82271252 to W.L.,No.8217091029 to T.W.and No.82204542 to L.H.)the Key Medical Research Projects of Jiangsu Health and Health Commission(Grant No.K2023066 to L.Z.)the Taishan Industrial Talent Project(Grant No.2020-371722-73-03-097290 to W.L.).
文摘Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited by the need for wound exposure and by increased pain.Our preliminary study demonstrated the innovative application of remote NMES(rNMES)to the skeletal muscle of the distal calf,which showed the potential to accelerate wound healing in remote areas.rNMES was effective in human clinical trials in our previous work,although the underlying mechanisms remain unclear.As rNMES is often used to stimulate muscle contraction in long-term bedridden patients,we analyzed data from the Gene Expression Omnibus(GEO)database and found that exercise promotes midkine(MDK)expression in muscle.MDK is a small secreted heparin-binding protein that interacts with multiple cell surface receptors to promote growth.In the present study,we found that MDK significantly enhanced macrophage efferocytosis in a low-density lipoprotein receptor-related protein 1(LRP1)-dependent manner.Our findings demonstrate that rNMES upregulates MDK expression in skeletal muscles through the AMPK-ERK axis,facilitating its delivery to wounds through the circulatory system and promoting LRP1-mediated efferocytosis of apoptotic cells,thereby expediting wound healing.
文摘This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format.Subsequently,a remote data transmission link using 4G signals and data processing methods is introduced.The advantages of remote diagnosis are analyzed,and common methods such as correlation analysis,fault diagnosis,and fault prediction are explained in detail.Then,challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed,along with development prospects in technological innovation,algorithm optimization,and application promotion.This research provides ideas for remote analysis and diagnosis based on TCMS data,contributing to the safe and efficient operation of rail vehicles.
文摘Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.
文摘The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remote sensing image,characterized by complex content and well-defined structures,are particularly vulnerable to malicious attacks and information leakage.To address this issue,the author proposes an encryption method based on the enhanced single-neuron dynamical system(ESNDS).ESNDS generates highquality pseudo-random sequences with complex dynamics and intense sensitivity to initial conditions,which drive a structure of multi-stage cipher comprising permutation,ring-wise diffusion,and mask perturbation.Using representative GF-2 Panchromatic and Multispectral Scanner(PMS)urban scenes,the author conducts systematic evaluations in terms of inter-pixel correlation,information entropy,histogram uniformity,and number of pixel change rate(NPCR)/unified average changing intensity(UACI).The results demonstrate that the proposed scheme effectively resists statistical analysis,differential attacks,and known-plaintext attacks while maintaining competitive computational efficiency for high-resolution urban image.In addition,the cipher is lightweight and hardware-friendly,integrates readily with on-board and ground processing,and thus offers tangible engineering utility for real-time,large-volume remote-sensing data protection.
基金the AECS for its financial support of this study
文摘Organosilicone thin films were prepared through plasma polymerization(pp)in a plasma enhance chemical vapour deposition(PECVD)system,utilizing hexamethyldisilazane(HMDSN)as a monomer precursor,at varying distances(25 mm,35 mm,45 mm,55 mm,and 65 mm)from the plasma source to the substrate.Research has examined how the distance between the substrate and plasma source impacts the properties of thin films,including their thickness,surface morphology,and photoluminescence(PL).It was discovered that as the distance increased,both film thickness and PL intensity also increased.Additionally,the film was observed to be more uniform and smoother when deposited 45 mm below the plasma source.
基金supported by the Jiangsu Province Frontier Leading Technology Basic Research Special Project-Research on the New Optoelectronic Imaging and Information Processing Basic Theory and Method(No:BK20192003).
文摘This review summarizes studies of hydrothermal alteration minerals at the Qiucun gold deposit in southeastern China and focuses on characterization and mapping of the deposit using hyperspectral remote sensing.The deposit exhibits multistage fluid-rock interaction,as evidenced by systematic alteration assemblages,including silicification,sericitization by white micas,the development of argillaceous clays,variable chloritization,and locally significant carbonate alteration.We describe the genetic importance of such mineral groups and emphasize their diagnostic Visible and Near-Infrared to Short-Wave Infrared(VNIR-SWIR)spectral signatures,especially Al-OH,Mg-OH/Fe-OH,and CO3 absorption bands,which make it possible to distinguish between minerals,not to mention the fact that,in some instances,compositional trends may be predicted.This review’s methodological advances are discussed beginning with data collection at satellite,airborne,and ground levels,proceeding to processing procedures,such as atmospheric and topographic correction,and culminating in spectral analysis,including continuum removal,spectral matching,and unmixing/classification techniques.An integrated study of hyperspectral findings reveals that alteration minerals develop spatially coherent zones that are strongly controlled by fault/fracture structures and host-rock reactivity,producing proximal silicification/sericitization cores and larger silicified/larcenies of argillaceous rocks owing to diverse apex coverings of carbonate.This should be combined with petrography and geochemistry to address overprinting,mixed pixels,and surface weathering,and to couple mineral maps with ore-forming processes.The review finds that hyperspectral remote sensing offers a solid modeling platform for the deposit-scale alteration at Qiucun and other hydrothermal gold systems,and outlines the directions for future research to integrate quantitatively and more threedimensional alteration characterization.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金supported by the National Public Welfare Forest Desert Shrubbery Monitoring Project。
文摘Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.
文摘Shallow landslides are strongly controlled by near-surface lithological variability,yet conventional geological maps are often too generalized to support accurate susceptibility assessment in complex terrains.This review synthesizes recent advances in remote sensing–based lithological mapping and evaluates their integration into landslide susceptibility modeling.Evidence from the literature indicates that remote sensing-derived lithological products,particularly those incorporating mineralogical information and higher spatial resolution,consistently outperform traditional geological maps in improving model accuracy and spatial detail,especially in heterogeneous environments.However,key challenges remain,including scale mismatches between surface observations and subsurface controls,limited ground validation,uncertainty propagation,and restricted model transferability across regions.The review identifies multi-sensor data fusion and explainable machine learning as the most promising directions for advancing lithological discrimination and model reliability.Future progress depends on integrating remote sensing with process-based understanding,improving validation strategies,and standardizing uncertainty reporting.These developments are essential for enabling more robust,scalable,and operationally relevant landslide susceptibility assessments in complex terrains.Lastly,we describe the directions of research that focus on multi-sensor fusion,explainable machine learning,UAV(Unmanned Aerial Vehicle)-enabled validation,and standardized uncertainty reporting that can help articulate landslide susceptibility assessment,making them even more robust and operationally significant.
基金Supported by Natural Science Foundation General Project of Heilongjiang Province(C2018050).
文摘As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.
文摘Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.
基金funded by the Hainan Province Science and Technology Special Fund under Grant ZDYF2024GXJS292.
文摘Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).
文摘Urban Heat Islands(UHI)are a significant environmental challenge in rapidly urbanizing cities,exacerbated by climate change and urbanization.The UHI effect causes the high temperatures of urban regions,causing high energy consumption,health hazards,and degradation of the environment.Remote sensing technology has found it invaluable to monitor and control UHI because it has been used to give spatially continuous data of land surface temperatures,vegetation,and urban morphology.This review paper summarizes the recent innovations in remote sensing techniques of UHI monitoring,empirical evidence of the UHI trends in various climates,and mitigation and adaptation strategies based on remote sensing.Also,it determines the gaps in the existing research,namely the data integration,mixed-pixel issues,and the socio-political barriers,and points out the emerging technologies that suggest potential solutions.The article ends by suggesting an all-encompassing model of urban heat resilience comprising remote sensing,urban planning,and fair policy formulation in tackling the increasing UHI issues amid global warming.
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28050400)Jilin Province Key Research and Development Project(No.20230202040NC)Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites(No.2017-000052-73-01-001735)。
文摘Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.
基金funded by the Henan Province Key R&D Program Project,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”,grant number 231111210400.
文摘High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.
基金supported by University Key Lab for Geomatics Technology&Optimize Resources Utilization in Fujian Province(KJG20104A)Fujian Forestry Science and Technology project(2023FKJ15)Fuzhou Forestry Science and Technology Research Project(2130206).
文摘Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strategies.However,traditional ground-based surveys are limited in spatial coverage and efficiency,hindering effective forest management.To overcome these limitations,this study developed an integrated assessment framework that couples ground-based modeling with remote sensing inversion to achieve large-scale site quality mapping.Field investigations on Pingtan Island,Fujian Province,China,were used to establish a ground-based evaluation model.Soil fertility was quantified using Principal Component Analysis(PCA),and principal components were classified into discrete fertility grades through K-means clustering.These grades,together with topographic variables,were incorporated into a site quality classification model constructed using Quantification Theory I.The point-based model was subsequently extrapolated using Landsat 9 imagery to generate a spatially continuous site quality map.Spatial autocorrelation(Moran’s Ⅰ)and LISA clustering were further employed to interpret spatial patterns.Results indicate that coastal sandy soils in the study area are generally nutrient-poor,with tree growth primarily constrained by total nitrogen,organic matter,available phosphorus,and total phosphorus.The five most influential site factors,ranked by importance,are soil fertility,distance from the coastline,aspect,slope gradient,and elevation.Optimal conditions for C.equisetifolia growth include fertile soil,location>1000 m from the coastline,south-facing or semi-sunny slopes,slope gradients<15°,and elevations between 10-100 m.Only 11.94%of the area was classified as high-quality(Grade I),while 61.74%fell into moderate or poor grades(Grades Ⅲ and Ⅳ),indicating that most plantations are located on suboptimal sites.This study provides scientific support for improving the precision and sustainability of coastal shelterbelt planning and management,offering practical insights for afforestation strategies,forest restoration,and ecological forestry development in coastal zones.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘Spared regions of the damaged central nervous system undergo dynamic remodelling and exhibit a remarkable potential for therapeutic exploitation1.Lesion-remote astrocytes(LRAs),which interact with viable neurons and glia,undergo reactive transformations whose molecular and functional properties are poorly understood2.Here,using multiple transcriptional profiling methods,we investigated LRAs from spared regions of mouse spinal cord following traumatic spinal cord injury.
