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
Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at diff...Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.展开更多
INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This colla...INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This collapse resulted in a pavement failure of approximately 17.9 m in length and covering an area of about 184.3 m^(2)(Chinanews,2024).展开更多
Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation.The remote sensing ecological index(RSEI)model of the Guangxi Beibu Gulf Economic Zone(GBGEZ)during 2001-2020 was establi...Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation.The remote sensing ecological index(RSEI)model of the Guangxi Beibu Gulf Economic Zone(GBGEZ)during 2001-2020 was established and evaluated using four indices:dryness,wetness,greenness,and heat.This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing.We found that:(1)From 2001 to 2020,the eco-environmental quality(EEQ)of GBGEZ tended to improve,and the spatial difference tended to expand.The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas,and the EEQ in the east and west is better than that in the middle.The contribution of greenness,wetness,and dryness to the improvement of EEQ in the study region increased year by year.(2)From 2001to 2020,the order of the contribution of the EEQ index in the GBGEZ was dryness,wetness,greenness,and heat.(3)The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ.展开更多
Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited recepti...Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.展开更多
The marshes of southern Iraq are of great value due to their roles in the economy,environment,heritage,tourism,and agriculture.However,the region has witnessed remarkable transformations in land cover,influenced by hu...The marshes of southern Iraq are of great value due to their roles in the economy,environment,heritage,tourism,and agriculture.However,the region has witnessed remarkable transformations in land cover,influenced by human interventions and natural environmental factors.In this research,the Central Marshlands were selected for study and monitoring.These Marshes form the Mesopotamian Marshes,a vital part of the Tigris-Euphrates river system.This area 2 formerly covered an area of approximately 3,000 km and was once home to the lives of Marsh Arabs and their animals.The primary objective of this study was to compile a set of satellite images covering the same marshland region over several decades.The data used includes images captured by various Landsat missions:MSS(1975),TM(1983&1993),ETM+(2003),and the Operational Land Imager(OLI)from Landsat 8(2015).Satellite images were combined and pre-processed through steps such as layer stacking to create composite images from multiple bands.Several image classification methods were applied,and the classification results showed a significant and unprecedented increase in the percentage of water in the marsh,reaching 16%in 2003.This was combined with vegetation identification techniques,including the identification of vegetation boundaries to detect areas of dense vegetation.In addition,the relative depth of the water was measured to estimate marsh water levels,with the best result obtained in 2003.The normalized mean vegetation index(NDVI)calculated in this study had its best value in 1984 due to the spread of reeds and papyrus during this period.Papyrus is the raw material in the sugar industry,providing a significant economic boost.展开更多
Dryland regions face complex interactions between urbanization and ecological changes,where effective coordination is essential for enhancing sustainability and resilience.However,most studies concentrate on the natio...Dryland regions face complex interactions between urbanization and ecological changes,where effective coordination is essential for enhancing sustainability and resilience.However,most studies concentrate on the national or provincial scales,with insufficient research on county-level coordination,limiting the ability to provide targeted polifrom a precise perspective.This study addresses this gap by analyzing 39 counties within the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration(HBOYUA),a typical dryland urban cluster in China.We use daytime and nighttime remote sensing images to track the spatio-temporal evolution of urbanization and ecological conditions from 1992 to 2023.A novel quantitative framework based on an improved coupling coordination degree(CCD)is proposed to assess their coordination relationship.The results reveal that:(1)Urbanization and ecological quality both exhibited fluctuating upward trends,with spatial heterogeneity increasing for urbanization and decreasing for the eco-environment.Regions with better ecological conditions had higher urbanization levels.(2)The overall coordinated level improved from imbalance(0.36)to low-level coordination(0.55),although its spatial distribution remained uneven,with central urban areas showing higher CCD than surrounding counties.(3)Socioeconomic factors exerted greater effects on CCD than natural factors,with GDP and land surface temperature(LST)playing a significant role in interaction analysis.(4)In western arid regions,urbanization did not necessarily harm ecosystems;instead,ecological conditions improved alongside urbanization.This research offers targeted and valuable references for county and city governments in resource allocation and sustainable development.The proposed methodology is also adaptable for urban resilience studies in other regions.展开更多
A massive amount of plastic waste has presented an immense management challenge.This escalating ecological damage,coupled with the detrimental effects of plastics infiltrating the marine food web,poses a significant t...A massive amount of plastic waste has presented an immense management challenge.This escalating ecological damage,coupled with the detrimental effects of plastics infiltrating the marine food web,poses a significant threat to human livelihoods.To combat this,there is a call for the development of plastic detection algorithms using remote sensing data.Here we tested a new index,referred to index_(MP),to detect clusters of floating macro plastics in the ocean using satellite imagery.The index_(MP)was applied to convolution high-pass filtered(3×3)Sentinel 2 Level 1 C images,showing the potential to reduce atmospheric interference and enhance the object edges,thereby improving the clarity of detection.In the analysis,we used three scatter plots to identify and assess plastic pixels.To differentiate the common features of plastic from non-plastic objects,the Sentinel 2 bands 5,8,and 9 were plotted against index_(MP)calculated and convolution high-pass filtered Level 1 C(CHPIC)images.The plastic pixels,clustering in the three scatter plots,showed positive‘X’,i.e.,CHPIC image value and‘Y’,i.e.,each band 5,8,and 9 reflectance values,along with a CHPIC image value exceeding 0.05.Using the index_(MP)and scatter plot analysis,we identified plastic pixels containing 14%or more plastic bottles.Detection of other types of plastics,such as fishing nets and plastic bags,required pixel proportions greater than 50%.Hence,plastic bottles were notably responsive even at a low pixel fraction.We further explored the classification of plastic and non-plastic objects by analyzing reed(plant)pixels;the differentiation between plastic and reed was conducted in the band 5 and 9 scatter plots.展开更多
The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between change...The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern Iran.Employing the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to 2020.The convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land use.The obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear regression.The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories.This variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use changes.The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°C.The developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level features.Furthermore,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,respectively.In conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.展开更多
With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil app...With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil application of HRS imagery in the fields of agriculture,forestry,and environmental monitoring.China is playing an important role in this evolution,especially in recent years,with the successful launch and operation of a series of civil hyper-spectral spacecraft and satellites,including the Shenzhou-3 spacecraft,the Gaofen-5 satellite,the SPARK satellite,the Zhuhai-1 satellite network for environmental and resources monitoring,the FengYun series of satellites for meteorological observation,and the Chang’E series of spacecraft for planetary exploration.The Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and microsatellites.This paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the applications.In addition,the future development trends of HRS in China are also discussed and analyzed.展开更多
Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolu...Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolution,high spatial resolution,and high spectral resolution.To the best of our knowledge,however,no satellite owns all the above character-istics.Thus,it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation.In this study,we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model,filling the gap of no existing urban remote sensing framework.In this study,we present four applications to elaborate on the specific applications of the proposed model:1)a spatiotemporal fusion model for synthesizing ideal data,2)a spatio-spectral observation model for urban vegetation biomass estimation,3)a temporal-spectral observation model for urban flood mapping,and 4)a spatio-temporal-spectral model for impervious surface extraction.We believe that the proposed model,although in a conceptual stage,can largely benefit urban observation by providing a new data fusion paradigm.展开更多
Requirements for monitoring the coastal zone environment are first summarized. Then the appli- cation of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coa...Requirements for monitoring the coastal zone environment are first summarized. Then the appli- cation of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coast beaches and bottom matter, target recognition, mine detection, oil spill identification and ocean color remote sensing. Finally, what is needed to follow on in application of hyperspectral remote sensing to coast environment is recommended.展开更多
Coal remote sensing technology was founded in the period of coal resources survey after the founding of People’s Republic of China.Aerophoto Grammetry&Remote Sensing Bureau of China National Administration of Coa...Coal remote sensing technology was founded in the period of coal resources survey after the founding of People’s Republic of China.Aerophoto Grammetry&Remote Sensing Bureau of China National Administration of Coal Geology was established,specializing in the application and promotion of coal remote sensing technology.With the rapid development of coal geological exploration in China,coal remote sensing technology has evolved from coal geology based survey to comprehensive survey that factors in resources,environment,ecology and so on.This paper summarizes the general situation,theories,development process,key research and future of remote sensing technology for coal mining in China.Spanning over 50 years,the history of China’s coal remote sensing technology can be divided into five stages:aero-geological mapping,coal remote sensing theory experimental research,application research and promotion,architecture planning and productionisation,“3S”technology integration and application.This paper expounds the main technical progress,application fields,major projects and major achievements in various historical periods,and points out that the coal remote sensing has entered a unified development stage of“Aviation,Aerospace and Ground”,with a focus on high-resolution remote sensing,hyperspectral remote sensing,radar remote sensing,“3S”technology integration and multi-means comprehensive exploration and evaluation.In the future,coal remote sensing technology will develop rapidly in data mass,technology integration,evaluation intelligence,integration application programming,system visualization,etc.Coal remote sensing technology has entered the industrial development from technology application.展开更多
The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center(LASAC),Mi...The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center(LASAC),Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna,Austria.Under this agreement panchromatic and multi-spectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research.So far,nearly 500 GB of data have been uploaded to the server.This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies.Some of them are already completed,others are still ongoing.They include a geometric accuracy validation of ZY-3 data,an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos,and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria.An accuracy study of DTMs from ZY-3 stereo data,as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.展开更多
Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and a...Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.展开更多
Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the govern...Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system,China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology.The development of domestic high-resolution remote sensing satellites shows a vigorous trend,and consequently,a relatively stable and perfect high-resolution earth observation system has been formed.The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods.In this paper,the development status,along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed,and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed,providing a reference for the subsequent development of high-resolution remote sensing satellites in China.展开更多
In this paper,I propose a personal view on the general contents of remote sensing science and technology,which includes sensor research and manufacturing,remotely sensed data acquisition,data processing,information ex...In this paper,I propose a personal view on the general contents of remote sensing science and technology,which includes sensor research and manufacturing,remotely sensed data acquisition,data processing,information extraction and remote sensing applications.Serving as the basis for all these components is radiative transfer process modeling and inversion.Also of importance is the effective visualization of remotely sensed data and their efficient distribution to end users.In all these areas,there are critical research questions.In particular,I consider 4 fundamental areas for improved application of remote sensing.These include the scale and angular issues in remote sensing,removal of topographic effects on the radiance and geometry of remotely sensed imagery and the related question of multisource and multitemporal data registration,integrating knowledge and remotely sensed data into effective information extraction,and four dimensional data assimilation techniques.Strategies of information extraction can be broadly divided into manual visual analysis and computer-based analysis.The computer based information analysis include radiative transfer model inversion,image classification,regression analysis,three dimensional information extraction,shape analysis and change detection.Successful information extraction is the key to the success of remote sensing.There are many important issues that need to be solved including how to make better use of the spatial and temporal data present in remotely sensed data in information extraction.How to effectively combine the strength of both computer analysis and human interpretation?Finally,4D data assimilation is the new direction that allows for the integration of instantaneous observation with process-based climate,hydrological and ecological models.Further work along this direction will enhance the contribution of remote sensing in global change studies.In return,the quality of remotely sensed parameters can be improved.展开更多
This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS trans...This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component,respectively.The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation,and undertaking the inverse IHS transformation.The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image.On the basis of the visual and statistical analysis results of the experimental study,we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion.It has potential advantages in image mapping optimization,object recognition,and weak information sharpening.展开更多
The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and ra...The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.展开更多
The coastal ecosystem health assessment is a field of increasing importance.In this paper,a preliminary assessment of ecosystem health in Zhejiang coastal water zone was made,mainly based on remote sensing data and GI...The coastal ecosystem health assessment is a field of increasing importance.In this paper,a preliminary assessment of ecosystem health in Zhejiang coastal water zone was made,mainly based on remote sensing data and GIS technique.Its spatial and quantitative evaluation was facilitated by the progress of remote sensing and GIS technique development.Firstly,human activities,hydrology and ecosystem problems in the study area were discussed and analyzed.Secondly,from 4 aspects of human stress,physical,chemical and biological responses to anthropogenic activities and natural stress,several indicators such as water transparency（Secchi Disk Depth,SDD）,suspended substance concentration,dissolved inorganic nitrogen,active phosphate,chlorophyll,harmful algae bloom,as well as distribution of sewage,sea lanes and port were employed.Thirdly,the Analytic Hierarchical Process was used for indicator weight calculation,and the ecosystem health criteria were established according to the integrative analysis of national water quality criteria,similar coastal ecosystem health research in other places or data inherent properties.The results indicated that from 2005 to 2007 the coastal water ecosystem health value in Zhejiang Province was unhealthy and needs ecological restoration by human intervention.展开更多
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.
基金supported by the National Natural Science Foundation of China(42030102,42371321).
文摘Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
基金supported by the National Natural Science Foundation of China(Nos.42371094,41907253)partially supported by the Interdisciplinary Cultivation Program of Xidian University(No.21103240005)the Postdoctoral Fellowship Program of CPSF(No.GZB20240589)。
文摘INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This collapse resulted in a pavement failure of approximately 17.9 m in length and covering an area of about 184.3 m^(2)(Chinanews,2024).
基金Guangxi Natural Science Foundation,No.2020GXNSFAA297176National Natural Science Foundation of China,No.U21A2022,No.42101369Youth Teacher Scientific Research Ability Improvement Project of Guangxi,No.2021KY0393。
文摘Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation.The remote sensing ecological index(RSEI)model of the Guangxi Beibu Gulf Economic Zone(GBGEZ)during 2001-2020 was established and evaluated using four indices:dryness,wetness,greenness,and heat.This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing.We found that:(1)From 2001 to 2020,the eco-environmental quality(EEQ)of GBGEZ tended to improve,and the spatial difference tended to expand.The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas,and the EEQ in the east and west is better than that in the middle.The contribution of greenness,wetness,and dryness to the improvement of EEQ in the study region increased year by year.(2)From 2001to 2020,the order of the contribution of the EEQ index in the GBGEZ was dryness,wetness,greenness,and heat.(3)The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ.
基金supported by the National Natural Science Foundation of China(Nos.42371449,41801386).
文摘Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.
文摘The marshes of southern Iraq are of great value due to their roles in the economy,environment,heritage,tourism,and agriculture.However,the region has witnessed remarkable transformations in land cover,influenced by human interventions and natural environmental factors.In this research,the Central Marshlands were selected for study and monitoring.These Marshes form the Mesopotamian Marshes,a vital part of the Tigris-Euphrates river system.This area 2 formerly covered an area of approximately 3,000 km and was once home to the lives of Marsh Arabs and their animals.The primary objective of this study was to compile a set of satellite images covering the same marshland region over several decades.The data used includes images captured by various Landsat missions:MSS(1975),TM(1983&1993),ETM+(2003),and the Operational Land Imager(OLI)from Landsat 8(2015).Satellite images were combined and pre-processed through steps such as layer stacking to create composite images from multiple bands.Several image classification methods were applied,and the classification results showed a significant and unprecedented increase in the percentage of water in the marsh,reaching 16%in 2003.This was combined with vegetation identification techniques,including the identification of vegetation boundaries to detect areas of dense vegetation.In addition,the relative depth of the water was measured to estimate marsh water levels,with the best result obtained in 2003.The normalized mean vegetation index(NDVI)calculated in this study had its best value in 1984 due to the spread of reeds and papyrus during this period.Papyrus is the raw material in the sugar industry,providing a significant economic boost.
基金National Natural Science Foundation of China,No.42330106。
文摘Dryland regions face complex interactions between urbanization and ecological changes,where effective coordination is essential for enhancing sustainability and resilience.However,most studies concentrate on the national or provincial scales,with insufficient research on county-level coordination,limiting the ability to provide targeted polifrom a precise perspective.This study addresses this gap by analyzing 39 counties within the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration(HBOYUA),a typical dryland urban cluster in China.We use daytime and nighttime remote sensing images to track the spatio-temporal evolution of urbanization and ecological conditions from 1992 to 2023.A novel quantitative framework based on an improved coupling coordination degree(CCD)is proposed to assess their coordination relationship.The results reveal that:(1)Urbanization and ecological quality both exhibited fluctuating upward trends,with spatial heterogeneity increasing for urbanization and decreasing for the eco-environment.Regions with better ecological conditions had higher urbanization levels.(2)The overall coordinated level improved from imbalance(0.36)to low-level coordination(0.55),although its spatial distribution remained uneven,with central urban areas showing higher CCD than surrounding counties.(3)Socioeconomic factors exerted greater effects on CCD than natural factors,with GDP and land surface temperature(LST)playing a significant role in interaction analysis.(4)In western arid regions,urbanization did not necessarily harm ecosystems;instead,ecological conditions improved alongside urbanization.This research offers targeted and valuable references for county and city governments in resource allocation and sustainable development.The proposed methodology is also adaptable for urban resilience studies in other regions.
基金Supported by the Guangdong Special Support Program for Key Talents Team Program(No.2019BT02H594)the PI Project of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(No.GML2021GD0810)the Major Project of National Social Science Foundation of China(No.21ZDA097)。
文摘A massive amount of plastic waste has presented an immense management challenge.This escalating ecological damage,coupled with the detrimental effects of plastics infiltrating the marine food web,poses a significant threat to human livelihoods.To combat this,there is a call for the development of plastic detection algorithms using remote sensing data.Here we tested a new index,referred to index_(MP),to detect clusters of floating macro plastics in the ocean using satellite imagery.The index_(MP)was applied to convolution high-pass filtered(3×3)Sentinel 2 Level 1 C images,showing the potential to reduce atmospheric interference and enhance the object edges,thereby improving the clarity of detection.In the analysis,we used three scatter plots to identify and assess plastic pixels.To differentiate the common features of plastic from non-plastic objects,the Sentinel 2 bands 5,8,and 9 were plotted against index_(MP)calculated and convolution high-pass filtered Level 1 C(CHPIC)images.The plastic pixels,clustering in the three scatter plots,showed positive‘X’,i.e.,CHPIC image value and‘Y’,i.e.,each band 5,8,and 9 reflectance values,along with a CHPIC image value exceeding 0.05.Using the index_(MP)and scatter plot analysis,we identified plastic pixels containing 14%or more plastic bottles.Detection of other types of plastics,such as fishing nets and plastic bags,required pixel proportions greater than 50%.Hence,plastic bottles were notably responsive even at a low pixel fraction.We further explored the classification of plastic and non-plastic objects by analyzing reed(plant)pixels;the differentiation between plastic and reed was conducted in the band 5 and 9 scatter plots.
文摘The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern Iran.Employing the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to 2020.The convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land use.The obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear regression.The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories.This variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use changes.The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°C.The developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level features.Furthermore,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,respectively.In conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.
基金This work was supported by National Natural Science Foundation of China under Grant Nos.42071350,41820104006,41771385 and 41622107supported by Postdoctoral Research Foundation of China.
文摘With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil application of HRS imagery in the fields of agriculture,forestry,and environmental monitoring.China is playing an important role in this evolution,especially in recent years,with the successful launch and operation of a series of civil hyper-spectral spacecraft and satellites,including the Shenzhou-3 spacecraft,the Gaofen-5 satellite,the SPARK satellite,the Zhuhai-1 satellite network for environmental and resources monitoring,the FengYun series of satellites for meteorological observation,and the Chang’E series of spacecraft for planetary exploration.The Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and microsatellites.This paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the applications.In addition,the future development trends of HRS in China are also discussed and analyzed.
基金This work is supported by the National Key Research and Development Program of China[grant number 2018YFB2100501]the Key Research and Development Program of Yunnan province in China[grant number 2018IB023]+2 种基金the Research Project from the Ministry of Natural Resources of China[grant number 4201⁃⁃240100123]the National Natural Science Foundation of China[grant numbers 41771452,41771454,41890820,and 41901340]the Natural Science Fund of Hubei Province in China[grant number 2018CFA007].
文摘Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolution,high spatial resolution,and high spectral resolution.To the best of our knowledge,however,no satellite owns all the above character-istics.Thus,it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation.In this study,we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model,filling the gap of no existing urban remote sensing framework.In this study,we present four applications to elaborate on the specific applications of the proposed model:1)a spatiotemporal fusion model for synthesizing ideal data,2)a spatio-spectral observation model for urban vegetation biomass estimation,3)a temporal-spectral observation model for urban flood mapping,and 4)a spatio-temporal-spectral model for impervious surface extraction.We believe that the proposed model,although in a conceptual stage,can largely benefit urban observation by providing a new data fusion paradigm.
基金The National "973" Program of China under contract No.2009CB723902the Key Projects of the Knowledge Innovation Program of Chinese Academy of Sciences under contract No.KZCX1-YW-14-2.
文摘Requirements for monitoring the coastal zone environment are first summarized. Then the appli- cation of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coast beaches and bottom matter, target recognition, mine detection, oil spill identification and ocean color remote sensing. Finally, what is needed to follow on in application of hyperspectral remote sensing to coast environment is recommended.
文摘Coal remote sensing technology was founded in the period of coal resources survey after the founding of People’s Republic of China.Aerophoto Grammetry&Remote Sensing Bureau of China National Administration of Coal Geology was established,specializing in the application and promotion of coal remote sensing technology.With the rapid development of coal geological exploration in China,coal remote sensing technology has evolved from coal geology based survey to comprehensive survey that factors in resources,environment,ecology and so on.This paper summarizes the general situation,theories,development process,key research and future of remote sensing technology for coal mining in China.Spanning over 50 years,the history of China’s coal remote sensing technology can be divided into five stages:aero-geological mapping,coal remote sensing theory experimental research,application research and promotion,architecture planning and productionisation,“3S”technology integration and application.This paper expounds the main technical progress,application fields,major projects and major achievements in various historical periods,and points out that the coal remote sensing has entered a unified development stage of“Aviation,Aerospace and Ground”,with a focus on high-resolution remote sensing,hyperspectral remote sensing,radar remote sensing,“3S”technology integration and multi-means comprehensive exploration and evaluation.In the future,coal remote sensing technology will develop rapidly in data mass,technology integration,evaluation intelligence,integration application programming,system visualization,etc.Coal remote sensing technology has entered the industrial development from technology application.
基金This work was supported by the National Key R&D Program of China for Strategic International Cooperation in Science and Technology Innovation(Grant No.2016YFE0205300)as well as a grant under the Eurasia Pacific UNINET program of the Austrian Federal Ministry of Education,Science and Research to the University of Vienna(Grant No.EPU 32/2017).
文摘The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center(LASAC),Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna,Austria.Under this agreement panchromatic and multi-spectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research.So far,nearly 500 GB of data have been uploaded to the server.This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies.Some of them are already completed,others are still ongoing.They include a geometric accuracy validation of ZY-3 data,an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos,and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria.An accuracy study of DTMs from ZY-3 stereo data,as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.
基金financially supported by the funding appropriated from USDA-ARS National Program 305 Crop Productionthe 948 Program of Ministry of Agriculture of China (2016-X38)
文摘Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.
基金This work is supported by the National Natural Science Foundation of China[grant numbers 91738302 and 91838303]the National Science Fund for Distinguished Young Scholars[grant number 61825103]Thanks for the support of China Centre for Resources Satellite Data and Application(CRESDA).
文摘Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system,China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology.The development of domestic high-resolution remote sensing satellites shows a vigorous trend,and consequently,a relatively stable and perfect high-resolution earth observation system has been formed.The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods.In this paper,the development status,along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed,and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed,providing a reference for the subsequent development of high-resolution remote sensing satellites in China.
基金National Natural Science Foundation of China(30590370)National High-Tech Program(2006AA12Z112)National Scientific Support program(2006BAJ01B02)
文摘In this paper,I propose a personal view on the general contents of remote sensing science and technology,which includes sensor research and manufacturing,remotely sensed data acquisition,data processing,information extraction and remote sensing applications.Serving as the basis for all these components is radiative transfer process modeling and inversion.Also of importance is the effective visualization of remotely sensed data and their efficient distribution to end users.In all these areas,there are critical research questions.In particular,I consider 4 fundamental areas for improved application of remote sensing.These include the scale and angular issues in remote sensing,removal of topographic effects on the radiance and geometry of remotely sensed imagery and the related question of multisource and multitemporal data registration,integrating knowledge and remotely sensed data into effective information extraction,and four dimensional data assimilation techniques.Strategies of information extraction can be broadly divided into manual visual analysis and computer-based analysis.The computer based information analysis include radiative transfer model inversion,image classification,regression analysis,three dimensional information extraction,shape analysis and change detection.Successful information extraction is the key to the success of remote sensing.There are many important issues that need to be solved including how to make better use of the spatial and temporal data present in remotely sensed data in information extraction.How to effectively combine the strength of both computer analysis and human interpretation?Finally,4D data assimilation is the new direction that allows for the integration of instantaneous observation with process-based climate,hydrological and ecological models.Further work along this direction will enhance the contribution of remote sensing in global change studies.In return,the quality of remotely sensed parameters can be improved.
文摘This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component,respectively.The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation,and undertaking the inverse IHS transformation.The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image.On the basis of the visual and statistical analysis results of the experimental study,we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion.It has potential advantages in image mapping optimization,object recognition,and weak information sharpening.
基金Project supported by the National Natural Science Foundation of China (No.40571115)the National High Tech-nology Research and Development Program (863 Program) of China (Nos.2006AA120101 and 2007AA10Z205)
文摘The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.
基金The National Natural Science Foundation of China under contract No. 41001271
文摘The coastal ecosystem health assessment is a field of increasing importance.In this paper,a preliminary assessment of ecosystem health in Zhejiang coastal water zone was made,mainly based on remote sensing data and GIS technique.Its spatial and quantitative evaluation was facilitated by the progress of remote sensing and GIS technique development.Firstly,human activities,hydrology and ecosystem problems in the study area were discussed and analyzed.Secondly,from 4 aspects of human stress,physical,chemical and biological responses to anthropogenic activities and natural stress,several indicators such as water transparency（Secchi Disk Depth,SDD）,suspended substance concentration,dissolved inorganic nitrogen,active phosphate,chlorophyll,harmful algae bloom,as well as distribution of sewage,sea lanes and port were employed.Thirdly,the Analytic Hierarchical Process was used for indicator weight calculation,and the ecosystem health criteria were established according to the integrative analysis of national water quality criteria,similar coastal ecosystem health research in other places or data inherent properties.The results indicated that from 2005 to 2007 the coastal water ecosystem health value in Zhejiang Province was unhealthy and needs ecological restoration by human intervention.
