Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in th...Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.展开更多
The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great...The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.展开更多
[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IR...[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.展开更多
Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
Lakes in the Tibetan Plateau are considered sensitive responders to global warming Variations in physical features of lake systems such as surface area and water level are very helpful in understanding regional respon...Lakes in the Tibetan Plateau are considered sensitive responders to global warming Variations in physical features of lake systems such as surface area and water level are very helpful in understanding regional responses to global warming in recent decades. In this study multi-source remote sensing data were used to retrieve the surface area and water level time series of five inland lakes in the south-central part of the Tibetan Plateau over the past dec- ades. Changes in water level and surface area of the lakes were investigated. The results showed that the water level of three lakes （Puma Yumco, Taro Co, Zhari Namco） increased, with expanding surface area, while the water levels of the other two lakes （Paiku Co, Mapam Yumco） fell, with shrinking area. The water levels of the lakes experienced remarkable changes in 2000-2012 as compared with 1976-1999. Spatially, lakes located at the southern fringe of the Tibetan Plateau showed consistency in water level changes, which was different from lakes in the central Tibetan Plateau.展开更多
This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based regi...This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.展开更多
Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established...Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6(Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0–5 m because of the influence of human activities. The inversion accuracy of 5–10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5–20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.展开更多
The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has ...The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.展开更多
Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two ...Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.展开更多
Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing tech...Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.展开更多
Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitat...Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitative classification rules(five ranks).Based on these two limitations,we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images,climate,parent material,terrain data,and machine learning.In hyper-arid and arid region,models established using Sentinel-2 and other geospatial data showed the highest accuracy with R^(2) of 0.85 and 0.62,respectively.In semi-arid,dry sub-humid,and humid regions,models performed best using Sentinel-1,Sentinel-2,and other geospatial data with R^(2) of 0.87,0.80,and 0.87,respectively.The accuracy of the global models is considerable with field validation in Iran and Xinjiang,and compared with digitized salinity maps in California,Brazil,Turkey,South Africa,and Shandong.The proportion of extremely saline soils in Europe is 10.21%,followed by South America(5.91%),Oceania(5.80%),North America(4.05%),Asia(1.19%),and Africa(1.11%).Climatic conditions,groundwater,and salinity index are key covariates in global soil salinity estimation.Use of radar data improves estimation accuracy in wet regions.The map of global soil salinity at 10 m provides a detailed,high-precision basis for soil property investigation and resource management.展开更多
The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America.Despite much progress in targeting habitat management in sta...The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America.Despite much progress in targeting habitat management in staging and wintering areas,methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required.This is particularly true for species that breed in remote,inaccessible areas such as the American black duck,an intensively managed game bird in Eastern North America.Although evidence suggests that black ducks prefer productive,nutrient-rich waterbodies,such as beaver ponds,information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge.Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap.In this study,we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario.The use of multi-source data,including a Digital Elevation Model,a Sentinel-2 Multi-Spectral Image,and RadarSat 2 Polarimetric data,enabled us to identify individual beaver ponds on the landscape.Our model correctly identified an average of 83.0%of the known beaver dams and 72.5%of the known beaver ponds based on validation with an independent dataset.This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents.Furthermore,the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various doma...Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.展开更多
Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric featur...Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.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
This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By e...This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.展开更多
Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic...Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.展开更多
Multifarious regions around the world are exposed to natural hazards and disasters,each with unique characteristics.A higher frequency of extreme hydro-meteorological events,most probably related to climate change,and...Multifarious regions around the world are exposed to natural hazards and disasters,each with unique characteristics.A higher frequency of extreme hydro-meteorological events,most probably related to climate change,and an increase in vulnerable population have been addressed as potential causes of such disasters.To mitigate the consequences of these disasters,Disaster Risk Management,including hazard assessment,elements-at-risk mapping,vulnerability and risk assessment of spatial components as well as Earth Observation(EO)products and Geographic Information Systems(GIS),should be considered.Multihazard assessment entails the evaluation of relationships between various hazards,including interconnected or cascading events,as well as focusing on various levels from global to local community levels,as each level manifests particular objectives and spatial data.This paper presents an overview of the diverse types of spatial data and explores the methods applied in hazard and risk assessments,with volcanic eruptions serving as a specific example.The rapid development of scientific research and the advancement of Earth Observation satellites in recent years have revolutionized the concepts of geologists and researchers.These satellites now play an indispensable role in supporting first responders during major disasters.The coordination of satellite deployment ensures a swift response along with allowing for the timely delivery of critical images.In tandem,remote sensing technologies and geographic information systems(GIS)have emerged as essential tools for geospatial analysis.The application of remote sensing and GIS for the detection of natural disasters was examined through a review of academic papers,offering an analysis of how remote sensing is utilized to assess natural hazards and their link to climate change.展开更多
With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,s...With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.展开更多
基金Project(201412016)supported by the Special Fund for Public Projects of National Administration of Surveying,Mapping and Geoinformation of ChinaProject(51174287)supported by the National Natural Science Foundation of China
文摘Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.
基金This work was supported by the National Advance Research Program(Item No.Y1601-1).
文摘The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.
基金Supported by Science and Technology Project of Lianyungang City(SH0917)
文摘[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
基金We thank the Climate Data Center, National Meteorological Information Center, China Me- teorological Administration, for providing the long-time meteorological data of the four field stations. This research was jointly supported by The ＂Strategic Priority Research Program （B）＂ of the Chinese Academy of Sciences （Grant XDB03030406）, and the National Natural Science Foundation of China （Grant 41371218, 41165011）.
文摘Lakes in the Tibetan Plateau are considered sensitive responders to global warming Variations in physical features of lake systems such as surface area and water level are very helpful in understanding regional responses to global warming in recent decades. In this study multi-source remote sensing data were used to retrieve the surface area and water level time series of five inland lakes in the south-central part of the Tibetan Plateau over the past dec- ades. Changes in water level and surface area of the lakes were investigated. The results showed that the water level of three lakes （Puma Yumco, Taro Co, Zhari Namco） increased, with expanding surface area, while the water levels of the other two lakes （Paiku Co, Mapam Yumco） fell, with shrinking area. The water levels of the lakes experienced remarkable changes in 2000-2012 as compared with 1976-1999. Spatially, lakes located at the southern fringe of the Tibetan Plateau showed consistency in water level changes, which was different from lakes in the central Tibetan Plateau.
文摘This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.
基金Under the auspices of the Program for Jilin University Science and Technology Innovative Research Team(No.JLUSTIRT,2017TD-26)Plan for Changbai Mountain Scholars of Jilin Province,China
文摘Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6(Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0–5 m because of the influence of human activities. The inversion accuracy of 5–10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5–20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.
文摘The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.
基金supported by National Key R&D Program of China(2022YFD2000100)National Natural Science Foundation of China(42401400)Zhejiang Provincial Key Research and Development Program(2023C02018).
文摘Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.
基金Under the auspices of the National Key Research and Development Program of China(No.2023YFC3208500)Shanghai Municipal Natural Science Foundation(No.22ZR1421500)+3 种基金National Natural Science Foundation of China(No.U2243207)National Science and Technology Basic Resources Survey Project(No.2023FY01001)Open Research Fund of State Key Laboratory of Estuarine and Coastal Research(No.SKLEC-KF202406)Project from Science and Technology Commission of Shanghai Municipality(No.22DZ1202700)。
文摘Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.
基金supported by the National Key Research and Development Program(grant numbers 2018YFE0107000 and 2023YFD1900102)the National Science Foundation of China(grant numbers 42261016 and 41061031)+4 种基金the Bingtuan Science and Technology Program(grant number 2020CB032)the Tarim University President’s Fund(grant number TDZKCX202205)the China Scholarship Council(CSC)the Academic Rising Star Program for Doctoral Students of Zhejiang Universitythe Outstanding Ph.D.Dissertation Funding of Zhejiang University.
文摘Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitative classification rules(five ranks).Based on these two limitations,we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images,climate,parent material,terrain data,and machine learning.In hyper-arid and arid region,models established using Sentinel-2 and other geospatial data showed the highest accuracy with R^(2) of 0.85 and 0.62,respectively.In semi-arid,dry sub-humid,and humid regions,models performed best using Sentinel-1,Sentinel-2,and other geospatial data with R^(2) of 0.87,0.80,and 0.87,respectively.The accuracy of the global models is considerable with field validation in Iran and Xinjiang,and compared with digitized salinity maps in California,Brazil,Turkey,South Africa,and Shandong.The proportion of extremely saline soils in Europe is 10.21%,followed by South America(5.91%),Oceania(5.80%),North America(4.05%),Asia(1.19%),and Africa(1.11%).Climatic conditions,groundwater,and salinity index are key covariates in global soil salinity estimation.Use of radar data improves estimation accuracy in wet regions.The map of global soil salinity at 10 m provides a detailed,high-precision basis for soil property investigation and resource management.
基金supported by the Natural Sciences and Engineering Research Council of Canada[RGPIN-2021-03624].
文摘The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America.Despite much progress in targeting habitat management in staging and wintering areas,methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required.This is particularly true for species that breed in remote,inaccessible areas such as the American black duck,an intensively managed game bird in Eastern North America.Although evidence suggests that black ducks prefer productive,nutrient-rich waterbodies,such as beaver ponds,information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge.Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap.In this study,we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario.The use of multi-source data,including a Digital Elevation Model,a Sentinel-2 Multi-Spectral Image,and RadarSat 2 Polarimetric data,enabled us to identify individual beaver ponds on the landscape.Our model correctly identified an average of 83.0%of the known beaver dams and 72.5%of the known beaver ponds based on validation with an independent dataset.This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents.Furthermore,the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
文摘Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.
基金financially supported by the Youth Innovation Promotion Association CAS(No.2021325)the National Natural Science Foundation of China(Nos.52179117,U21A20159)the Research project of Panzhihua Iron and Steel Group Mining Co.,Ltd.(No.2021-P6-D2-05)。
文摘Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.
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This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.
基金supported by the Office of Naval Research(Grant Nos.N00014-16-1-2094 and N00014-24-1-2547)the Lawrence Livermore National Laboratory(Grant No.B622827)the National Science Foundation.Y.-S.J.acknowledges support from KRISS(Grant Nos.25011026 and 25011211).
文摘Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.
文摘Multifarious regions around the world are exposed to natural hazards and disasters,each with unique characteristics.A higher frequency of extreme hydro-meteorological events,most probably related to climate change,and an increase in vulnerable population have been addressed as potential causes of such disasters.To mitigate the consequences of these disasters,Disaster Risk Management,including hazard assessment,elements-at-risk mapping,vulnerability and risk assessment of spatial components as well as Earth Observation(EO)products and Geographic Information Systems(GIS),should be considered.Multihazard assessment entails the evaluation of relationships between various hazards,including interconnected or cascading events,as well as focusing on various levels from global to local community levels,as each level manifests particular objectives and spatial data.This paper presents an overview of the diverse types of spatial data and explores the methods applied in hazard and risk assessments,with volcanic eruptions serving as a specific example.The rapid development of scientific research and the advancement of Earth Observation satellites in recent years have revolutionized the concepts of geologists and researchers.These satellites now play an indispensable role in supporting first responders during major disasters.The coordination of satellite deployment ensures a swift response along with allowing for the timely delivery of critical images.In tandem,remote sensing technologies and geographic information systems(GIS)have emerged as essential tools for geospatial analysis.The application of remote sensing and GIS for the detection of natural disasters was examined through a review of academic papers,offering an analysis of how remote sensing is utilized to assess natural hazards and their link to climate change.
文摘With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.
