The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem becau...The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem because of involving many objective functions,many constraints,and complex search space.Moreover,this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses.Different types ofMulti-Objective Optimization Algorithms(MOOAs)based on Artificial Intelligence(AI)have been frequently employed,but their ability and performance have not been evaluated and compared properly.This paper aims to employ and compare three commonly used MOOAs i.e.NSGA-II,MOPSO,and MOEA/D in urban land-use allocation problems.Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages.The objective functions of this study are compatibility,dependency,suitability,and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment.Evaluation of results is based on the dispersion of the solutions,diversity of the solutions’space,and comparing the number of dominant solutions in Pareto-Fronts.The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses.However,the run time of NSGA-II is the worst,related to the Diversity Metric(DM)which represents the regularity of the distance between solutions at the highest degree.Moreover,MOPSO provides the best Scattering Diversity Metric(SDM)which shows the diversity of solutions in the solution space.Furthermore,In terms of algorithm execution time,MOEA/D performed better than the other two.So,Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.展开更多
Change Detection(CD)provides a research basis for environmental monitoring,urban expansion and reconstruction as well as disaster assessment,by identifying the changes of ground objects in different time periods.Tradi...Change Detection(CD)provides a research basis for environmental monitoring,urban expansion and reconstruction as well as disaster assessment,by identifying the changes of ground objects in different time periods.Traditional CD focused on the Binary Change Detection(BCD),focusing solely on the change and no-change regions.Due to the dynamic progress of earth observation satellite techniques,the spatial resolution of remote sensing images continues to increase,Multi-class Change Detection(MCD)which can reflect more detailed land change has become a hot research direction in the field of CD.Although many scholars have reviewed change detection at present,most of the work still focuses on BCD.This paper focuses on the recent progress in MCD,which includes five major aspects:challenges,datasets,methods,applications and future research direction.Specifically,the background of MCD is first introduced.Then,the major difficulties and challenges in MCD are discussed and delineated.The benchmark datasets for MCD are described,and the available open datasets are listed.Moreover,MCD is further divided into three categories and the specific techniques are described,respectively.Subsequently,the common applications of MCD are described.Finally,the relevant literature in the main journals of remote sensing in the past five years are analyzed and the development and future research direction of MCD are discussed.This review will help researchers understand this field and provide a reference for the subsequent development of MCD.Our collections of MCD benchmark datasets are available at:https://zenodo.org/record/6809804#.YsfvxXZByUk.展开更多
The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accur...The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accuracy for UWB localization system in indoor environment.So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy.In this paper,a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed.The proposed FCN-Attention algorithm utilizes a Fully Convolution Network(FCN)for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy.The proposed algorithm is evaluated using an open-source dataset,a local collected dataset and a mixed dataset created from these two datasets.The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24%on the open-source dataset,100%on the local collected dataset and 92.01%on the mixed dataset,which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper.展开更多
Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests o...Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests of Domain Users(BIDUs)can help elucidate users’access intentions and provide suitable recommendations.Previous research has found that access frequency of spatial features is an indicator of users’browsing interests;however,highfrequency spatial features are sparsely distributed,resulting in inaccurate extraction of browsing interests.Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation.First,to extract spatial features in tiles,we proposed a k-nearest neighbor method for Point-of-Interest(POI)extraction and a template-based method for Land Uses/Land Covers extraction.Then,we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness.Finally,a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs.Compared with the baseline models,the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction.Our findings can help construct domain user profiles and support the development of intelligent PMSPs.展开更多
In this paper,we will conclude the results of Bufeng-1(BF-1)A/B data processing,calibration workflow,and validation of the calibrated sea surface winds,land surface soil moisture,and sea surface height measurements.Si...In this paper,we will conclude the results of Bufeng-1(BF-1)A/B data processing,calibration workflow,and validation of the calibrated sea surface winds,land surface soil moisture,and sea surface height measurements.Since 2019,the BF-1 mission has operated in-orbit for over 4 years.The Earth reflected delay Doppler maps(DDMs)are continuously collected to perform global sea surface and land observations.At the same time,the intermediate frequency(IF)raw data are also obtained for 12 seconds every pass in diagnostic mode.To begin with,a brief description of the spaceborne Global Navigation Satellite System Reflectometry(GNSS-R)technique will be provided in the introduction.Next,we will present the overview of Chinese BF-1 mission and the data specifications used in our research.In the next section,the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications.Then,the status of Chinese Beidou System(BDS)Equivalent Isotropic Radiated Power(EIRP)acquisition programme is then introduced.Furthermore,the latest sea surface height(SSH)measurements results including two modes(group delay and carrier phase)and wind speed derivation based on machine learning(ML)method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University(DTU)mean sea surface(MSS)products and European Centre for Medium-Range Weather Forecasts(ECMWF)ERA5 reanalysis.The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications.Finally,the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.展开更多
Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civili...Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu’er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R^(2)of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m×20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R^(2)=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.展开更多
Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and min...Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance.Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models.The preference pattern is different from the intention pattern,in that it does not emphasize the user mobility pattern of revisiting POIs,which is a common behavior and kind of intention for users.An effective module is needed to predict when and where users will repeat visits.In this paper,we propose a Spatio-Temporal Intention Learning Self-Attention Network(STILSAN)for next POI recommendation.STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time.Meanwhile,we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs.Experiments are conducted on two real-world check-in datasets.The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.展开更多
Cloud coverage has become a significant factor affecting the availability of remote-sensing images in many applications.To mitigate the adverse impact of cloud coverage and recover ground information obscured by cloud...Cloud coverage has become a significant factor affecting the availability of remote-sensing images in many applications.To mitigate the adverse impact of cloud coverage and recover ground information obscured by clouds,this paper presents a curvature-driven cloud removal method.Considering that each image can be regarded as a curved surface and the curvature can reflect the texture information well due to its dependence on the surface’s undulation degree,the presented method transforms image from natural domain to curvature domain for information reconstruction to maintain details of reference image.In order to improve the overall consistency and continuity of cloud removal results,the optimal boundary for cloud coverage area replacement is determined first to make the boundary pass through pixels with minimum curvature difference.Then,the curvature of missing area is reconstructed based on the curvature of reference image,and the reconstructed curvature is inversely transformed to natural domain to obtain a cloud-free image.In addition,considering the possible significant radiometric differences between different images,the initial cloud-free result will be further refined based on specific checkpoints to improve the local accuracy.To evaluate the performance of the proposed method,both simulated experiments and real data experiments are carried out.Experimental results show that the proposed method can achieve satisfactory results in terms of radiometric accuracy and consistency.展开更多
Drone technology opens the door to major changes and opportunities in our society.But this technology,like many others,needs to be administered and regulated to prevent potential harm to the public.Therefore,national ...Drone technology opens the door to major changes and opportunities in our society.But this technology,like many others,needs to be administered and regulated to prevent potential harm to the public.Therefore,national and local governments around the world established regulations for operating drones,which bans drone use from specific locations or limits their operation to qualified drone pilots only.This study reviews the types of restrictions on drone use that are specified in federal drone regulations for the US,the UK,and France,and in state regulations for the US.The study also maps restricted areas and assesses compliance with these regulations by analyzing the spatial contribution patterns to three crowd-sourced drone portals,namely SkyPixel,Flickr,and DroneSpot,relative to restricted areas.The analysis is performed both at the national level and at the state/regional level within each of the three countries,where statistical tests are conducted to compare compliance rates between the three drone portals.This study provides new insight into drone users’awareness of and compliance with drone regulations.This can help governments to tailor information campaigns for increased awareness of drone regulations among drone users and to determine where increased control and enforcement of drone regulations is necessary.展开更多
Validation studies of global Digital Elevation Models(DEMs)in the existing literature are limited by the diversity and spread of landscapes,terrain types considered and sparseness of groundtruth.Moreover,there are kno...Validation studies of global Digital Elevation Models(DEMs)in the existing literature are limited by the diversity and spread of landscapes,terrain types considered and sparseness of groundtruth.Moreover,there are knowledge gaps on the accuracy variations in rugged and complex landscapes,and previous studies have often not relied on robust internal and external validation measures.Thus,there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications.In this study,we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution,readily available,free-access and global coverage 1 arc-second(30 m)DEMs:NASADEM,ASTER GDEM,Copernicus GLO-30,and ALOS World 3D(AW3D).The assessment is carried out at landscapes spread across Cape Town,Southern Africa(urban/industrial,agricultural,mountain,peninsula and grassland/shrubland)and forested national parks in Gabon,Central Africa(low-relief tropical rainforest and high-relief tropical rainforest).The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution,and error ranking.In Cape Town,Copernicus DEM generally had the least vertical error with an overall Mean Error(ME)of 0.82 m and Root Mean Square Error(RMSE)of 2.34 m while ASTER DEM had the poorest performance.However,ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon.Generally,the DEM errors have a moderate to high positive correlation in forests,and a low to moderate positive correlation in mountains and urban areas.Copernicus DEM showed superior vertical accuracy in forests with less than 40%tree cover,while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%.This study is a robust regional assessment of these global DEMs.展开更多
Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and...Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards.This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining subsidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)technology.The results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(IoU)indicators;(2)monitoring water conservancy and hydropower engineering.The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side.The dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation.The overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical imagery.The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.展开更多
Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classi...Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.展开更多
To achieve the goal that China and Nepal jointly announce the new height of Mount Qomolangma(MQ),the campaign of Qomolangma Height Survey(QHS)was carried out from 2019 to 2020.A high precision geoid model realizing th...To achieve the goal that China and Nepal jointly announce the new height of Mount Qomolangma(MQ),the campaign of Qomolangma Height Survey(QHS)was carried out from 2019 to 2020.A high precision geoid model realizing the common height datum for both sides is necessary for determining the unique height of MQ.However,high altitude and rugged topography make it extremely difficult to conduct terrestrial gravity measurements in this region,the accuracy of geoid model is restricted by terrestrial gravity data gaps.In the campaign of 2020 QHS,the airborne gravity survey was carried out over MQ and its surrounding areas,the airborne gravity data covering an area of 12,700 km^(2) were successfully collected.For the first time,the high precision observations of terrestrial gravity and BeiDou Navigation Satellite System(BDS)at the peak of MQ were collected.These datasets pave the way for the precise determination of the orthometric height of MQ.According to the definition of the International Height Reference System(IHRS),we developed the IHRS-based gravimetric quasigeoid model by combining the airborne and terrestrial gravity data.Validations against highly accurate GNSS leveling data at 61 benchmarks demonstrate that the accuracy of the quasigeoid model is 3.8 cm,and the addition of airborne gravity data improves the accuracy by 51.3%.Based on the IHRS,the final orthometric height of the snow surface of the peak of MQ is determined to be 8848.86 m.展开更多
Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depic...Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.展开更多
Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems.The monitoring of forest health is,therefore,indispensable for the long-term conservation of forests and th...Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems.The monitoring of forest health is,therefore,indispensable for the long-term conservation of forests and their sustainable management.In this regard,evaluating the amount and quality of dead wood is of utmost interest as they are favorable indicators of biodiversity.Apparently,remote sensing-based Machine Learning(ML)techniques have proven to be more efficient and sustainable with unprecedented accuracy in forest inventory.However,the application of these techniques is still in its infancy with respect to dead wood mapping.This study,for the first time,automatically categorizing individual coniferous trees(Norway spruce)into five decay stages(live,declining,dead,loose bark,and clean)from combined Airborne Laser Scanning(ALS)point clouds and color infrared(CIR)images using three different ML methods−3D point cloud-based deep learning(KPConv),Convolutional Neural Network(CNN),and Random Forest(RF).First,CIR colorized point clouds are created by fusing the ALS point clouds and color infrared images.Then,individual tree segmentation is conducted,after which the results are further projected onto four orthogonal planes.Finally,the classification is conducted on the two datasets(3D multispectral point clouds and 2D projected images)based on the three ML algorithms.All models achieved promising results,reaching overall accuracy(OA)of up to 88.8%,88.4%and 85.9%for KPConv,CNN and RF,respectively.The experimental results reveal that color information,3D coordinates,and intensity of point clouds have significant impact on the promising classification performance.The performance of our models,therefore,shows the significance of machine/deep learning for individual tree decay stages classification and landscape-wide assessment of the dead wood amount and quality by using modern airborne remote sensing techniques.The proposed method can contribute as an important and reliable tool for monitoring biodiversity in forest ecosystems.展开更多
With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data ...With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.展开更多
Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance.Synthetic Aperture Radar(SAR)images are often used to monitor the...Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance.Synthetic Aperture Radar(SAR)images are often used to monitor the characteristics of glaciers based on the backscattering coefficient.However,the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered.This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Xizang Interior Mountains by using Sentinel-1 C-band SAR data.The investigated regions have different climate patterns.We compare backscatter coefficient distributions for wet snow and firn,derived from maximum likelihood classification under various polarizations,alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn.However,a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer.We furthermore show,by analyzing weather stations on glaciers,that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit.In contrast,firn ratios across different orbits show less variation in winter,and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences.These findings demonstrate glaciers’sensitivity to temperature fluctuations and the radar wave’s responsiveness to surface characteristics.Consequently,when employing SAR for glacier monitoring,it is crucial to consider the influence of orbit and polarization,in combination with temperature variations,and whether the season is winter or summer.展开更多
Recent research shows that China is experiencing significant greening,with afforestation initiatives being the main cause.Quantitative calculation of vegetation change influencing factors and evaluation of the contrib...Recent research shows that China is experiencing significant greening,with afforestation initiatives being the main cause.Quantitative calculation of vegetation change influencing factors and evaluation of the contribution of afforestation to vegetation greening in China are critical to coping with climate change and improving the implementation and efficacy of forestry projects.We investigated the temporal and spatial dynamics of the Normalized Difference Vegetation Index(NDVI)from 1982 to 2020,and quantified the contribution of afforestation initiatives,a typical human activity,to the dynamic changes of vegetation.The results showed that NDVI in China has primarily increased in the last 39 years.57%of the pixels increased,27%were stable and unchanged,and 16%decreased.Climate change was responsible for 72.34%of vegetation restoration,while human activities were responsible for 27.36%of vegetation restoration,according to residual analysis.In the future,only 14%of the regions showed continuous growth of the NDVI,while the remaining regions showed obvious antipersistence(59%will go from increasing to decreasing,and 22%will go from decreasing to increasing).The contribution of climate factors to vegetation change will decrease in the future,and human activities will become more complex.Except for Huaihe River and Taihu Lake(SPHRTL),other forestry projects showed an increasing trend of NDVI after the implementation of ecological engineering.However,due to differences in climate conditions and ecological engineering implementation,there are differences in the benefits of forestry projects.Some forestry project areas still have obvious vegetation degradation,and appropriate forestry management is necessary.This work provides a quantitative analysis of vegetation change and its driving factors in China,which will help to cope with future climate change and provide a reference for the implementation and management of ecological projects.展开更多
Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the pattern...Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the patterns of interchanges,which are indispensable parts of urban road networks.In the SC-GCNN model,an interchange is modeled as a graph,wherein nodes and edges represent the interchange segments and their connections,respectively.Then,a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes.Finally,a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns.The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap.The classification accuracy was 87.06%,which was higher than that of the image-based AlexNet,GoogLeNet,and Random Forest models.展开更多
Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resy...Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights,providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects.As an emerging technology,it is different from the traditional interferometric SAR(InSAR)technology and has advantages in reconstructing the three-dimensional structure of the illuminated media.Over the past two decades,many TomoSAR methods have been proposed to improve the vertical resolution,aiming to distinguish the locations of different scatters in the unit pixel.In order to cope with the forest mission of European Space Agency(ESA)that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests,it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology.In this paper,we adopt three typical algorithms,namely,Capon,Multiple Signal Classification(MUSIC),and Compressed Sensing(CS),to evaluate the performance in forest height and underlying topography inversion.The P-band airborne full-polarization(FP)SAR data of LopèNational Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment.Furthermore,we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile.The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions.Compared with LiDAR results,the root-mean-square error(RMSE)of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m,which performs the best among the three algorithms.展开更多
文摘The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas.This problem is an NP(nondeterministic polynomial time)-hard problem because of involving many objective functions,many constraints,and complex search space.Moreover,this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses.Different types ofMulti-Objective Optimization Algorithms(MOOAs)based on Artificial Intelligence(AI)have been frequently employed,but their ability and performance have not been evaluated and compared properly.This paper aims to employ and compare three commonly used MOOAs i.e.NSGA-II,MOPSO,and MOEA/D in urban land-use allocation problems.Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages.The objective functions of this study are compatibility,dependency,suitability,and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment.Evaluation of results is based on the dispersion of the solutions,diversity of the solutions’space,and comparing the number of dominant solutions in Pareto-Fronts.The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses.However,the run time of NSGA-II is the worst,related to the Diversity Metric(DM)which represents the regularity of the distance between solutions at the highest degree.Moreover,MOPSO provides the best Scattering Diversity Metric(SDM)which shows the diversity of solutions in the solution space.Furthermore,In terms of algorithm execution time,MOEA/D performed better than the other two.So,Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.
基金supported by the National Natural Science Foundation of China[grant number 41901306]the Key Lab of Spatial Data Mining&Information Sharing of Ministry of Education[grant number 2022LSDMIS09].
文摘Change Detection(CD)provides a research basis for environmental monitoring,urban expansion and reconstruction as well as disaster assessment,by identifying the changes of ground objects in different time periods.Traditional CD focused on the Binary Change Detection(BCD),focusing solely on the change and no-change regions.Due to the dynamic progress of earth observation satellite techniques,the spatial resolution of remote sensing images continues to increase,Multi-class Change Detection(MCD)which can reflect more detailed land change has become a hot research direction in the field of CD.Although many scholars have reviewed change detection at present,most of the work still focuses on BCD.This paper focuses on the recent progress in MCD,which includes five major aspects:challenges,datasets,methods,applications and future research direction.Specifically,the background of MCD is first introduced.Then,the major difficulties and challenges in MCD are discussed and delineated.The benchmark datasets for MCD are described,and the available open datasets are listed.Moreover,MCD is further divided into three categories and the specific techniques are described,respectively.Subsequently,the common applications of MCD are described.Finally,the relevant literature in the main journals of remote sensing in the past five years are analyzed and the development and future research direction of MCD are discussed.This review will help researchers understand this field and provide a reference for the subsequent development of MCD.Our collections of MCD benchmark datasets are available at:https://zenodo.org/record/6809804#.YsfvxXZByUk.
基金supported by the National Key Research and Development Program of China[grant No.2016YF B0502200]the Postdoctoral Research Foundation of China[grant No.2020M682480]the Fundamental Research Funds for the Central Universities[grant No.2042021kf0009]。
文摘The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accuracy for UWB localization system in indoor environment.So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy.In this paper,a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed.The proposed FCN-Attention algorithm utilizes a Fully Convolution Network(FCN)for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy.The proposed algorithm is evaluated using an open-source dataset,a local collected dataset and a mixed dataset created from these two datasets.The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24%on the open-source dataset,100%on the local collected dataset and 92.01%on the mixed dataset,which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper.
基金supported by the National Natural Science Foundation of China[grant numbers:U20A209141771426]Zhizhuo Research Fund on Spatial-Temporal Artificial Intelligence[grant number ZZJJ202204]LIESMARS Special Research Funding.
文摘Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests of Domain Users(BIDUs)can help elucidate users’access intentions and provide suitable recommendations.Previous research has found that access frequency of spatial features is an indicator of users’browsing interests;however,highfrequency spatial features are sparsely distributed,resulting in inaccurate extraction of browsing interests.Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation.First,to extract spatial features in tiles,we proposed a k-nearest neighbor method for Point-of-Interest(POI)extraction and a template-based method for Land Uses/Land Covers extraction.Then,we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness.Finally,a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs.Compared with the baseline models,the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction.Our findings can help construct domain user profiles and support the development of intelligent PMSPs.
基金supported by the ESA&NRSCC Dragon 5 Cooperation[Grant No.58070]the National Natural Science Foundation of China[Grant No.42101409]+2 种基金China Spacesat[Grant No.SK2020014]funded by MCIN/AEI/10.13039/501100011033 with contributions by“European Union Next Generation EU/PRTR”[Grant No.RYC2019-027000-I]is also supported by Spanish National Research Council[Grant No.20215AT007].
文摘In this paper,we will conclude the results of Bufeng-1(BF-1)A/B data processing,calibration workflow,and validation of the calibrated sea surface winds,land surface soil moisture,and sea surface height measurements.Since 2019,the BF-1 mission has operated in-orbit for over 4 years.The Earth reflected delay Doppler maps(DDMs)are continuously collected to perform global sea surface and land observations.At the same time,the intermediate frequency(IF)raw data are also obtained for 12 seconds every pass in diagnostic mode.To begin with,a brief description of the spaceborne Global Navigation Satellite System Reflectometry(GNSS-R)technique will be provided in the introduction.Next,we will present the overview of Chinese BF-1 mission and the data specifications used in our research.In the next section,the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications.Then,the status of Chinese Beidou System(BDS)Equivalent Isotropic Radiated Power(EIRP)acquisition programme is then introduced.Furthermore,the latest sea surface height(SSH)measurements results including two modes(group delay and carrier phase)and wind speed derivation based on machine learning(ML)method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University(DTU)mean sea surface(MSS)products and European Centre for Medium-Range Weather Forecasts(ECMWF)ERA5 reanalysis.The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications.Finally,the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.
基金supported by the National Key Research and Development Program of China[grant numbers 2021YFE0117700 and 2022YFF1302100]the ESA-MOST China Dragon 5 Cooperation[grant number 59313]National Science and Technology Major Project of China's High Resolution Earth Observation System[grant numbers 30-Y30A02-9001-20/22-7 and 21-Y20B01-9001-19/22].
文摘Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu’er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R^(2)of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m×20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R^(2)=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.
基金supported by Chongqing Technology Innovation and Application Development Project[grant number cstc2021jscx-dxwtBX0023]funding from Chongqing Changan Automobile Co.,Ltd.,Dongfeng Motor Corporation,and Dongfeng Changxing Tech Co.,Ltd.
文摘Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance.Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models.The preference pattern is different from the intention pattern,in that it does not emphasize the user mobility pattern of revisiting POIs,which is a common behavior and kind of intention for users.An effective module is needed to predict when and where users will repeat visits.In this paper,we propose a Spatio-Temporal Intention Learning Self-Attention Network(STILSAN)for next POI recommendation.STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time.Meanwhile,we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs.Experiments are conducted on two real-world check-in datasets.The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.
基金supported by the National Natural Science Foundation of China[grant number 41971422 and 42090011]Key Research and Development Plan Project of Hubei Province[grant number 2020BIB006]+1 种基金Hubei Provincial Natural Science Foundation[grant number 2020CFA001]LIESMARS Special Research Funding.
文摘Cloud coverage has become a significant factor affecting the availability of remote-sensing images in many applications.To mitigate the adverse impact of cloud coverage and recover ground information obscured by clouds,this paper presents a curvature-driven cloud removal method.Considering that each image can be regarded as a curved surface and the curvature can reflect the texture information well due to its dependence on the surface’s undulation degree,the presented method transforms image from natural domain to curvature domain for information reconstruction to maintain details of reference image.In order to improve the overall consistency and continuity of cloud removal results,the optimal boundary for cloud coverage area replacement is determined first to make the boundary pass through pixels with minimum curvature difference.Then,the curvature of missing area is reconstructed based on the curvature of reference image,and the reconstructed curvature is inversely transformed to natural domain to obtain a cloud-free image.In addition,considering the possible significant radiometric differences between different images,the initial cloud-free result will be further refined based on specific checkpoints to improve the local accuracy.To evaluate the performance of the proposed method,both simulated experiments and real data experiments are carried out.Experimental results show that the proposed method can achieve satisfactory results in terms of radiometric accuracy and consistency.
文摘Drone technology opens the door to major changes and opportunities in our society.But this technology,like many others,needs to be administered and regulated to prevent potential harm to the public.Therefore,national and local governments around the world established regulations for operating drones,which bans drone use from specific locations or limits their operation to qualified drone pilots only.This study reviews the types of restrictions on drone use that are specified in federal drone regulations for the US,the UK,and France,and in state regulations for the US.The study also maps restricted areas and assesses compliance with these regulations by analyzing the spatial contribution patterns to three crowd-sourced drone portals,namely SkyPixel,Flickr,and DroneSpot,relative to restricted areas.The analysis is performed both at the national level and at the state/regional level within each of the three countries,where statistical tests are conducted to compare compliance rates between the three drone portals.This study provides new insight into drone users’awareness of and compliance with drone regulations.This can help governments to tailor information campaigns for increased awareness of drone regulations among drone users and to determine where increased control and enforcement of drone regulations is necessary.
基金supported by the(i)Commonwealth Scholarship Commission and the Foreign,Commonwealth and Development Office in the UK[Grant number NGCN-2021-239](ii)University of Cape Town Postgraduate Funding Office.
文摘Validation studies of global Digital Elevation Models(DEMs)in the existing literature are limited by the diversity and spread of landscapes,terrain types considered and sparseness of groundtruth.Moreover,there are knowledge gaps on the accuracy variations in rugged and complex landscapes,and previous studies have often not relied on robust internal and external validation measures.Thus,there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications.In this study,we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution,readily available,free-access and global coverage 1 arc-second(30 m)DEMs:NASADEM,ASTER GDEM,Copernicus GLO-30,and ALOS World 3D(AW3D).The assessment is carried out at landscapes spread across Cape Town,Southern Africa(urban/industrial,agricultural,mountain,peninsula and grassland/shrubland)and forested national parks in Gabon,Central Africa(low-relief tropical rainforest and high-relief tropical rainforest).The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution,and error ranking.In Cape Town,Copernicus DEM generally had the least vertical error with an overall Mean Error(ME)of 0.82 m and Root Mean Square Error(RMSE)of 2.34 m while ASTER DEM had the poorest performance.However,ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon.Generally,the DEM errors have a moderate to high positive correlation in forests,and a low to moderate positive correlation in mountains and urban areas.Copernicus DEM showed superior vertical accuracy in forests with less than 40%tree cover,while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%.This study is a robust regional assessment of these global DEMs.
基金supported by the National Key Research and Development Program of China[grant number 2021YFE0116800]ESA-MOST China Dragon-5 Program[grant number 56796]+1 种基金the National Natural Science Foundation of China[grant number 41977415]the SIAP Project[grant number 1/SAMA/2020/2019(POCI-62-2019-01)]by AMA IP(Portuguese Administrative Modernization Agency).
文摘Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards.This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining subsidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)technology.The results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(IoU)indicators;(2)monitoring water conservancy and hydropower engineering.The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side.The dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation.The overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical imagery.The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.
基金supported by the National Natural Science Foundation of China[grant number 42071354]supported by the Fundamental Research Funds for the Central Universities[grant number 2042022dx0001]supported by the Fundamental Research Funds for the Central Universities[grant number WUT:223108001].
文摘Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.
基金supported by the National Natural Science Foundation of China[grant numbers 41974010,42074020]the basic scientific research operating program of Chinese Academy of Surveying and Mapping。
文摘To achieve the goal that China and Nepal jointly announce the new height of Mount Qomolangma(MQ),the campaign of Qomolangma Height Survey(QHS)was carried out from 2019 to 2020.A high precision geoid model realizing the common height datum for both sides is necessary for determining the unique height of MQ.However,high altitude and rugged topography make it extremely difficult to conduct terrestrial gravity measurements in this region,the accuracy of geoid model is restricted by terrestrial gravity data gaps.In the campaign of 2020 QHS,the airborne gravity survey was carried out over MQ and its surrounding areas,the airborne gravity data covering an area of 12,700 km^(2) were successfully collected.For the first time,the high precision observations of terrestrial gravity and BeiDou Navigation Satellite System(BDS)at the peak of MQ were collected.These datasets pave the way for the precise determination of the orthometric height of MQ.According to the definition of the International Height Reference System(IHRS),we developed the IHRS-based gravimetric quasigeoid model by combining the airborne and terrestrial gravity data.Validations against highly accurate GNSS leveling data at 61 benchmarks demonstrate that the accuracy of the quasigeoid model is 3.8 cm,and the addition of airborne gravity data improves the accuracy by 51.3%.Based on the IHRS,the final orthometric height of the snow surface of the peak of MQ is determined to be 8848.86 m.
基金supported by the National Natural Science Foundation of China[grant number 42001386,42271407]within the ESA-MOST China Dragon 5 Cooperation(ID:59313).
文摘Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.
基金supported by the National Natural Science Foundation of China[Grant No.42171361]the Research Grants Council of the Hong Kong Special Administrative Region,China[Grant No.PolyU 25211819]supported by The Hong Kong Polytechnic University,China[Grant No.1-ZVN6,1-ZECE].
文摘Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems.The monitoring of forest health is,therefore,indispensable for the long-term conservation of forests and their sustainable management.In this regard,evaluating the amount and quality of dead wood is of utmost interest as they are favorable indicators of biodiversity.Apparently,remote sensing-based Machine Learning(ML)techniques have proven to be more efficient and sustainable with unprecedented accuracy in forest inventory.However,the application of these techniques is still in its infancy with respect to dead wood mapping.This study,for the first time,automatically categorizing individual coniferous trees(Norway spruce)into five decay stages(live,declining,dead,loose bark,and clean)from combined Airborne Laser Scanning(ALS)point clouds and color infrared(CIR)images using three different ML methods−3D point cloud-based deep learning(KPConv),Convolutional Neural Network(CNN),and Random Forest(RF).First,CIR colorized point clouds are created by fusing the ALS point clouds and color infrared images.Then,individual tree segmentation is conducted,after which the results are further projected onto four orthogonal planes.Finally,the classification is conducted on the two datasets(3D multispectral point clouds and 2D projected images)based on the three ML algorithms.All models achieved promising results,reaching overall accuracy(OA)of up to 88.8%,88.4%and 85.9%for KPConv,CNN and RF,respectively.The experimental results reveal that color information,3D coordinates,and intensity of point clouds have significant impact on the promising classification performance.The performance of our models,therefore,shows the significance of machine/deep learning for individual tree decay stages classification and landscape-wide assessment of the dead wood amount and quality by using modern airborne remote sensing techniques.The proposed method can contribute as an important and reliable tool for monitoring biodiversity in forest ecosystems.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.[42275147].
文摘With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.
基金supported by the Natural Science Foundation of China[grant number 41971393]the Dragon 5 programme[grant number 400059344/21/I-NB]supported by ESA and NRSCC/MOST and by Key scientific and technological research projects in the Xinjiang Production and Construction Corps:[grant number 2023AB074]。
文摘Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance.Synthetic Aperture Radar(SAR)images are often used to monitor the characteristics of glaciers based on the backscattering coefficient.However,the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered.This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Xizang Interior Mountains by using Sentinel-1 C-band SAR data.The investigated regions have different climate patterns.We compare backscatter coefficient distributions for wet snow and firn,derived from maximum likelihood classification under various polarizations,alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn.However,a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer.We furthermore show,by analyzing weather stations on glaciers,that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit.In contrast,firn ratios across different orbits show less variation in winter,and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences.These findings demonstrate glaciers’sensitivity to temperature fluctuations and the radar wave’s responsiveness to surface characteristics.Consequently,when employing SAR for glacier monitoring,it is crucial to consider the influence of orbit and polarization,in combination with temperature variations,and whether the season is winter or summer.
基金funded by the National Natural Science Foundation of China[grant number 42271318,42271354,41971402]the Science and Technology Project of Department of Natural Resources of Hubei Province[grant number ZRZY2023KJ41]+1 种基金the National Key Research and Development Program of China[grant number 2018YFC1506506]the ESA-MOST Dragon Program[grant number 58815],and the LIESMARS Special Research Funding.
文摘Recent research shows that China is experiencing significant greening,with afforestation initiatives being the main cause.Quantitative calculation of vegetation change influencing factors and evaluation of the contribution of afforestation to vegetation greening in China are critical to coping with climate change and improving the implementation and efficacy of forestry projects.We investigated the temporal and spatial dynamics of the Normalized Difference Vegetation Index(NDVI)from 1982 to 2020,and quantified the contribution of afforestation initiatives,a typical human activity,to the dynamic changes of vegetation.The results showed that NDVI in China has primarily increased in the last 39 years.57%of the pixels increased,27%were stable and unchanged,and 16%decreased.Climate change was responsible for 72.34%of vegetation restoration,while human activities were responsible for 27.36%of vegetation restoration,according to residual analysis.In the future,only 14%of the regions showed continuous growth of the NDVI,while the remaining regions showed obvious antipersistence(59%will go from increasing to decreasing,and 22%will go from decreasing to increasing).The contribution of climate factors to vegetation change will decrease in the future,and human activities will become more complex.Except for Huaihe River and Taihu Lake(SPHRTL),other forestry projects showed an increasing trend of NDVI after the implementation of ecological engineering.However,due to differences in climate conditions and ecological engineering implementation,there are differences in the benefits of forestry projects.Some forestry project areas still have obvious vegetation degradation,and appropriate forestry management is necessary.This work provides a quantitative analysis of vegetation change and its driving factors in China,which will help to cope with future climate change and provide a reference for the implementation and management of ecological projects.
基金supported by the National Natural Science Foundation of China[grant numbers 42071450 and 42001415].
文摘Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the patterns of interchanges,which are indispensable parts of urban road networks.In the SC-GCNN model,an interchange is modeled as a graph,wherein nodes and edges represent the interchange segments and their connections,respectively.Then,a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes.Finally,a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns.The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap.The classification accuracy was 87.06%,which was higher than that of the image-based AlexNet,GoogLeNet,and Random Forest models.
基金supported by ESA-MOST Dragon Programme 5[grant number 59332].
文摘Due to the strong penetrability,long-wavelength synthetic aperture radar(SAR)can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media.SAR tomography(TomoSAR)technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights,providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects.As an emerging technology,it is different from the traditional interferometric SAR(InSAR)technology and has advantages in reconstructing the three-dimensional structure of the illuminated media.Over the past two decades,many TomoSAR methods have been proposed to improve the vertical resolution,aiming to distinguish the locations of different scatters in the unit pixel.In order to cope with the forest mission of European Space Agency(ESA)that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests,it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology.In this paper,we adopt three typical algorithms,namely,Capon,Multiple Signal Classification(MUSIC),and Compressed Sensing(CS),to evaluate the performance in forest height and underlying topography inversion.The P-band airborne full-polarization(FP)SAR data of LopèNational Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment.Furthermore,we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile.The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions.Compared with LiDAR results,the root-mean-square error(RMSE)of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m,which performs the best among the three algorithms.
