Block Adjustment(BA)is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery.For block adjustment with few ground-control points or without grou...Block Adjustment(BA)is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery.For block adjustment with few ground-control points or without ground control,the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products.The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods,high positioning precision,and low acquisition cost,providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA.This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery(OSSI)and spaceborne laser altimeter data.Firstly,we elaborate the principle and necessity of this work through a literature review of existing methods.Then,the framework of our geo-positioning models.Secondly,four key technologies of the proposed model are expounded in order,including the acquisition and management of global Laser Control Points,the association of LCPs and OSSI,the block adjustment model combining LCPs with OSSI,and the accuracy estimation and quality control of the combined BA.Next,the combined BA experiment using Ziyuan-3(ZY-3)OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province,China.Experimental results prove that our method can automatically select LCPs with high accuracy.The elevation deviation of the combined BA eventually achieved the Mean Error(ME)of 0.06 m and the Root Mean Square Error(RMSE)of 1.18 m,much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment.A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.展开更多
Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from...Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.展开更多
This study applied a computerized parametric methodology to monitor, map, and quantify land degradation by salinization risk detection techniques at a 1：250 000 mapping scale using geo-information technology. The nor...This study applied a computerized parametric methodology to monitor, map, and quantify land degradation by salinization risk detection techniques at a 1：250 000 mapping scale using geo-information technology. The northern part of the Shaanxi province in China was taken as a case. Multi-temporal remotely sensed materials of both Landsat TM and thematic maps （ETM＋） were used as the bases to provide comprehensive views of surface conditions such as vegetation cover and salinization detection. With ERDAS ver. 9.1 software, the Normalized Differential Salinity Index （NDSl） and Salinity Index （S.I.） were computed and then evaluated for land degradation by salinization. Arc/Info ver. 9.2 software was used along with field observation data （GPS） for analysis. Using spatial analysis methods, results showed that 19 973.1 km^2 （72%） of land had no risk of land degradation by salinization, 3 684.7 km^2 （13%） had slight land degradation by salinization risk, 2 797.9 km^2 （10%） had moderate land degradation by salinization risk, and 1 218.9 km^2 （4%） of the total land area was at a high risk of land degradation by salinization. The study area, in general, is exposed to a high risk of soil salinization.展开更多
The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap ...The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap by developing the Housing Contradiction Evaluation Weighted Index(HCEWI)model,making three key contributions to high-resolution housing monitoring.First,we establish a tripartite theoretical framework integrating dynamic population pressure(PPI),housing supply potential(HSI),and functional diversity(HHI).The PPI innovatively combines mobile signaling data with principal component analysis to capture real-time commuting patterns,while the HSI introduces a novel dual-criteria system based on Local Climate Zones(LCZ),weighted by building density and residential function ratio.Second,we develop a spatiotemporal coupling architecture featuring an entropy-weighted dynamic integration mechanism with self-correcting modules,demonstrating robust performance against data noise.Third,our 25-month longitudinal analysis in Shenzhen reveals significant findings,including persistent bipolar clustering patterns,contrasting volatility between peripheral and core areas,and seasonal policy responsiveness.Methodologically,we advance urban diagnostics through 500-meter grid monthly monitoring and process-oriented temporal operators that reveal“tentacle-like”spatial restructuring along transit corridors.Our findings provide a replicable framework for precision housing governance and demonstrate the transformative potential of mobile signaling data in implementing China’s“city-specific policy”approach.We further propose targeted intervention strategies,including balance regulation for high-contradiction zones,Transit-Oriented Development(TOD)activation for low-contradiction clusters,and dynamic land conversion mechanisms for transitional areas.展开更多
The vegetation ecosystem of the Qinghai–Tibet Plateau in China,considered to be the′′natural laboratory′′of climate change in the world,has undergone profound changes under the stress of global change.Herein,we a...The vegetation ecosystem of the Qinghai–Tibet Plateau in China,considered to be the′′natural laboratory′′of climate change in the world,has undergone profound changes under the stress of global change.Herein,we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity(NPP)in the Qinghai–Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models.Subsequently,we quantitatively distinguished the relative effects of climate change(such as precipitation,temperature and evapotranspiration)and human activities(such as grazing and ecological construction)on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data.The average annual NPP in the Qinghai–Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000–2015.With respect to the inter-annual changes,the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015,with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015.In the Qinghai–Tibet Plateau,the regions with the increase in NPP(change rate higher than 10%)were mainly concentrated in the Three-River Source Region,the northern Hengduan Mountains,the middle and lower reaches of the Yarlung Zangbo River,and the eastern parts of the North Tibet Plateau,whereas the regions with the decrease in NPP(change rate lower than–10%)were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau.The gravity center of NPP in the Qinghai–Tibet Plateau has moved southwestward during 2000–2015,indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part.Further,a significant correlation was observed between NPP and climate factors in the Qinghai–Tibet Plateau.The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai–Tibet Plateau,and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai–Tibet Plateau.Furthermore,the relative effects of climate change and human activities on the NPP changes in the Qinghai–Tibet Plateau exhibited significant spatial differences in three types of zones,i.e.,the climate change-dominant zone,the human activity-dominant zone,and the climate change and human activity interaction zone.These research results can provide theoretical and methodological supports to reveal the driving mechanisms of the regional ecosystems to the global change in the Qinghai–Tibet Plateau.展开更多
Sensitivity analysis is important for determining the parameters in the model calibration process.In our study,a variance-based global sensitivity analysis(extended Fourier amplitude sensitivity test,EFAST)was applied...Sensitivity analysis is important for determining the parameters in the model calibration process.In our study,a variance-based global sensitivity analysis(extended Fourier amplitude sensitivity test,EFAST)was applied to an agro-hydrological model(the SWAP(Soil-Water-Atmosphere-Plant model)model).The sensitivities of 20 parameters belonging to 4 categories(soil hydraulics,solute transport,root water uptake,and environmental stresses)for the simulated accumulated transpiration,dry matter(DM),and yield of sunflowers were analyzed under three nitrogen application rates(N1,N2,and N3),four salinity levels(S1,S2,S3,and S4),and three root distributions(R1,R2,and R3).The results indicated that for predominantly loamy soils,the high-impact parameters for accumulated transpiration,DM,and yield were the soil hydraulic parameters(αand n),critical stress index for compensatory root water uptake(ωc),the salt level at which salt stress starts(Pi),the decline of root water uptake above Pi(SSF),residual water content(θr),saturated water content(θs),and relative uptake of solutes by roots(TSCF).We also found that nitrogen application did not change the order of the parameter impacts on accumulated transpiration,DM,and yield.However,TSCF replacedαas the highest-impact parameter for the accumulated transpiration,DM,and yield at high salinity levels(S2 and S3).Furthermore,αwas also the highest-impact parameter for DM and yield under different root distributions,but the highest-impact parameters for transpiration wereωc,α,andθs under R1,R2,and R3,respectively.Nitrogen application could be neglected when considering the interactive effects of nitrogen application,salinity level,and root distribution on the transpiration,DM,and yield.Additionally,the mean values and uncertainties of the transpiration,DM,and yield were similar in all scenarios,except S3,which showed a sharp decrease in the mean values.We suggest determining the above eight parameters(α,n,ωc,Pi,SSF,θr,θs,and TSCF)and the saturated vertical hydraulic conductivity(Ks)based on rigorous calibrations with direct or indirect local measurements using economical methods(e.g.,a literature review),with limited observations for other parameters when using the SWAP model and other similar agro-hydrological models.展开更多
Gravity Recovery and Climate Experiment（GRACE） observations have been used to de-tect the co-seismic and post-seismic gravity field variations due to the Mw=9.3 Sumatra-Andaman earthquake that occurred on December 2...Gravity Recovery and Climate Experiment（GRACE） observations have been used to de-tect the co-seismic and post-seismic gravity field variations due to the Mw=9.3 Sumatra-Andaman earthquake that occurred on December 26,2004.This article focuses on investigating some gravita-tional effects caused by this huge earthquake.We computed the geoid height changes,the equivalent water height（EWH） changes,and the gravity changes using the GRACE Level-2 monthly spherical harmonic（SH） solutions released by University of Texas Center for Space Research（UTCSR）.The GRACE results agree well with the prediction by a dislocation model and are consistent with the results obtained by some previous scholars.In particular,we calculated the three components of the gravity gradient variations and found that they can recover the seismic-related signature more sensitively due to a certain degree of amplification of the signals.A positive-negative-positive mode predominates in the spatial distribution of the horizontal components of the gravity gradient variations,which is possibly attributed to the anomalies in the crustal density distribution caused by the uplift-subduction effect of the dip-slip earthquake.Moreover,the latitude components of the gravity gradient changes show strong suppression of the north-south stripes,which is due to the along-orbit measurements of the two GRACE satellites.We conclude that the posi-tive-negative-positive mode in latitude gravity gradient changes would be a more sensitive fea-ture to detect the deformations of some major dip-slip earthquakes by GRACE data.展开更多
The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Sc...The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.展开更多
Detecting the timing and amount of deformation is critical for understanding the physical causes and eventually warning of possible landslide hazards. Monitoring of deformation of structures and ground surface displac...Detecting the timing and amount of deformation is critical for understanding the physical causes and eventually warning of possible landslide hazards. Monitoring of deformation of structures and ground surface displacements during landslides can be accomplished by using different types of systems and techniques. Besides geotechnical or physical techniques, remote sensing techniques can be classified as satellite techniques, photo-grammetric techniques, geodetic techniques, ground based techniques, and so on. To study and govern growing geological disasters in China, especially in the Three Gorges area, Three Gorges Research Center for Geo-hazard （TGRG） is establishing an infra structure to ef-fectively and comprehensively analyze the mechanism of landslide deformation, focused on the Huangtupo landslide, using of various ad-vanced monitoring systems and techniques. In this article, the framework and latest advances of integration of multi remote sensing techniques in the infrastructure are presented. Different remote sensing techniques, data processing and integrating methods, and the latest results are discussed in detail. At last, reviews on current work and suggestions for further work are put forward.展开更多
We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time o...We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time of 2 days) in the monitoring of total suspended sediment (TSS) concentrations in dynamic water bodies using Poyang Lake, the largest freshwater lake in China, as an example. Field surveys conducted during October 17-26, 2009 showed a wide range of TSS concentration (3-524 mg/L). Atmospheric correction was implemented using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in ENVI with the aid of aerosol information retrieved from concurrent Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) surveys, which worked well at the CCD bands with relatively high reflectance. A practical exponential retrieval algorithm was created between satellite remote sensing reflectance and in-situ measured TSS concentration. The retrieved results for the whole water area matched the in-situ data well at most stations. The retrieval errors may be related to the problem of scale matching and mixed pixel. In three selected subregions of Poyang Lake, the distribution trend of retrieved TSS was consistent with that of the field investigation. It was shown that HJ-1A/1B CCD imagery can be used to estimate TSS concentrations in Poyang Lake over synoptic scales after applying an appropriate atmospheric correction method and retrieval algorithm.展开更多
Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote s...Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.展开更多
Dynamic flood disaster simulation is an emerging and promising technology significantly useful in urban planning,risk assessment,and integrated decision support systems.It is still an important issue to integrate the ...Dynamic flood disaster simulation is an emerging and promising technology significantly useful in urban planning,risk assessment,and integrated decision support systems.It is still an important issue to integrate the large assets such as dynamic observational data,numerical flood simulation models,geographic information technologies,and computing resources into a unified framework.For the intended end user,it is also a holistic solution to create computer interpretable representations and gain insightful understanding of the dynamic disaster processes,the complex impacts,and interactions of disaster factors.In particular,it is still difficult to access and join harmonized data,processing algorithms,and models that are provided by different environmental information infrastructures.In this paper,we demonstrate a virtual geographic environments-based integrated environmental simulation framework for flood disaster management based on the notion of interlinked resources,which is capable of automated accumulating and manipulating of sensor data,creating dynamic geo-analysis and three-dimensional visualizations of ongoing geo-process,and updating the contents of simulation models representing the real environment.The prototype system is evaluated by applying it as a proof of concept to integrate in situ weather observations,numerical weather and flood disaster simulation models,visualization,and analysis of the real time flood event.Case applications indicate that the developed framework can be adopted for use by decision-makers for short-term planning and control since the resulting simulation and visualization are completely based on the latest status of environment.展开更多
Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learn...Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.展开更多
Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full ...Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full 3D GIS platform： GeoScope, which provides a professional solution for the massive full three-dimensional geospatial data integration, management, analysis, visualization, and applications. GeoScope is characterized by： （1） extendible software architecture based on the hierarchical message bus, facilitates multimodal integrative applications of 2D GIS and 3D GIS; （2） unified 3D city models, support multiscale se- mantic representation of outdoor ＆ indoor and aboveground ＆ underground 3D objects; （3） high-efficient 3D geospatial database engine, supports integrated management of massive 3D geospatial data for real-time applications; and （4） high-performance visualization engine exploiting the massively parallel computation architecture of modem GPUs, supports real-time realistic rendering of large-scale complicated 3D geospatial environments. The successful pilot application of GeoScope is also illustrated with the 3D city models of 8494 knl2 of the whole Wuhan City, the largest city in middle China.展开更多
City lights,fishing boats,and oil fields are the major sources of nighttime lights,therefore the nighttime light images provide a unique source to map human beings and their activities from outer space.While most of t...City lights,fishing boats,and oil fields are the major sources of nighttime lights,therefore the nighttime light images provide a unique source to map human beings and their activities from outer space.While most of the scholars focused on application of nighttime light remote sensing in urbanization and regional development,the actual fields are much wider.This paper summarized the applications of nighttime light remote sensing into fields such as the estimation of socioeconomic parameters,monitoring urbanization,evaluation of important events,analyzing light pollution,fishery,etc.For estimation of socioeconomic parameters,the most promising progress is that Gross Domestic Product and its growth rate have been estimated with statistical data and nighttime light data using econometric models.For monitoring urbanization,urban area and its dynamics can be extracted using different classification methods,and spatial analysis has been employed to map urban agglomeration.As sharp changes of nighttime light are associated with important socioeconomic events,the images have been used to evaluate humanitarian disasters,especially in the current Syrian and Iraqi wars.Light pollution is another hotspot of nighttime light application,as the night light is related to some diseases and abnormal behavior of animals,and the nighttime light images can provide light pollution information on large scales so that it is much easier to analyze the effects of light pollutions.In each field,we listed typical cases of the applications.At last,future studies of nighttime light remote sensing have been predicted.展开更多
Based on gravity frequency shift effect predicted by general relativity theory, this study discusses an approach for determining the gravity potential(geopotential) difference between arbitrary two points P and Q by r...Based on gravity frequency shift effect predicted by general relativity theory, this study discusses an approach for determining the gravity potential(geopotential) difference between arbitrary two points P and Q by remote comparison of two precise optical clocks via optical fiber frequency transfer. After synchronization, by measuring the signal's frequency shift based upon the comparison of bidirectional frequency signals from P and Q oscillators connected with two optical atomic clocks via remote optical fiber frequency transfer technique, the geopotential difference between the two points could be determined, and its accuracy depends on the stabilities of the optical clocks and the frequency transfer comparison technique. Due to the fact that the present stability of optical clocks achieves 1.6×10-18 and the present frequency transfer comparison via optical fiber provides stabilities as high as 10-19 level, this approach is prospective to determine geopotential difference with an equivalent accuracy of 1.5 cm. In addition, since points P and Q are quite arbitrary, this approach may provide an alternative way to determine the geopotential over a continent, and prospective potential to unify a regional height datum system.展开更多
Based upon seven superconducting gravimeter （SG） records of 20 000 h length after the 2004 Sumatra earthquake, four methods, namely the ensemble empirical mode decomposition （EEMD）, the multi-station experiment （...Based upon seven superconducting gravimeter （SG） records of 20 000 h length after the 2004 Sumatra earthquake, four methods, namely the ensemble empirical mode decomposition （EEMD）, the multi-station experiment （MSE） technique, the autoregressive （AR） method and the product spec- trum analysis （PSA） method, are chosen jointly together to detect the inner core translational modes （1S1）. After the conventional pretreatment, each of the seven simultaneous residual gravity series is di- vided into five segments with an 80% overlap, and then EEMD is applied to all the 35 residual SG se- ries as a dyadic filter bank to get 35 filtered series. After then, according to different stations and dif- ferent time windows, five new simultaneous gravity datasets are obtained. After using MSE for each of the five new datasets, the AR method is used to demodulate some known harmonic signals from the new sequences that obtained by using MSE, and three demodulated product spectra are obtained. Then, according to two criterions, two clear spectral peaks at periods of 4.548 9±2.3×10^-5 and 3.802 3±3.2×10^-5 h corresponding respectively to the singlets m=-1 and m=＋l are identified from various spectral peaks, and they are close to the predictions of the 1066A model given by Rieutord （2002）, but no spectral peak corresponding to the singlet m=0 is found. We conclude that the selected two peaks might be the ob- served singlets of the Slichter triplet.展开更多
Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it...Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals(SDGs).Urban sprawl has resulted in unsustainable urban development patterns from social,environmental,and economic perspectives.This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality,Tanzania.Random Forest(RF)method was applied to accomplish imagery classification and location-based social media(Twitter usage)data were obtained through a Twitter Application Programming Interface(API).Morogoro urban municipality was classified into built-up,vegetation,agriculture,and water land cover classes while the classification results were validated by the generation of 480 random points.Using the Kernel function,the study measured the location of Twitter users within a 1 km buffer from the center of the city.The results indicate that,expansion of the city(built-up land use),which is primarily driven by population expansion,has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.In addition,social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area.The outcome of the study suggests that the combination of remote sensing,social sensing,and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro,and Africa city where data for urban planning is often unavailable,inaccurate,or stale.展开更多
Image-based relocalization is a renewed interest in outdoor environments,because it is an important problem with many applications.PoseNet introduces Convolutional Neural Network(CNN)for the first time to realize the ...Image-based relocalization is a renewed interest in outdoor environments,because it is an important problem with many applications.PoseNet introduces Convolutional Neural Network(CNN)for the first time to realize the real-time camera pose solution based on a single image.In order to solve the problem of precision and robustness of PoseNet and its improved algorithms in complex environment,this paper proposes and implements a new visual relocation method based on deep convolutional neural networks(VNLSTM-PoseNet).Firstly,this method directly resizes the input image without cropping to increase the receptive field of the training image.Then,the image and the corresponding pose labels are put into the improved Long Short-Term Memory based(LSTM-based)PoseNet network for training and the network is optimized by the Nadam optimizer.Finally,the trained network is used for image localization to obtain the camera pose.Experimental results on outdoor public datasets show our VNLSTM-PoseNet can lead to drastic improvements in relocalization performance compared to existing state-of-theart CNN-based methods.展开更多
The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. ...The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. In this study, we investigate if there is a systematic difference coming from the choice between the Vienna Mapping Function 1(VMF1) and the Global Mapping Function(GMF) for the modeling of Zenith Total Delay(ZTD) estimates, as well as the Integrated Precipitable Water Vapor(IPWV) estimates that are deduced from them. As ZTD estimates cannot be fully separated from coordinate estimates, we also investigated the coordinate repeatability between subsequent measurements.For this purpose, we monitored twelve GNSS stations on a global scale, for each of the three climatic zones(polar, mid-latitudes and tropical), with four stations on each zone. We used an automated processing based on the Bernese GNSS Software Version 5.2 by applying the Precise Point Positioning(PPP)approach, L3 Ionosphere-free linear combination, 7 cutoff elevation angle and 2 h sampling. We noticed an excellent agreement with the ZTD estimates and coordinate repeatability for all the stations w.r.t to CODE(the Center for Orbit Determination in Europe) and USNO(US Naval Observatory) products, except for the Antarctic station(Davis) which shows systematic biases for the GMF related results. As a final step, we investigated the effect of using two mapping functions(VMF1 and GMF) to estimate the IPWV,w.r.t the IPWV estimates provided by the Integrated Global Radiosonde Archive(IGRA). The GPS-derived IPWV estimates are very close to the radiosonde-derived IPWV estimates, except for one station in the tropics(Tahiti).展开更多
基金supported by the National Science Fund for Distinguished Young Scholars[grant number 61825103]the Fundamental Research Funds for The Central Universities[grant number 2042022kf1002].
文摘Block Adjustment(BA)is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery.For block adjustment with few ground-control points or without ground control,the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products.The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods,high positioning precision,and low acquisition cost,providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA.This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery(OSSI)and spaceborne laser altimeter data.Firstly,we elaborate the principle and necessity of this work through a literature review of existing methods.Then,the framework of our geo-positioning models.Secondly,four key technologies of the proposed model are expounded in order,including the acquisition and management of global Laser Control Points,the association of LCPs and OSSI,the block adjustment model combining LCPs with OSSI,and the accuracy estimation and quality control of the combined BA.Next,the combined BA experiment using Ziyuan-3(ZY-3)OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province,China.Experimental results prove that our method can automatically select LCPs with high accuracy.The elevation deviation of the combined BA eventually achieved the Mean Error(ME)of 0.06 m and the Root Mean Square Error(RMSE)of 1.18 m,much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment.A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.
基金supported by the National Natural Science Foundation of China (41975044)the Open Research Fund of the State Laboratory of Information Engineering in Surveying,Mapping,Remote Sensing,Wuhan University (20R02)+2 种基金the Fundamental Research Funds for the Central Universities,China University of Geosciences (Wuhan)(111-G1323520290)funded by SNSA (Dnr 96/16)the EU-Aid funded CASSECS Project。
文摘Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.
基金the Geo-information Science and Technology Program (No. IRT 0438)
文摘This study applied a computerized parametric methodology to monitor, map, and quantify land degradation by salinization risk detection techniques at a 1：250 000 mapping scale using geo-information technology. The northern part of the Shaanxi province in China was taken as a case. Multi-temporal remotely sensed materials of both Landsat TM and thematic maps （ETM＋） were used as the bases to provide comprehensive views of surface conditions such as vegetation cover and salinization detection. With ERDAS ver. 9.1 software, the Normalized Differential Salinity Index （NDSl） and Salinity Index （S.I.） were computed and then evaluated for land degradation by salinization. Arc/Info ver. 9.2 software was used along with field observation data （GPS） for analysis. Using spatial analysis methods, results showed that 19 973.1 km^2 （72%） of land had no risk of land degradation by salinization, 3 684.7 km^2 （13%） had slight land degradation by salinization risk, 2 797.9 km^2 （10%） had moderate land degradation by salinization risk, and 1 218.9 km^2 （4%） of the total land area was at a high risk of land degradation by salinization. The study area, in general, is exposed to a high risk of soil salinization.
基金National Natural Science Foundation of China(No.42101346)Undergraduate Training Programs for Innovation and Entrepreneurship of Wuhan University(GeoAI Special Project)(No.202510486196).
文摘The rapid urbanization and structural imbalances in Chinese megacities have exacerbated the housing supplydemand mismatch,creating an urgent need for fine-scale diagnostic tools.This study addresses this critical gap by developing the Housing Contradiction Evaluation Weighted Index(HCEWI)model,making three key contributions to high-resolution housing monitoring.First,we establish a tripartite theoretical framework integrating dynamic population pressure(PPI),housing supply potential(HSI),and functional diversity(HHI).The PPI innovatively combines mobile signaling data with principal component analysis to capture real-time commuting patterns,while the HSI introduces a novel dual-criteria system based on Local Climate Zones(LCZ),weighted by building density and residential function ratio.Second,we develop a spatiotemporal coupling architecture featuring an entropy-weighted dynamic integration mechanism with self-correcting modules,demonstrating robust performance against data noise.Third,our 25-month longitudinal analysis in Shenzhen reveals significant findings,including persistent bipolar clustering patterns,contrasting volatility between peripheral and core areas,and seasonal policy responsiveness.Methodologically,we advance urban diagnostics through 500-meter grid monthly monitoring and process-oriented temporal operators that reveal“tentacle-like”spatial restructuring along transit corridors.Our findings provide a replicable framework for precision housing governance and demonstrate the transformative potential of mobile signaling data in implementing China’s“city-specific policy”approach.We further propose targeted intervention strategies,including balance regulation for high-contradiction zones,Transit-Oriented Development(TOD)activation for low-contradiction clusters,and dynamic land conversion mechanisms for transitional areas.
基金supported by the Natural Science Foundation of Shandong Province(ZR2018BD001)the Project of Shandong Province Higher Educational Science and Technology Program(J18KA181)+4 种基金the Key Research Program of Frontier Science of Chinese Academy of Sciences(QYZDY-SSW-DQC007)the Open Fund of Key Laboratory of Geographic Information Science(Ministry of Education),East China Normal University(KLGIS2017A02)the Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University(17I04)the Open Fund of Key Laboratory of Geomatics and Digital Technology of Shandong Provincethe National Key R&D Program of China(2017YFA0604804)
文摘The vegetation ecosystem of the Qinghai–Tibet Plateau in China,considered to be the′′natural laboratory′′of climate change in the world,has undergone profound changes under the stress of global change.Herein,we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity(NPP)in the Qinghai–Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models.Subsequently,we quantitatively distinguished the relative effects of climate change(such as precipitation,temperature and evapotranspiration)and human activities(such as grazing and ecological construction)on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data.The average annual NPP in the Qinghai–Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000–2015.With respect to the inter-annual changes,the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015,with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015.In the Qinghai–Tibet Plateau,the regions with the increase in NPP(change rate higher than 10%)were mainly concentrated in the Three-River Source Region,the northern Hengduan Mountains,the middle and lower reaches of the Yarlung Zangbo River,and the eastern parts of the North Tibet Plateau,whereas the regions with the decrease in NPP(change rate lower than–10%)were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau.The gravity center of NPP in the Qinghai–Tibet Plateau has moved southwestward during 2000–2015,indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part.Further,a significant correlation was observed between NPP and climate factors in the Qinghai–Tibet Plateau.The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai–Tibet Plateau,and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai–Tibet Plateau.Furthermore,the relative effects of climate change and human activities on the NPP changes in the Qinghai–Tibet Plateau exhibited significant spatial differences in three types of zones,i.e.,the climate change-dominant zone,the human activity-dominant zone,and the climate change and human activity interaction zone.These research results can provide theoretical and methodological supports to reveal the driving mechanisms of the regional ecosystems to the global change in the Qinghai–Tibet Plateau.
基金the financial support from the Major Program of the National Natural Science Foundation of China(Nos.51879196 and 51790533)the China Postdoctoral Science Foundation Funded Project(No.2020M682475)the Fundamental Research Funds for the Central Universities,China(No.IWHR-SKL-KF201814)。
文摘Sensitivity analysis is important for determining the parameters in the model calibration process.In our study,a variance-based global sensitivity analysis(extended Fourier amplitude sensitivity test,EFAST)was applied to an agro-hydrological model(the SWAP(Soil-Water-Atmosphere-Plant model)model).The sensitivities of 20 parameters belonging to 4 categories(soil hydraulics,solute transport,root water uptake,and environmental stresses)for the simulated accumulated transpiration,dry matter(DM),and yield of sunflowers were analyzed under three nitrogen application rates(N1,N2,and N3),four salinity levels(S1,S2,S3,and S4),and three root distributions(R1,R2,and R3).The results indicated that for predominantly loamy soils,the high-impact parameters for accumulated transpiration,DM,and yield were the soil hydraulic parameters(αand n),critical stress index for compensatory root water uptake(ωc),the salt level at which salt stress starts(Pi),the decline of root water uptake above Pi(SSF),residual water content(θr),saturated water content(θs),and relative uptake of solutes by roots(TSCF).We also found that nitrogen application did not change the order of the parameter impacts on accumulated transpiration,DM,and yield.However,TSCF replacedαas the highest-impact parameter for the accumulated transpiration,DM,and yield at high salinity levels(S2 and S3).Furthermore,αwas also the highest-impact parameter for DM and yield under different root distributions,but the highest-impact parameters for transpiration wereωc,α,andθs under R1,R2,and R3,respectively.Nitrogen application could be neglected when considering the interactive effects of nitrogen application,salinity level,and root distribution on the transpiration,DM,and yield.Additionally,the mean values and uncertainties of the transpiration,DM,and yield were similar in all scenarios,except S3,which showed a sharp decrease in the mean values.We suggest determining the above eight parameters(α,n,ωc,Pi,SSF,θr,θs,and TSCF)and the saturated vertical hydraulic conductivity(Ks)based on rigorous calibrations with direct or indirect local measurements using economical methods(e.g.,a literature review),with limited observations for other parameters when using the SWAP model and other similar agro-hydrological models.
基金supported by the National Natural Science Foundation of China (Nos. 40974015,40637034)the Fund of Key Laboratory of Geodynamic Geodesy, Chinese Academy of Sciences (No. 09-18)the Fund of Key Laboratory of Geospace Environment and Geodesy,Ministry of Education,China (No. 07-12)
文摘Gravity Recovery and Climate Experiment（GRACE） observations have been used to de-tect the co-seismic and post-seismic gravity field variations due to the Mw=9.3 Sumatra-Andaman earthquake that occurred on December 26,2004.This article focuses on investigating some gravita-tional effects caused by this huge earthquake.We computed the geoid height changes,the equivalent water height（EWH） changes,and the gravity changes using the GRACE Level-2 monthly spherical harmonic（SH） solutions released by University of Texas Center for Space Research（UTCSR）.The GRACE results agree well with the prediction by a dislocation model and are consistent with the results obtained by some previous scholars.In particular,we calculated the three components of the gravity gradient variations and found that they can recover the seismic-related signature more sensitively due to a certain degree of amplification of the signals.A positive-negative-positive mode predominates in the spatial distribution of the horizontal components of the gravity gradient variations,which is possibly attributed to the anomalies in the crustal density distribution caused by the uplift-subduction effect of the dip-slip earthquake.Moreover,the latitude components of the gravity gradient changes show strong suppression of the north-south stripes,which is due to the along-orbit measurements of the two GRACE satellites.We conclude that the posi-tive-negative-positive mode in latitude gravity gradient changes would be a more sensitive fea-ture to detect the deformations of some major dip-slip earthquakes by GRACE data.
基金This work was supported in part by the National key R and D plan on strategic international scientific and technological innovation cooperation special project[grant number 2016YFE0202300]the National Natural Science Foundation of China[grant number 61671332,41771452,51708426,41890820,41771454]+1 种基金the Natural Science Fund of Hubei Province in China[grant number 2018CFA007]the Independent Research Projects of Wuhan University[grant number 2042018kf0250].
文摘The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.
基金supported by the National Basic Research Program of China (No. 2011CB707001)the National Natural Science Foundation of China (Nos. 41102209, 41102210, 41071291, 40802045 and 40821061)+5 种基金the Postdoctoral Science Foundation of China (No. 20110491232)the Natural Science Foundation of Hubei Province (No. 2010CDB04105)the Key Technology R & D Program of Wuhan (No. 201110821237)the Young Scientist Program of Wuhan (No. 201150431074)the Fundamental Research Funds for the Central Universities (Nos. CUG100705 and CUG100313)Additional support was provided by Ministry of Education of China (No. B07039)
文摘Detecting the timing and amount of deformation is critical for understanding the physical causes and eventually warning of possible landslide hazards. Monitoring of deformation of structures and ground surface displacements during landslides can be accomplished by using different types of systems and techniques. Besides geotechnical or physical techniques, remote sensing techniques can be classified as satellite techniques, photo-grammetric techniques, geodetic techniques, ground based techniques, and so on. To study and govern growing geological disasters in China, especially in the Three Gorges area, Three Gorges Research Center for Geo-hazard （TGRG） is establishing an infra structure to ef-fectively and comprehensively analyze the mechanism of landslide deformation, focused on the Huangtupo landslide, using of various ad-vanced monitoring systems and techniques. In this article, the framework and latest advances of integration of multi remote sensing techniques in the infrastructure are presented. Different remote sensing techniques, data processing and integrating methods, and the latest results are discussed in detail. At last, reviews on current work and suggestions for further work are put forward.
基金Supported by the National Basic Research Program of China(973Program)(No.2011CB707106)the National Natural Science Foundation of China(Nos.41071261,41023001,41021061,40906092,40971193,41101415)+3 种基金the Opening Foundation of Institute of Remote Sensing and Earth Sciences,Hangzhou Normal University(No.PDKF2010YG06)the Fundamental Research Funds for the Central Universities,the China Postdoctoral Science Foundation(No.20100480861)LIESMARS Special Research Funding,the Natural Science Foundation of Hubei Province,China(No.2009CDB107)the Natural Science Foundation of Zhejiang Province,China(No.Y5090143)
文摘We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time of 2 days) in the monitoring of total suspended sediment (TSS) concentrations in dynamic water bodies using Poyang Lake, the largest freshwater lake in China, as an example. Field surveys conducted during October 17-26, 2009 showed a wide range of TSS concentration (3-524 mg/L). Atmospheric correction was implemented using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in ENVI with the aid of aerosol information retrieved from concurrent Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) surveys, which worked well at the CCD bands with relatively high reflectance. A practical exponential retrieval algorithm was created between satellite remote sensing reflectance and in-situ measured TSS concentration. The retrieved results for the whole water area matched the in-situ data well at most stations. The retrieval errors may be related to the problem of scale matching and mixed pixel. In three selected subregions of Poyang Lake, the distribution trend of retrieved TSS was consistent with that of the field investigation. It was shown that HJ-1A/1B CCD imagery can be used to estimate TSS concentrations in Poyang Lake over synoptic scales after applying an appropriate atmospheric correction method and retrieval algorithm.
文摘Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.
基金This study is supported by the National High Technology Research and Development Program of China(863 Program)(Nos.2012AA121305 and 2013AA120701)the National Natural Science Foundation of China(Nos.41471320 and 41201440).
文摘Dynamic flood disaster simulation is an emerging and promising technology significantly useful in urban planning,risk assessment,and integrated decision support systems.It is still an important issue to integrate the large assets such as dynamic observational data,numerical flood simulation models,geographic information technologies,and computing resources into a unified framework.For the intended end user,it is also a holistic solution to create computer interpretable representations and gain insightful understanding of the dynamic disaster processes,the complex impacts,and interactions of disaster factors.In particular,it is still difficult to access and join harmonized data,processing algorithms,and models that are provided by different environmental information infrastructures.In this paper,we demonstrate a virtual geographic environments-based integrated environmental simulation framework for flood disaster management based on the notion of interlinked resources,which is capable of automated accumulating and manipulating of sensor data,creating dynamic geo-analysis and three-dimensional visualizations of ongoing geo-process,and updating the contents of simulation models representing the real environment.The prototype system is evaluated by applying it as a proof of concept to integrate in situ weather observations,numerical weather and flood disaster simulation models,visualization,and analysis of the real time flood event.Case applications indicate that the developed framework can be adopted for use by decision-makers for short-term planning and control since the resulting simulation and visualization are completely based on the latest status of environment.
基金This work is supported by the National Natural Science Foundation of China[grant number 61801336]the China Postdoctoral Science Foundation[grant number 2019M662717 and 2017M622521]the China Postdoctoral Program for Innovative Talent[grant number BX201700182].
文摘Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.
基金the National High Technology Research and Development Program of China (863 Program) (No. 2008AA121600)the National BasicResearch Program of China (973 Program)(No. 2010CB731801)the National Natural Science Foundation of China (No. 40871212)
文摘Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full 3D GIS platform： GeoScope, which provides a professional solution for the massive full three-dimensional geospatial data integration, management, analysis, visualization, and applications. GeoScope is characterized by： （1） extendible software architecture based on the hierarchical message bus, facilitates multimodal integrative applications of 2D GIS and 3D GIS; （2） unified 3D city models, support multiscale se- mantic representation of outdoor ＆ indoor and aboveground ＆ underground 3D objects; （3） high-efficient 3D geospatial database engine, supports integrated management of massive 3D geospatial data for real-time applications; and （4） high-performance visualization engine exploiting the massively parallel computation architecture of modem GPUs, supports real-time realistic rendering of large-scale complicated 3D geospatial environments. The successful pilot application of GeoScope is also illustrated with the 3D city models of 8494 knl2 of the whole Wuhan City, the largest city in middle China.
基金This work was supported by the Natural Science Foundation of Hubei Province(China)[grant number 2014CFB726]a Special Fund by Surveying and Mapping and Geo-information Research in the Public Interest(China)[grant number 201512026].
文摘City lights,fishing boats,and oil fields are the major sources of nighttime lights,therefore the nighttime light images provide a unique source to map human beings and their activities from outer space.While most of the scholars focused on application of nighttime light remote sensing in urbanization and regional development,the actual fields are much wider.This paper summarized the applications of nighttime light remote sensing into fields such as the estimation of socioeconomic parameters,monitoring urbanization,evaluation of important events,analyzing light pollution,fishery,etc.For estimation of socioeconomic parameters,the most promising progress is that Gross Domestic Product and its growth rate have been estimated with statistical data and nighttime light data using econometric models.For monitoring urbanization,urban area and its dynamics can be extracted using different classification methods,and spatial analysis has been employed to map urban agglomeration.As sharp changes of nighttime light are associated with important socioeconomic events,the images have been used to evaluate humanitarian disasters,especially in the current Syrian and Iraqi wars.Light pollution is another hotspot of nighttime light application,as the night light is related to some diseases and abnormal behavior of animals,and the nighttime light images can provide light pollution information on large scales so that it is much easier to analyze the effects of light pollutions.In each field,we listed typical cases of the applications.At last,future studies of nighttime light remote sensing have been predicted.
基金supported by the National Natural Science Foundation of China (Nos. 41631072, 41721003, 41574007, and 41429401)the Discipline Innovative Engineering Plan of Modern Geodesy and Geodynamics (No. B17033)+1 种基金the DAAD Thematic Network Project (No. 57173947)the International Space Science Institute (ISSI) 2017–2019
文摘Based on gravity frequency shift effect predicted by general relativity theory, this study discusses an approach for determining the gravity potential(geopotential) difference between arbitrary two points P and Q by remote comparison of two precise optical clocks via optical fiber frequency transfer. After synchronization, by measuring the signal's frequency shift based upon the comparison of bidirectional frequency signals from P and Q oscillators connected with two optical atomic clocks via remote optical fiber frequency transfer technique, the geopotential difference between the two points could be determined, and its accuracy depends on the stabilities of the optical clocks and the frequency transfer comparison technique. Due to the fact that the present stability of optical clocks achieves 1.6×10-18 and the present frequency transfer comparison via optical fiber provides stabilities as high as 10-19 level, this approach is prospective to determine geopotential difference with an equivalent accuracy of 1.5 cm. In addition, since points P and Q are quite arbitrary, this approach may provide an alternative way to determine the geopotential over a continent, and prospective potential to unify a regional height datum system.
基金supported by the National Natural Science Foundation of China(No.41174011)the National Natural Science Foundation of China(Nos.41128003,41021061,40974015)+2 种基金the National 973 Project of China(No.2013CB733305)the Fundamental Research Funds for the Central Universities(No.2012214020203)the Open Research Fund Program of the Key Laboratory of Geospace Environment and Geodesy,Ministry of Education,China(Nos.12-02-04,12-02-02)
文摘Based upon seven superconducting gravimeter （SG） records of 20 000 h length after the 2004 Sumatra earthquake, four methods, namely the ensemble empirical mode decomposition （EEMD）, the multi-station experiment （MSE） technique, the autoregressive （AR） method and the product spec- trum analysis （PSA） method, are chosen jointly together to detect the inner core translational modes （1S1）. After the conventional pretreatment, each of the seven simultaneous residual gravity series is di- vided into five segments with an 80% overlap, and then EEMD is applied to all the 35 residual SG se- ries as a dyadic filter bank to get 35 filtered series. After then, according to different stations and dif- ferent time windows, five new simultaneous gravity datasets are obtained. After using MSE for each of the five new datasets, the AR method is used to demodulate some known harmonic signals from the new sequences that obtained by using MSE, and three demodulated product spectra are obtained. Then, according to two criterions, two clear spectral peaks at periods of 4.548 9±2.3×10^-5 and 3.802 3±3.2×10^-5 h corresponding respectively to the singlets m=-1 and m=＋l are identified from various spectral peaks, and they are close to the predictions of the 1066A model given by Rieutord （2002）, but no spectral peak corresponding to the singlet m=0 is found. We conclude that the selected two peaks might be the ob- served singlets of the Slichter triplet.
基金This work is supported by the National Natural Science Foundation of China[Grants Number 41771452,41771454 and 41890820]the Natural Science Fund of Hubei Province in China[Grant Number 2018CFA007].
文摘Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals(SDGs).Urban sprawl has resulted in unsustainable urban development patterns from social,environmental,and economic perspectives.This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality,Tanzania.Random Forest(RF)method was applied to accomplish imagery classification and location-based social media(Twitter usage)data were obtained through a Twitter Application Programming Interface(API).Morogoro urban municipality was classified into built-up,vegetation,agriculture,and water land cover classes while the classification results were validated by the generation of 480 random points.Using the Kernel function,the study measured the location of Twitter users within a 1 km buffer from the center of the city.The results indicate that,expansion of the city(built-up land use),which is primarily driven by population expansion,has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.In addition,social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area.The outcome of the study suggests that the combination of remote sensing,social sensing,and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro,and Africa city where data for urban planning is often unavailable,inaccurate,or stale.
基金This work is supported by the National Key R&D Program of China[grant number 2018YFB0505400]the National Natural Science Foundation of China(NSFC)[grant num-ber 41901407]+1 种基金the LIESMARS Special Research Funding[grant number 2021]the College Students’Innovative Entrepreneurial Training Plan Program[grant number S2020634016].
文摘Image-based relocalization is a renewed interest in outdoor environments,because it is an important problem with many applications.PoseNet introduces Convolutional Neural Network(CNN)for the first time to realize the real-time camera pose solution based on a single image.In order to solve the problem of precision and robustness of PoseNet and its improved algorithms in complex environment,this paper proposes and implements a new visual relocation method based on deep convolutional neural networks(VNLSTM-PoseNet).Firstly,this method directly resizes the input image without cropping to increase the receptive field of the training image.Then,the image and the corresponding pose labels are put into the improved Long Short-Term Memory based(LSTM-based)PoseNet network for training and the network is optimized by the Nadam optimizer.Finally,the trained network is used for image localization to obtain the camera pose.Experimental results on outdoor public datasets show our VNLSTM-PoseNet can lead to drastic improvements in relocalization performance compared to existing state-of-theart CNN-based methods.
基金the innovation carrier project by Zhejiang provincial science and Technology Department (2017F10008)the French Space Agency (CNES) for their funding, through a DAR grant to the Geodesy Observatory of Tahiti
文摘The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. In this study, we investigate if there is a systematic difference coming from the choice between the Vienna Mapping Function 1(VMF1) and the Global Mapping Function(GMF) for the modeling of Zenith Total Delay(ZTD) estimates, as well as the Integrated Precipitable Water Vapor(IPWV) estimates that are deduced from them. As ZTD estimates cannot be fully separated from coordinate estimates, we also investigated the coordinate repeatability between subsequent measurements.For this purpose, we monitored twelve GNSS stations on a global scale, for each of the three climatic zones(polar, mid-latitudes and tropical), with four stations on each zone. We used an automated processing based on the Bernese GNSS Software Version 5.2 by applying the Precise Point Positioning(PPP)approach, L3 Ionosphere-free linear combination, 7 cutoff elevation angle and 2 h sampling. We noticed an excellent agreement with the ZTD estimates and coordinate repeatability for all the stations w.r.t to CODE(the Center for Orbit Determination in Europe) and USNO(US Naval Observatory) products, except for the Antarctic station(Davis) which shows systematic biases for the GMF related results. As a final step, we investigated the effect of using two mapping functions(VMF1 and GMF) to estimate the IPWV,w.r.t the IPWV estimates provided by the Integrated Global Radiosonde Archive(IGRA). The GPS-derived IPWV estimates are very close to the radiosonde-derived IPWV estimates, except for one station in the tropics(Tahiti).
