Assessment of total suspended sediment concentrations in Poyang Lake using HJ-1A/1B CCD imagery

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.

Progresses on SAR Remote Sensing of Tropical Forests:Forest Biomass Retrieval and Analysis of Changing Weather Conditions

This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation(ID:32278),sub-project Multi-baseline SAR Processing for 3 D/4 D Reconstruction(ID:322782).The work here reported focuses on two important aspects of SAR remote sensing of tropical forests,namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions.Recent studies have shown that by using SAR tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest Above Ground Biomass(AGB).However,the two parameters that determine this linear relationship might vary for different tropical forest sites.For purpose of solving this problem,we investigate the possibility of using Li DAR derived AGB to help training the two parameters.Experimental results obtained by processing data from the Tropi SAR campaign support the feasibility of the proposed concept.This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging,for which we simulate BIOMASS repeat pass tomography using ground-based Tropi SCAT data with a revisit time of 3 days and rainy days included.The resulting backscattered power variation at 30 m is within 1.5 d B.For this forest site,this error is translated into an AGB error of about 50~80 t/hm^(2),which is 20%or less of forest AGB.

Earth Observation Brain(EOB):an intelligent earth observation system

Since the twenty-first century,with the rapid development of high-resolution earth observation satellites,the earth observation satellite system has developed from the initial single satellite observation model to the current satellite constellation formed by light and small satellites observation model.All-weather and all-directional fine earth observation can now be realized.In the future,the satellite constellation,communication satellites,navigation satellites,and aircrafts are linked through dynamic linking network to form an air-space information network to realize real-time services of intelligent air-space information.To further enhance the perception,cognition,and quick response ability of the network,we propose the concept and model of the Earth Observation Brain(EOB)−the intelligent earth system based on events perception in this paper.Then,some key technologies needed to be solved in the EOB are also described.An application example is illustrated to show the process of perception and cognition in the primary stage of the EOB.In the future,EOB can observe what change of what object,the when and where to push these right information to mobile terminal of right people at the right time and right place.Global users can obtain any data,information,and knowledge in real-time through the EOB.

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.

On generalised and specialised spatial information grids: are geo-services ready?

After the set-up of a spatial data infrastructure(SDI)and a national information infrastructure(NII)in many countries,the provision of geo-services became one of the most important and attractive tasks.With the integration of global positioning system(GPS),geographic information system(GIS)and remote sensing(RS),we can,in principle,answer any geo-spatial related question:when and where what object has which changes?An intelligent geo-service agent could provide end-users with the most necessary information in the shortest time and at the lowest cost.Unfortunately there is still a long way to go to achieve such goals.The central component in such geo-services is the integration of the spatial information system with a computing grid via wire-and wireless communication networks.This paper will mainly discuss the grid technology and its integration with spatial information technology,expounding potential problems and possible resolutions.A novel categorising of information grids in the context of geospatial information is proposed:generalised and specialised spatial information grids.

Geoinformatics education in China

The paper gives an overview of the current status of education in geoinformatics in China.First,the paper provides a brief introduction to the history of geoinformatics education in China and a general review of the scientific and technological development.It then presents how the development affects the education and training in China.In the paper,universities and institutes in China that can award academic degrees related to geoinformatics are summarized,and undergraduate majors are briefly introduced.Next,the paper reports the work having been done by the national expert group on Surveying and Mapping,including the revision of discipline catalog and guide for graduate education and requirements.A list of typical curricula in geoinformatics education is suggested.Activities on promoting the graduate student exchange platform are presented.Finally,a case study of geoinformatics education in Wuhan University is discussed.

Iteration convergence condition modeling for single spaceborne SAR image direct positioning

Single SAR image direct positioning is to determine the ground coordinate for each pixel in the SAR image assisted with a reference DEM.During this procedure,an iterative procedure is essentially needed to solve the uncertainty in elevation of each pixel in the SAR image.However,such an iterative procedure may suffer from the problem of divergence in shaded and serious layover areas.To investigate this problem,we performed a theoretical analysis on the convergence conditions that has not been intensively studied till now.The Range-Doppler(RD)model was simplified and then the general surface is degenerated into a planar surface.Mathematical deduction was then carried out to derive the convergence conditions and the impact factors for the convergence speed were evaluated.The theoretical findings were validated by experiments for both simulated and real scenarios.

Absolute geolocation accuracy of high-resolution spotlight TerraSAR-X imagery–validation in Wuhan

TerraSAR-X(TSX)can acquire high-resolution SAR images and due to its high orbit precision as well as its ability to acquire data from different off-nadir viewing angles,the high-precision stereo geolocation can be obtained.In this study,we investigate the absolute geolocation accuracy of TSX high-resolution images in Wuhan,China.We present a direct stereo SAR geolocation method and analyze the 2D and 3D geoposition accuracy of two corner reflectors.The sub-meter localization accuracy was achieved using only atmospheric correction information available in the TSX metadata.

Distributed cache replacement method for geospatial data using spatiotemporal localitybased sequence

Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.

Geospatial big data for urban planning and urban management

The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines.For example,a huge number of Landsat images are utilized in mapping high-resolution global forest cover and the global forest changes in the twenty-first century are explored(Hansen et al.2013),which is impossible without the support of geospatial big data and the related automatic processing techniques.Based on the huge enterprise registration data in China,the economic and social development situations and trends are revealed by the non-statistic data and novel approaches(Li et al.2018).City-wide fine-grained urban population distribution at building level is achieved by integrating and fusing multisource geospatial big data(Yao et al.2017),which is usually not desired in traditional research.Geospatial Big Data provides a new transforming paradigm of scientific research especially at the crossroads of broad disciplines,including but not limited to the humanities,the physical sciences,engineering,and so on.

Statistical multiresolution analysis in amplitude- frequency domain

A concept of statistical multiresolution analysis in amplitude-frequency domain is proposed, which is to employ the wavelet transform on the statistical character of a signal in amplitude domain. In terms of the theorem of generalized ergodicity, an algorithm to estimate the transform coefficients based on the amplitude statistical multiresolution analysis (AMA) is presented. The principle of applying the AMA to Synthetic Aperture Radar (SAR) image processing is described, and the good experimental results imply that the AMA is an efficient tool for processing of speckled signals modeled by the multiplicative noise.

Spatiotemporal change patterns of urban lakes in China’s major cities between 1990 and 2015

China has experienced unprecedented urbanization in the past decades,resulting in dramatic changes in the physical,limnological,and hydrological characteristics of lakes in urban landscapes.However,the spatiotemporal dynamics in distribution and abundance of urban lakes in China remain poorly understood.Here,we characterized the spatiotemporal change patterns of urban lakes in China’s major cities between 1990 and 2015 using remote-sensing data and landscape metrics.The results showed that the urban lake landscape patterns have experienced drastic changes over the past 25 years.The total surface area of the urban lakes has decreased by 17,620.02 ha,a decrease of 24.22%,with a significant increase in the landscape fragmentation and a reduction in shape complexity.We defined three lake-shrinkage types and found that vanishment was the most common lake-shrinkage pattern,followed by edge-shrinkage and tunneling in terms of lake area.Moreover,we also found that urban sprawl was the dominant driver of the lake shrinkage,accounting for 67.89%of the total area loss,and the transition from lakes to cropland was also an important factor(19.86%).This study has potential for providing critical baseline information for government decision-making in lake resources management and urban landscape design.

Geoprocessing in Cloud Computing platforms-a comparative analysis

The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and cost
effective geoprocessing services to geospatial users.This paper provides a comparative analysis of geoprocessing in Cloud Computing platforms
Microsoft Windows Azure and Google App Engine.The analysis compares differences in the data storage,architecture model,and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms;emphasizes the importance of virtualization;recommends applications of hybrid geoprocessing Clouds,and suggests an interoperable solution on geoprocessing Cloud services.The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern,once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies.The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms.The tested services are developed using geoprocessing algorithms from different vendors,GeoSurf and Java Topology Suite.The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.

Tessellations in GIS:Part I—putting it all together

This article attempts to describe the role of tessellated models of space within the discipline of geographic information systems(GIS)—a speciality coming largely out of geography and land surveying,where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps.We look at some of the basic operations in GIS,including dynamic and kinetic applications.We examine issues of topology and data structures and produce a tessellation model that may be widely applied both to traditional“object”and“field”data types.Based on this framework,it can be argued that tessellation models are fundamental to our understanding and processing of geographical space,and provide a coherent framework for understanding the“space”in which we exist.This first article examines static structures,and a subsequent article looks at“change”—what happens when things move.

Potential loess landslide deformation monitoring using L-band SAR interferometry

Multi-temporal InSAR technique can implement continuous earth surface deformation detection with long time scale and wide geographical coverage.In this paper,we first employ the Small Baseline Subset method to survey potential landslides in Guide County,Qinghai Province,which is identified as a loess landslide prone area for geological and climate conditions.Two anomalous deformation regions are detected by L-band Phased Array and L-band Synthetic Aperture Radar stacks.Then,qualitative and quantitative evaluations of the measuring points are given for understanding the distribution regularity of deformation.Finally,preliminary correlation between the time-series deformation and triggering factors is analyzed to explore the driving mechanism for landslide movement.The results demonstrate that L-band SAR has high potential in landslide monitoring applications and can be used as the basis for landslide recognizing,precursory information extracting,and early warning.

Indoor scene texturing based on single mobile phone images and 3D model fusion

Realistic texture mapping and coherent up-to-date rendering is one of the most important issues in indoor 3-D modelling.However,existing texturing approaches are usually performed manually during the modelling process,and cannot accommodate changes in indoor environments occurring after the model was created,resulting in outdated and misleading texture rendering.In this study,a structured learning-based texture mapping method is proposed for automatic mapping a single still photo from a mobile phone onto an alreadyconstructed indoor 3-D model.The up-to-date texture is captured using a smart phone,and the indoor structural layout is extracted by incorporating per-pixel segmentation in the FCN algorithm and the line constraints into a structured learning algorithm.This enables real-time texture mapping according to parts of the model,based on the structural layout.Furthermore,the rough camera pose is estimated by pedestrian dead reckoning(PDR)and map information to determine where to map the texture.The experimental results presented in this paper demonstrate that our approach can achieve accurate fusion of 3-D triangular meshes with 2-D single images,achieving low-cost and automatic indoor texture updating.Based on this fusion approach,users can have a better experience in virtual indoor3-D applications.

Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives

A spatial web portal(SWP)provides a web-based gateway to discover,access,manage,and integrate worldwide geospatial resources through the Internet and has the access characteristics of regional to global interest and spiking.Although various technologies have been adopted to improve SWP performance,enabling high-speed resource access for global users to better support Digital Earth remains challenging because of the computing and communication intensities in the SWP operation and the dynamic distribution of end users.This paper proposes a cloud-enabled framework for high-speed SWP access by leveraging elastic resource pooling,dynamic workload balancing,and global deployment.Experimental results demonstrate that the new SWP framework outperforms the traditional computing infrastructure and better supports users of a global system such as Digital Earth.Reported methodologies and framework can be adopted to support operational geospatial systems,such as monitoring national geographic state and spanning across regional and global geographic extent.

Information processing for unmanned aerial vehicles(UAVs)in surveying,mapping,and navigation

Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping.

A novel water index for urban high-resolution eight-band WorldView-2 imagery

Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images still a challenging problem.This paper develops a novel water index named as NDWI-MSI,combining a new normalized difference water index(NDWI)and a recently developed morphological shadow index(MSI),to delineate water bodies from eight-band WorldView-2 imagery.The newly available bands(e.g.coastal,yellow,red-edge,and near-infrared 2)of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations.Through our testing,a new NDWI is defined in this study.In addition,MSI,a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas.The NDWI-MSI is created by combining NDWI and MSI,which is able to highlight water bodies and simultaneously suppress shadow areas.In experiments,it is shown that the new water index can achieve better performance than traditional NDWI,and even supervised classifiers,for example,maximum likelihood classifier,and support vector machine.

An Elliptic Centerness for Object Instance Segmentation in Aerial Images

Instance segmentation in aerial images is an important and challenging task.Most of the existing methods have adapted instance segmentation algorithms developed for natural images to aerial images.However,these methods easily suffer from performance degradation in aerial images,due to the scale variations,large aspect ratios,and arbitrary orientations of instances caused by the bird’s-eye view of aerial images.To address this issue,we propose an elliptic centerness(EC)for instance segmentation in aerial images,which can assign the proper centerness values to the intricate aerial instances and thus mitigate the performance degradation.Specifically,we introduce ellipses to fit the various contours of aerial instances and measure these fitted ellipses by two-dimensional anisotropic Gaussian distribution.Armed with EC,we develop a one-stage aerial instance segmentation network.Extensive experiments on a commonly used dataset,the instance segmentation in aerial images dataset(iSAID),demonstrate that our proposed method can achieve a remarkable performance of instance segmentation while introducing negligible computational cost.