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Effective solutions to a global 3. visual system in networking environments

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science China Earth sciences 2005年第11期48卷 2032-2039页

作者： ZHANG Liqiang1,2. ZHANG Yan3. YANG Chongjun2. LIU Suhong1, REN Yingchao2. RUI Xiaoping2.& LIU Donglin2.1. state key {2. of remote {3. sciences, School of Geography and remote {3., Beijing Normal University, Beijing {13., China {14.s, Institute of remote {3. Applications, Chinese Academy of sciences, Beijing 101001, China 3. Institute of Geographical sciences and Natural Resources Research, Chinese Academy of sciences, Beijing 101001, China 1. state key {2. of {3.{4. sciences School of Geography and Remote Sensing Beijing Normal University 100875 Beijing China 2. Chinese Academy of sciences State Key Laboratory of Remote Sensing SciencesInstitute of Remote Sensing Applications 101001 Beijing China 3. Institute of Geographical sciences and Natural Resources Research Chinese Academy of Sciences 101001 Beijing China

The development of Web-based global 3. visual system has made progresses. However, there exist few mature and effective ways for managing, transmitting and visualizing massive spatial data. Based on the related work, the paper illustrates the ellipsoidal quadtree technique for rapid access multi-scale and multiple level geographical data, integrates the streaming with level-of-detail rendering method for transmitting the data on the network, and im-plements large-scale terrain surface simplification using M-band wavelet transforms and mul-tiresolution triangulations. We fulfil a web-based global terrain visual system using COM com-ponents on the basis of the above techniques. The system has a good prospect in the military simulation and city plans.

关键词： massive spatial data, streaming transmission, system architecture, M-band wavelet

Monitoring of rice crop using ENVISAT ASAR data

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science China Earth sciences 2006年第7期49卷 755-763页

作者： DONG Yanfang1, 2. SUN Guoqing1,3.& PANG Yong4.1. state {3. {2. of {4. {3. science, Institute of {4. {3. Applications, Chinese Academy of sciences, Beijing 100101, China 2. Graduate University of Chinese Academy of sciences, Beijing 10003., China 3. Department of Geography, University of Maryland, College Park, MD 2.74.0} USA 4. Forest {4. {3. Lab, Research Institute of Forest Resource and Information Technology, Chinese Academy of For-estry, Beijing 100091, China 1. state key {2. of {3.{4. science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China 2. Graduate University of Chinese Academy of sciences Beijing 100039 China 3. Department of Geography University of Maryland College Park MD 20742 USA 4. Forest remote sensing Lab Research Institute of Forest Resource and Information Technology Chinese Academy of Forestry Beijing 100091 China

Because of the cloudy conditions during the rice growth period, rice is one of the agri-cultural crops most suited to monitoring with the SAR instruments. Backscatter response measured by SAR is correlated with rice conditions, including height, density, biomass and structure, which are variable at different growing stages. In this paper, multi-date ENVISAT ASAR Alternating Polarization Mode (APMode) imageries were acquired during the rice crop growing cycle. At the same time, the rice parameters were measured in field. A continuous canopy model was used to compute the back-scattering from rice fields during the growth cycle, and the relationship between rice parameters and radar backscattering coefficients from both ASAR and modeling was analyzed. The effects of polari-zation, incidence angle and polarization on radar backscattering coefficients were analyzed. It was found that simulated radar backscatter has similar trends as ASAR data. This will be meaningful for the further research of rice parameters estimation from ASAR data. Different features show signifi-cantly different signatures in ASAR images and they follow some certain laws, so rice area can be accurately mapped by using multi-temporal SAR images, then rice yield can be estimated.

关键词： rice, ASAR, radar backscattering, model.

Estimating surface evapotranspiration using combined MODIS and CBERS-02.data

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science China(Technological sciences) 2005年第S2期48卷 145-160页

作者： XIN Xiaozhou1,2. LIU Qinhuo1, TANG Yong1,3. TIAN Guoliang1, GU Xingfa1, LI Xiaowen1,2. ZHENG Hongsheng4.& CHEN Jiayi4.1. state key {2. of remote {3. science, Institute of remote {3. Applications, Chinese Academy of sciences, Beijing 100101, China 2. Research Center for remote {3. and GIS, Department of Geography, Beijing Normal University, Beijing {14., China 3. Graduate University of Chinese Academy of sciences, Beijing 10003., China 4. Department of Atmospheric Physics, Peking University, Beijing 100871, China 1. state key {2. of {3.{4. science Institute of Remote Sensing Applications Chinese Academy of Sciences 100101 Beijing China2. Research Center for Remote Sensing and GIS Department of Geography Beijing Normal University 100875 Beijing China3. Graduate University of Chinese Academy of Sciences 100039 Beijing China4. Department of Atmospheric Physics Peking University 100871 Beijing China

Spatial scale error is one of the most serious problems in the estimates of land surface heat fluxes of sensible and latent from satellite-borne data such as MODIS 1km resolution reflectance and emissive data. One of the feasible and economic ways to decrease the spatial scale error is to use high resolution land use class data together with the MODIS data. CBERS-02.data were used to produce land use class of Baiyangdian area, Hebei Province, China in the autumn of 2.04. The area ratio of each class in MODIS pixel was calculated, and used to derive the heat fluxes of the mixed pixel. The results showed that the estimated heat fluxes of soil, sensible and latent have been changed remarkably after using the high resolution land class data. It could be concluded from the comparison between simulated and ground-measured fluxes as well as the theoretical analysis that high resolution land class data are useful to diminishing the scale error of heat fluxes estimated from low resolution satellite data.

关键词： surface evapotranspiration spatial scale error MODIS CBERS land class.

Semi-Supervised Classification based on Gaussian Mixture Model for remote imagery

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science China(Technological sciences) 2010年第S1期53卷 85-90页

作者： XIONG Biao1,ZHANG XiaoJun1 & JIANG WanShou1,2.1 state key {2. of Information Engineering in Surveying,Mapping and {4. {3.,Wuhan University,Wuhan 4.0079,China 2.state key {2. of {4. {3. {13.,Beijing,China 1. state key {2. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China2. State Key Laboratory of Remote Sensing Science Beijing China

Semi-Supervised Classification (SSC),which makes use of both labeled and unlabeled data to determine classification borders in feature space,has great advantages in extracting classification information from mass *** this paper,a novel SSC method based on Gaussian Mixture Model (GMM) is proposed,in which each class’s feature space is described by one *** show the proposed method can achieve high classification accuracy with small amount of labeled ***,for the same accuracy,supervised classification methods such as Support Vector Machine,Object Oriented Classification,*** be provided with much more labeled data.

关键词： remote sensing image classification Semi-Supervised Classification Gaussian Mixture Model EM algorithms

Multiple time scale evaluation of the energy balance during the maize growing season,and a new reason for energy imbalance

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science China Earth sciences 2009年第1期52卷 108-117页

作者： GUO JianXia1,2.BIAN LinGen3.& DAI YongJiu2.1 Meteorological Observation Center of China Meteorological Administration,Beijing 100081,China 2.{3. {4. {2. of remote {3. science,School of Geography and remote {3. science,Beijing Normal University,Beijing 100875,China 3.Chinese Academy of Meteorological sciences,Beijing 100081,China Meteorological Observation Center of China Meteorological Administration Beijing 100081 China state key {2. of {3.{4. science School of Geography and Remote Sensing Science Beijing Normal University Beijing 100875 China

Energy imbalance is a common problem associated with the measurement of surface energy using the eddy covariance *** the evaluation of the energy balance,people usually pay more attention to the statistical result that the effective energy(the sum of sensible and latent heat) is systematically lower than the available energy(the difference of net radiation and ground heat flux).However,little attention has been paid to the existence of the reversed situation when the effective energy is larger than the available energy or their contribution to the overall energy closure *** this paper,based on the analysis of the energy balance on multiple time scales across the maize growth season,we conclude that the non-synchronization of energy components is the main reason for the existence of the reversed *** shifting the phase of the effective energy components half an hour ahead,the rates of energy closure over all time scales are improved and dramatically reduce the number of the half-hourly samples when the energy ratio exceeds 1 or is below *** to the characteristics of the energy distribution and transformation over multiple time scales,latent heat is always the main type of energy cost,and the residual of the energy balance increases with the growth of the maize plant surpassing the sensible heat for seventy *** is suggested that the heat storage and photosynthetic energy play an important role in the energy balance during the growing period of maize.

关键词： maize energy balance multiple time scale energy hysteresis

Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

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science China Earth sciences 2010年第5期53卷 721-730页

作者： WANG DongWei1,2.3. WANG JinDi1,2.& LIANG ShunLin4.1 {3. key {2. of remote {3. science, Jointly Sponsored by Beijing Normal University and Institute of remote {3. Applications, CAS, Beijing 100875, China 2.School of Geography, Beijing key {2. of Environmental remote {3. and Digital City, Beijing Normal University, Beijing 100875, China 3.Haihe River Water Conservancy Commission, Tianjin 3.0170, China 4.Department of Geography, University of Maryland, College Park, MD 2.74.0} USA 1. state key {2. of {3.{4. science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications CAS Beijing 100875 China2. School of Geography Beijing Key Laboratory of Environmental Remote Sensing and Digital City Beijing Normal University Beijing 100875 China3. Haihe River Water Conservancy Commission Tianjin 300170 China4. Department of Geography University of Maryland College Park MD 20742 USA

Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this research is to assimilate multiple canopy reflectance values observed at different growth stages and a priori LAI values into a coupled crop growth and radiative transfer model sequentially using a variational data assimilation algorithm. Adjoint method is used to minimize the cost function. Any other information source can be easily incorporated into the inversion cost function. The validation results show that the time series of MODIS canopy reflectance can greatly reduce the uncertainty of the inverted LAI values. Compared with MODIS LAI product at Changping and Shunyi Counties of Beijing, this method has significantly improved the estimated LAI temporal profile.

关键词： inversion leaf area index crop growth model time series MODIS

Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

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science China Earth sciences 2010年第S1期53卷 92-98页

作者： ZHANG Hao1,LI XiaoWen1,2.CAO ChunXiang1,YANG Hua2.GAO MengXu1,3.ZHENG Sheng1,3.XU Min1,3.XIE DongHui2.JIA HuiCong1,JI Wei1,ZHAO Jian1,3.CHEN Wei1,3.& NI XiLiang1,3.1 {3. {4. {2. of remote {3. science,Jointly Sponsored by Institute of remote {3. Applications,Chinese Academy of sciences and Beijing Normal University,Beijing 100101,China 2.{3. {4. {2. of remote {3. science,Jointly Sponsored by Beijing Normal University and Institute of remote {3. Applications of Chinese Academy of sciences,Beijing 100875,China 3.Graduate University of Chinese Academy of sciences,Beijing 10004.,China 1. state key {2. of {3.{4. science Jointly Sponsored by Institute of Remote Sensing Applications Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China2. State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications of Chinese Academy of Sciences Beijing 100875 China3. Graduate University of Chinese Academy of Sciences Beijing 100049 China

The spatial distribution of sub-pixel components has an impact on retrieval accuracy,and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index(LAI).To investigate this effect,we constructed three realistic scenarios with the same LAI values and other properties,except that the simulated plants had different *** implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor(BRF) datasets based upon these simulated *** inversion was conducted using these data,which showed that spatial distribution affects retrieval *** inversion was also conducted for LAI based on charge-coupled device(CCD) data from the Environment and Disaster Monitor Satellite(HJ-1),which depicted both forest and drought-resistant crop land *** showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI *** spatial distribution of global fractal dimension index,which can be used to describe the area of sub-pixel components and their spatial distribution modes,shows good consistency with the coarse resolution LAI inversion error.

关键词： HJ-1 leaf area index scale effect spatial distribution

Information flow and controlling in regularization inversion of quantitative remote sensing

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science China Earth sciences 2005年第1期48卷 74-83页

作者： YANG Hua, XU Wangli, ZHAO Hongrui, CHEN Xue & WANG Jindi Research Center for remote sensing and GIS, School of Geography, Beijing Normal University, {2. {3.{4. of remote sensing science, Beijing {3.{4. for remote sensing of Environment and Digital Cities, Beijing {12., China Department of Mathematics, Beijing Normal University, Beijing {12., China 1. Research Center for remote sensing and GIS School of Geography Beijing Normal University State Key Laboratory of Remote Sensing Science Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities 100875 Beijing China 2. Department of Mathematics Beijing Normal University 100875 Beijing China

In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quantitative remote sensing model inversion is, thus control the information flow. Aiming at this, the paper takes the linear kernel-driven model inversion as an example. At first, the information flow in different inversion methods is calculated and analyzed, then the effect of information flow controlled by multi-stage inversion strategy is studied, finally, an information matrix based on USM is defined to control information flow in inversion. It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly. Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow. In regularization inversion of remote sensing, information matrix based on USM may be a better tool for quantitatively controlling information flow.

关键词： regularization inversion, information flow, Shannon entropy decrease, information matrix.

The quantification of remote sensing

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science China(Technological sciences) 2005年第S2期48卷 1-11页

作者： GU Xingfa1,2.3.TIAN Guoliang1,2. LI Xiaowen1,2.& GUO Jianning{3.1. state {{3. {2. of remote {3. science, Jointly Sponsored by the Institute of remote {3. Applications of Chinese Academy of sciences and Beijing Normal University, Beijing 100101, China 2. The Center for National Spaceborne Demonstration, Beijing 100101, China 3. Institut National de la Recherche Agronomique, Climat Sol Environnement, 8{3.1{3.Avignon, France {3. China Center for Resource Satellite Data and Application, Beijing 10083., China Chinese Acad Sci Inst Remote Sensing Applicat State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Beijing Normal Univ State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Ctr Natl Spaceborne Demonstrat Beijing 100101 Peoples R China Inst Natl Rech Agron F-84914 Avignon France China Ctr Resource Satellite Data & Applicat Beijing 100830 Peoples R China

The paper firstly defined the remote sensing information quantification, analyzed the necessity of developing remote sensing quantification, figured out the application guidelines requirement, and pointed out the importance of quantification research. Then taking the remote sensing application research of CBERS-02.data quantification as the example, the paper described the whole quantification system of “remotely sensed digital signal-radiation information-field parameter inversion”. Finally the paper gave the prospect for the development trend of the quantitative remote sensing.

关键词： remote sensing information quantification CBERS-02 radiometric calibration surface parame- ter reversion.