检索结果-南通市图书馆

Information flow and controlling in regularization inversion of quantitative remote sensing

在线全文

学校读者我要写书评

暂无评论

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, {2. Normal University, State {3. laboratory of remote sensing Science, {2. {3. laboratory for remote sensing of {12. and {13. cities, {2. 100875, china Department of Mathematics, {2. Normal University, {2. 100875, 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.

Synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data

在线全文

学校读者我要写书评

暂无评论

Science china(Technological Sciences) 2008年第S2期51卷 176-187页

作者： BAO YunFei1,2,CAO ChunXiang1,ZHANG Hao1,CHEN ErXue3.HE QiSheng1,2,HUANG HuaBing1,LI ZengYuan3.LI XiaoWen1,4 & GONG Peng1 1 State key laboratory of remote {3. Science,jointly sponsored by the Institute for remote {3. Applications of Chinese Academy of Sciences and beijing Normal University,beijing 100101,china 2 Graduate University of the Chinese Academy of Sciences,beijing 100049,china 3.Institute of Forest Resource Information Technique,the Chinese Academy of Forestry,beijing 100091,china 4 Research Center for remote {3. and GIS,Department of Geography and beijing key laboratory for remote {3. of environment and digital Sites,beijing Normal University,beijing 100875,china State key laboratory of {2. {3. Science jointly sponsored by the Institute for Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China Institute of Forest Resource Information Technique the Chinese Academy of Forestry Beijing 100091 China Research Center for remote sensing and GIS Department of Geography and Beijing Key Laboratory for Remote Sensing of Environment and Digital Sites Beijing Normal University Beijing 100875 China

We proposed a method to separate ground points and vegetation points from discrete return,small footprint airborne laser scanner data,called skewness change *** method,which makes use of intensity of laser scanner data,is especially applicable in steep,and forested *** does not take slope of forested area into account,while other algorithms consider the change of slope in steep forested *** ground points and vegetation points can be used to estimate digital terrain model(DTM) and fractional vegetation cover,respectively.A few vegetation points which were classified into the ground points were removed as noise before the generation of *** method was tested in a test area of 10000 square meters.A LiteMapper -5600 laser system was used and a flight was carried out over a ground of 700―800 *** this tested area,a total number of 1546 field measurement ground points were measured with a total station TOPCON GTS-602.and TOPCON GTS -7002.for validation of DTM and the mean error value is -18.5 cm and the RMSE(root mean square error) is ±2..9 cm.A data trap sizes of 4m in diameter from airborne laser scanner data was selected to compute vegetation fraction *** of fractional vegetation cover was carried out using 15 hemispherical photographs,which are georeferenced to centimeter accuracy by differential *** gap fraction was computed over a range of zenith angles 10° using the gap light analyzer(GLA) from each hemispherical *** R2.for the regression of fractional vegetation cover from these ALS data and the respective field measurements is *** this study presents a method for synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data.

关键词： LIDAR intensity skewness synchronous DTM fractional vegetation cover

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

在线全文

学校读者我要写书评

暂无评论

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 State 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 2.742. USA 1. State key laboratory of {2. {3. 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 {2., 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

在线全文

学校读者我要写书评

暂无评论

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 State key laboratory of remote {3. Science,Jointly Sponsored by Institute of remote {3. Applications,Chinese Academy of Sciences and {12. Normal University,{12. 100101,china 2.State key laboratory of remote {3. Science,Jointly Sponsored by {12. Normal University and Institute of remote {3. Applications of Chinese Academy of Sciences,{12. 100875,china 3.Graduate University of Chinese Academy of Sciences,{12. 100049,china 1. State key laboratory of {2. {3. 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 {2. 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

Development of environmental monitoring satellite systems in china

在线全文

学校读者我要写书评

暂无评论

Science china Earth Sciences 2010年第S1期53卷 1-7页

作者： LI GuoPing1 & CAO ChunXiang2.1 School of Earth and Space Sciences,Peking University,beijing 100871,china 2.State {2. {3. of remote sensing Science,Jointly Sponsored by the Institute of remote sensing Applications of Chinese Academy of Sciences and beijing Normal University,beijing 100101,china 1. School of Earth and Space Sciences Peking University Beijing 100871 China2. State Key Laboratory of Remote Sensing Science Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China

With the increase in global environmental problems,the necessity and urgency of remote {2. technology being applied to environmental monitoring has been widely recognized around the *** has launched the environment and disaster monitoring and forecasting small satellite constellation HJ-1A/B and the FY3.atmosphere and environmental satellite,but they still cannot fully satisfy requirements for environmental *** paper summarizes the current status of satellite environmental monitoring in china and the existing problems of inadequate load design and low data utilization efficiency,and discusses the demand for environmental monitoring *** on the development of foreign satellite systems for environmental monitoring,the future development and {12. tasks of the environmental monitoring satellite system in china is discussed,as are some related initiatives.

关键词： environmental satellite environmental monitoring global climate change specialty load satellite system

The quantification of remote sensing

在线全文

学校读者我要写书评

暂无评论

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 Jianning4 1. State key {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, 84914 Avignon, France 4. 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.

Biomass estimation and uncertainty analysis based on CBERS-02.CCD camera data and field measurement

在线全文

学校读者我要写书评

暂无评论

Science china(Technological Sciences) 2005年第S2期48卷 116-128页

作者： CHEN Liangfu1,2. GAO Yanhua1,3. CHENG Yu1,3. WEI Zheng1,3. XIAO Qing1,4, LIU Qinhuo1, YU Tao1, LIU Qijing5, GU Xingfa1 & TIAN Guoliang1 1. State key laboratory of remote {3. Science, Institute of remote {3. Applications, Chinese Academy of Sciences, beijing 100101, china 2. Geography and environment College, Jiangxi Normal University, Nanchang 3.002., china 3. Graduate University of Chinese Academy of Sciences, beijing 10003., china 4. beijing Research Institute of Uranium Geology, beijing 100875, china 5. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, beijing 100101, china 1. State key laboratory of {2. {3. Science Institute of Remote Sensing Applications Chinese Academy of Sciences 100101 Beijing China 2. Geography and environment College Jiangxi Normal University 330027 Nanchang China 3. Graduate University of Chinese Academy of Sciences 100039 Beijing China 4. beijing Research Institute of Uranium Geology 100875 Beijing China 5. Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences 100101 Beijing China

The study site is located in Qianyanzhou experimental station and the surrounding area. Based on CBERS-02.satellite data and field measurement, we not only discussed the relationship between NDVI and biomass of two species of coniferous plantations, namely, Pinus massoniana Lamb and Pinus elliottii Engelm, but also introduced the biomass models based on NDVI. The comparison between measured biomass in Qianyanzhou and biomass derived from CBERS-02.CCD data showed that it is feasible to estimate biomass based on NDVI. But its limitations cannot be ignored. This kind of model depends on the dominant vegetation species. There are some effect factors in estimating biomass based on NDVI. This paper analyzes these factors based on fine-resolution CBERS-02.CCD data and some conclusions are drawn: In Qianyanzhou, the area with good vegetation coverage, the nonlinearity of NDVI has little influence on scaling-up of NDVI. As a result of surface heterogeneity, scaling-up can cause NDVI within each pixel to change. Because scaling-up can cause pixel attribute to change, the applicability of biomass model is one of the sources of error in estimating biomass.

关键词： NDVI biomass scaling effect scaling-up.

Study of coastal water zone ecosystem health in Zhejiang Province based on remote sensing data and GIS

在线全文

学校读者我要写书评

暂无评论

Acta Oceanologica Sinica 2010年第5期29卷 27-34页

作者： CHEN Zhenghua PAN Delu BAI Yan School of the environment Gangxi University Nanning 530004 China State environmental Protection key {2. of Satellite {3. sensing Ministry of Environmental Protection Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China State key laboratory of Satellite Ocean {2. Dynamics Second Institute of Oceanography State Oceanic Administration Hangzhou 310012 China

The coastal ecosystem health assessment is a field of increasing *** this paper,a preliminary assessment of ecosystem health in Zhejiang coastal water zone was made,mainly based on remote sensing data and GIS *** spatial and quantitative evaluation was facilitated by the progress of remote sensing and GIS technique ***,human activities,hydrology and ecosystem problems in the study area were discussed and ***,from 4 aspects of human stress,physical,chemical and biological responses to anthropogenic activities and natural stress,several indicators such as water transparency（Secchi Disk Depth,SDD）,suspended substance concentration,dissolved inorganic nitrogen,active phosphate,chlorophyll,harmful algae bloom,as well as distribution of sewage,sea lanes and port were ***,the Analytic Hierarchical Process was used for indicator weight calculation,and the ecosystem health criteria were established according to the integrative analysis of national water quality criteria,similar coastal ecosystem health research in other places or data inherent *** results indicated that from 2.05 to 2.07 the coastal water ecosystem health value in Zhejiang Province was unhealthy and needs ecological restoration by human intervention.

关键词： coastal water zone ecosystem health indicator remote sensing GIS

Parallel algorithm for real-time contouring from grid DEM on modern GPUs

在线全文

学校读者我要写书评

暂无评论

Science china(Technological Sciences) 2010年第S1期53卷 33-37页

作者： CHEN Zhuo,SHEN Lei,ZHAO YanQing & YANG ChongJun State key laboratory of {2. {3. Science,Jointly Sponsored by the Institute of {2. {3. Applications of Chinese Academy of Sciences and beijing Normal University,beijing 100101,china 1. State key laboratory of {2. {3. Science Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China

A real-time algorithm for constructing contour maps from grid DEM data is *** runs completely within the programmable 3. visualization *** interpolation is paralleled by rasterizer units in the graphics card,and contour line extraction is paralleled by pixel *** each frame of the rendering,we first make an elevation gradient map out of original terrain vertex data,then figure out the final contour lines with image-space processing,and directly blend the results on the original scene to obtain a final scene with contour map using *** implement this method in our global 3.-{2.earth system with Direct3.?9.0c API and tested on some consumer level PC *** arbitrary scene with certain LOD level,the process takes less than 10 ms,giving topologically correct,anti-aliased contour lines.

关键词： contour map contour lines paralleled computing 3. terrain GPU digital earth