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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 {4., Beijing normal university, State key laboratory of remote sensing Science, Beijing key laboratory for remote sensing of environment and digital Cities, Beijing {14., china department of Mathematics, Beijing normal university, Beijing {14., 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

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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 sensing Science,jointly sponsored by the Institute for remote sensing Applications of Chinese Academy of Sciences and Beijing normal university,Beijing 100101,china 2 Graduate university of the Chinese Academy of Sciences,Beijing 10004.,china 3 Institute of Forest Resource Information Technique,the Chinese Academy of Forestry,Beijing 100091,china 4.research Center for remote sensing and gis,{14. of geography and Beijing key laboratory for remote sensing of environment and digital {24.,Beijing normal university,Beijing 100875,china State key laboratory of remote sensing 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 154. 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 ±20.9 cm.A data trap sizes of 4. 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

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

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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 sensing Science, Institute of remote sensing Applications, Chinese Academy of Sciences, Beijing 100101, china 2. geography and environment College, Jiangxi normal university, Nanchang 330027, china 3. Graduate university of Chinese Academy of Sciences, Beijing 100039, china 4. Beijing {14. Institute of Uranium Geology, Beijing {14., china 5. Institute of Geographic Sciences and Natural Resources {14., Chinese Academy of Sciences, Beijing 100101, china 1. State key laboratory of remote sensing 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.

Comments on scaling effect of reciprocity in BRDF study

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Progress in Natural Science:Materials International 2004年第3期 95-96页

作者： ZHAO Hongrui* and WANG Jindi(research Center for remote sensing and gis, {4. of geography, Beijing normal university, Beijing key laboratory for remote sensing of environment and digital Cities, Beijing {14., china) researchCenterforremotesensingandgis DepartmentofGeographyBeijingNormalUniversityBeijingKeyLaboratoryforRemoteSensingofEnvironmentandDigitalCitiesBeijing100875China

Reciprocity principle is a common theory in electromagnetics and optics. It is also one of the general principles of the radiation transformation theory. However, in many remote sensing studies, this principle cannot be always supported by experimental data, especially when the scaling effect of the remote sensing image pixel is considered. The debate over the issue of whether the reciprocity principle can be used as a fundamental standard in evaluating the effectiveness of remote sensing observations has lasted for years. Using geometrical optics model, Li and Wan proved the existence of the scaling effect in applying the reciprocity principle to a remote sensing image pixel in 1998. In 2002, Snyder challenged Li’s proof, and attempted to prove that reciprocity principle is universally valid without any scale limitation. In this paper, we will argue with Snyder’s theory and point out that Snyder’s theory had neglected an important condition used in Li’s proof and, as a result, drawn the wrong conclusion. Here we will restate Li’s condition in his demonstration and offer a further proof to verify that reciprocity principle cannot be applied unconditionally to the study of bi directional reflectance distribution function (BRDF) of land surfaces in field or satellite remote sensing observation scale.

关键词： BRDF reciprocity scaling effect.

Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

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Science china Earth Sciences 2010年第S1期53卷 16-25页

作者： GUO ZhiFeng1,CHI Hong1,2 & SUN GuoQing1,3 1State key laboratory of remote sensing Science,Jointly Sponsored by the Institute of remote sensing Applications of Chinese Academy of Sciences and Beijing normal university,Institute of remote sensing Applications,Chinese Academy of Sciences,Beijing 100101,china 2Graduate university of Chinese Academy of Sciences,Beijing 10004.,china 3department of {14. university of Maryland,College Park,MD 2074.,USA 1. 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 Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China2. Graduate University of Chinese Academy of Sciences Beijing 100049 China3. Department of Geography University of Maryland College Park MD 20742 USA

The ecosystem in northeastern china and the Russian Far East is a hotspot of scientific research into the global carbon *** aboveground biomass(AGB) is an important component in the land surface carbon *** this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern china by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory *** resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf *** models were used to estimate biomass for all GLAS footprints of forest located in the study *** GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 *** data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74.t *** pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this *** was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estim

关键词： Disaster and environment Monitoring and Forecast Satellite ICESat GLAS waveform boreal forest forest aboveground biomass multiple linear regression

使用PB数据管道对象实现多数据库备份与更新

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计算机工程与应用 2004年第26期40卷 163-165页

作者：赵祥刘素红屈永华王培娟北京师范大学地理学与遥感科学学院遥感与地理信息系统研究中心北京100875 北京师范大学地理学与遥感科学学院遥感科学国家重点实验室北京100875 北京师范大学地理学与遥感科学学院环境遥感与数字城市北京市重点实验室北京100875

论文介绍了PowerBuilder数据管道(DataPipeline)对象的主要特点与作用,结合数据库存储过程的使用,提出后台多数据库备份与更新的策略及关键技术,并给出其实现方法,为多数据库维护与使用提供了一个实例。

关键词： PowerBuilder数据管道多数据库备份与更新

Using remote sensing to research Beijing wetlands dynamics

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Science china(Technological Sciences) 2006年第z2期49卷 97-107页

作者： ZHAO Wenji, GONG Zhaoning, GONG Huili, LI Xiaojuan, ZHANG Songmei & LI Jing key Lab of Resource environment and gis of Beijing, Capital normal {4., Beijing 100037, china Northeast Institute of geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, china College of Resources Science and Technology, Beijing normal {4., Beijing 100875, china Capital normal Univ Key Lab Resource Environm Beijing 100037 Peoples R China Capital normal Univ GIS Beijing Beijing 100037 Peoples R China Chinese Acad Sci NE Inst Geog & Agroecol Changchun 130012 Peoples R China Beijing normal Univ Coll Resources Sci & Technol Beijing 100875 Peoples R China

In Beijing, where wetlands are important to municipal freshwater conservation and biodiversity retention, three different types of wetlands were identified: riverside wetlands, wetlands surrounding lakes and reservoirs, and wetlands in municipal *** sensing technology was applied in combination with field investigations to monitor and analyze the changes in these wetlands, and a combination of fusion technologies,Landsat TM/ETM+ and IKONOS imaging, was used to investigate and map them. This study indicates that not only have wetland areas been reduced by half, but also their ecological environments have been degraded because of rapid economic development and population increase. Suggestions based on this research are made to reconstruct the ecological environment of the wetlands and return them to their previous state.

关键词： Beijing, wetland resource, remote sensing technology, ecological environment, dynamics.

A preliminary knowledge-driven prediction model of snail distribution in the Poyang Lake region

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Chinese Science Bulletin 2008年第1期53卷 115-123页

作者： ZHAO An1,2,3 & BAO ShuMing1 1 The key Lab of Poyang Lake Ecological environment and Resource Development, Jiangxi normal university, Nanchang 330022, {4. 2 Institute of Geographic Sciences and Resources research, Chinese Academy of Sciences, Beijing 100101, {4. 3 School of geography and environmental Sciences, Jiangxi normal university, Nanchang 330022, {4. The key Lab of Poyang Lake Ecological environment and Resource Development Jiangxi Normal University Nanchang China Institute of Geographic Sciences and Resources research Chinese Academy of Sciences Beijing China School of geography and environmental Sciences Jiangxi Normal University Nanchang China

Present monitoring and prediction of schistosomiasis’s intermediate parasite, snail, are based on re-mote sensing image’s spectral signatures, and the calculation result is in fact an incom-plete-constraints solution. TM image of the Poyang Lake region on October 31, 2005 was combined with gis thematic data (DEM, boundary of the Poyang Lake, vegetation, soil and land use) to make a prediction on snail spatial distribution in the region by remote sensing, geo-informatics and knowl-edge-driven modeling according to mechanism of snail occurrence. Result shows that with change of overall fuzzy membership of snail occurrence from high to low, snail occurrence of the snail samples of validation group goes up to 81% within 10% high fuzzy membership range, denoting high efficiency of the model in predicting snail occurrence.

关键词：鄱阳湖区域蜗牛分布模糊分类血吸虫病

基于Multigen/Vega的校园VR仿真系统的实现——以中国矿业大学为例

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系统仿真学报 2004年第9期16卷 1995-1998页

作者：宋金玲肖寒盛业华王锦地北京师范大学地理学与遥感科学学院遥感与地理信息系统研究中心遥感科学国家重点实验室环境遥感与数字城市北京市重点实验室北京100875 中国矿业大学环测学院徐州221008 南京师范大学地理科学学院南京210097

虚拟现实技术对于城市规划和城市的可持续发展有着非常重要的意义,它的长足进步,为真实景观的再现提供了有效手段。本论文在已有的二维地理信息系统数据的基础上进行校园数据的获取与组织,利用虚拟现实技术实现校园全景的三维重建,采用... 详细信息

虚拟现实技术对于城市规划和城市的可持续发展有着非常重要的意义,它的长足进步,为真实景观的再现提供了有效手段。本论文在已有的二维地理信息系统数据的基础上进行校园数据的获取与组织,利用虚拟现实技术实现校园全景的三维重建,采用层次型与面向对象相结合的三维数据结构,构造了校园全景的仿真模型,并实现了校园全景的驱动显示,为校园规划和可持续发展更可靠的保证。

关键词：虚拟现实 Multigen Vega 仿真