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photosynthetically active radiation retrieval based on HJ-1A/B satellite data

science china Earth sciences 2010年第S1期53卷 81-91页

作者： LI Li1,XIN XiaoZhou1,SU GaoLi1,2 & LIU QinHuo1 1 state key Laboratory of remote sensing {4.,institute of remote sensing applications,chinese academy of sciences,beijing 100101,china 2 Zhejiang Climate Center,Hangzhou 310017,china 1. state key Laboratory of remote sensing {4. Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China2. Zhejiang Climate Center Hangzhou 310017 China

photosynthetically active radiation(pAr) is essential for plant photosynthesis and carbon cycle,and is also important for meteorological and environmental *** advance china's disaster and environmental monitoring capabilities,the HJ-1A/B satellites have been placed in Earth *** of their environmental monitoring objectives is the study of *** simulated direct solar,scattered and environment radiation between 4.0 and 700 nm under different atmospheric parameters(solar zenith angle,atmospheric water vapor,atmospheric ozone,aerosol optical thickness,surface elevation and surface albedo),and then established a look-up table between these input parameters and *** on the look-up table,we used HJ-1A/B aerosol and surface albedo outputs to derive the corresponding *** of inversed instantaneous and observed pAr values using HJ-1 Heihe experimental data had a root mean square error of 25.2 W m-2,with a relative error of 5.9%.The root mean square error for accumulated daily pAr and observed values was 0.4. MJ m-2,with a relative error of 3.5%.Our approach improved significantly the computational efficiency,compared with using directly radiation transfer *** also studied the sensitivity of various input parameters to photosynthetically active radiation,and found that solar zenith angle and atmospheric aerosols were sensitive pAr *** albedo had some effect on pAr,but water vapor and ozone had minimal impact on pAr.

关键词： photosynthetically active radiation 6S HJ-1A/B satellites

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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 {4.,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,Department of Geography and beijing key Laboratory for remote sensing of Environment and Digital Sites,beijing Normal University,beijing 100875,china state key Laboratory of remote sensing {4. 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

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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 {4.,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 Geography University of Maryland,College park,MD 2074.,USA 1. state key Laboratory of remote sensing {4. 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

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Loose architecture of multi-level massive geospatial data based on virtual quadtree

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science china(Technological sciences) 2008年第S1期51卷 114-123页

作者： YANG ChongJun,WU Sheng,rEN YingChao,FU Li,ZHANG FuQing,WANG Gang,TAN Jian,LIU DongLin,MA ChaoJi & LIANG Li state key Laboratory of remote sensing {4.,Jointly Sponsored by the institute of remote sensing applications of chinese academy of sciences and beijing Normal University,beijing 100101,china 1. state key Laboratory of remote sensing {4. Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China

This paper proposed a virtual quadtree (VQT) based loose architecture of multi-level massive geospatial data for integrating massive geospatial data dispersed in the departments of different hierarchies in the same sector into a unified GIS (Geographic Information System) platform. By virtualizing the nodes of the quad-tree,the VQT separates the structure of data organization from data storage,and screens the difference between the data storage in local computer and in the re-mote computers in network environment. And by mounting,VQT easily integrates the data from the remote computers into the local VQT so as to implement seam-less integration of distributed multi-level massive geospatial data. Based on that mode,the paper built an application system with geospatial data over 1200 GB distributed in 12 servers deployed in 12 cities. The experiment showed that all data can be seamlessly rapidly traveled and performed zooming in and zooming out smoothly.

关键词： massive geospatial data virtual quadtree loose architecture

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principle of small target detection for hyperspectral imagery

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science china Earth sciences 2007年第8期50卷 1225-1231页

作者： GENG Xiurui1,2,3 & ZHAO YongChao2,3,4.1 research Center for remote sensing and GIS, Department of Geography, beijing Normal University, beijing 100875, {4. 2 state key Laboratory of remote sensing science, chinese academy of sciences, beijing 100101, {4. 3 institute of Erectronics, chinese academy of sciences, beijing 100080, {4. 4.Center for for Assessment and Monitoring of Forest and Environmental resources and Division of Ecosystem sciences University of California Berkeley, CA 94.20-3114. USA 1. research Center for remote sensing and GIS Department of Geography Beijing Normal University Beijing 100875 China 2. state key Laboratory of remote sensing {4. Chinese Academy of Sciences Beijing 100101 China 3. institute of Erectronics Chinese Academy of Sciences Beijing 100080 China 4. Center for for Assessment and Monitoring of Forest and Environmental resources and Division of Ecosystem sciences University of California Berkeley CA 94720-3114 USA

This paper generalizes the progress of algorithms in small target detection for hyperspectral imaging, and finds that whitening the image is the key point of many methods in small target detection. An al-gorithm is presented to detect desired targets by converting large targets into small ones based on the weighted sample autocorrelation matrix.

关键词： hyperspectral whiten target detection

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A study on correlativity between Qinghai-Tibet plateau thermal infrared remote sensing data and underground temperature

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science china(Technological sciences) 2006年第z2期49卷 114-120页

作者： HAN Liqun, BI Siwen & SONG Shixin School of Information Engineering, beijing Technology and Business University, beijing 100037, china key Laboratory of {4. sensing Information sciences, institute of remote sensing applications, chinese academy of sciences, beijing 100101, china beijing Technol & Business Univ Sch Informat Engn Beijing 100037 Peoples R China chinese Acad Sci Inst Remote Sensing Applicat Key Lab Remote Sensing Informat Sci Beijing 100101 Peoples R China

Based on an analysis of the correlativity between Qinghai-Tibet plateau thermal infrared remote sensing data (QpTIrSD) and underground temperature field distribution, the main factors which obviously influence underground-layer temperatures were derived. Using neural network technology, a model was built to compute underground temperatures via parameters out of the inversion of thermal infrared remote sensing (TIrS) and then analyze the correlativity between above-ground parameters and underground temperatures. This method offers a new way to apply TIrS in monitoring the suture zone of a large-area massif as well as to research structural thermal anomalies.

关键词： Qinghai-Tibet plateau, TIrS, artificial neural network, temperature.

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Iterative implementation of the adaptive regularization yields optimality

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science china Mathematics 2005年第4期48卷 485-492页

作者： MA Qinghua & WANG Yanfei Department of Information sciences, College of Arts and science of beijing Union University, beijing 100038, {4. National key Laboratory on remote sensing science, institute of remote sensing applications, chinese academy of sciences, beijing 100101, {4. Department of Mathematics, University of Central Florida, p. O. Box 161364. Orlando, FL 32816-1364. USA 1. Department of Information sciences College of Arts and Science of Beijing Union University 100038 Beijing China 2. National key Laboratory on remote sensing science Institute of Remote Sensing Applications Chinese Academy of Sciences 100101 Beijing China

The adaptive regularization method is first proposed by ryzhikov et al. for the deconvolution in elimination of multiples. This method is stronger than the Tikhonov regularization in the sense that itis adaptive, i.e. it eliminates the small eigenvalues of theadjoint operator when it is nearly singular. We will show in this paper that the adaptive regularization can be implemented iterately. Some properties of the proposed non-stationary iterated adaptive regularization method are analyzed. The rate of convergence for inexact data is proved. Therefore the iterative implementation of the adaptive regularization can yield optimality.

关键词： ill-posed problems, non-stationary iterated adaptive regularization, optimality.

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Detection of the urban heat island in beijing using HJ-1B satellite imagery

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science china Earth sciences 2010年第S1期53卷 67-73页

作者： YANG Jun1,GONG peng2,ZHOU JinXing3,HUANG HuaBing2 & WANG Lei2 1 key Laboratory of Silviculture and Conservation,Ministry of Education,beijing Forestry University,beijing 100083,china 2 {4. key Laboratory of remote sensing science,jointly sponsored by the institute of remote sensing applications,chinese academy of sciences and beijing Normal University,beijing 100101,china 3 {14. of Desertification Studies,chinese academy of Forestry,beijing 100091 china 1. key Laboratory of Silviculture and Conservation Ministry of Education Beijing Forestry University Beijing 100083 China2. State Key Laboratory of Remote Sensing Science jointly sponsored by the Institute of Remote Sensing Applications Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China3. Institute of Desertification Studies Chinese Academy of Forestry Beijing 100091 China

Satellite images are used extensively in studying the urban heat island(UHI) *** evaluated the suitability of thermal infrared(TIr) data from the HJ-1B satellite for detecting UHI using a case study in *** modified algorithms for retrieving the land surface temperature(LST) from HJ-1B data were *** results were compared with LST images derived from a Landsat TM thermal band and the MODIS LST *** spatial pattern of UHI generated using HJ-1B data matched well with that produced using TM and MODIS *** the two algorithms,the mono-window algorithm performed better but further tests are *** more frequent coverage than TM and higher spatial resolution than MODIS,the HJ-1B TIr data present a unique opportunity to study thermal environments in cities in china and neighboring countries.

关键词： surface temperature NDVI mono-window algorithm single-channel algorithm

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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 Laboratory of remote sensing {4., institute of remote sensing applications, chinese academy of sciences, beijing 100101, china 2. research Center for remote sensing and GIS, Department of Geography, beijing Normal University, beijing 100875, china 3. Graduate University of chinese academy of sciences, beijing 100039, china 4. Department of Atmospheric physics, peking University, beijing 100871, china 1. state key Laboratory of remote sensing {4. 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 2004. 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.

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Study on movement inertia in the Earth multi-body system

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science china(Technological sciences) 2006年第z2期49卷 70-79页

作者： CHEN Xiaofei, BI Siwen & GONG Huili key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, beijing 100037, china National key Laboratory of {4. sensing science, institute of remote sensing applications (IrSA), chinese academy of sciences, beijing 100101, china Capital Normal Univ Minist Educ Key Lab D Informat Acquisit & Applicat Beijing 100037 Peoples R China chinese Acad Sci Inst Remote Sensing Applicat Natl Key Lab Remote Sensing Sci Beijing 100101 Peoples R China

This paper summarizes the first movement and mass centre of stratum-block movement inertia in the Earth multi-body system and introduces its application. It also elaborates the moment of inertia and the products of inertia: the inertia dyadic transformation of reference system and the parallel axis theorem of stratum-block movement inertia of the Earth multi-body system. It provides an academic foundation for research on the dynamic model of Earth multi-body system.

关键词： Earth multi-body system, stratum block, movement inertia.

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