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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 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 100049,china 3Department of Geography university of Maryland,College Park,MD 20742,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

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 100049,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 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 ±20.9 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

Cascade recursion models of computing the temperatures of underground layers

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science china(Technological sciences) 2006年第z2期49卷 30-38页

作者： HAN Liqun, BI Siwen & SONG Shixin School of Information Engineering, beijing Technology and Business university, beijing 100037, china state key laboratory of remote sensing science, 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 State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China

An RBF neural network was used to construct computational models of the underground temperatures of different layers, using ground-surface parameters and the temperatures of various underground layers. Because series recursion models also enable researchers to use above-ground surface parameters to compute the temperatures of different underground layers, this method provides a new way of using thermal infrared remote sensing to monitor the suture zones of large areas of blocks and to research thermal anomalies in geologic structures.

关键词： RBF network, underground temperature, modeling, cascade, recursion.

Improvement of a 3D radar backscattering model using matrix-doubling method

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science china Earth sciences 2010年第7期53卷 1029-1035页

作者： NI WenJian1,2, GUO ZhiFeng1 & SUN GuoQing3 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, beijing 100101, china 2 Graduate university of chinese academy of sciences, beijing 100049, china 3 Department of Geography, university of Maryland, College Park, MD 27042, 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 Beijing 100101 China2. Graduate University of Chinese Academy of Sciences Beijing 100049 China3. Department of Geography University of Maryland College Park MD 27042 USA

Radiative transfer models have been widely used to simulate the radar backscattering from forested areas. A three-dimensional radar backscatter model of forest canopy developed in previous studies takes full account of spatial position of trees in a forest stand, and the interactions among crown, trunk and ground surface. The model predicted well for the co-polarized backscatter measurements, but underestimated the backscattering for cross-polarization, primarily because only the first-order scattering within tree crowns was considered in the model. The backscattering at cross-polarization depends strongly on multiple scatter- ing within tree crowns. To produce good estimations for cross-polarized component, the matrix-doubling method is employed here to compute multiple-scattering within the crown. The modified model is compared with the original model, and the field forest measurements and AIRSAR data are used for validation of the modified model. The cross-polarization backscattering is improved in different degrees for different crown structures and at different bands.

关键词： remote sensing forestry electromagnetic propagation radar backscattering model matrix-doubling

Latent ancestral graph of structure vector autoregressive models

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Journal of Systems Engineering and Electronics 2010年第2期21卷 233-238页

作者： Wei Gao1,2and Zheng Tian2,3 1.School of Statistics,Xi’an university of Finance&Economics,Xi’an 710061,P.R.china 2.Department of Applied Mathematics,Northwestern Polytechnical university,Xi’an 710072,P.R.china 3.state key laboratory of remote sensing science,institute of remote sensing applications,chinese academy of sciences,beijing 100101,P.R.china School of Statistics Xi'an University of Finance & EconomicsXi'an 710061P.R.China Department of Applied Mathematics Northwestern Polytechnical UniversityXi'an 710072P.R.China Department of Applied Mathematics Northwestern Polytechnical UniversityXi'an 710072P.R.China state key laboratory of remote sensing science Institute of Remote Sensing ApplicationsChinese Academy of SciencesBeijing 100101P.R.China

A class of latent ancestral graph for modelling the dependence structure of structural vector autoregressive （VAR） model affected by latent variables is proposed. The graphs are mixed graphs with possibly two kind of edges, namely directed and bidirected edges. The vertex set denotes random variables at dif- ferent times. In Gaussian case, the latent ancestral graph leads to a simple parameterization model. A modified iterative conditional fitting algorithm is presented to obtain maximum likelihood esti- mation of the parameters. Furthermore, a log-likelihood criterion is used to select the most appropriate models. Simulations are performed using illustrative examples and results are provided to demonstrate the validity of the methods.

关键词： multivariate time series latent ancestral graph itera- tive conditional fitting.

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 key laboratory of remote sensing 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 For-estry, beijing 100091, china 1. state key laboratory of remote sensing 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.

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

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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 science,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 science 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 400 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 0.49 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

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 remote 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.

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, china National key laboratory on remote sensing science, institute of remote sensing applications, chinese academy of sciences, beijing 100101, china 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.