The estimation of above-ground biomass(AGB) and carbon storage is very important for arid and semi-arid ecosystems.HJ-1A/B satellite data combined with field measurement data was used for the estimation of shrub AGB and carbon storage in the Mu Us desert,China.The correlations of shrub AGB and spectral reflectance of four bands as well as their combined vegetation indexes were respectively analyzed and stepwise regression analysis was employed to establish AGB prediction equation.The prediction equation based on ratio vegetation index(RVI)was proved to be more suitable for shrub AGB estimation in the Mu Us desert than others.Shrub AGB and carbon storage were mapped using the RVI based prediction model in final.The statistics showed the western Mu Us desert has relatively high AGB and carbon storage,and that the gross shrub carton storage in Mu Us desert reaches 16 799 200 t,which has greatly contributed to the carbon fixation in northern China.
XU Min 1,2,CAO ChunXiang 1,TONG QingXi 1,LI ZengYuan 3,ZHANG Hao 1,HE QiSheng 1,2,GAO MengXu 1,2,ZHAO Jian 1,2,ZHENG Sheng 1,2,CHEN Wei 1,2 & ZHENG LanFen 1 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,Beijing 100101,China
We proposed a method to separate ground points and vegetation points from discrete return,small footprint airborne laser scanner data,called skewness change algorithm.The method,which makes use of intensity of laser scanner data,is especially applicable in steep,and forested areas.It does not take slope of forested area into account,while other algorithms consider the change of slope in steep forested area.The 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 DTM.This 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 m.In 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 cover.Validation of fractional vegetation cover was carried out using 15 hemispherical photographs,which are georeferenced to centimeter accuracy by differential GPS.The gap fraction was computed over a range of zenith angles 10° using the gap light analyzer(GLA) from each hemispherical photograph.The R2 for the regression of fractional vegetation cover from these ALS data and the respective field measurements is 0.7554.So this study presents a method for synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data.
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
Plague,caused by the gram-negative bacterium Yersinia pestis,is a serious and rapidly progressing illness in humans that can be fatal if not treated effectively.The Qinghai-Tibet Plateau is the largest area of natural Himalayan marmot(Marmota himalayana) plague foci in China and covers more than 630000 km2.Akesai County in Gansu Province is a part of this natural focus of plague and was chosen as a study area.Our study used an ecological niche modeling(ENM) approach to predict the potential distribution of the Himalayan marmot.Environment and Disaster Monitor Satellite(HJ-1) data was used to investigate environment factors that affect plague host animal activity.Host animal point data from active surveillance was combined with environmental variables from the HJ-1 satellite and other databases,and the models of the potential distribution of Himalayan marmot were produced with the Genetic Algorithm for Rule-Set Production(GARP).The probability of marmot presence was divided into 0-5%,5%-20%,20%-40%,40%-80%,and 80%-100% subgroups.Areas with 80%-100% probability exhibited the greatest potential for the presence of Himalayan marmot.According to the predicted potential distribution of Himalayan marmot in the study area,active surveillance of plague hosts and plague control and prevention could be more efficient.
GAO MengXu1,4,LI XiaoWen1,2,CAO ChunXiang1,ZHANG Hao1,LI Qun3,ZHOU Hang3 HE QiSheng1,4,XU Min1,4,ZHAO Jian1,4,ZHENG Sheng1,4 & CHEN Wei1,4 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,Beijing 100101,China
The leaf area index(LAI) is an important ecological parameter that characterizes the interface between vegetation canopy and the atmosphere.In addition,it is used by most process-oriented ecosystem models.This paper investigates the potential of HJ-1 CCD data combined with linear spectral unmixing and an inverted geometric-optical model for the retrieval of the shrub LAI in Wushen Banner of Inner Mongolia in the Mu Us Sandland.MODTRAN(Moderate Resolution Atmospheric Radiance and Transmittance Model) was used for atmospheric correction.Shrubland was extracted using the threshold of the normalized difference vegetation index,with which water bodies and farmland were separated,in combination with a vegetation map of the People's Republic of China(1:1000000).Using the geometric-optical model,we derive the per-pixel reflectance as a simple linear combination of two components,namely sunlit background and other.The fraction of sunlit background is related to the shrub LAI.With the support of HJ-1 CCD data,we employ linear spectral unmixing to obtain the fraction of sunlit background in an atmospherically corrected HJ image.In addition,we use the measured shrub canopy structural parameters for shrub communities to invert the geometric-optical model and retrieve the pixel-based shrub LAI.In total,18 sample plots collected in Wushen Banner of Inner Mongolia are used for validation.The results of the shrub LAI show good agreement with R2 of 0.817 and a root-mean-squared error of 0.173.
CHEN Wei1,2,CAO ChunXiang1,HE QiSheng1,2,GUO HuaDong3,ZHANG Hao1,2,LI RenQiang4,ZHENG Sheng1,2,XU Min1,2,GAO MengXu1,2,ZHAO Jian1,2,LI Sha1,NI XiLiang1,2,JIA HuiCong1,JI Wei1,TIAN Rong1,2,LIU Cheng1,2,ZHAO YuXing5 & LI JingLu6 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
A novel influenza A (H1N1) has been spreading worldwide. Early studies implied that international air travels might be key cause of a severe potential pandemic without appropriate containments. In this study, early outbreaks in Mexico and some cities of United States were used to estimate the preliminary epidemic parameters by applying adjusted SEIR epidemiological model, indicating transmissibility infectivity of the virus. According to the findings, a new spatial allocation model totally based on the real-time airline data was established to assess the potential spreading of H1N1 from Mexico to the world. Our estimates find the basic reproductive number R0 of H1N1 is around 3.4, and the effective reproductive number fall sharply by effective containment strategies. The finding also implies Spain, Canada, France, Panama, Peru are the most possible country to be involved in severe endemic H1N1 spreading.
A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in China's Mainland for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.
