Quantitative Retrieval Methods And Data Assimilation Technology For Vegetational Parameters In Alpine Wetland

Characterized by the high altitude, cold and dry weather, unitary and heterogeneousdistributed vegetation, the alpine wetland is extreme fragile and vulnerable. Once theecological environment is destroyed, the problems of degradation and reversesuccession are arose. The state of alpine wetland can be indicated by the condition ofvegetation, which can be represented directly or indirectly by the parameters of thevegetation, such as leaf area index (LAI). Therefore, it is meaningful to monitor thestate of the alpine wetland dynamically by the parameters of vegetation in space andtime.In this study, we made the purpose to retrieve the LAI of vegetation in space andtime in Wutumeiren prairie located in Qinghai province based on multi-source remotesensing images and field measurements. The methods and technology of quantitativeretrieval and data assimilation were used in the process. Then, a prototype system ofvegetation retrieval was developed. The major accomplishments are listed as follow.(1) The Landsat TM and MODIS data were used to retrieve the LAI in alpinewetland and to map spatial distribution of LAI of study area in early July and lateAugust,2011based on the ACRM model and the algorithm of look-up table. Thetechnology of sensitive analysis of ACRM model, the classification of vegetation andregularization of retrieved results were used to deal with the problems of heterogeneousdistributed vegetation and ill-posed inversion. The retrieved results were very promisingcompared to the field measured LAI values that the correlation of the measured LAIvalues and retrieved LAI values (R2) reached to0.95, and the root-mean-square-error(RMSE) was0.33for late August,2011, while the R2reached up to0.82and RMSE was0.25for early July,2011.(2) The moderate-resolution imaging spectroradiometer (MODIS) data were usedto retrieval LAI based on ACRM model. Then, the retrieved LAI values were importedinto the dynamic model though the algorithm of ensemble Kalman Filter to update thestate variables of the model to estimate the temporal distribution of LAI in the studyarea during the time of2011. The fitted logistic model, rather than complicated vegetation growth model, was used as the dynamic model to deal with the problem ofthe lack of prior information. The assimilated result was very promising compared tothe field measured LAI in early July,2011that the R2reached up to0.79, and the RMSEwas0.30.(3) A prototype system of vegetation quantitative retrieval was developed, whichintegrated the models and algorithms that included in the process of quantitativeparameters retrieval and data assimilation. The prototype system was capable ofderiving and simulating key parameters of herbaceous vegetation distribution in spaceand time series.