The Inversion Of Leaf Area Index (LAI) In The Greater Hinggan Mountains

Forests, as a principal component of terrestrial ecosystems, account for nearly25%of the world’s land and play an important role in the ecological balance and environment. Forests are the main important material basis and environment guarantee for the human, but also structure the most complex and most biologically diverse natural ecosystems. It is practically and theoretically significant for people to enhance the supervision of importance the forest and scientific utilization of forest resources, to in-depth study the forest and description the changes of carbon, water and energy.Leaf Area Index (LAI) is defined as the one-sided green leaf area per unit ground surface area, and it is one of the important parameters of canopy structure. LAI has become a hot academic issue in recently years. Leaf is the main alimentative organ of the plant for the material energy exchange with the outside world major organs. And photosynthesis occurs almost exclusively in the leaves. Transpiration also is forced from the leaves through hydathodes. LAI is already considered reference index for describe the forest resources worldwide, study the energy cycle and matter circulation between vegetation and the outside world. This article is in view of the greater hinggan mountains area, inversing LAI use the remote sensing data. The main research achievements are summarized as followed.Based on deeply understanding of LAI2200and TRAC’s principle, we designed experiment according to the tree of northeast part leaves in winter. Consistent with the same solar height angle in the same sample, got branches and woody-to-total area ratio of the sample through the measuring used TRAC in the winter and summer respectively. The reference ratio of branches and leaves area of the Fraxinus mandshurica forest in the experiment forestry farm is0.24, and larch forest’s is0.3.Collect more scene different time image to divide into four superiority tree forest categories used maximum likelihood classifier and decision tree method, based on significantly different phonological characteristics in the greater hinggan mountains area. And classification accuracy is up to76.85%.Established the statistical models used TM remote sensing image and field measured data based classified, include linear regression model, multivariate linear model and exponential model. This study shows that inversion accuracy of tree types is better than the global field’s. The exponential model is the best model for inversion of Leaf area index and the NDVI is the best fit result.To meet the requirement of high spatial resolution-MISR remote sensing data, combine the measured data form ground sample plots (30m*30m), and got verify data at large scales. Established the statistical models of three types in the greater hinggan mountains area based five scale model of Professor Chen jingming. Established the LAI lookup table for various observation angels of MISR data, and combine the classification data to inversion the LAI in the greater hinggan mountains area, the goodness of fit for eventually is76%.