Methods Of Modeling Forest Biomass Based On Remote Sensing Information

The research aims to construct forset biomass model based on remote sensing information, topogaphy and climate data according to the theory of geographical similitude.At present the methods of modeling forset biomass are three kinds. The first one is using statistic method by correlation analysis and regression to obtain linear equation. This kind models can not explain the mechanism and lack of logic between the parameters. Many statistic rules have been gotten from different conditions and different regions. The models can not scaling in spatial and time domain, So that they only apply to the certain region, certain time and certain thing.The second is neural network model which is complete black system and no help for explaining the mechanism and has the same shortcomings with the first kind. The third is the mechanism model, ie process-based model, which simulate the bionomics process. This kind models have many parameters that are difficult to get, and complex equation, so the practicality is limited. This research adopts geographical similitude standards for modeling to solve the problems mentioned above.The biomass data and forest information used for model computed are converted from the sixth forest inventory 199 pieces sample plots investigated in 2001 located in mountain area north beijing. The remote sensing information derived from landsat5 TM image captured on Aug 31th 2001and Sep 8th 2004 and rectified to orthoimage . the topographic data such as slope, aspect computed from the DEM 1:250,000. The climate data that are processed to image in GRID format are the 30 years average value from 1971 to 2001 observed by beijing climate center. The independence factors groups and biomass model are established according to geographical similitude standards . The geographical indexes and geographical parameter are computed. The biomass of the plots 3 years later is predicted by the model through calculating the remote sensing information in 2004. while trying the new modeling method, using statistic method by correlation analysis and step regression, linear equation was gotten and compared with the model established based on theory of geographical similitude in model accuracy. This research firstly establish the forest biomass remote sensing model based on the theory of geographical similitude phenomena. This modeling method not only uses the rules discovered but also considers the random and fuzzy of the factors. The model is consistent with the growth equation used in forest for many years coincidently. The independence factors groups in forest specialty and remote sensing were derived, such as age, Photosynthesis, forest absorbing reflection ratio of TM image. Especially Photosynthesis independence factors group is combined from NASA-CASA model and MaAinai NPP model. The vegetation covering ratio f_g is firstly used in the remote sensing biomass model. The different tree species in different age class models were established which were used to predicted 3 years later biomass of the plots. The vegetation index (NDVI) was transformed successfully between different time according to MODIS vegetation index season changes and used in prediction computed. The model is based on image pixels, so can calculate image and produce biomass image.