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Estimation of Forest Biomass and Its Temporal and Spatial Distribution Patterns Analysis in Maoershan Forest Farm

Author: SongLiZuo
Tutor: WangWeiFang
School: Northeast Forestry University
Course: Forest Management
Keywords: Forest biomass Topographic factors Interpolation Regression model BP neural network model
CLC: S718.5
Type: Master's thesis
Year: 2010
Downloads: 96
Quote: 0
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Abstract


In this thesis, Northeast Forestry University cap child Mountain Experimental Forest Farm TM image and the 1990 and 2004 fixed sample survey data , the use of geographic information systems interpolation , multiple regression equation method and neural network model was performed to estimate forest biomass in the region . The first Northeast Forestry University the cap child Mountain Experimental Forest in 1990 and 2004 fixed sample survey data - and a biomass prediction model , calculated by Lin at all sorts of biomass per unit area . ' And then randomly selected 192 samples use interpolation to get the rest of the study area plots of forest biomass . After paired samples t-test , no significant difference in the two sets of data obtained interpolation forest biomass estimation . Select environmental factors , including remote sensing factor of 16 variables principal component analysis , stepwise regression , screened six main components to the establishment of a multiple regression equation , equation coefficient test equation test , autocorrelation test , find R = 0.812 . In MATLAB7.0 environment by constructing the BP neural network model to estimate the biomass plots come to an accuracy of 96.063% . Generate the study area in 1990 and 2004 , biomass distribution , for comparison, drawn after 15 years of operating cap child Mountains biomass showed an increasing trend in the use of interpolation . To analyze the relationship between biomass and topographical factors , we can see biomass showed a significant correlation with altitude and slope .

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CLC: > Agricultural Sciences > Forestry > Forestry basic science > Forest Biology > Forest Ecology
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