Proceedings of the 2018 3rd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2018)

Forest Biomass Estimation Based on Remote Sensing Method

Authors
Fuxiang Liu, Yuanyuan Zhang
Corresponding Author
Fuxiang Liu
Available Online July 2018.
DOI
https://doi.org/10.2991/icesame-18.2018.11How to use a DOI?
Keywords
Forest biomass; Landsat TM data; Multiple Linear Regression; BP neutral net
Abstract
Using the Landsat 5 TM images in 2002 as source data, the paper constructed individual tree biomass models of seven principal species based on the data from field surveying and fixed Plots in Tahe and Amur forest Region in Daxiangan Mountains. The remote sensing biomass model between TM images and data from forest fixed Plots was developed by the methods of multiple linear regression and BP neutral net. The result showed that R in multiple linear regression model was 0.764 and the model passed the F test, D-W test and multi-collinearity test. In the independent sample estimation, the neutral net model with the precision of 91.25% was significantly higher than multiple linear regression model with the precision of 81.02%. Although the “black-box” neutral net model could not give the concrete analytical equation, this kind of model with high precision might be applied to estimate the forest biomass in large level forest biomass.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Fuxiang Liu
AU  - Yuanyuan Zhang
PY  - 2018/07
DA  - 2018/07
TI  - Forest Biomass Estimation Based on Remote Sensing Method
BT  - 2018 3rd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2018)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/icesame-18.2018.11
DO  - https://doi.org/10.2991/icesame-18.2018.11
ID  - Liu2018/07
ER  -