Forest Biomass Estimation Based on Remote Sensing Method
Fuxiang Liu, Yuanyuan Zhang
Available Online July 2018.
- https://doi.org/10.2991/icesame-18.2018.11How to use a DOI?
- Forest biomass; Landsat TM data; Multiple Linear Regression; BP neutral net
- 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.
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 - Proceedings of the 2018 3rd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2018) PB - Atlantis Press SP - 53 EP - 58 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 -