The Study on Estimating Grassland NPP based on BP Neural Network Algorithm
Authors
Zhijun Tong, Xiangqian Li, Xingpeng Liu, Jiquan Zhang
Corresponding Author
Zhijun Tong
Available Online October 2018.
- DOI
- 10.2991/rac-18.2018.46How to use a DOI?
- Keywords
- grassland, NPP, estimate, BPNN, XilinGOL
- Abstract
Grassland NPP is an important component of the carbon cycle of terrestrial ecosystems, which can directly measure the carrying capacity of grassland ecosystem. Considering the disadvantages of the existing NPP estimated models, this study adopt BP neural network model to estimate the NPP for XilinGol grassland based on the meteorological, field sample, DEM and remote sensing data. Our results showed that: (1) The trained BPNN not only has excellent performance for estimating NPP for the trained dataset, but also has good generation and portability, which can be applied for other pixels; (2)The spatial pattern of the grassland NPP has obviously horizontal zonal regularity, which rises from 0 to >300gC/m2 from west to east.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhijun Tong AU - Xiangqian Li AU - Xingpeng Liu AU - Jiquan Zhang PY - 2018/10 DA - 2018/10 TI - The Study on Estimating Grassland NPP based on BP Neural Network Algorithm BT - Proceedings of the 8th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2018) PB - Atlantis Press SP - 295 EP - 300 SN - 2352-5428 UR - https://doi.org/10.2991/rac-18.2018.46 DO - 10.2991/rac-18.2018.46 ID - Tong2018/10 ER -