Method for Burned Forest Biomass Estimation at Subcompartment Level Using GF-1 images and GIS DatasetsXianlin Qin*, Lingyu Ying, Guifen Sun and Xiaofeng Zu
Research Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing, 100091, PR China
- *Corresponding Author:
- Xianlin Qin
Research Institute of Forest Resource Information Technique
Chinese Academy of Forestry, Beijing, 100091, PR China
E-mail: [email protected]
Received date: July 02, 2016; Accepted date: July 22, 2016; Published date: July 25, 2016
Citation: Qin X, Ying L, Sun G, Zu X (2016) Method for Burned Forest Biomass Estimation at Subcompartment Level Using GF-1 images and GIS Datasets. J Geogr Nat Disast 6:181 doi:10.4172/2167-0587.6-181
Copyright: © 2016 Qin X, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
To estimate the burned biomass and get the burned fuel types information by forest fire for the need of Chinese forestry management, basing on the fuel load from digital reference data, the combustion factor gotten from fieldwork, and the results of burned scar mapping by using the No.1 High-Resolution satellite (GF-1) of Chinese, the burned biomass estimation method at the subcompartment level has been developed using satellite images and Geography datasets. The method has been validated by the selected forest fire, which had taken place in Huangcaobai of Anning City, Yunnan province in year 2012.The total burned biomass is about 1.18 × 108 kg by using the panchromatic and multispectral scanners (PMS) image of GF-1; however, it is about 1.11 × 108 kg by using the Wide Coverage Image (WFV) of GF-1. The difference between them is 7.10 × 106 kg. This study also supplies a method for the single forest fire case when the fire radiative power (FRP) or fire radiative energy (FRE) of detected active fire points by using sparse low spatial resolution satellite images doesn’t satisfied the condition of Power Law distribution or Gaissian function.