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Method for national fuel types classification based on multi-source data

  • role： First author第一作者
  • Affiliation：

    Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China

  • Email：lixiaotong36@163.com
  • Introduction：李晓彤，研究方向为林业遥感、森林火险预警技术。E-mail：lixiaotong36@163.com
LI Xiaotong,  
  • Affiliation：

    Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China

LIU Qian,  
  • role： Corresponding author通信作者
  • Affiliation：

    Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China

  • Email：noaags@ifrit.ac.cn
  • Introduction：覃先林，研究方向为林业遥感及森林灾害监测。E-mail：noaags@ifrit.ac.cn
QIN Xianlin*,  
  • Affiliation：

    Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China

LIU Shuchao,  
  • Affiliation：

    Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China

WANG Chongyang

résumé

The rate and intensity of forest fire spread can be predicted, and forest fire prevention measures can be formulated according to the classifying results of fuel types. Accurately exploring the types and spatial distribution of fuel is crucial for predicting the occurrence of forest fires, predicting forest fire behavior, commanding fire-fighting, and biological fire prevention. At present, most researches on fuel types classification based on remote sensing in China are carried out in local areas, but research based on the national scale will become one of the trends in this field. To meet the needs of China’s national-scale fuel types mapping, a fuel type classification system was developed combined with the characteristics of vegetation distribution and phenology in Chinabased on the previous research. The fuels in China, including forest, shrub, and grass, were classified and mapped based on MODIS products and the Chinese national vegetation regionalization map using geographical spatial analysis technology. A method for national fuel types classification in forests, shrubs and grasses based on remote sensing and geospatial analysis was explored. Non-tree cover, average vegetation canopy height and area occupied by each fuel types were calculated, using the product datasets of MODIS VCF（Vegetation Continuous Fields）and forest canopy height. The classification results were validated using field survey data and other data products. The results show that the total accuracies of the classification result at levels 1, 2, and 3 are 90.89%, 84.14%, and 68.16%, respectively; the Kappa coefficients of the classification result at levels 1, 2, and 3 are 0.81, 0.74, and 0.6, respectively. The national-scale fuel types, including forest, shrub, and grass, were classified and mapped by using the multi-source data and geographical spatial analysis technology. The study will provide technical support for the prevention and management of forest and grassland fire in China.

mots-clés

remote sensing;Fuel type;remote sensing classification;spatial analysis technology;MCD12Q1;MOD44B

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