Spatial accessibility of anthropogenic fire ignition sources of grassland fire in northeast China
Zhengxiang Zhang A , Jianjie Li A , Shan Yu B and Jianjun Zhao A C
+ Author Affiliations
- Author Affiliations
A Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun City, 130024, China.
B Inner Mongolia Autonomous Region Key Laboratory of Remote Sensing and Geography Information System, Inner Mongolia, Huhhot 010022, China.
C Corresponding author. Email: zhangzx040@nenu.edu.cn
International Journal of Wildland Fire 30(10) 763-775 https://doi.org/10.1071/WF20125
Submitted: 3 August 2020 Accepted: 13 July 2021 Published: 3 August 2021
Abstract
Fires can have an enormous impact on grassland systems, affecting their ecology as well as their economic productivity. As most grassland fires are caused by human activities, understanding the relationship between anthropogenic activities that cause fires and where fire ignitions occur is essential in determining where grassland fires pose the greatest risk. Any potential model to predict the spatial distribution and intensity of anthropogenic activities that cause grassland fire ignition needs to take into account the size of residential areas, roads and area of land that is cultivated. The spatial accessibility of human activities that cause grassland fire ignitions was predicted by the model to represent the ability of human driving factors that influence the occurrence of grassland fire ignitions. An index of spatial accessibility of anthropogenic fire ignition sources was overlapped with artificially generated neural networks. Within the index, five categories were created to adequately assess the level of ignition risk to grassland fires: extremely low, low, medium, high and extremely high. The percentages of actual fires in each ignition risk zone from low to high were 2.94%, 18.82%, 20.01%, 22.35% and 35.88%. This methodology provides new insight into how human factors affect the occurrence of wildland fire.
Keywords: multilayer perceptron, potential model theory, time cost surface, fire distribution, human factors.
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