Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

Author:

Shuo Zhen1,Jingyu Zhang2,Zhengxiang Zhang1,Jianjun Zhao1

Affiliation:

1. Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University , Changchun 130024 , China

2. Faculty of Geographical Sciences, Beijing Normal University , Beijing 100857 , China

Abstract

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

Publisher

Walter de Gruyter GmbH

Subject

General Earth and Planetary Sciences,Environmental Science (miscellaneous)

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