Spatial distribution characteristics of lichens in forest of Dabie mountain, Anhui province, China

2019 ◽  
Vol 52 (1) ◽  
Author(s):  
Yuliang Wang ◽  
Shoucheng Huang ◽  
Xing Jian
Keyword(s):  
Spatial Distribution ◽  
Anhui Province ◽  
Distribution Characteristics ◽  
Dabie Mountain ◽  
Spatial Distribution Characteristics

scholarly journals Trends and Spatial Distribution Characteristics of Sustainability in Eastern Anhui Province, China

2014 ◽  
Vol 6 (12) ◽  
pp. 8398-8414 ◽  
Author(s):  
Jian Zhang ◽  
Guishan Yang ◽  
Lijie Pu ◽  
Buzhuo Peng
Keyword(s):  
Spatial Distribution ◽  
Anhui Province ◽  
Distribution Characteristics ◽  
Spatial Distribution Characteristics

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

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

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.

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