scholarly journals Spatial distribution characteristics and dynamics of Eichhornia crassipes in the Shuikou Reservoir, Fujian Province

2012 ◽  
Vol 24 (3) ◽  
pp. 391-399 ◽  
Author(s):  
CHEN Xiao ◽  
◽  
PAN Wenbin ◽  
WANG Mu
Keyword(s):  
Spatial Distribution ◽  
Eichhornia Crassipes ◽  
Distribution Characteristics ◽  
Fujian Province ◽  
Spatial Distribution Characteristics

Spatial distribution characteristics of an evergreen broad-leaved forest in the Wuyi Mountains, Fujian Province, southeastern China

2018 ◽  
Vol 38 (5) ◽  
Author(s):  
陈婷婷 CHEN Tingting ◽  
徐辉 XU Hui ◽  
杨青 YANG Qing ◽  
陈水飞 CHEN Shuifei ◽  
葛晓敏 GE Xiaomin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Distribution Characteristics ◽  
Southeastern China ◽  
Fujian Province ◽  
Broad Leaved Forest ◽  
Spatial Distribution Characteristics ◽  
Leaved Forest

scholarly journals Quantifying the Distribution Characteristics of Geochemical Elements and Identifying Their Associations in Southwestern Fujian Province, China

Minerals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 183 ◽  
Author(s):  
Jian Wang ◽  
Renguang Zuo
Keyword(s):  
Spatial Distribution ◽  
Multifractal Spectrum ◽  
Distribution Characteristics ◽  
Fujian Province ◽  
Spatial Distribution Characteristics ◽  
Geological Conditions ◽  
Distribution Parameters ◽  
Geochemical Elements ◽  
Geochemical Dispersion ◽  
Southwestern Fujian

The distribution of geochemical elements in the surficial media is the end product of geochemical dispersion under complex geological conditions. This study explored the frequency and spatial distribution characteristics of geochemical elements and their associations. It quantifies the frequency distribution via mean, variance, skewness and kurtosis, followed by measuring the spatial distribution characteristics (i.e., spatial autocorrelation, heterogeneity and self-similarity) via semivariogram, q-statistic and multifractal spectrum, and further identify the elemental associations based on these distribution parameters using hierarchical clustering. A criterion was defined to identify the importance of parameters in the clustering procedure. A case study processing a geochemical dataset of stream sediment samples collected in southwestern Fujian province of China was carried out to illustrate and validate the procedure. The results indicate that studies of the frequency and spatial distribution characteristics of geochemical elements can enhance the knowledge of geochemical dispersions. The associations identified based on the frequency and spatial distribution parameters are different from those obtained by conventional cluster analysis. Spatial distribution characteristics cannot be neglected when investigating the distribution patterns of geochemical elements and their associations. The findings can enhance the knowledge of the geochemical dispersion in the study area and might benefit the following-up mineral exploration.

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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