Title: Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation
Abstract: Abstract Road density (i.e., km/km2) is a useful broad index of the road network in a landscape and has been linked to several ecological effects of roads. However, previous studies have shown that road density, estimated by grid computing, has weak correlation with landscape fragmentation. In this article, we propose a new measure of road density, namely, kernel density estimation function (KDE) and quantify the relation between road density and landscape fragmentation. The results show that road density estimated by KDE (km/km2) elucidates the spatial pattern of the road network in the region. Areas with higher road density are dominated by a larger proportion of built-up landscape and less possession of forest and vice versa. Road networks segregated the landscape into smaller pieces and a greater number of patches. Furthermore, Spearman rank correlation model indicates that road density (km/km2) is positively related to landscape fragmentation. Our results suggest that road density, estimated by KDE, may be a better correlate with effects of the road on landscape fragmentation. Through KDE, the regional spatial pattern of road density and the prediction of the impact of the road on landscape fragmentation could be effectively acquired. Keywords: road densityroad networkKDElandscape fragmentationcore area of the Pearl River Delta Acknowledgements This research was funded by the National Natural Science Foundation of China (41171446; 31170445) and the Research Fund of LREIS, CAS (2010KF0006SA). We thank Dr. Qin Lu for helping with editing and proofreading the manuscript, which helped improve the presentation of the article. Dr. Jianbin Wei and the two anonymous reviewers provided valuable comments for improving this manuscript.
Publication Year: 2013
Publication Date: 2013-02-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 153
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