Urban forest landscape patterns in Ma'anshan City, China

Abstract

The landscape pattern of Ma'anshan City was analyzed based on theories and methodologies of landscape ecology, remote sensing, global positioning, and a geographic information system (GIS). The study area encompassed the entire built-up area of 63.88 km²; a north–south transect 3-km wide and 13-km long was established along the long axis of the city. Five major landscape elements were assessed: urban land, urban forest, agriculture, water, and grass. Urban land was the dominant land cover type, and occupied 67% of total land area; while patches of urban forest occupied 16%, with a landscape element dominance of 0.42. Urban forest was classified according to land-use category and location into six types: scenic forest, yard forest, recreational forest, roadside forest, shelter forest, and nurseries. There were 2464 urban forest patches, the largest being 185.1 ha, with an average of 0.43 ha. The low nearest neighbor index and high patch density indicated that urban forest patches tend to be aggregated and have a high degree of fragmentation. This study also demonstrated that the spatial pattern of urbanization could be quantified using a combination of landscape metrics and gradient analysis. Urban forest has distinct spatial characters that are dependent on specific landscape metrics along the urbanization gradient.

Keywords:

• land use pattern
• urban forest
• landscape metrics
• gradient analysis

Acknowledgements

This research was supported by the National Key Technologies R & D Program of China during the 11th Five-Year Plan (No. 2006BAD03A06), and the Key Project of the National Societal Science Foundation of China (No. 06ZD024). We wish to express our sincere thanks to Jim Kielbaso, Joe McBride, Hong Qiang, and Hui Chen for their valuable comments on a previous version of this manuscript. We also thank Kefu Xu, Andong Hong, Wenyou Wu for their assistance with preparation of the satellite data. Lei Zhang, Lulu Guan, and Xia Zhao worked on remote sensing and field survey for this project. Thanks also to the anonymous reviewers.