Sub-pixel vs. super-pixel-based greenspace mapping along the urban–rural gradient using high spatial resolution Gaofen-2 satellite imagery: a case study of Haidian District, Beijing, China

ABSTRACT

Greenspace in urban areas is closely related to urban ecosystems, economy, culture, and society. Recently, rapid urban development and expansion are always dominated by a series of human–environment interactions, which can lead to various spatial patterns of urban greenspace especially along the urban–rural gradient. Urban–rural greenspace mapping is therefore of great importance to provide a comprehensive insight for urban planners and managers. In our study, we adopted both the sub-pixel and super-pixel strategies to map the greenspace in Haidian District, Beijing, China. Specifically, the fully constrained linear spectral unmixing and object-based classification methods were implemented as the representatives of sub-pixel and super-pixel strategies, respectively. The high spatial resolution Gaofen-2 multispectral imagery collected in September, 2015 was used in this study. The results showed that the overall accuracies of greenspace mapping by the super-pixel method were higher than those by the sub-pixel method in the selected dense urban, sub-urban, and rural subsets. Obviously, the super-pixel method was more advantageous for mapping greenspace from the high spatial resolution imagery, especially for patches of greenspace in rural and mountain areas. When further comparing these two methods using the medium spatial resolution Landsat-8 imagery, we concluded that the sub-pixel method failed to keep the same levels of greenspace mapping accuracies as those using the high spatial resolution Gaofen-2 imagery but outperformed the super-pixel method especially in the dense urban and sub-urban subsets due to their high degrees of greenspace fragmentation. Furthermore, the sub-pixel method also demonstrated its merits in terms of automation and operability compared to the super-pixel method.

Acknowledgements

This research was supported by The National Natural Science Foundation of China [No. 31500580] and the China Postdoctoral Science Foundation [No. 2016M591091] granted to Dr. Weida Yin at the School of Landscape Architecture, Beijing Forestry University. And also supported by Beijing Urban and Rural Ecological Environment Laboratory.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [No. 31500580], the China Postdoctoral Science Foundation [No. 2016M591091], granted to Dr. Weida Yin at the School of Landscape Architecture, Beijing Forestry University. And also supported by Beijing Urban and Rural Ecological Environment Laboratory and Beijing Key Laboratory of Urban Spatial Information Engineering [No. 2016201], Beijing Laboratory of Urban and Rural Ecological Environment (Special Fund for Beijing Common Construction), and Fundamental Research Funds for the Central Universites [No. 2017ZY19].