The rapid but “invisible” changes in urban greenspace: A comparative study of nine Chinese cities
Graphical abstract
Introduction
Urban greenspace refers to any vegetation found in the urban environment, including woodland, grassland, wetland, garden and other vegetated areas (Kabisch and Haase, 2013; Taylor and Hochuli, 2017). Greenspace in urban areas provides myriad ecosystem services (ES) that are central to human well-being and urban sustainability (Groenewegen et al., 2006; Ouyang et al., 2016; Pathak et al., 2011; Thompson et al., 2012; Wu, 2013; Wolf and Housley, 2014; Zhou et al., 2014; Yan et al., 2016). Ecosystem services provided by urban greenspace play a vital role in counteracting environmental problems caused by increasing urban density or by climate change (Kabisch, 2015). These include regulating services such as urban heat island mitigation, air pollution reduction, storm water runoff interception (Huang and Cadenasso, 2016; Park et al., 2017; Yao et al., 2015; Yan et al., 2016; Zhou et al., 2011, Zhou et al., 2017b), biodiversity conservation (Ziter, 2016), and health benefits such as relieving stress and anxiety (Coutts and Hahn, 2015). The combination of environmental and social values has motivated many cities to maintain and expand urban greenspace, both within and surrounding the cities. However, the spatial pattern and change of urban greenspace can significantly affect the ES provided by the greenspace. For example, numerous studies that focus on effects of spatial pattern of greenspace on land surface temperature have shown that the composition and configuration of greenspace had significant effects on regulating services on microclimate (e.g., Chen et al., 2014; Maimaitiyiming et al., 2014; Zhou et al., 2017a, Zhou et al., 2017b). In addition, the spatial pattern of greenspace is strongly related to adequacy of greenspace in cities, and thereby influences the cultural ecosystem services provided by greenspace (Lo and Jim, 2010). Therefore, to evaluate the efficacy of their efforts, and fully understand the ES provided by urban greenspace, it is crucial to first accurately characterize and quantify the spatial pattern and change of urban greenspace (Lovell and Taylor, 2013; Wu, 2014; Pickett et al., 2017; Qian et al., 2015b; Zhou et al., 2017a).
Quantifying spatiotemporal pattern of urban greenspace frequently relies on remote sensing. Numerous studies have been conducted on urban greenspace mapping and change analysis. These studies have used a variety of remotely sensed image data having different spatial resolutions, ranging from sub-meter to 1000 m (e.g., Stefanov et al., 2001; Zhang et al., 2003; Zhou et al., 2008; Wang et al., 2018). While changes in urban greenspace have been an important research topic, most previous studies focused on loss of greenspace associated with urban expansion (Hurd et al., 2001; Kong and Nakagoshi, 2006; Miller, 2012; Portillo-Quintero et al., 2012; Seto et al., 2002; Yuan et al., 2005; Zhou and Wang, 2011; Yang et al., 2014). These studies usually found that changes in greenspace occurred in the urban-rural periphery, coincident with urban expansion (Peng et al., 2016b; Portillo-Quintero et al., 2012; Seto et al., 2002; Yuan et al., 2005; Miller, 2012). Greenspace in cities proper, or urban core areas, however, remained largely unchanged (Li et al., 2011; Xu et al., 2011; Zhou and Wang, 2011). These results are somewhat contradictory to the perceptions in many cities. On the one hand, there are still great pressures from development on conversion of greenspace to buildings and infrastructures, particularly in cities in developing countries (Qian et al., 2015a). On the other hand, many cities have devoted great efforts to increasing urban greenspace (Beijing Landscape Bureau, 2007; Van Den Hoek et al., 2014). Consequently, urban greenspace may be dynamic, even in highly urbanized areas.
These contradictory results may be due to the data used to quantify the dynamics of urban greenspace (Qian et al., 2015b). Most of the previous studies of urban greenspace dynamics have used data derived from medium-resolution remotely sensed imagery. While these data are very useful for quantifying the coarse-scale loss of greenspace associated with urban expansion, they may be inadequate in characterizing changes of urban greenspace in built areas, where most of the changes in urban greenspace may involve small areas (Qian et al., 2015b). However, these small patches of greenspace, similar to large greenspaces such as parks and urban forests, can provide important ecological functions and ecosystem services (Pickett, 2010; Niemelä, 2014; Wu, 2014). Considering the “invisible” greenspace patches, which can only be revealed by high spatial resolution remote sensing data, can help better understand and assess ecosystem services provided by urban greenspace (Qian et al., 2015b).
Recognizing the importance of accurate quantification of the spatial pattern and change of urban greenspace at fine scales, high spatial resolution image data, such as SPOT, IKONOS, QUICKBIRD, WorldView, and aerial imagery, have been increasingly used for fine-resolution urban greenspace mapping and change analysis (e.g., Zhou et al., 2008; MacFaden et al., 2012; Ramos-Gonzalez, 2014; Qian et al., 2015a). In addition to large greenspaces, patches of small-sized urban green cover can be accurately mapped from high spatial resolution imagery (Mathieu et al., 2007; Zhou and Troy, 2008; Zhou et al., 2008; MacFaden et al., 2012). Using multitemporal high spatial resolution image data, fine-scale changes in urban greenspace can also be detected (e.g., Zhou et al., 2008; Qian et al., 2015a). These studies have mostly focused on a single city. However, Nowak and Greenfield (2012) compared the change in tree and impervious cover in 20 U.S. cities. They used a random sampling approach focusing on changes in direction and rate of coverage but not the spatial pattern. Few studies, however, have examined the fine-scale spatial pattern of urban greenspace and its change for cross-city comparisons. Consequently, a quantitative understanding of the fine-scale spatiotemporal pattern of urban greenspace across different cities remains elusive.
This paper presents a comparison study of the spatiotemporal patterns of urban greenspace in nine major cities in China, using high spatial resolution image data collected in 2005 and 2010. The study cities are located in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) urban megaregions, described in details below. It tests the hypothesis that urban greenspace in well-developed city regions may be experiencing great changes, due to the combination of pressure for development and renewal, as well as the increasing efforts to increase urban greenspace in many cities (United Nations, 2014; Locke et al., 2010; Pataki, 2013). The changes in urban greenspace based on high resolution image data were further compared and contrasted with those from the most commonly used 30 m resolution Landsat Thematic Mapper (TM) data. Specially, the aims of this study are: 1) to quantify the spatiotemporal patterns of urban greenspace in urban core areas, and examine how they varied within and across different cities; and 2) to investigate and compare the efficacy of data with different spatial resolution on detecting such patterns. Results from this study have important implications for urban greenspace management and planning.
Section snippets
Study area
This research focuses on nine cities in China, including the three largest cities, Beijing (BJ), Tianjin (TJ) and Tangshan (TS) in the Beijing-Tianjin-Heibei (BTH) urban megaregion, and the six largest cities, Shanghai (SH), Nanjing (NJ), Hangzhou (HZ), Suzhou (SZ), Wuxi (WX), Changzhou (CZ)in the Yangtze River Delta (YRD) urban megaregion (Fig. 1). The BTH urban megaregion is located in the eastern part of North China, with a total population of 10.53 million, and gross domestic product (GDP)
Medium resolution TM data greatly underestimated the cover of greenspace
The results showed that the percent cover of urban greenspace mapped from the two datasets with different spatial resolution was very different. According to the TM data, the percent cover of urban greenspace ranged from 9.90% in Shanghai to 28.64% in Nanjing for 2005, with a mean of 20.44%, and from 9.85% in Tianjin to 20.52% in Nanjing for 2010, with a mean of 15.94% (Table 3). In contrast, percent cover of urban greenspace derived from the 2.5 m high resolution image data was much higher than
The highly dynamic urban greenspace in Chinese cities
Relatively few studies have been conducted to examine the within-city urban greenspace dynamics for Chinese cities, especially using high spatial resolution imagery (Qian et al., 2015b). Our results based on high spatial resolution image data indicated that urban greenspace in all the nine cities was highly dynamic. Within a relatively short five-year time period, all of the nine cities experienced tremendous absolute changes in gain or loss of urban greenspace, even though the net change might
Conclusion
Previous studies have largely focused on loss of greenspace due to urban expansion, using medium resolution imagery and focusing on the growing urban fringes. This study presents a comparison study of the spatiotemporal patterns of urban greenspace in nine major cities in China, using 2.5 m high spatial resolution ALOS and SPOT image data collected in 2005 and 2010, respectively. The changes in urban greenspace were further compared and contrasted with those based on the most commonly used 30 m
Acknowledgements
This research was funded by the National Natural Science Foundation of China (Grant No. 41422104, 4177011341 and 41590841), the project “Developing key technologies for establishing ecological security patterns at the Beijing-Tianjin-Hebei urban megaregion” of the National key research and development program (2016YFC0503004), the Key Research Program of Frontier Sciences, CAS (QYZDB-SSW-DQC034), and the China Ecosystem Survey (2000−2010) (grant no. STSN-12-00). The support of the Urban
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