Land use and socio-economic determinants of urban forest structure and diversity

Fan, Chao; Johnston, Mark; Darling, Lindsay; Scott, Lydia; Liao, Felix Haifeng

doi:10.1016/j.landurbplan.2018.09.012

Cited by 65 publications

(27 citation statements)

References 52 publications

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“…For example, travel mode, travel distance, and travel distribution could be affected by land development density, land use intensity, and land use mixing degree [45,46]. The social, economic, and ecological efficiency of urban land has formed a relatively mature evaluation system, and the evaluation objects are basically concentrated in urban construction land [47,48] and industrial land [49]. The previous research also shows that there are two problems with the evaluation methods of construction land use efficiency.…”

Section: The Effects Of Urban Land Use On Urban Trafficmentioning

confidence: 99%

Coupling Coordination Relationships between Urban-industrial Land Use Efficiency and Accessibility of Highway Networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China

Gao

et al. 2019

Sustainability

100

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The implementation of the Beijing, Tianjin, and Hebei coordinated development strategy has seriously increased the influence of land use and urban traffic. Thus, understanding the coordination between urban land and transportation systems is important for the efficient and sustainable development of cities, especially in this rapidly urbanizing era. Urban–industrial land and highway networks are, respectively, primary types of urban land and transportation systems, and have significant impacts on social and economic development. However, limited studies have been conducted to examine the relationships between urban–industrial land and highway networks. Therefore, this paper aims to examine the coupling coordination relationship between urban–industrial land use efficiency, and the accessibility of the highway networks of cities. Specifically, in the context of the Beijing–Tianjin–Hebei (BTH) urban agglomeration, the coupling coordination between urban-industrial land use efficiency and accessibility of the highway traffic network was empirically analyzed. The results show that: (i) The differences in urban-industrial land use efficiency in the BTH region are significant. Capital cities in the BTH urban agglomeration have higher economic, social, and comprehensive efficiency, while in industrial cities, the use of urban–industrial land should prioritize ecological and environmental issues. (ii) Because of its good geographical location Beijing has the best accessibility, with an accessibility index of 1.416, while Qinhuangdao had the lowest accessibility index of 0.039. (iii) In most BTH cities, the urban-industrial comprehensive land use level has fallen behind the highway network development level. The results of this study can provide references for the coordinated development of the BTH urban agglomeration.

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Section: The Effects Of Urban Land Use On Urban Trafficmentioning

confidence: 99%

Coupling Coordination Relationships between Urban-industrial Land Use Efficiency and Accessibility of Highway Networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China

Gao

et al. 2019

Sustainability

100

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“…With the rapid development of urbanization and the pursuit of high-quality living conditions for inhabitants, urban green spaces have played a significant role in ecology, economy, socio-culture, health, and environmental benefits [1][2][3][4][5]. The ways in which urban green spaces can benefit cities and their inhabitants have become key issues in urban planning [6].…”

Section: Introductionmentioning

confidence: 99%

Is an Environment with High Biodiversity the Most Attractive for Human Recreation? A Case Study in Baoji, China

et al. 2019

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Evidence has been accumulating of the psychological and physiological benefits and well-being gained by individuals from recreational activities in urban green spaces due to their ability to sustain biodiversity, but maximizing both biodiversity and recreational values of green spaces has become increasingly difficult in practice. In order to better maximize the biodiversity and recreational value of urban green space, this study was conducted through the utilization of an onsite questionnaire to understand people’s perceptions of and preferences for biodiversity and recreational values of urban green spaces in Baoji City, China. The results showed that respondents were able to correctly perceive biodiversity and preferred to engage in recreational activities in the high biodiversity environment. However, the respondents’ perceptions of the eight perceived sensory dimensions (PSDs) in the different habitats were divergent, and an environment which is high in biodiversity does not necessarily have higher PSDs. Moreover, ‘living environment’ and ‘frequency to the park’ had significant impacts on perception of and preference for urban biodiversity. In addition, ‘education level’, ‘living environment’, ‘age’, and other indicators were more likely to influence the perception of the eight PSDs. Therefore, the presented findings can be applied by urban landscape planners to assess the qualities of urban green spaces in order to maintain urban biodiversity and meet the satisfaction of human recreation in the future.

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“…Datasets used in many previous studies on urban forests, in individual cities, were of a very high resolution: Satellite for observation of Earth (SPOT) images with 10 m/2.5 m resolution [46], QuickBird images with a 0.61 m resolution [47], SPOT-5 images with 5 m resolution [21], and multispectral images with 1 m resolution [22]. However, these data were not open access, resulting in the high cost of mapping at the country scale.…”

Section: Datamentioning

confidence: 99%

“…Much effort has been made in measuring urban forest cover; for example, field surveys [18], remote sensing [19][20][21][22], and drone shooting [23] have all been used to gain information on urban forest cover. Among them, remote sensing is efficient and useful in mapping forested urban areas for large scale-maps: Canetti et al [21] used RapidEye and Satellite for observation of Earth (SPOT 5) images to quantify multi-temporal urban forest cover in Araucaria (a city in Brazil), based on the support vector machine algorithms; Chen et al [24] drew the urban green space in the neighborhoods of five Chinese megacities using Google Earth images with the spatial resolution of 0.26 m; Fan et al [22] quantified the tree canopy of Cook County in the United States, using multispectral images with the spatial resolution of 1 m from the National Agriculture Imagery Program and Light Detecting and Ranging data. However, these explorations mainly focus on a single city, and there are, at present, no data on Chinese urban forest cover with a high spatial resolution at the national scale.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 Images with Google Earth Engine

Duan

Tan

Guo

et al. 2019

Forests

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Urban forests are vitally important for sustainable urban development and the well-being of urban residents. However, there is, as yet, no country-level urban forest spatial dataset of sufficient quality for the scientific management of, and correlative studies on, urban forests in China. At present, China attaches great importance to the construction of urban forests, and it is necessary to map a high-resolution and high-accuracy dataset of urban forests in China. The open-access Sentinel images and the Google Earth Engine platform provide a significant opportunity for the realization of this work. This study used eight bands (B2–B8, B11) and three indices of Sentinel-2 in 2016 to map the urban forests of China using the Random Forest machine learning algorithms at the pixel scale with the support of Google Earth Engine (GEE). The 7317 sample points for training and testing were collected from field visits and very high resolution images from Google Earth. The overall accuracy, producer’s accuracy of urban forest, and user’s accuracy of urban forest assessed by independent validation samples in this study were 92.30%, 92.27%, and 92.18%, respectively. In 2016, the percentage of urban forest cover was 19.2%. Nearly half of the cities had an urban forest cover between 10% and 20%, and the average percentage of large cities whose urban populations were over 5 million was 24.8%. Cities with less than half of the average were mainly distributed in northern and western parts of China, which should be focused on in urban greening planning.

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Land use and socio-economic determinants of urban forest structure and diversity

Cited by 65 publications

References 52 publications

Coupling Coordination Relationships between Urban-industrial Land Use Efficiency and Accessibility of Highway Networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China

Coupling Coordination Relationships between Urban-industrial Land Use Efficiency and Accessibility of Highway Networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China

Is an Environment with High Biodiversity the Most Attractive for Human Recreation? A Case Study in Baoji, China

Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 Images with Google Earth Engine

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