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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

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Front. Earth Sci.    2021, Vol. 15 Issue (3) : 543-552    https://doi.org/10.1007/s11707-021-0896-7
RESEARCH ARTICLE
UAV-based spatial pattern of three-dimensional green volume and its influencing factors in Lingang New City in Shanghai, China
Sijun ZHENG1,2, Chen MENG3,4, Jianhui XUE1(), Yongbo WU1, Jing LIANG2, Liang XIN5, Lang ZHANG2
1. College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
2. Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200241, China
3. School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
4. Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, Shanghai 200241, China
5. Shanghai Institute of Surveying and Mapping, Shanghai 200063, China
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Abstract

Three-dimensional green volume (TDGV) reflects the quality and quantity of urban green space and its provision of ecosystem services; therefore, its spatial pattern and the underlying influential factors play important roles in urban planning and management. However, little is known about the factors contributing to the spatial pattern of TDGV. In this paper, TDGV and land use intensity (LUI) extracted from high spatial resolution (0.05 m) remotely sensed data acquired by an unmanned aerial vehicle (UAV), anthropogenic factors and natural factors were utilized to identify the spatial pattern of TDGV and the potential influencing factors in Lingang New City, a rapidly developed coastal town in Shanghai. The results showed that most of the TDGV was distributed in the western part of this new city and that its spatial variations were significantly axial. TDGV corresponded well with the chronologies of land formation, urban planning, and construction in the city. Generalized least squares (GLS) analysis of TDGV (grid cell size: 100 × 100 m) and its influencing factors showed that the TDGV in this new city was significantly negatively correlated with both LUI and distance from roads and significantly positively correlated with land formation time and distance from water. Distance from buildings did not affect TDGV. Additionally, the degree of influence decreased in the following order: distance from water>land formation time>distance from roads>LUI. These results indicate that the spatial pattern of TDGV in this new town was mainly affected by natural factors (i.e., the distance from water and land formation time) and that the artificial disturbances caused by rapid urbanization did not decrease the regional TDGV. The main factors shaping the spatial distribution of TDGV in this city were local natural factors. Our findings suggest that the improvement in local soil and water conditions should be emphasized in the construction of new cities in coastal areas to ensure the efficient provision of ecological services by urban green spaces.
Keywords low-altitude remote sensing      LUI      urban space layout      land formation time      dominant factor     
Corresponding Author(s): Jianhui XUE   
Online First Date: 23 September 2021    Issue Date: 17 January 2022
 Cite this article:   
Sijun ZHENG,Chen MENG,Jianhui XUE, et al. UAV-based spatial pattern of three-dimensional green volume and its influencing factors in Lingang New City in Shanghai, China[J]. Front. Earth Sci., 2021, 15(3): 543-552.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0896-7
https://academic.hep.com.cn/fesci/EN/Y2021/V15/I3/543
Fig.1  Location of Shanghai Lingang New City and its coverage of different land use types.
Fig.2  Layout of transect belts, grid cells, and ground control points. Each sample grid in each transect belt was numbered in order (E1-E7, W1-W11, N1-N6, S1-S7, SE1-SE7, NE1-NE9, NW1-NW9, and SW1-SW10) with Dishui Lake at the center.
Fig.3  Bird’s eye view of Lingang New City from east to west.
Unused/unsuitable land Forestland/water Agricultural land Construction land
Area/m2 11469617.14 131296354.60 10703716.95 17596630.30
TDGV/m3 2704005.10 12563090.73 2847261.99 15863733.42
Proportion of TDGV/% 7.96 36.97 8.38 46.69
Tab.1  Area and TDGV of different land use types in Lingang New City. TDGV: three-dimensional green volume
Fig.4  Spatial distribution of three-dimensional green volume in Lingang New City, Shanghai.
Fig.5  Trends in three-dimensional green volume per unit area in eight transect belts with Dishui Lake at the center.
Fig.6  Spatial distribution of LUI in Lingang New City, Shanghai.
Fig.7  Spatial distribution of patch distances from buildings, roads and water in Lingang New City, Shanghai.
AIC logLik Range Nugget Degrees of freedom
15896.73 −7939.36 281.08 0.028 5186
Tab.2  Statistical results for the optimal model
Value Std. Error t-value p-value
(Intercept) 5.912874 0.28438128 20.792065 0
LUI −0.106673** 0.0380966 −2.800057 0.0051
Land_Year 0.042955** 0.0048025 8.944345 0
log(D_water+ 1) 0.51576** 0.02348222 21.963852 0
log(D_road+ 1) −0.378231** 0.02545828 −14.856899 0
log(D_house+ 1) −0.005438 0.02273008 −0.239255 0.8109
Tab.3  Regression coefficients of the optimal model
AIC logLik Range Nugget Degrees of freedom
15884.97 −7934.48 199.96 0.21 5186
Tab.4  Standardized GLS model results
Value Std. Error t-value p-value
(Intercept) 7.039121 0.1592458 44.20287 0
scale(LUI) −0.096466 0.03562694 −2.70768 0.0068
scale(Land_Year) 0.516165 0.05769519 8.94640 0
scale(log(D_water+ 1) 0.909142 0.04004476 22.70315 0
scale(log(D_road+ 1) −0.459215 0.03113147 −14.75084 0
Tab.5  Regression coefficients of the standardized GLS model
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