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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 223-230     DOI: 10.6046/gtzyyg.2018.02.30
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Study of water storage effect of roof greening in the construction of Fuzhou sponge city
Lu LIN1(), Zhanghua XU1,2,3,4(), Xuying HUANG1, Fukang LYU5, Qianfeng WANG1,4, Qian LIN5
1. College of Environment and Resources,Fuzhou University,Fuzhou 350116,China
2.Postdoctoral Research Station of Information and Communication Engineering, Fuzhou University, Fuzhou 350116, China
3. Key Lab of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou 350116, China
4.Center for Region and Urban and Rural Planning,Fuzhou 350116,China
5.Zhicheng College,Fuzhou University,Fuzhou 350002,China
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Abstract  

The urban roof greening has the effects such as water interception and ecological environment improvement, and can be an important part of sponge city construction. Taking Gulou, Taijiang, Cangshan Districts of Fuzhou City as the study objects and the remote sensing image of Landsat8 OLI as the main data, the authors extracted the roof greening rate based on sequential maximum angle convex cone(SMACC), constructed the relational models of roof greening rate and global vegetation moisture index(GVMI) humidity indicator, and then simulated and analyzed the roof greening rates. The results show that the roof greening rate in the three districts of Fuzhou is overall low, with an average of only 17.34%; the proportion of greening rate of 10%~20% is 66.55%, and only 5.11% is higher than 50%. The greening rates are different, and there are also changes in humidity, indicating that the roof vegetation has remarkable water interception capacity. The quadratic fumction model of roof humidity h and greening rate r is the optimization model. When the roof greening rate is higher than 16.30%, the intercepting effect begins to be obvious. In the process of greening rate increasing from 30% to 60%, the increasing speed of intercepting capacity becomes the fastest, with an average of up to 57.9%. Two typical blocks were selected and the roof greening rates were simulated and analyzed, which further proves the rationality of the above model. The result confirms the intercepting capacity of roof greening and determines the roof greening threshold under the target of water interception, which provides important reference for sponge city construction.
Keywords sponge city      roof greening rate      humidity      global vegetation moisture index(GVMI)      simulation      Fuzhou City     
:  TP79TU981  
Corresponding Authors: Zhanghua XU     E-mail: n160620002@fzu.edu.cn;fafuxzh@163.com
Issue Date: 30 May 2018
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Lu LIN
Zhanghua XU
Xuying HUANG
Fukang LYU
Qianfeng WANG
Qian LIN
Cite this article:   
Lu LIN,Zhanghua XU,Xuying HUANG, et al. Study of water storage effect of roof greening in the construction of Fuzhou sponge city[J]. Remote Sensing for Land & Resources, 2018, 30(2): 223-230.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.30     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/223
Fig.1  Processed OLI image of three districts in Fuzhou City
Fig.2  IBI and building information extracted with IBI of three districts in Fuzhou City
类别 参考合
计/个		 分类合
计/个		 正确
数/个		 生产者
精度/%		 使用者
精度/%
建筑 172 171 155 90.12 90.64
非建筑 328 329 300 79.37 79.16
合计 500 500 455 ─ ─
总体精度=91.00% Kappa=0.902 3
Tab.1  Accuracy assessment of building information extracted with IBI
Fig.3  Building OLI image of three districts in Fuzhou City
Fig.4  Greening abundance of three districts in Fuzhou City
屋顶编号 屋顶实际
绿化率		 绿化丰度 估测精度
1 10.16 11.95 82.38
2 19.60 15.63 79.74
3 30.45 32.18 94.32
4 41.89 47.21 87.30
5 50.07 51.36 97.43
6 62.74 66.53 93.96
7 70.10 71.82 97.55
8 79.95 84.97 93.72
Tab.2  Estimation accuracy evaluation of green abundance on roof greening rate(%)
Fig.5  GVMI of three districts in Fuzhou City
等级 屋顶绿化率/% 随机点/个 比例/% 平均湿度/%
低 [0,10) 4 395 37.89 6.44
[10,20) 3 323 28.66 6.14
中 [20,30) 2 007 17.31 6.15
[30,40) 953 8.24 6.23
[40,50) 324 2.79 16.54
高 [50,60) 326 2.81 24.53
[60,70) 178 1.54 28.62
[70,80] 67 0.58 35.33
合计 11 573 100.00 ─
Tab.3  Correspondence relationship between roof greening rate and humidity
Fig.6  Relational models of roof humidity and greening rate
模型 模型拟合度 模型参数
R2 P c b1 b2 b3
线性模型h=b1r+c 0.236 0 0.036 0.234 ─ ─
对数模型h=b1ln r+c 0.057 0 0.109 0.015 ─ ─
倒数模型h=b1/r+c 0.001 0.009 0.077 -1.251e-5 ─ ─
二次曲线模型h=b2r2-b1r+c 0.434 0 0.081 -0.327 1.014 ─
三次曲线模型h=b3r3+b2r2+b1r+c 0.426 0 0.080 -0.317 0.973 0.043
复合模型h=cbr1 0.052 0 0.035 5.868 ─ ─
成长模型h=eb1r+c 0.052 0 -3.347 1.769 ─ ─
指数模型h=ceb1r 0.052 0 0.035 1.769 ─ ─
Tab.4  Fit goodness and parameters of roof humidity and greening rate
Fig.7  Humidity growth rate of quadratic curve model
小区名称 屋顶平均
绿化率		 GVMI
湿度		 二次曲线模
型估测湿度		 估测
精度
凯旋花园 27.12 7.74 6.68 86.30
新农村公寓 10.67 4.51 5.76 72.28
Tab.5  Verification of roof humidity and greening rate mode in typical blocks(%)
Fig.8  Roof greening rate simulation for typical blocks
新农村公寓小区 凯旋花园小区
屋顶绿化率 湿度增长率 屋顶绿化率 湿度增长率
[10.67,20.67) 22.70 [27.12,37.12) 49.69
[20.67,30.67) 34.98 [37.12,47.12) 53.98
[30.67,40.67) 53.21 [47.12,57.12) 49.07
[40.67,50.67) 52.55 [57.12,67.12) 41.31
[50.67,60.67) 46.13 [67.12,77.12) 35.89
[60.67,70.67) 39.56 [77.12,87.12) 31.13
[70.67,80.67) 34.07 [87.12,97.12] 27.34
[80.67,90.67] 29.69
Tab.6  Correspondence relationship between roof humidity growth rate and greening rate in typical blocks(%)
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