Remote sensing monitoring of impervious surface percentage in Hangzhou during 1990—2017

doi:10.6046/gtzyyg.2020.02.31

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Abstract

The impervious surface percentage is an important indicator of regional urbanization and ecological environment changes. The spatial and temporal distribution of impervious surface percentage can reveal the current and future development potential of the city, and provide a reference for urban environmental protection and green sustainable development. In this paper, Hangzhou was selected as the study area, and one Sentinel-2B satellite image was used to extract the impervious surface percentage as reference data. Based on the four-phase Landsat satellite imagery, the authors used the random forest algorithm to invert the 30 m spatial resolution impervious surface percentage datasets from 1990 to 2017 in Hangzhou. The accuracy verification results show that the mean absolute error is from 6.3% to 6.7%, and the root mean square error is from 13.40% to 14.25%, indicating that the inversion model has great accuracy and can accurately reflect the spatial distribution of the impervious surface. Based on the impervious surface weighted mean center, standard deviation ellipse and landscape pattern index, the authors analyzed the spatial and temporal patterns of impervious surface in Hangzhou. The results are as follows: The impervious surface percentage in Hangzhou was increasing from 1990 to 2017, and the annual average growing of impervious surface percentage was the fastest from 2010 to 2017, mainly concentrated in Yuhang and Xiaoshan; Due to the unbalanced development of Hangzhou between 1990 and 2017, the impervious surface weighting mean centre moved to northeast at first, then moved to south, and finally moved to north; The northwest-southeast profile was the main direction of urban growth and the gathering trend was relatively stable in Hangzhou; The change of landscape pattern shows that the impervious landscapes of all types were increasing, and were distributed in a balanced trend; The natural surface, medium density and super high density impervious landscapes became less aggregated and increasingly fragmented; The aggregation of the impervious landscapes of all types was relatively stable, the highest degree of aggregation was the natural surface, and the lowest was the medium density impervious landscape.

Keywords impervious surface change detection time series Hangzhou random forest

TP79

Corresponding Authors: Jiaqi ZUO E-mail: 123354087@qq.com

Issue Date: 18 June 2020

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	Yuting YANG
	Hailan CHEN
	Jiaqi ZUO

Cite this article:

Yuting YANG,Hailan CHEN,Jiaqi ZUO. Remote sensing monitoring of impervious surface percentage in Hangzhou during 1990—2017[J]. Remote Sensing for Land & Resources, 2020, 32(2): 241-250.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.31 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/241

Fig.1 Image of study area combined with Landsat 8 B5(R),B4(G),B3(B) in 2017

Tab.1 The images used in the research

光谱指数	指数作用	表达式^①
BAI	用于检测人工建筑面	BAI= $BLUE - NIR BLUE + NIR$
MNDWI	用于增强水体和其他地物的区别	MNDWI= $GREEN - MIR GREEN + MIR$
NDVI	用于增强植被和其他地物的区别	NDVI= $NIR - RED NIR + RED$
NDBI	对于城镇用地信息有较好的检测效果	NDBI= $MIR - NIR MIR + NIR$
SAVI	考虑了土壤亮度背景,能够增强不透水面和植被的区分,对于城市内植被的提取具有良好的效果。	SAVI= $1.5 (NIR - RED) NIR + RED + 0.5$
IBI	增强了不透水面特征并且有效抑制了背景噪声	IBI= $(NDBI - SAVI + MNDWI 2) (NDBI + SAVI + MNDWI 2)$

Tab.2 The spectral index

Fig.2 Selection the pixels with constant impervious surface percentage from 1990 to 2017

Tab.3 The classification of sentinel-2B image in 2017

Fig.3 ISP reference data extracted from Sentinel-2B image in 2017

Tab.4 Errors of different years(%)

Fig.4 The impervious surface percentage in Hangzhou from 1990 to 2017

Tab.5 Growth of impervious surface percentage in different periods(km²)

Fig.5 Impervious surface weighted mean centres in different years

Tab.6 SDE parameters of impervious surface maps from 1990 to 2017

Fig.6 SDEs of impervious surface in different years

Fig.7 The landscape pattern changes of impervious surface in Hangzhou from 1990 to 2017

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