<trans-title>Urban Green Space Classification and Landscape Pattern Measurement based onGF-2 Image</trans-title>

Xuemiao WANG; Qingyan MENG; Shaohua ZHAO; Juan LI; Linlin ZHANG; Xu CHEN

doi:10.12082/dqxxkx.2020.200122

Journal of Geo-information Science, Volume. 22, Issue 10, 1971(2020)

Urban Green Space Classification and Landscape Pattern Measurement based onGF-2 Image

Xuemiao WANG^1,2, Qingyan MENG^1、*, Shaohua ZHAO³, Juan LI¹, Linlin ZHANG^1,2, and Xu CHEN^1,2

Author Affiliations

¹Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

²University of Chinese Academy of Sciences, Beijing 100049, China

³State Environmental Protection Key Lab of Satellite Remote Sensing, Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China

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Figures & Tables(13)

Fig. 1. Study area location and remote sensing image

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Fig. 2. Characteristics of different types of vegetation in summer and winter

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Fig. 3. The technical route of this study

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Fig. 4. Diagram of moving window method

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Fig. 5. Importance of summer and winter image features

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Fig. 6. Importance of multi-temporal image features

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Fig. 7. Urban vegetation classification map of Beijing Vice-City Center in 2018

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Fig. 8. Calculation results of green space landscape metrics of Beijing Vice-City Center in 2018(class level)

Fig. 9. [in Chinese]

Fig. 10. UGI and SHDI distribution map of study area

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Table 1. Overall classification accuracy obtained by using different number of features (%)
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Table 1. Overall classification accuracy obtained by using different number of features (%)
特征个数夏季单时相冬季单时相多时相
5 72.4 54.0 74.4
10 72.6 58.8 85.7
15 77.5 59.3 87.0
20 77.5 61.7 87.7
25 77.5 62.1 87.2
30 78.3 63.4 87.0
35 - - 85.7
40 - - 87.0

Table 2. Classification accuracy of urban vegetation classif-ication using summer, winter and multi-temporal image
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Table 2. Classification accuracy of urban vegetation classif-ication using summer, winter and multi-temporal image
夏季冬季多时相
PA UA PA UA PA UA
阔叶树 0.74 0.80 0.65 0.49 0.89 0.89
针叶树 0.73 0.81 0.94 0.76 0.90 0.92
农田 0.63 0.68 0.76 0.60 0.69 0.82
草地 0.92 0.86 0.62 0.79 0.93 0.88
灌木 0.90 0.75 0.14 0.50 0.97 0.86

Table 3. Calculation results of green space landscape metrics of Beijing Vice-City Center in 2018 (landscape level)

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Table 3. Calculation results of green space landscape metrics of Beijing Vice-City Center in 2018 (landscape level)

	PLAND/%	PD/(个/100ha)	ED/(m/ha)	FRAC	SHDI	AI
城市绿心	65.012	677.050	701.849	1.252	1.504	95.395
居住区	38.098	1612.312	689.839	1.249	1.415	93.600
办公区	37.060	784.271	668.851	1.250	1.396	95.096
商业区	26.907	1833.961	720.051	1.251	1.331	93.364
研究区	43.805	1008.982	673.226	1.252	1.485	94.758

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Xuemiao WANG, Qingyan MENG, Shaohua ZHAO, Juan LI, Linlin ZHANG, Xu CHEN. Urban Green Space Classification and Landscape Pattern Measurement based onGF-2 Image[J]. Journal of Geo-information Science, 2020, 22(10): 1971

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Paper Information

Received: Mar. 16, 2020

Accepted: --

Published Online: Apr. 23, 2021

The Author Email: Qingyan MENG (mengqy@radi.ac.cn)

DOI:10.12082/dqxxkx.2020.200122

Topics

Table 1. Overall classification accuracy obtained by using different number of features (%)

Table 1. Overall classification accuracy obtained by using different number of features (%)

Table 2. Classification accuracy of urban vegetation classif-ication using summer, winter and multi-temporal image

Table 2. Classification accuracy of urban vegetation classif-ication using summer, winter and multi-temporal image

Table 3. Calculation results of green space landscape metrics of Beijing Vice-City Center in 2018 (landscape level)

Table 3. Calculation results of green space landscape metrics of Beijing Vice-City Center in 2018 (landscape level)