Research on Monitoring Technology and Analysis of Urban Green Space Ecology and Environment

Research Article
Open access

Research on Monitoring Technology and Analysis of Urban Green Space Ecology and Environment

Zhiming Pan 1*
  • 1 Chang'an University    
  • *corresponding author bluradioheads@outlook.com
Published on 10 November 2023 | https://doi.org/10.54254/2754-1169/27/20231260
AEMPS Vol.27
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-83558-075-2
ISBN (Online): 978-1-83558-076-9

Abstract

Urban green land plays an active role in the process of improving urban ecological environment and the harmony between human and nature. With the economic development, industrial progress and the improvement of people's living standard, urban greenness as the regulator of urban environment has been generally concerned. The dynamic monitoring of urban green land is conducive to scientific and effective urban management, and provides scientific basis and evaluation criteria for urban green land system planning. In this paper, we use remote sensing as the observation means to systematically carry out the research of urban green land ecological environment monitoring technology and analysis. Firstly, it introduces the pre-processing techniques of remote sensing data for urban monitoring, and selects the area method, grid cell method, buffer zone method and moving window method as the key techniques for spatial measurement of urban greenness spaces, and finally evaluates the ecological effects of urban green land with the greenness environment index model of urban buildings. This paper provides the key techniques and evaluation process of urban green space remote sensing in a more systematic way, which will play an important role in garden city evaluation, ecological city evaluation, garden planning, smart city and ecological city support, urban building planning, and sponge map city construction.

Keywords:

urban green land, remote sensing monitoring, ecological effects

Pan,Z. (2023). Research on Monitoring Technology and Analysis of Urban Green Space Ecology and Environment. Advances in Economics, Management and Political Sciences,27,231-241.
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References

[1]. Li Wei. Extraction and analysis of urban green land information based on remote sensing images [D]; East China Normal University, (2007).

[2]. Meng Qingyan. Spatial remote sensing of urban greenness [M]. Beijing: Science Press, (2022).

[3]. Lv Miaoer, Pu Yingxia, Huang Xingyuan. Remote sensing application of urban green space monitoring [J]. Chinese Tradision Gardens, 2000, (05): 41-4.

[4]. Meng Qingyan. Urban Greenness Spatial Remote Sensing -- Making Urban Life Better: Speech Delivered at "Theme Forum on Urban Environmental Issues and 2020 Discovering the Beauty of Cities" [EB/OL]. (2020-11-08).

[5]. Li Miaomiao. Study on estimation method of vegetation coverage by remote sensing [D]. Graduate School of the Chinese Academy of Sciences, (2003).

[6]. Yu Liang. Research on Ground object classification and rapid modeling based on vehicle-mounted laser scanning data [D]. Wuhan University, (2011).

[7]. Qiao Jizang, Liu Xiaoping, Zhang Yihan. Ground object classification based on LiDAR height texture and neural network [J], Journal of Remote Sensing, 15(03): 539-53 (2011).

[8]. Li Zhifeng, Zhu Guchang, Dong Taifeng. Application of GLCM-based texture features to remote sensing image classification [J], Geology and Exploration, 47(03): 456-61 (2011).

[9]. Clergeau P, Jokimäki J, Snep R. Using hierarchical levels for urban ecology [J]. Trends in Ecology & Evolution, 2006, 21(12): 660-661

[10]. Wang Yanjiao, Yan Feng, Zhang Peikun, et al. Urban heat island change in Beijing based on vegetation index and surface albedo [J], Environmental Science Research, 22(02): 215-20 (2009).

[11]. MackeyC W, Lee W, Smith R B. Remotely sensing the cooling effects of city scale efforts to reduce urban heat island[J]. Building & Environment, 2012, 49(3): 348-358.

[12]. Li X,Zhang Qianqian,Zhang Weijie. Research on urban green land planning based on CITYgreen [J]. Shanxi architecture. 2011(07)

[13]. Research on the application of I-Trees model in the assessment of urban green space ecological service functions [J]. Shandong Forestry Science and Technology, 2018,48(06)

[14]. Simplified Chinese User's Manual-i-Tree

[15]. Li Manchun, Zhou Libin, Mao Liang. An assessment and prediction model of urban green land eco-efficiency based on RS and GIS [J], China Environmental Monitoring, (03): 48-51 (2003).

[16]. Li Fengxia. Research on the evaluation system and value estimation of urban green space eco-efficiency in Xi'an [D]; Xi'an University of Architecture and Technology, (2018).

Cite this article

Pan,Z. (2023). Research on Monitoring Technology and Analysis of Urban Green Space Ecology and Environment. Advances in Economics, Management and Political Sciences,27,231-241.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 7th International Conference on Economic Management and Green Development

ISBN：978-1-83558-075-2(Print) / 978-1-83558-076-9(Online)
Editor：Canh Thien Dang
Conference website: https://www.icemgd.org/
Conference date: 6 August 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.27
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Li Wei. Extraction and analysis of urban green land information based on remote sensing images [D]; East China Normal University, (2007).

[2]. Meng Qingyan. Spatial remote sensing of urban greenness [M]. Beijing: Science Press, (2022).

[3]. Lv Miaoer, Pu Yingxia, Huang Xingyuan. Remote sensing application of urban green space monitoring [J]. Chinese Tradision Gardens, 2000, (05): 41-4.

[4]. Meng Qingyan. Urban Greenness Spatial Remote Sensing -- Making Urban Life Better: Speech Delivered at "Theme Forum on Urban Environmental Issues and 2020 Discovering the Beauty of Cities" [EB/OL]. (2020-11-08).

[5]. Li Miaomiao. Study on estimation method of vegetation coverage by remote sensing [D]. Graduate School of the Chinese Academy of Sciences, (2003).

[6]. Yu Liang. Research on Ground object classification and rapid modeling based on vehicle-mounted laser scanning data [D]. Wuhan University, (2011).

[7]. Qiao Jizang, Liu Xiaoping, Zhang Yihan. Ground object classification based on LiDAR height texture and neural network [J], Journal of Remote Sensing, 15(03): 539-53 (2011).

[8]. Li Zhifeng, Zhu Guchang, Dong Taifeng. Application of GLCM-based texture features to remote sensing image classification [J], Geology and Exploration, 47(03): 456-61 (2011).

[9]. Clergeau P, Jokimäki J, Snep R. Using hierarchical levels for urban ecology [J]. Trends in Ecology & Evolution, 2006, 21(12): 660-661

[10]. Wang Yanjiao, Yan Feng, Zhang Peikun, et al. Urban heat island change in Beijing based on vegetation index and surface albedo [J], Environmental Science Research, 22(02): 215-20 (2009).

[11]. MackeyC W, Lee W, Smith R B. Remotely sensing the cooling effects of city scale efforts to reduce urban heat island[J]. Building & Environment, 2012, 49(3): 348-358.

[12]. Li X,Zhang Qianqian,Zhang Weijie. Research on urban green land planning based on CITYgreen [J]. Shanxi architecture. 2011(07)

[13]. Research on the application of I-Trees model in the assessment of urban green space ecological service functions [J]. Shandong Forestry Science and Technology, 2018,48(06)

[14]. Simplified Chinese User's Manual-i-Tree

[15]. Li Manchun, Zhou Libin, Mao Liang. An assessment and prediction model of urban green land eco-efficiency based on RS and GIS [J], China Environmental Monitoring, (03): 48-51 (2003).

[16]. Li Fengxia. Research on the evaluation system and value estimation of urban green space eco-efficiency in Xi'an [D]; Xi'an University of Architecture and Technology, (2018).

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