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ESDA (Exploratory Spatial Data Analysis) of Vegetation Cover in Urban Areas—Recognition of Vulnerabilities for the Management of Resources in Urban Green Infrastructure

Author

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  • Ana Clara M. Moura

    (Architecture School, Geoprocessing Laboratory, Federal University of Minas Gerais, Belo Horizonte, MG 30130-141, Brazil)

  • Bráulio M. Fonseca

    (IGC, Post-Graduate Program in Analysis and Modeling of Environmental Systems, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil)

Abstract

From the mapping of urban vegetation cover by high-resolution orthoimages, using IR band and NDVI classification (Normalized Difference Vegetation Index), added to three-dimensional representation obtained by LiDAR capture (Light Detection and Ranging), the volumetric values of vegetal cover are obtained as a base to construct spatial analysis in the district of Pampulha, in Belo Horizonte, investigating the role it plays in the neighborhood. The article aims to analyze the relationship between vegetation cover, income distribution and population density, as a support to urban environmental quality management. It applies Exploratory Spatial Data Analysis (ESDA) to identify the presence of clusters and patterns of spatial distribution and to examine spatial autocorrelation. The results confirm the concentration of vegetation cover in areas of high income and lower population density but the main contribution of the study is the use of a method to analyze the spatial behavior of this distribution. Calculating Moran global index and local index (LISA), these spatial combinations are mainly used to identify transformation pressures, which may result in the definition of priorities for public actions and the construction of proposals for parameterization of vegetation cover to support plans related to green infrastructure in urban areas.

Suggested Citation

  • Ana Clara M. Moura & Bráulio M. Fonseca, 2020. "ESDA (Exploratory Spatial Data Analysis) of Vegetation Cover in Urban Areas—Recognition of Vulnerabilities for the Management of Resources in Urban Green Infrastructure," Sustainability, MDPI, vol. 12(5), pages 1-22, March.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1933-:d:327978
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    References listed on IDEAS

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    2. Bernard Fingleton (ed.), 2003. "European Regional Growth," Advances in Spatial Science, Springer, number 978-3-662-07136-6, Fall.
    3. Cem Ertur & Julie Gallo, 2003. "An Exploratory Spatial Data Analysis of European Regional Disparities, 1980–1995," Advances in Spatial Science, in: Bernard Fingleton (ed.), European Regional Growth, chapter 2, pages 55-97, Springer.
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    Cited by:

    1. Rui Zhang & Yong Ma & Jie Ren, 2022. "Green Development Performance Evaluation Based on Dual Perspectives of Level and Efficiency: A Case Study of the Yangtze River Economic Belt, China," IJERPH, MDPI, vol. 19(15), pages 1-24, July.
    2. Rongtian Zhang & Xiaolin Zhang, 2022. "Spatial–Temporal Differentiation and the Driving Mechanism of Rural Transformation Development in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(5), pages 1-16, February.

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