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测量城市邻里中绿地的时空异质性和内部特征:一种使用灰度共生矩阵的新方法。

Measuring spatio-temporal heterogeneity and interior characteristics of green spaces in urban neighborhoods: A new approach using gray level co-occurrence matrix.

机构信息

Humboldt Universität zu Berlin, Department of Geography, Lab for Landscape Ecology, Rudower Chaussee 16, 12489 Berlin, Germany; Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, D-04318 Leipzig, Germany.

Department of Urban Studies and Planning, University of Sheffield, Western Bank, S10 2TN Sheffield, UK; Institute of Geography, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany.

出版信息

Sci Total Environ. 2023 Jan 10;855:158608. doi: 10.1016/j.scitotenv.2022.158608. Epub 2022 Sep 8.

Abstract

Urban green space (UGS) is a complex and highly dynamic interface between people and nature. The existing methods of quantifying and evaluating UGS are mainly implemented on the surface features at a landscape scale, and most of them are insufficient to thoroughly reflect the spatial-temporal relationships, especially the internal characteristics changes at a small scale and the neighborhood spatial relationship of UGS. This paper thus proposes a method to evaluate the internal dynamics and neighborhood heterogeneity of different types of UGS in Leipzig using the gray level co-occurrence matrix (GLCM) index. We choose GLCM variance, contrast, and entropy to analyze five main types of UGS through a holistic description of their vegetation growth, spatial heterogeneity, and internal orderliness. The results show that different types of UGS have distinct characteristics due to the changes of surrounding buildings and the distance to the built-up area. Within a one-year period, seasonal changes in UGS far away from built-up areas are more obvious. As for the larger and dense urban forests, they have the lowest spatial heterogeneity and internal order. On the contrary, the garden areas present the highest heterogeneity. In this study, the GLCM index depicts the seasonal alternation of UGS on the temporal scale and shows the spatial form of each UGS, being in line with local urban planning contexts. The correlation analysis of indices also proves that each type of UGS has its distinct temporal and spatial characteristics. The GLCM is valid in assessing the internal characteristics and relationships of various UGS at the neighborhood scales, and using the methodology developed in our study, more studies and field experiments could be fulfilled to investigate the assessment accuracy of our GLCM index approach and to further enhance the scientific understanding on the internal features and ecological functions of UGS.

摘要

城市绿地(UGS)是人与自然之间复杂而高度动态的界面。现有的量化和评估 UGS 的方法主要在景观尺度上实现,并且大多数方法都不足以彻底反映时空关系,尤其是小尺度上的空间特征变化和 UGS 的邻里空间关系。因此,本文提出了一种使用灰度共生矩阵(GLCM)指数评估莱比锡不同类型 UGS 的内部动态和邻里异质性的方法。我们选择 GLCM 方差、对比度和熵来通过整体描述植被生长、空间异质性和内部有序性来分析五种主要类型的 UGS。结果表明,由于周围建筑物的变化和与建成区的距离,不同类型的 UGS 具有明显的特征。在一年的时间内,远离建成区的 UGS 的季节性变化更为明显。对于较大且密集的城市森林,它们的空间异质性和内部有序性最低。相反,花园区域呈现出最高的异质性。在本研究中,GLCM 指数描绘了 UGS 在时间尺度上的季节性变化,并展示了每个 UGS 的空间形式,符合当地城市规划背景。指数的相关分析也证明了每种类型的 UGS 都具有其独特的时空特征。GLCM 可用于评估各种 UGS 在邻里尺度上的内部特征和关系,并且可以使用我们研究中开发的方法,进行更多的研究和现场实验,以调查我们的 GLCM 指数方法的评估准确性,并进一步增强对 UGS 的内部特征和生态功能的科学理解。

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