4.7 Article

A Synthetic Landscape Metric to Evaluate Urban Vegetation Quality: A Case of Fuzhou City in China

Journal

FORESTS
Volume 13, Issue 7, Pages -

Publisher

MDPI
DOI: 10.3390/f13071002

Keywords

urban vegetation; vegetation quality index; landscape metrics; normalized difference vegetation index (NDVI); principal component analysis

Categories

Funding

  1. National Natural Science Foundation of China [31971639, 41901221]
  2. Natural Science Foundation of Fujian Province [2019J01406]
  3. Foundation for National Science and Technology Basic Resources Investigation Project [2019FY202108]

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Urban vegetation plays a crucial role in regulating urban climate and improving the urban environment. This study proposes a synthetic vegetation quality index (VQI) to effectively and quickly assess urban vegetation quality and detects its changes over time. The results demonstrate the reliability and applicability of the VQI in evaluating urban vegetation quality and its impact on the urban thermal environment.
Urban vegetation plays a very important role in regulating urban climate and improving the urban environment. There is an urgent need to construct an effective index to quickly detect urban vegetation quality changes. In this study, a synthetic vegetation quality index (VQI) was proposed using a holistic approach based on the quality of vegetation itself and the spatial relationship with its surroundings, composed of four selected variables: normalized difference vegetation index (NDVI), patch aggregation index (AI), patch density (PD), and percentage of landscape (PLAND). Principal component analysis (PCA) was employed to calculate weights for each variable due to its objectivity. Then, taking Fuzhou City, southeast China as the case study, the scale effects of the VQI under different moving window sizes (500 m, 1 km, 2 km, horizontal ellipsis , 5 km) and the spatiotemporal changes were explored. The results showed that a VQI with a window size of 3 km had the highest correlations with all the selected indicators. Meanwhile, the representativeness and the effectiveness of the VQI were validated by the percentage eigenvalues of PC1, as well as Pearson correlation analysis and bivariate spatial autocorrelation analysis. We also revealed that the proposed VQI had the greatest explanatory power for land surface temperature (LST) among all the factors in both studied years (2000 and 2016), with the VQI's interpretation of LST being 0-44% better than any individual indicator except for AI in 2000. Additionally, our work revealed that the location of vegetation has a great impact on the urban thermal environment. The VQI can assess urban vegetation quality effectively and quickly.

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