A novel method to quantify urban surface ecological poorness zone: A case study of several European cities

A set of factors cause the Surface Ecological Status (SES) of urban areas to become largely different from the surrounding rural areas. Hence, the degree of poorness of SES in urban areas versus surrounding rural areas forms a zone, which is named Urban Surface Ecological Poorness Zone (USEPZ). The main objective of this study was to propose a new method to quantify USEPZ Intensity (USEPZI). To this end, Landsat-8 satellite images. water vapor products, and High Resolution Imperviousness Layer (HRIL) datasets of Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome cities were used. Firstly, Single Channel (SC) algorithm, Tasseled cap transformation, and spectral indices were used to model the surface biophysical characteristics including Land Surface Temperature (1ST). Wetness, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Soil Index (NDSI). Then, SES was modeled based on the combination of surface biophysical characteristics using Remote Sensing-based Ecological Index (RSEI). Finally, the USEPZI was modeled bawd on the linear regression function obtained from RSEI-Impervious Surface Percentage (ISP) feature space. The spatial variability of the ISP, LST, NDVI, NDSI and Wetness of the selected cities was found to be heterogeneous. The coefficient of determination (R-2) between RSEI and ISP values for Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome cities were obtained to be 0.99, 0.97, 0.98.0.99, 0.98,0.99.0.99, and 0.94, respectively. Also, the USEPZI values of these dties were 0.14.0.31, 0.41,0.26. 0.40,0.81, 0.44 and 0.46, respectively. Our findings show that the significant differences in their SES and USEPZI are due to the surface biophysical characteristics. The USEPZI in the selected cities with humid climate conditions was higher than the selected cities in dry climate conditions. Also, the use of the RSEI-ISP feature space is quite useful in modeling USEPZI of cities in different conditions. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study aimed to propose a new method to quantify Urban Surface Ecological Poorness Zone Intensity (USEPZI) using remote sensing data and imagery. By successfully modeling the surface biophysical characteristics of cities and simulating their surface ecological status, a valuation model for urban surface ecological poorness zones can be established using a linear regression function.