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Volume 11
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10.3390/land11010046
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Article

A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks

by
Xincheng Zheng
1,
Zeyao Zou
1,
Chongmin Xu
1,
Sen Lin
1,
Zhilong Wu
1,
Rongzu Qiu
1,
Xisheng Hu
1,* and
Jian Li
2
1
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
2
College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(1), 46; https://doi.org/10.3390/land11010046
Submission received: 1 December 2021 / Revised: 23 December 2021 / Accepted: 27 December 2021 / Published: 29 December 2021
(This article belongs to the Topic Climate Change and Environmental Sustainability)
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Abstract

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments.
Keywords: remote-sensing-based ecological index; geographically weighted regression; kernel density; road network; ecoenvironmental system remote-sensing-based ecological index; geographically weighted regression; kernel density; road network; ecoenvironmental system

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MDPI and ACS Style

Zheng, X.; Zou, Z.; Xu, C.; Lin, S.; Wu, Z.; Qiu, R.; Hu, X.; Li, J. A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks. Land 2022, 11, 46. https://doi.org/10.3390/land11010046

AMA Style

Zheng X, Zou Z, Xu C, Lin S, Wu Z, Qiu R, Hu X, Li J. A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks. Land. 2022; 11(1):46. https://doi.org/10.3390/land11010046

Chicago/Turabian Style

Zheng, Xincheng, Zeyao Zou, Chongmin Xu, Sen Lin, Zhilong Wu, Rongzu Qiu, Xisheng Hu, and Jian Li. 2022. "A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks" Land 11, no. 1: 46. https://doi.org/10.3390/land11010046

APA Style

Zheng, X., Zou, Z., Xu, C., Lin, S., Wu, Z., Qiu, R., Hu, X., & Li, J. (2022). A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks. Land, 11(1), 46. https://doi.org/10.3390/land11010046

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