Abstract
The contribution degree of different surface factors (complexity and heterogeneity) in the urban interior to the urban thermal environment has become an issue of increasing concern under changing climate. In this paper, the multiple linear regression analysis methods to analyze the contribution degree of different surface factors to the urban thermal environment were based on seven urban built-up areas. At the same time, the LST of the same type of factors in the same city will have a difference of ±2.5°C due to the different surrounding features. At the same time, the LST of the same ground object in the same city will be ±2.5°C different because of the difference of the surrounding ground object. The environmental LST and the mean LST of other surface factors were significantly correlated, and the root mean square error was 3.52. This study first classifies the ground features with different attributes, conducts LST statistics for each category, and conducts multivariate linear analysis, instead of setting some fuzzy exponent and forcing a threshold to calculate. The purpose is to explore the contribution of different reflectivity ground objects to the urban thermal environment.
Highlights
Academic Editor: Stefania Bonafoni e contribution degree of different surface factors in the urban interior to the urban thermal environment has become an issue of increasing concern under changing climate
Their study showed that landscape composition and arrangement both have an impact on land surface temperature (LST), landscape composition is more important than landscape layout; one component indicator together with no more than four landscape layout indicators can well lead to the Advances in Meteorology prediction of LST [23,24,25,26,27]. ese research results can help landscape ecologists effectively use landscape indicators and promote landscape planners to make balanced use of land use types (LUTs) in urban planning [28,29,30,31,32,33]
In relevant studies on the urban thermal environment, we found that the urban thermal environment was closely related to the significant built-up areas of the city. erefore, based on the Google Earth Engine (GEE) platform combined with night light data, normalized difference building index (NDBI) data, normalized difference vegetation index (NDVI) data, and normalized difference water index (NDWI) data, this study adopted the Wake Cobweb algorithm to extract the research area and used the impervious layer dataset released by the China National Earth Observation Data Center to verify that the accuracy is above 80% and extracted the outer contour of the built-up area through morphological corrosion and expansion operations
Summary
Academic Editor: Stefania Bonafoni e contribution degree of different surface factors (complexity and heterogeneity) in the urban interior to the urban thermal environment has become an issue of increasing concern under changing climate. The multiple linear regression analysis methods to analyze the contribution degree of different surface factors to the urban thermal environment were based on seven urban built-up areas. Setting some undefined indexes and some mandatory thresholds can be representative to some extent, the contribution value of specific ground objects to the urban thermal environment cannot be truly explored. For this status quo, we tried to solve this problem with a data-intensive, data-driven approach. This study clustered 29 Landsat-8 multispectral remote sensing data covering seven urban built-up areas and used high spatial resolution optical images and GIS data to interpret their surface factors. We used ridge regression to analyze and measure the contribution coefficient of the mean LST of various surface factors to the thermal environment. e main objective of this paper is to clarify the contribution of various land features to the urban environment through accurate land surface classification
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