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Open AccessArticle

Determining the Boundary and Probability of Surface Urban Heat Island Footprint Based on a Logistic Model

1
Key Laboratory of Indoor Air Environment Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
2
School of Architecture, Tianjin University, Tianjin 300272, China
3
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
5
University of Chinese Academy of Sciences, Beijing 100049, China
6
School of Geographic Science and Engineering, Hohai University, Nanjing 211100, China
7
Guangdong Province Key Laboratory for Land Use and Consolidation, The College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1368; https://doi.org/10.3390/rs11111368
Received: 17 April 2019 / Revised: 27 May 2019 / Accepted: 3 June 2019 / Published: 6 June 2019
Studies of the spatial extent of surface urban heat island (SUHI or UHISurf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical limitations on the ability of their algorithms to accurately determine the impacts of the shape, topography, and landscape heterogeneity of the city. The key to determining the FP boundary is identifying background temperatures in reference rural regions. Due to the instability of remote sensing data, these background temperatures should be determined automatically rather than manually, to eliminate artificial bias. To address this need, we developed an algorithm that adequately represents the decay of land surface temperature (LST) from the urban center to surrounding rural regions, and automatically calculates thresholds for reference rural LSTs in all directions based on a logistic curve. In this study, we applied this algorithm with data from the Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS) 8-day level 3 (L3) LST global grid product to delineate precise SUHI FPs for the Beijing metropolitan area during the summers of 2004–2018 and determine the interannual and diurnal variations in FP boundaries and their relationship with SUHI intensity. View Full-Text
Keywords: surface urban heat island; footprint; threshold; boundary; logistic model; MODIS surface urban heat island; footprint; threshold; boundary; logistic model; MODIS
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Qiao, Z.; Wu, C.; Zhao, D.; Xu, X.; Yang, J.; Feng, L.; Sun, Z.; Liu, L. Determining the Boundary and Probability of Surface Urban Heat Island Footprint Based on a Logistic Model. Remote Sens. 2019, 11, 1368.

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