Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data

Shasha Wang; Deyong Hu; Chen Yu; Shanshan Chen; Yufei Di

doi:10.1016/j.scitotenv.2020.139457

Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data

Sci Total Environ. 2020 Sep 10:734:139457. doi: 10.1016/j.scitotenv.2020.139457. Epub 2020 May 16.

Authors

Shasha Wang¹, Deyong Hu², Chen Yu¹, Shanshan Chen³, Yufei Di¹

Affiliations

¹ College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China; Beijing Key Laboratory of Resources Environment and Geographic Information System, Beijing 100048, China.
² College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China; Beijing Key Laboratory of Resources Environment and Geographic Information System, Beijing 100048, China. Electronic address: deyonghu@cnu.edu.cn.
³ Institute of Remote Sensing and Geographic Information System, School of Earth and Space Science, Peking University, Beijing 100871, China.

PMID: 32464384
DOI: 10.1016/j.scitotenv.2020.139457

Abstract

Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R²) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R² value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R² value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000-2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2-5 W·m^-2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0-2 W·m^-2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.

Keywords: Anthropogenic heat flux; Energy consumption; Remote sensing data; Time-series estimation.