Estimation of anthropogenic heat flux of Fujian Province (China) based on Luojia 1-01 nighttime light data

被引:0
作者
Lin Z. [1 ]
Xu H. [2 ,3 ]
Lin C. [1 ]
机构
[1] College of Architecture and Urban Planning, Fujian University of Technology, Fuzhou
[2] Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing, College of Environmental and Safety Engineering, Fuzhou University, Fuzhou
[3] Fujian Provincial Key Laboratory of Remote Sensing Soil Erosion and Disaster Prevention, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou
基金
中国国家自然科学基金;
关键词
AHF; anthropogenic heat; Fujian Province; Luojia; 1-01; nighttime light imagery; remote sensing;
D O I
10.11834/jrs.20210295
中图分类号
学科分类号
摘要
Nighttime light (NTL) data are important for estimating Anthropogenic Heat Flux (AHF). However, the commonly used DMSP/OLS and Suomi-NPP/VIIRS NTL data are restricted by their coarse spatial resolution and therefore, cannot exhibit the spatial details of AHF at city scale. The 130 m high-resolution NTL data obtained by the Luojia 1-01 satellite launched in June 2018 show potential to solve this problem. Therefore, this study aims to construct an AHF estimation model using the NTL data of Luojia 1-01 for Fujian Province based on three indexes, namely, normalized nighttime light data (NTLnor), Human Settlement Index (HSI), and Vegetation Adjusted NTL Urban Index (VANUI). To determine the best estimation model of AHF, the AHF of 84 county-level cities of Fujian Province has also been calculated using energy-consumption statistics data and then correlated with the corresponding data of three indexes. Results show that (1) based on a five-fold cross validation approach, VANUI power estimation model achieves the highest R2 along with the smallest RMSE; therefore, it has the highest accuracy among the three indexes; (2) according to the VANUI power estimation model, the average annual AHF of Fujian Province in 2018 is 0.88 W/m2, of which Xiamen has the highest average annual AHF of 10.98 W/m2, followed by Quanzhou, Putian, Fuzhou, and Zhangzhou, with the annual average of 0.98—1.95 W/m2, whereas the figures of Ningde, Longyan, Sanming, and Nanping are relatively low, ranging from 0.38—0.46 W/m2; (3) Luojia 1-01 NTL data can reveal the AHF differentiation details at a city scale. The AHF values of different land properties and functions show the following order: urban commercial area > large municipal public facility area > urban main road > urban residential area > suburban residential area. Studies have shown that the AHF estimation model developed by Luojia 1-01 NTL data can achieve high accuracy of the city-scale estimation of AHF. © 2022 National Remote Sensing Bulletin. All rights reserved.
引用
收藏
页码:1236 / 1246
页数:10
相关论文
共 39 条
[1]  
Block A, Keuler K, Schaller E, Impacts of anthropogenic heat on regional climate patterns, Geophysical Research Letters, 31, 12, (2004)
[2]  
Bohnenstengel S I, Hamilton I, Davies M, Belcher S E, Impact of anthropogenic heat emissions on London's temperatures, Quarterly Journal of the Royal Meteorological Society, 140, 679, pp. 687-698, (2014)
[3]  
Cao Z Y, Wu Z F, Kuang Y Q, Huang N S, Correction of DMSP/OLS night-time light images and its application in China, Journal of Geo-information Science, 17, 9, pp. 1092-1102, (2015)
[4]  
Chander G, Markham B, Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges, IEEE Transactions on Geoscience and Remote Sensing, 41, 11, pp. 2674-2677, (2003)
[5]  
Chavez P S, Image-based atmospheric corrections revisited and improved, Photogrammetric Engineering and Remote Sensing, 62, 9, pp. 1025-1035, (1996)
[6]  
Chen B, Chen L F, Dong L, Shi G Y, Estimating the global distribution of anthropogenic heat release and exploring its possible climatic effect, Chinese Journal of Atmospheric Sciences, 40, 2, pp. 289-295, (2016)
[7]  
Chen B, Shi G Y, Wang B, Zhao J Q, Tan S C, Estimation of the anthropogenic heat release distribution in China from 1992 to 2009, Acta Meteorologica Sinica, 26, 4, pp. 507-515, (2012)
[8]  
Chen S S, Hu D Y, Parameterizing anthropogenic heat flux with an energy-consumption inventory and multi-source remote sensing data, Remote Sensing, 9, 11, (2017)
[9]  
Chen Y B, Zheng Z H, Wu Z F, Qian Q L, Review and prospect of application of nighttime light remote sensing data, Progress in Geography, 38, 2, pp. 205-223, (2019)
[10]  
Elvidge C D, Baugh K E, Kihn E A, Kroehl H W, Davis E R, Mapping city lights with nighttime data from the DMSP Operational Linescan System, Photogrammetric Engineering and Remote Sensing, 63, 6, pp. 727-734, (1997)