Relationship of air temperature to NDVI and NDBI in Guangzhou City using spatial autoregressive model
Jianhui XU1,2,3(), Yi ZHAO4,5, Minghong XIAO6, Kaiwen ZHONG1,2,3, Huihua RUAN7
1. Guangzhou Institute of Geography, Guangzhou 510070, China 2. Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China 3. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China 4. Guangzhou Institute of Geochemistry, China Academy of Sciences, Guangzhou 510640, China 5. University of Chinese Academy of Sciences, Beijing 100049, China 6. Guangxi Institute of Geographic Information Surveying and Mapping, Liuzhou 545006, China 7. Guangdong Meteorological Observation Data Center, Guangzhou 510080, China
To study the spatio-temporal pattern of the air temperature in Guangzhou City, the authors used MODIS monthly normalized difference vegetation index (NDVI) acquired in 2015 and extracted the normalized difference built-up index (NDBI) with Landsat8 OLI data. The correlation analysis method was used to explore the relationship between air temperature and NDVI, NDBI. The experimental results show that there is a negative relation between NDVI and air temperature and a positive relation between NDBI and air temperature. On such a basis, the spatial lag model (SLM) and spatial error model (SEM) were established to discuss the spatial relations between air temperature and NDVI, NDBI in different seasons, respectively. The SLM and SEM results were compared with the ordinary least square regression (OLS) model, which shows the best performance of the SLM and SEM models. The SLM model with higher R2 and lower AIC values performs slightly better than the SEM model. NDVI has more influence on air temperature from spring to autumn than NDBI. In the SLM model, the positive and significant spatial autoregressive coefficients indicate an active influence from neighboring meteorological stations.
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2018,
Vol. 30
), Yi ZHAO4,5, Minghong XIAO6, Kaiwen ZHONG1,2,3, Huihua RUAN7
