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Analysis of remotely-sensed ecological indexes' influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi’an, China

Zhu, Xinming, Wang, Xuhong, Yan, Dajiang, Liu, Zhuang, Zhou, Yongfang
International journal of remote sensing 2019 v.40 no.9 pp. 3421-3447
Landsat, case studies, decision making, ecosystems, environmental factors, issues and policy, physical phenomena, principal component analysis, regression analysis, remote sensing, soil water, surface temperature, vegetation index, China
The spatio-temporal pattern of surface ecological status affects urban thermal environment distribution significantly. Urban thermal pattern, however, is a complicated physical phenomenon involving a series of terrestrial environmental parameters. Thus, it is insufficient to employ only one ecological parameter for depicting the variation of land surface temperature (LST). This paper begins with the analysis of four ecological parameters' influence on LST using regression analysis, based on 24 Landsat images which cover Xi’an of China from 1992 to 2014. These four parameters include greenness degree (i.e. the soil adjusted vegetation index, SAVI), soil moisture degree (i.e. the normalized difference moisture index, NDMI), dryness degree (i.e. the normalized difference soil index, NDSI) and resident aggregation degree (i.e. the normalized difference build-up index, NDBI). Besides, contribution intensity index was introduced to investigate the contribution effect of four ecological parameters on LST, and a new ecological index, integrated ecological index (IEI), was founded using the principal component analysis technique to integratedly represent its spatial and mathematical correlations with LST. Results indicate that four ecological parameters all possessed pronounced performance in impacting LST pattern in all dates: SAVI and NDMI were found to be correlated negatively with LST, whereas NDBI and NDSI correlated positively with LST. Additionally, SAVI had a profound impact on LST distribution compared with the other three parameters, and there was the biggest heating contribution in the lowest SAVI category. Further finding suggests that IEI as a new ecological index can be used to integratedly estimate the spatio-temporal change of LST, manifesting a negative correlation with LST. Our study thinks that the comprehensive characterization of surface ecological status is conducive to benefit us to better understand the spatio-temporal mechanism of thermal environment and ecosystem and to help urban decision-makers to execute effective conservation policies for the ecosystem.