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The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.     Keywords: spatial heterogeneity, urban ecology, urban remote sensing

Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery