10.1016/j.compenvurbsys.2020.101542 ">

Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE)

Document Type


Publication Date



Geography and Geology

Publication Title

Computers, Environment and Urban Systems


© 2020 Elsevier Ltd Google Earth Engine (GEE) has been increasingly used in environmental and urban studies due to its cloud-based geospatial processing capability and accessibility to a large collection of geospatial datasets like Landsat, Modis, etc. However, at present, ecological and urban modeling efforts based on GEE are facing three grave challenges: current illustrations of GEE are to a large extent “straightforward mapping” applications; technical complexities that ecological or urban modelers have to overcome in order to effectively and easily use GEE to develop image processing based environmental models; and the majority of ecological and urban modelers are not aware of new analytical approaches that are becoming available because of the unprecedent geospatial processing capability and large collection of big geospatial datasets GEE has brought to them. The great potential of GEE to support ecological and urban modeling is less explored. In this study, we augmented GEE functions with a few sets of user-customized functions for improving image classification accuracy, estimating ecosystem services, and modeling urban growth sustainability. The paper is the first effort of modeling urban sustainability based on the concept of ecosystem service value (ESV) and in the cloud with GEE; is the first application of classifying GEE Landsat time-series images to compute yearly ESV; and creates the first set of cloud tools to augment GEE for ecologists and urban modelers to model urban sustainability from GEE and ESV. The paper also chose Hohhot City, Inner Mongolia as a case study to model urban sustainability in a time-series 12 years (2005–2016). The case study successfully estimated ecosystem service values and analyzed urban growth sustainability. It also revealed spatial disparities and temporal dynamics of urban growth sustainability in Hohhot City. The study provides an easy-to-adapt illustration on using GEE for image-based ecological and urban modeling.

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