Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems

Abstract

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…The GEE greatly improves the processing efficiency when using substantial amounts of remote sensing data. In recent years, the GEE was used in land cover mapping [49][50][51][52][53][54][55][56][57][58], agricultural applications [59][60][61][62][63], disaster management, and earth sciences studies [64][65][66]. This remote sensing data processing cloud platform makes the rapid processing of Sentinel-2 images covering large areas possible.…”

Landscape Changes and their Impacts on Land Surface Temperature in Tropical Hill Stations in South Asia: A Case Study of Nuwara Eliya, Sri Lanka

“…We hypothesized that the use more imageries produces clearer output images to understand the LST patterns in the study area. Past studies have shown the aptness of this method to generate LST in different areas [39] [40]. All extracted images were georectified using the WGS84/UTM 44N projection system before further processing.…”

“…The use of more satellite images captured in multiple time point can potentially provide more specific information to understand the changing pattern of LST [4]. Still, it is not easy to analyze extensive earth observation data set due to spatial and temporal resolution [39] [40]. Thus, big data analysis platforms can be used as an alternative for conducting accurate result [39].…”

“…Still, it is not easy to analyze extensive earth observation data set due to spatial and temporal resolution [39] [40]. Thus, big data analysis platforms can be used as an alternative for conducting accurate result [39]. Google Earth Engine (GEE) provides potential to process a large number of satellite images, and researchers can easily access to free public data archives more than thirty years of historical data [39].…”

“…Thus, big data analysis platforms can be used as an alternative for conducting accurate result [39]. Google Earth Engine (GEE) provides potential to process a large number of satellite images, and researchers can easily access to free public data archives more than thirty years of historical data [39]. Hence, we have used GEE to extract the annual median LST for three-time points such as 1996, 2006, and 2017 based on the several images captured during the selected years.…”