AUTHOR=Liu Zhisong , Wang Liyan , Li Bin 

TITLE=Quality Assessment of Ecological Environment Based on Google Earth Engine: A Case Study of the Zhoushan Islands

JOURNAL=Frontiers in Ecology and Evolution

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.918756

DOI=10.3389/fevo.2022.918756

ISSN=2296-701X

ABSTRACT=With the development of society, the impact of human activities on the ecological environment is becoming increasingly intense, so the dynamic monitoring of the status of the ecological environment is of great significance to the management and protection of urban ecology. As an objective and rapid ecological quality monitoring and evaluation technique, the remote sensing–based ecological index (RSEI) has been widely used in the field of ecological research. However, due to the influence of unfavorable meteorological conditions such as the cloudy conditions in the city of Zhoushan, it is difficult to obtain large-scale seasonal and cloud-free Landsat remote sensing images. To monitor the quality of the ecological environment in Zhoushan from 2000 to 2020, the Google Earth Engine (GEE) platform was used for cloud computing to obtain the RSEI, which can reflect the quality of the ecological environment. The results show that (1) from 2000 to 2020, the average RSEI value in Zhoushan decreased from 0.748 to 0.681, indicating that the overall ecological environment exhibited a degradation trend. In general, the average RSEI value decreased from 2000 to 2010 and increased slowly from 2010 to 2020. (2) From 2000 to 2020, the change in the area of each ecological environment level indicate that the quality of the ecological environment in Zhoushan exhibited a degradation trend. The proportion of the area with an excellent eco-environment grade decreased by 13.54%, and the proportion of the area with poor and fair eco-environment grades increased by 3.43%. (3) The results show that the GEE platform, as a cloud computing platform for remote sensing, expands the application of the RSEI in the evaluation of the quality of the eco-environment to a larger scale and a longer time series.