Mapping of Ecological Environment Based on Google Earth Engine Cloud Computing Platform and Landsat Long-Term Data: A Case Study of the Zhoushan Archipelago

Abstract

In recent years, with the rapid advancement of China’s urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas.

Evaluation and Prediction of Ecological Restoration Effect of Beijing Wangping Coal Mine Based on Modified Remote Sensing Ecological Index

“…The index, which quantifies the relative trend of a time series, was initially invented for the study of practical problems in water resources. Because the index can represent time series sustainability, it has been gradually utilized in the prediction studies of future ecological quality in recent years [34]. We calculated the Hurst index for the study area using a rescaled polarity (R/S) analysis.…”

Remote Sensing Ecological Quality and Its Response to the Rocky Desertification in the World Heritage Karst Sites

“…Chen et al calculated the RSEI status of the Zhoushan Islands from 1985 to 2020 based on the GEE (Google Earth Engine) platform. Their analysis revealed that the overall ecological environment of the Zhoushan Islands showed a downward trend, but the RSEI values were concentrated, with small interannual fluctuations and relatively stable time-series [20]. Based on previous research [21,22], using the RSEI model for spatial long-term environmental monitoring and analysis can achieve significant results in WHKSs with fragmented terrain, wide regional scope, and strong contrast of human activity cold and hot spots.…”

Monitoring and analysis of eco-environmental quality in Yulin City from 2000 to 2020 based on Remote Sensing Ecological Index (RSEI)

“…RSEI and its improvements are widely used in the assessment and analysis of eco-environmental quality at different scales [12][13][14][15]. Meanwhile, the use of remote sensing cloud computing platforms, such as Google Earth Engine (GEE), greatly improves the efficiency of remote sensing data processing and analysis [16], which makes the application of large-scale and long time series RSEI more efficiently [17][18][19][20].…”