Yifan Wang's research while affiliated with Fuzhou University and other places

The COVID-19 pandemic in China in the winter-spring of 2019–2020 has decreased and even stopped many human activities. This study investigates whether there were any changes in the water quality of the Lower Min River (China) during the lockdown period. The time-series remote sensing images from November 2019 to April 2020 was used to examine the dynamics of the river’s total suspended solids (TSS) concentrations in the period. A new remote sensing-based prototype was developed to recalibrate an existing algorithm for retrieving TSS concentrations in the river. The Nechad and the Novoa algorithms were used to validate the recalibrated algorithm. The results show that the recalibrated algorithm is highly consistent with the two algorithms. All of the three algorithms indicate significant fluctuation in TSS concentrations in the Lower Min River during the study period. February (COVID-19 lockdown period) has witnessed a 48% fall in TSS concentration. The TSS in March–April showed a progressive and recovery back to normal levels of pre-COVID-19. The spatiotemporal change of TSS has worked as a good indicator of human activities, which revealed that the decline of TSS in the lockdown period was due largely to the substantially-reduced discharges from industrial estates, densely-populated city center, and river’s shipping. Remote sensing monitoring of the spatiotemporal changes of TSS helps understand important contributors to the water-quality changes in the river and the impacts of anthropogenic activities on river systems.

Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann-Kendall test and Theil-Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km 2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.