Mapping the vegetation distribution and dynamics of a wetland using adaptive-stacking and Google Earth Engine based on multi-source remote sensing data

Wetland vegetation is susceptible to climate change and human disturbance, and has experienced significant losses and degradation. However, the spatial patterns of dynamics for China's inland lake wetlands remain unknown. In this paper, an adaptive-stacking algorithm based on Google Earth Engine was proposed to map the vegetation distribution of Dongting Lake wetland using Sentinel-1/2 and DEM data. Subsequently, LandTrendr was utilized to analyze vegetation dynamics over the 1999-2018 based on Landsat normalized combustion ratio time-series. By overlaying the latest vegetation types and spatial distribution of vegetation dynamics, the main types of vegetation change were examined. Results showed that the overall accuracy and kappa coefficient of adaptive-stacking classification were 94.59% and 0.92, respectively, which were higher than those of support vector machine and random forest. The overall accuracy of change detection for three types (vegetation gain, vegetation loss, and no changes) was 83.67%. In the past 20 years, 2,604.43 and 5,458.84 km2 land has experienced vegetation loss and gain, respectively. The increase in the areas of forest, reed, and sedge in wetland were 1330.39, 86.42, 136.97 km2, respectively. We found that the overall recovery condition of the wetland vegetation around Dongting Lake was good, which demonstrated the key role played by the national wetland ecological protection policy.

Automated summary

This study utilized an adaptive-stacking algorithm and LandTrendr to analyze the vegetation dynamics in the Dongting Lake wetland in China, demonstrating a good overall recovery of vegetation.