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Spatiotemporal evolution of mangrove forests in Hainan Island

作者:Chang Fu

Mangrove ecosystems play a dominant role in global tropical and subtropical coastal wetlands. Remote sensing has a central role in mangrove conservation, as it is the preferred tool for monitoring spatiotemporal distribution changes. Through Landsat remote sensing image data, this study employed support vector machine (SVM) machine learning and Res-UNet deep learning to monitor the changes of mangrove forests and crown surface cover were monitored in Hainan Island from 1991 to 2021. Additionally, based on the crown surface cover area of mangrove forests in Hainan Island, the influencing mechanisms were analyzed via dynamic changes and landscape patterns. The following is the data package of our research:

1. The data in the folder (Res-UNet Deep Learning Classification) the classification results of Res-UNet deep learning algorithm, as well as its accuracy verification results.

2. The data in the folder (Influential Mechanisms) contains the total population, urban population, and rural population, GDP and gross output fishery value and climatic data and Pearson correlation analysis index for Hainan Island during the study period. The total population, urban population, and rural population, GDP, gross output fishery value, and shelter forests planting area of Hainan Island were obtained from the Annual Statistical Report of Hainan Province, and the climate data is downloaded from WorldClim data website (https://www.worldclim.org/data/index.html).

3. The data in the folder (Landscape Patterns) is the landscape pattern index and the annual change rate of mangrove forest crown surface cover in Hainan Island.

4. The data in the folder (SVM Machine Learning Classification) is the classification results of SVM machine learning algorithm, as well as its accuracy verification results.

5. The data in the folder (Ground Survey ) are the distribution range of mangrove forests and the distribution of dominant mangrove tree species obtained by the team members in the ground survey in 2020.   

Please contact 'zixuanqiu@hainanu.edu.cn' for the extract password.

Download linkhttp://doi.org/10.6084/m9.figshare.21405531