Beyond tree cover: Characterizing southern China's forests using deep learning
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Dokumenter
- Beyond tree cover
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Mapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time-series of images. This knowledge is important, because ecological and economic values differ between forests. A new generation of low cost very high spatial resolution satellite images and the advent of deep learning enables improved abilities for distinguishing objects based on their structure, which could potentially also be applied to map different forest classes related to type, species and use. Here we use GF-1 images at 2 m resolution and map six forest classes including different planted species for the karst region in southwest China, covering 806,900 km2. We compare the results with field data and show that accuracies range between 78% and 90%. We show a dominance of plantations (15%) and secondary forests (70%), and only remnants of natural forests (6%). The possibility to map forest classes based on their crown structure derived from low cost very high-resolution satellite imagery paves the road towards sustainable forest management and restoration activities, supporting the creation of connected habitats, increasing biodiversity and improved carbon storage. No temporal information is needed for our approach, which saves costs and leads to rapid results that can be updated at a high temporal frequency.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Remote Sensing in Ecology and Conservation |
| Vol/bind | 9 |
| Udgave nummer | 1 |
| Sider (fra-til) | 17-32 |
| DOI | |
| Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
This work was supported by the National Key Research and Development Program of China (2018YFD1100103) and the National Natural Science Foundation of China (U20A2048, 41930652), the Strategic Priority Research Program of Chinese Academy of Sciences (grant number XDA19050502), the CAS Interdisciplinary Innovation Team (JCTD‐2021‐16), and the China Scholarship Council (202004910532). Funding Information
Funding Information:
This work was supported by the National Key Research and Development Program of China (2018YFD1100103) and the National Natural Science Foundation of China (U20A2048, 41930652), the Strategic Priority Research Program of Chinese Academy of Sciences (grant number XDA19050502), the CAS Interdisciplinary Innovation Team (JCTD‐2021‐16), and the China Scholarship Council (202004910532). We thank the Forest Bureau of the Yunnan, Guizhou and Guangxi Provinces for providing the statistical data. This work also received funding from the DFF Sapere Aude grant (no. 9064–00049B) and the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement no. 947757 TOFDRY). We also acknowledge support by the Villum Foundation through the project ‘Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics’ (DeReEco).
Publisher Copyright:
© 2022 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
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