Preprints
https://doi.org/10.5194/essd-2022-415
https://doi.org/10.5194/essd-2022-415
 
02 Jan 2023
02 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

30 m Map of Young Forest Age in China

Yuelong Xiao1, Qunming Wang1, Xiaohua Tong1, and Peter Atkinson2,3 Yuelong Xiao et al.
  • 1College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai, 200092, China
  • 2Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YR, UK
  • 3Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK

Abstract. Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land-atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby, laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed, and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436,967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use/land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1–31 years) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method was validated by comparing the forest age map with Hansen’s forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services, and important guidance for government departments, such as the National Forestry and Grassland Administration and National Development and Reform Commission in China.

Yuelong Xiao et al.

Status: open (until 27 Feb 2023)

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Yuelong Xiao et al.

Yuelong Xiao et al.

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Short summary
A model suitable for large-scale forest age mapping was developed to estimate young forest ages across China in 2020 at a spatial resolution of 30 m, based on 436,967 Landsat tiles across China from 1990 to 2020. The overall accuracy was 90.28 %. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services, and important guidance for government departments.