High resolution and spatially clear forest age maps are crucial for quantifying forest carbon storage and carbon sequestration potential.. The nationwide forest age estimation work previously conducted in China has been limited by sparse resolution and incomplete forest ecosystem coverage. Due to the complex species composition, vast forest area, insufficient field measurements, and incomplete methods, the resolution is sparse and the forest ecosystem coverage is incomplete, which limits the estimation of forest age nationwide in China. To address these challenges, we have developed a framework that combines machine learning algorithms (MLAs) with remote sensing time series analysis to estimate the age of forests in China. Initially, we determined and developed the optimal MLA for estimating forest age for various vegetation classifications based on field measurements of forest height, climate, terrain, soil, and forest age, and used these MLA to determine forest age information. Subsequently, we applied LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, and used the time since the last disturbance as a proxy for forest age. Finally, the LandTrendr forest age data was combined with the results of MLA to generate the 2020 Chinese forest age map.
Collected the seventh national forest inventory survey in China from 2004 to 2008（ http://www.forestry.gov.cn/ The data, last accessed on September 22, 2023, will be used to develop a model for estimating forest age. This list involves accurate monitoring of national forest resources based on a 667 m2 sample plot system covering the whole country. The main information collected from the plot includes tree species, forest age, average tree height, and geographic location. The age of the forest stand is determined based on the planting time or estimated using the diameter at breast height of the tree. We collected a total of 58033 fields ranging in age from 1 year to 480 years old. The average age of the sample is 34.0 years, with a standard deviation of 29.6 years. The sample plots are distributed in 8 vegetation zones, each zone containing at least 436 sample plots, used to construct MLAs for estimating forest age.
This framework combines machine learning algorithms (MLA) and remote sensing time series analysis to estimate the age of Chinese forests.
Apply LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, and use the time since the last disturbance as a proxy for forest age. Finally, the LandTrendr forest age data was combined with the results of MLA to generate the 2020 Chinese forest age map. The validation of independent field plots resulted in R2, ranging from 0.51 to 0.63. The average forest age nationwide is 56.1 years (with a standard deviation of 32.7 years). The forests in the high-altitude vegetation area of the Qinghai Tibet Plateau are the oldest, with an average vegetation age of 138.0 years, while the vegetation age in the warm temperate deciduous broad-leaved forest vegetation area is only 28.5 years. This 30 meter resolution forest age map provides important insights for a comprehensive understanding of the ecological benefits and sustainable management of China's forest resources.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB6448.2024
10.5281/zenodo.8354262
