| Abstrakt: |
A spatially explicit, high-resolution forest age map is critical for quantifying forest carbon stock and carbon sequestration potential. Previous endeavours to estimate forest age in China at national scale mainly concentrated on a sparse resolution or incomplete forest ecosystems because of complex species composition, vast forest areas, insufficient field measurements, and the lack of effective methods. To overcome these limitations, we construct a framework for estimating China's forest age by combining remote-sensing time series analysis with machine learning algorithms based on massive field measurements and remote-sensing dataset. Specifically, the LandTrendr time series analysis is first applied to detect forest disturbances from 1985 to 2020, with the time since the last disturbance serving as a proxy for forest age. Next, for pixels where no disturbance, machine learning algorithms are used to estimate forest age from independent variables, including forest height, climate, terrain, soil, and forest-age field measurements. Finally, MLA models are established for each vegetation division and used to estimate forest ages. Combining these two methods produces a spatially explicit 30 m resolution forestage map for China in the year of 2020. Validation against independent field plots produces a R -2 from 0.51 to 0.63. Nationally, the average forest age is 56.1 years (standard deviation = 32.7 years), where the Qinghai-Tibet Plateau alpine vegetation zone has the oldest forest with an average of 138.0 years, whereas the forest in the warm temperate deciduous-broadleaf forest vegetation zone averages only 28.5 years. This 30-m-resolution forest-age map provides vital information for accurately understanding the ecological benefits of China's forests and to sustainably manage China's forest resources. [ABSTRACT FROM AUTHOR] |
