Wang Zhihui, Shi Yonglei, Tang Qiuhong, Cheng Miaomiao, Zhang Yi
Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China.
Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China; Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China.
Sci Total Environ. 2024 Sep 15;943:173886. doi: 10.1016/j.scitotenv.2024.173886. Epub 2024 Jun 8.
Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It is always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time-series, resulting in changing trajectory of woody AGB estimates cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was firstly selected and interannual time-series of composited SR was smoothed using S-G filter for each pixel, and then optimal machine learning algorithm was selected to estimate woody AGB time-series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatial-temporal extended using random forest model combining environmental variables under current climate condition and CMIP6 climate models. Results show that: 1) minimum value composite based on NIRv during Jul.-Sep. is more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and Random Forest (RF) model has the best performance in estimating woody AGB (R = 0.75, RMSE = 4.74 t·ha) among sis commonly used machine learning models. 2) Annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R = 0.97, p < 0.001) indicating explicit growth regularity of woody vegetation, which provides physiological foundation for determining woody AGB potential. 3) Woody AGB potential can be accurately simulated by RF combining environmental variables (R = 0.95, RMSE = 2.89 t·ha), and current woody AGB still has a potential of small increase, whereas the overall losses of woody AGB potential were observed in 2030, 2040 and 2050 under CMIP6 SSP-RCP scenarios.
掌握木本地上生物量（AGB）的长期动态变化以及气候变化下的潜力，对于计算和提高旱地造林的碳固存至关重要。仅利用陆地卫星时间序列准确捕捉与草地混生的稀疏木本植被的AGB动态变化一直是一项巨大挑战，导致木本AGB估计值的变化轨迹无法准确反映旱地木本植被的生长规律。在本研究中，首先选择对木本AGB敏感的地表反射率（SR），对每个像素的合成SR年际时间序列使用S-G滤波器进行平滑处理，然后选择最优机器学习算法来估计木本AGB时间序列。基于AGB变化轨迹检测已达到AGB潜力的像素，并利用结合当前气候条件下的环境变量和CMIP6气候模型的随机森林模型对该潜力进行时空扩展。结果表明：1）7月至9月基于近红外植被指数（NIRv）的最小值合成更能解释旱地木本AGB的变化（R = 0.87，p < 0.01），在六种常用机器学习模型中，随机森林（RF）模型在估计木本AGB方面表现最佳（R = 0.75，RMSE = 4.74 t·ha）。2）年度木本AGB估计值能很好地拟合逻辑斯蒂增长曲线（R = 0.97，p < 0.001），表明木本植被具有明确的生长规律，这为确定木本AGB潜力提供了生理基础。3）利用RF结合环境变量可准确模拟木本AGB潜力（R = 0.95，RMSE = 2.89 t·ha），当前木本AGB仍有小幅增长潜力，而在CMIP6 SSP-RCP情景下，2030年、2040年和2050年观察到木本AGB潜力总体呈下降趋势。
