He Jingyuan, Fan Chunyu, Geng Yan, Zhang Chunyu, Zhao Xiuhai, von Gadow Klaus
Research Center of Forest Management Engineering of State Forestry Administration Beijing Forestry University Beijing China.
Faculty of Forestry and Forest Ecology Georg-August-University Göttingen Germany.
Ecol Evol. 2022 Jul 13;12(7):e9110. doi: 10.1002/ece3.9110. eCollection 2022 Jul.
Estimating forest above-ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30-ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.
估算森林地上生物量（AGB）生产力是森林生态研究中最基本的课题之一。基于中国东北地区一个30公顷的永久性野外样地，我们将AGB生产力作为输出进行建模，并使用该算法将地形、物种多样性、林分结构和一个林分密度变量作为输入，在一系列面积尺度上进行建模。随着粒度从10米增加到200米，我们发现驱动生物量生产力变化的解释变量的相对重要性有很大差异，并且模型精度逐渐提高。该地区生物量生产力建模的最小采样面积为140×140米。我们的研究表明，在可控方法下，当从森林调查典型尺度（10米）转变为更大尺度（200米）时，使用随机森林（RF）算法建模的地形、物种多样性、林分结构和林分密度变量与生物量生产力之间的关系会发生变化。这些结果应该会引起关注森林评估的科学家的极大兴趣。
