| 1 | Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China显示文摘Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently,it is necessary to quantitatively characterize the dynamicspatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree(BRT) model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region(Wuyishan National Nature Reserve, China). Long-term,stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass(t) increased from 4.62 9 106 to 5.30 9 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha-1(mean ± standard error) to 94.33 ± 0.41 t ha-1in the study region. The major factors that controlled biomass(in decreasing order of importance) were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolationmethods for mapping forest biomass increments at regional scales. | Yin Ren Shanshan Chen Xiaohua Wei Weimin Xi Yunjian Luo Xiaodong Song Shudi Zuo Yusheng Yang | 2016 | Journal of Forestry Research2016，27，4： | 5 |
| 2 | 基于光学-ALS变量组合和非参数模型的天然次生林地上生物量估算显示文摘【目的】通过组合机载激光雷达(airborne laser scanning,ALS)数据和Sentinel-2A数据提取特征变量,探讨估算天然次生林地上生物量(aboveground biomass,AGB)最佳的变量组合方式和估算方法。【方法】以2015年ALS数据、2016年Sentinel-2A数据和黑龙江帽儿山林场森林资源连续清查固定样地数据为数据源,通过ALS数据提取高度特征变量(all the LiDAR variables,记为A_(L)),Sentinel-2A数据提取若干植被指数变量(all the optical variables,记为A_(O)),然后将光学-ALS结合变量(combined optical and LiDAR index,COLI,记为ICOL)结合成为新的变量I_(COL1)和I_(COL2),以6组特征变量组合方式(A_(O)+A_(L)、I_(COL1)、I_(COL2)、I_(COL1)+A_(O)+A_(L)、I_(COL2)+A_(O)+A_(L)、I_(COL1)+I_(COL2)+A_(O)+A_(L))作为输入变量,分别使用多元线性逐步回归(stepwise multiple linear regression,SMLR)、K-最近邻法(K-nearest neighbor,K-NN)、支持向量回归(support vector regression,SVR)、随机森林(random forest,RF)和堆叠稀疏自编码器(stack sparse auto-encoder,SSAE)共5种方法构建了天然次生林AGB估算模型,探讨ICOLs变量以及不同模型对生物量估测精度的影响。【结果】结合变量ICOLs对于森林AGB的估算十分有效,加入ICOLs变量能够很大提高森林AGB模型的估算精度;与其他4种模型相比,无论使用哪些变量作为输入数据,SSAE模型的精度最高;当使用SSAE模型,以光学和ALS变量组合(I_(COL1)+I_(COL2)+A_(O)+A_(L))作为输入特征变量时,模型的准确性最高:R^(2)=0.83,均方根误差为11.06 t/hm^(2),相对均方根误差为8.23%。【结论】结合变量COLIs能够有效地提高天然次生林AGB的估算精度,而且深度学习模型(SSAE)在估算天然次生林AGB方面优于其他预测模型。总体而言,利用ALS和Sentinel-2A数据组合变量的SSAE模型可以较准确地估算森林AGB,为天然次生林地上生物量的估算和碳储量评估提供技术支持。 | 赵颖慧 郭新龙 甄贞 | 2021 | 南京林业大学学报(自然科学版)2021，45，4： | 1 |
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