Ge Jing, Hou Mengjing, Liang Tiangang, Feng Qisheng, Meng Xinyue, Liu Jie, Bao Xuying, Gao Hongyuan
State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China.
State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China.
Sci Total Environ. 2022 Jun 20;826:154226. doi: 10.1016/j.scitotenv.2022.154226. Epub 2022 Feb 28.
Although remote sensing has enabled rapid monitoring of grassland aboveground biomass (AGB) at a regional scale, it is still a difficult challenge to construct an accurate estimation model of grassland AGB in a vast region to support the AGB dynamics analysis over a long time series. In this study, extensive grassland AGB measurements (collected in North China during the grassland growing season of 2000-2019), MODIS data, and environmental factors (climate, topography and soil) were employed to construct the grassland AGB models using four machine learning algorithms (random forest, support vector machine, artificial neural network and extreme learning machine) combined with four variable selections. The spatial distributions of annual grassland AGB from 2000 to 2019 were simulated based on the optimal AGB model. The temporal change and future trend of AGB series from 2000 to 2019 were comprehensively analyzed by the slope model and Hurst exponent. The influences of natural and anthropogenic factors on grassland AGB dynamics were explored quantitatively using the Geodetector model. The results showed that (1) the random forest model constructed from the variables selected by the successive projections algorithm is the optimal grassland AGB model. (2) The 20-year average grassland AGB in North China showed an overall spatial distribution of being low in the central and western parts and high in the southeastern part. (3) The annual maximum grassland AGB in most regions (82.71%) showed an increasing trend during 2000-2019; and most of the grasslands with a decreasing trend of AGB were located in regions with low AGB values and arid climates. (4) The future trend of grassland AGB after the study period may be optimistic, as reflected by more grassland AGB was predicted to increase rather than decrease (70.38% vs. 29.62%). (5) The main driving factors of spatiotemporal dynamics of grassland AGB were precipitation, soil type, and livestock density; the interactive influence of two drivers on AGB showed mutual enhancement.
尽管遥感技术已能够在区域尺度上快速监测草地地上生物量（AGB），但要在广大区域构建准确的草地AGB估算模型以支持长时间序列的AGB动态分析，仍然是一项艰巨的挑战。在本研究中，利用广泛的草地AGB测量数据（于2000 - 2019年草地生长季在中国北方采集）、MODIS数据以及环境因素（气候、地形和土壤），采用四种机器学习算法（随机森林、支持向量机、人工神经网络和极限学习机）并结合四种变量选择方法来构建草地AGB模型。基于最优AGB模型模拟了2000年至2019年年度草地AGB的空间分布。通过斜率模型和赫斯特指数全面分析了2000年至2019年AGB序列的时间变化和未来趋势。利用地理探测器模型定量探究了自然和人为因素对草地AGB动态的影响。结果表明：（1）由连续投影算法选择的变量构建的随机森林模型是最优的草地AGB模型。（2）中国北方20年平均草地AGB总体空间分布为中西部低、东南部高。（3）2000 - 2019年期间，大多数区域（82.71%）的年度最大草地AGB呈增加趋势；AGB呈下降趋势的草地大多位于AGB值低且气候干旱的区域。（4）研究期后草地AGB的未来趋势可能较为乐观，预计更多草地AGB会增加而非减少（70.38%对29.62%）。（5）草地AGB时空动态的主要驱动因素是降水、土壤类型和牲畜密度；两个驱动因素对AGB的交互影响表现为相互增强。
