Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests
Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests
复制标题
估算中国亚热带森林地上生物量的机器学习和地统计学方法
DOI：
10.1186/s40663-020-00276-7
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发表时间：
2020-05
影响因子：
4.1
通讯作者：
Li Mingshi
中科院分区：
文献类型：
--
作者：
Su Huiyi;Shen Wenjuan;Wang Jingrui;Ali Arshad;Li Mingshi
BackgroundBoundaries生物量（AGB）是森林生态系统生产力和健康的一个基本指标，因此在评估森林碳储量和支持有针对性的森林管理plannes.MethodsHere的发展中起着至关重要的作用，我们提出了一个随机森林/co-kriging框架，集成了机器学习和地统计学方法的优势，以提高AGB在中国广东省北方的映射精度。我们使用陆地卫星时间序列观测数据、高级陆地观测卫星（ALOS）相控阵L波段合成孔径雷达（PALSAR）数据和国家森林资源清查（NFI）样地测量数据，生成三个时间点的森林AGB图（1992年、2002年和2010年）显示了广东亚热带森林中AGB的时空动态，中国。ResultsThe提出的模型是能够映射森林AGB使用光谱，纹理，地形变量和雷达后向散射系数在一个有效和可靠的方式。样地级AGB验证的均方根误差在15.62至53.78 t·ha− 1之间，平均绝对误差在6.54至32.32 t·ha− 1之间，偏差在-2.14至1.07 t·ha− 1之间，相对于随机森林算法的相对改进在3.8%至17.7%之间。在1992年的AGB地图中观测到最大的决定系数（0.81）和最小的平均绝对误差（6.54 t·ha− 1）。通过将PALSAR数据添加到2010年的建模变量集，将光谱饱和效应降至最低。通过添加高程作为协变量，协同克里格法在AGB残差预测方面优于普通克里格法，因为协同克里格法在研究区域的山谷和平原中获得了更好的插值结果。结论使用独立数据集验证三张AGB地图表明，随机森林/协同克里格法在AGB预测方面表现最佳，其次是随机森林与普通克里金法（随机森林/普通克里金法）和随机森林模型。提出的随机森林/协同克里格框架为复杂地形亚热带林区的AGB制图提供了一种准确可靠的方法。由此产生的AGB地图适合于有针对性地制定森林管理行动，以促进气候变化背景下的碳固存和可持续森林管理。
BackgroundAboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.MethodsHere, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China.ResultsThe proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha− 1, the mean absolute error ranged from 6.54 to 32.32 t∙ha− 1, the bias ranged from − 2.14 to 1.07 t∙ha− 1, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha− 1) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area.ConclusionsValidation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.
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影响因子：
3.7
作者：
C. Hoover;M. Ducey;R. Andy Colter;M. Yamasaki
通讯作者：
C. Hoover;M. Ducey;R. Andy Colter;M. Yamasaki
影响因子：
2.5
作者：
E. Ziegel
通讯作者：
E. Ziegel
影响因子：
3.9
作者：
N. Cressie;C. Gotway;M. Grondona
通讯作者：
N. Cressie;C. Gotway;M. Grondona
DOI：
10.1016/j.isprsjprs.2014.08.014
发表时间：
2015-04
影响因子：
12.7
作者：
Xiaolin Zhu;Desheng Liu
通讯作者：
Xiaolin Zhu;Desheng Liu
影响因子：
3.7
作者：
R. Hall;R. Skakun;E. Arsenault;B. Case
通讯作者：
R. Hall;R. Skakun;E. Arsenault;B. Case