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Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data.
Carbon Balance and Management ( IF 3.9 ) Pub Date : 2020-07-29 , DOI: 10.1186/s13021-020-00151-6
J Luis Hernández-Stefanoni 1 , Miguel Ángel Castillo-Santiago 2 , Jean Francois Mas 3 , Charlotte E Wheeler 4 , Juan Andres-Mauricio 1 , Fernando Tun-Dzul 1 , Stephanie P George-Chacón 1 , Gabriela Reyes-Palomeque 1 , Blanca Castellanos-Basto 1 , Raúl Vaca 5 , Juan Manuel Dupuy 1
Affiliation  

Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R2 = 0.44 vs R2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R2 = 0.26). This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

中文翻译:

通过整合LiDAR,ALOS PALSAR,气候和野外数据,改善热带干旱森林的地上生物量图。

有关热带森林中地上生物量(AGB)空间分布的可靠信息对于缓解气候变化和维持碳储量至关重要。最近在大陆和国家范围内绘制的AGB地图显示出很大的不确定性,尤其是在具有较高AGB值的热带地区。AGB地图中的错误与用于校准遥感产品的地块数据的质量以及雷达数据在高AGB森林中进行地图绘制的能力有关。在这里,我们提出了一种提高AGB地图精度的方法,并以尤卡坦半岛热带森林为例进行了测试,该方法的AGB地图精度低于墨西哥的其他森林类型。为了减少现场数据中的错误,已对国家森林清单(NFI)图进行了校正,以考虑使用小树。通过考虑生物量随时间的变化,可以解决NFI地块和图像采集之间的时间差异。为了克服与雷达后向散射饱和相关的问题,我们结合了雷达纹理度量和气候数据来提高AGB地图的准确性。最后,我们使用从LiDAR数据得出的生物量估算值来增加采样区的数量,以评估增加样本量是否可以提高AGB估算值的准确性。校正小树的NFI图数据以及田野测量与遥感测量之间的时间差异,可将生物量估计的相对误差降低12.2%。使用机器学习算法“随机森林”,并带有经过校正的场图数据,安装在高级陆地观测卫星1(ALOS)上的L波段合成孔径雷达(PALSAR)的反向散射和表面纹理,以及气候缺水数据,与以前的研究相比,提高了本研究中获得的地图的准确性(R2 = 0.44 vs R2 = 0.32)。但是,使用从LiDAR数据得出的样本图来增加样本量并不能提高AGB图的准确性(R2 = 0.26)。这项研究表明,所建议的方法有可能改善热带干旱森林的AGB图,并显示了未来研究中应考虑的AGB预测因子。我们的结果突出了利用生态学知识来纠正与样地级生物量估计以及现场数据与遥感数据之间的不匹配相关的误差的重要性。与以前的研究相比(R2 = 0.44 vs R2 = 0.32),气候缺水数据提高了本研究中获得的地图的准确性。但是,使用从LiDAR数据得出的样本图来增加样本量并不能提高AGB图的准确性(R2 = 0.26)。这项研究表明,所建议的方法有可能改善热带干旱森林的AGB图,并显示了未来研究中应考虑的AGB预测因子。我们的结果突出了利用生态学知识来纠正与样地级生物量估计以及现场数据与遥感数据之间的不匹配相关的误差的重要性。与以前的研究相比(R2 = 0.44 vs R2 = 0.32),气候缺水数据提高了本研究中获得的地图的准确性。但是,使用从LiDAR数据得出的样本图来增加样本量并不能提高AGB图的准确性(R2 = 0.26)。这项研究表明,所建议的方法有可能改善热带干旱森林的AGB图,并显示了未来研究中应考虑的AGB预测因子。我们的结果突出了利用生态学知识来纠正与样地级生物量估计以及现场数据与遥感数据之间的不匹配相关的误差的重要性。使用从LiDAR数据得出的样本图来增加样本量并不能提高AGB图的准确性(R2 = 0.26)。这项研究表明,所建议的方法有可能改善热带干旱森林的AGB图,并显示了未来研究中应考虑的AGB预测因子。我们的结果突出了利用生态学知识来纠正与样地级生物量估计以及现场数据与遥感数据之间的不匹配相关的误差的重要性。使用从LiDAR数据得出的样本图来增加样本量并不能提高AGB图的准确性(R2 = 0.26)。这项研究表明,所建议的方法可能会改善热带干旱森林的AGB图,并显示了未来研究中应考虑的AGB预测因子。我们的结果突出了利用生态学知识来纠正与样地级生物量估计以及现场数据与遥感数据之间的不匹配相关的误差的重要性。
更新日期:2020-07-29
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