Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
复制标题
DOI：
10.1016/j.rse.2021.112845
复制
发表时间：
2022-01-07
影响因子：
13.5
通讯作者：
Zgraggen, Carlo
中科院分区：
文献类型：
--
作者：
Duncanson, Laura;Kellner, James R.;Zgraggen, Carlo
NASA的全球生态系统动力学调查（GEDI）正在收集星载全波形激光雷达数据，其主要科学目标是准确估计森林地上生物量密度（AGBD）。本文介绍了用于创建GEDI的足迹级（类似于25米）AGBD（GEDI 04_A）产品，包括所使用的数据集和最终模型选择的程序的描述模型的发展。用于拟合我们的模型的数据来自全球分布的空间和时间一致的场和机载激光雷达数据集的汇编，据此，我们模拟了机载激光雷达的GEDI样波形，以建立校准数据库。我们使用这个数据库来扩展过去波形激光雷达研究的地理范围，并将地球仪按植物功能类型（PFT）划分为四个大层和六个地理区域。GEDI的波形-生物量模型采用参数化普通最小二乘（OLS）模型的形式，以模拟的相对高度（RH）指标作为预测变量。从一组详尽的候选模型中，我们选择了最好的输入预测变量，并为GEDI域中的每个地理层进行数据转换，以产生一组全面的预测足迹级模型。我们发现，模型选择经常倾向于在地面以上第98、90、50和10个高度的RH指标组合（分别为RH 98、RH 90、RH 50和RH 10），但包含较低的RH指标（例如RH 10）并不能显著提高模型性能。第二，在所有模型中强制包含RH 98是重要的，并且不会降低模型性能，并且性能最好的模型是简约的，通常只有1-3个预测因子。第三，与没有分层的全球模型相比，按地理域（PFT，地理区域）分层提高了模型性能。第四，对于绝大多数地层，最好的表现模型拟合使用字段AGBD和/或高度度量的平方根变换。不同地理阶层的模型性能差异很大，训练数据稀疏和/或AGBD值高的地区性能最差。这些模型被用于产生AGBD的全球预测，但将来将随着更多更好的训练数据的出现而得到改进。
NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.