Abstract
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 (~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.
Highlights
NASA’s Global Ecosystem Dynamics Investigation (GEDI) (Dubayah et al, 2020) mission has a primary science goal of mapping aboveground forest biomass across Earth’s temperate, subtropical, and tropical for ests
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 a training dataset to produce predictive footprint-level models that addressed three model development ques tions. What predictors should these models use to provide suffi cient explanatory power while preventing inclusion of too many independent variables that may lead to overfitting (Valbuena et al, 2017)? Further, what data transformations linearizes the relationship between predictors and aboveground biomass density (AGBD) without compromising model performance? what level of geographic stratification optimizes model performance while maintaining sufficient training data? This paper presents GEDI’s conceptual approach to footprint-level AGBD modeling, and the version 1 GEDI footprint biomass (GEDI04_A) models
Summary
NASA’s Global Ecosystem Dynamics Investigation (GEDI) (Dubayah et al, 2020) mission has a primary science goal of mapping aboveground forest biomass across Earth’s temperate, subtropical, and tropical for ests. Many remote sensing technologies have been used to quantify forest aboveground biomass density (AGBD [Mg/ha]) at various scales, including both passive opti cal sensors such as Landsat (Foody et al, 2003) and active sensors such as Synthetic Aperture Radar (SAR) (Mitchard et al, 2009), airborne lidar (Coops et al, 2007; Næsset et al, 2013) and spaceborne lidar (Baccini et al, 2012; Saatchi et al, 2011). Each of these technologies has asso ciated strengths and weaknesses for mapping AGBD, and are naturally synergistic. This increases the challenge for any EO mission to accurately map AGBD
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