Challenges to aboveground biomass prediction from waveform lidar

Abstract

Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD —that forest stands with different AGBD may produce highly similar waveforms —and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non- uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7-411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

Toward a forest biomass reference measurement system for remote sensing applications

“…The pivotal role of in situ data was recently exemplified in the case of GEDI waveform data. Accurately predicting AGCD from GEDI waveforms alone was shown to be suboptimal as two forest stands with similar waveforms can have very different AGCD (Bruening et al, 2021), and allometries heavily rely on in situ training data (Duncanson et al, 2022). Beyond such direct use, treeby-tree identity information can also be mobilized to calibrate and validate hyperspectral data (Draper et al, 2019;Jucker et al, 2018) Tropical sites should consequently be the cornerstone of the FBRM system, reasonably representing 65%-70% of all the potential FBRM sites.…”

A new approach to derive productivity of tropical forests using radar remote sensing measurements

“…We expect similar trends with different values of GPP and NPP due to climate variations. Furthermore, it is also possible to apply the approach to temperate and boreal forests [48,57,63] where we expect even stronger variations in GPP and NPP due to shorter growing seasons. Henniger et al use the forest factory approach [47,64] to generate virtual forests instead of simulating forest development over time.…”

A New Approach Combining a Multilayer Radiative Transfer Model with an Individual-Based Forest Model: Application to Boreal Forests in Finland

“…This also depends on a set of species-specific parameters and allometry equations. FORMIND has been extensively tested and applied to tropical forests [30][31][32][33][34][35][36][37][38], temperate forests [39][40][41], grasslands [42] and Boreal forests [43]. The parameterization of [39] includes all tree species of the investigated forest stands (North Karelia, Finland) and is used for our simulations on a 30 m × 30 m scale.…”