Dataset Information

The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions.

ABSTRACT: NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above-ground biomass density during its 2-year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large-footprint, full-waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root-mean-square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70-85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre-launch calibration and performance assessment of the GEDI mission.

SUBMITTER: Hancock S

PROVIDER: S-EPMC6472476 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions.

Hancock Steven S Armston John J Hofton Michelle M Sun Xiaoli X Tang Hao H Duncanson Laura I LI Kellner James R JR Dubayah Ralph R

Earth and space science (Hoboken, N.J.) 20190227 2

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above-ground biomass density during its 2-year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise ...[more]

PMID: 31008149

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Project description:Leaf area index (LAI) is an important biophysical parameter of vegetation and serves as a significant indicator for assessing forest ecosystems. Multi-source remote sensing data enables large-scale and dynamic surface observations, providing effective data for quantifying various indices in forest and evaluating ecosystem changes. However, employing single-source remote sensing spectral or LiDAR waveform data poses limitations for LAI inversion, making the integration of multi-source remote sensing data a trend. Currently, the fusion of active and passive remote sensing data for LAI inversion primarily relies on empirical models, which are mainly constructed based on field measurements and do not provide a good explanation of the fusion mechanism. In this study, we aimed to estimate LAI based on physical model using both spectral imagery and LiDAR waveform, exploring whether data fusion improved the accuracy of LAI inversion. Specifically, based on the physical model geometric-optical and radiative transfer (GORT), a fusion strategy was designed for LAI inversion. To ensure inversion accuracy, we enhanced the data processing by introducing a constraint-based EM waveform decomposition method. Considering the spatial heterogeneity of canopy/ground reflectivity ratio in regional forests, calculation strategy was proposed to improve this parameter in inversion model. The results showed that the constraint-based EM waveform decomposition method improved the decomposition accuracy with an average 12% reduction in RMSE, yielding more accurate waveform energy parameters. The proposed calculation strategy for the canopy/ground reflectivity ratio, considering dynamic variation of parameter, effectively enhanced previous research that relied on a fixed value, thereby improving the inversion accuracy that increasing on the correlation by 5% to 10% and on R2 by 62.5% to 132.1%. Based on the inversion strategy we proposed, data fusion could effectively be used for LAI inversion. The inversion accuracy achieved using both spectral and LiDAR data (correlation=0.81, R2 = 0.65, RMSE=1.01) surpassed that of using spectral data or LiDAR alone. This study provides a new inversion strategy for large-scale and high-precision LAI inversion, supporting the field of LAI research.

Dataset Information

The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions.

Publications

The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions.

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