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

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.

[1]  Xiaoli Sun,et al.  Gaussian approximation versus nearly exact performance analysis of optical communication systems with PPM signaling and APD receivers , 1988, IEEE Trans. Commun..

[2]  Philip Lewis,et al.  Measuring forests with dual wavelength lidar: A simulation study over topography , 2012 .

[3]  Patrick D. Gerard,et al.  Characterizing vertical forest structure using small-footprint airborne LiDAR , 2003 .

[4]  Gérard Dedieu,et al.  Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes , 2015, Remote. Sens..

[5]  Philip Lewis,et al.  Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests , 2010 .

[6]  K. Kraus,et al.  FROM SINGLE-PULSE TO FULL-WAVEFORM AIRBORNE LASER SCANNERS: POTENTIAL AND PRACTICAL CHALLENGES , 2004 .

[7]  Kevin J. Gaston,et al.  Measurement of fine-spatial-resolution 3D vegetation structure with airborne waveform lidar: Calibration and validation with voxelised terrestrial lidar , 2017 .

[8]  James R. Kellner,et al.  Canopy height and ground elevation in a mixed-land-use lowland Neotropical rain forest landscape , 2009 .

[9]  F. M. Danson,et al.  Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy , 2015 .

[10]  Klaus I. Itten,et al.  Assessment of the influence of flying altitude and scan angle on biophysical vegetation products derived from airborne laser scanning , 2008 .

[11]  Natascha Kljun,et al.  Vegetation height and cover fraction between 60° S and 60° N from ICESat GLAS data , 2012 .

[12]  Kevin J. Gaston,et al.  Is waveform worth it? A comparison of LiDAR approaches for vegetation and landscape characterization , 2015 .

[13]  J. Blair,et al.  Modeling laser altimeter return waveforms over complex vegetation using high‐resolution elevation data , 1999 .

[14]  M. Keller,et al.  Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+ , 2015, Carbon Balance and Management.

[15]  R. Dubayah,et al.  Estimation of tropical forest structural characteristics using large-footprint lidar , 2002 .

[16]  Ned Horning,et al.  Remote sensing for ecology and conservation. , 2010 .

[17]  Hao Tang,et al.  Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure , 2017, Proceedings of the National Academy of Sciences.

[18]  Philip Lewis,et al.  A threshold insensitive method for locating the forest canopy top with waveform lidar , 2011 .

[19]  J. Bryan Blair,et al.  Decomposition of laser altimeter waveforms , 2000, IEEE Trans. Geosci. Remote. Sens..

[20]  Paul R. Stysley,et al.  Laser production for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar , 2016, Defense + Security.

[21]  Scott J. Goetz,et al.  The Global Ecosystem Dynamics Investigation , 2014 .

[22]  Peter R. J. North,et al.  Slope Estimation from ICESat/GLAS , 2014, Remote. Sens..

[23]  Zhihua Wang,et al.  A 13.3 mW 500 Mb/s IR-UWB Transceiver With Link Margin Enhancement Technique for Meter-Range Communications , 2014, IEEE Journal of Solid-State Circuits.

[24]  J. Holmgren,et al.  Influence of footprint size and geolocation error on the precision of forest biomass estimates from space-borne waveform LiDAR , 2017 .

[25]  Stuart R. Phinn,et al.  Direct retrieval of canopy gap probability using airborne waveform lidar , 2013 .

[26]  Alan H. Strahler,et al.  Validating modeled lidar waveforms in forest canopies with airborne laser scanning data , 2018 .