GEDI L4C Footprint Level Waveform Structural Complexity Index, Version 2

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4C (L4C) Version 2 predictions of the Waveform Structural Complexity Index (WSCI) and estimates of prediction intervals for each footprint estimate at 95% confidence. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI04_C is the same as in the GEDI02_A product. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-17 to 2023-03-16. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint WSCI was derived from XGBoost regression models relating simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to a 3D structural complexity metric calculated from matched Airborne laser Scanning (ALS) point clouds. Four global WSCI models were trained on a plant functional type (PFT) basis (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the WSCI estimates, including the associated uncertainty metrics, quality flags, and other information about the GEDI L2A waveform for this selected algorithm setting group. Additional model outputs include WSCI predictions for each of the six GEDI L2A algorithm setting groups and associated prediction intervals. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. The data are provided in HDF5 format.

There are 74,860 data files in HDF5 format (.h5) with this dataset.

This dataset contains GEDI Level 4C (L4C) predictions of the WSCI and estimates of prediction intervals at 95% confidence within each sampled geolocated laser footprint. The footprints are located within the global latitude band observed by the ISS, nominally 51.6 degrees N and S and reported for the period 2019-04-17 to 2023-03-16. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint WSCI estimates were derived from machine learning models that relate GEDI Level 2A (L2A) relative height (RH) metrics to measures of 3D structural complexity from GEDI intersected Airborne Laser Scanning (ALS) plots. The training dataset consisted of more than 800,000 collocated GEDI/ALS footprints, used to train four global WSCI models representing different PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees and the combination of grasslands, shrubs, and woodlands).

Reported with the WSCI estimates for each of the eight beams are the associated uncertainty metrics, quality flags and other information about the GEDI L2A waveform for this selected algorithm setting group. Also provided are footprint geolocation variables and land cover input data including PFTs and the land cover type identifiers. Additional model outputs include the WSCI predictions for each of the six GEDI L2A algorithm setting groups with associated prediction intervals for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups.

Project: Global Ecosystem Dynamics Investigation (GEDI)

The Global Ecosystem Dynamics Investigation (GEDI) produces high resolution laser ranging observations of the 3D structure of the Earth. GEDI’s precise measurements of forest canopy height, canopy vertical structure, and surface elevation greatly advance our ability to characterize important carbon and water cycling processes, biodiversity, and habitat. GEDI was funded as a NASA Earth Ventures Instrument (EVI) mission. It was launched to the International Space Station in December 2018 and completed initial orbit checkout in April 2019.

Related Publication

Dubayah, R., J.B. Blair, S. Goetz, L. Fatoyinbo, M. Hansen, S. Healey, M. Hofton, G. Hurtt, J. Kellner, S. Luthcke, J. Armston, H. Tang, L. Duncanson, S. Hancock, P. Jantz, S. Marselis, P.L. Patterson, W. Qi, and C. Silva. 2020. The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography. Science of Remote Sensing 1:100002. https://doi.org/10.1016/j.srs.2020.100002

De Conto, T., R.O. Dubayah, J. Armston et al. Characterizing the structural complexity of the Earth's forests with spaceborne lidar, 21 May 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4445870/v1]

Related Datasets

Level 1B, Level 2A, and Level 2B data from GEDI are available from the Land Processes Distributed Active Archive Center at https://lpdaac.usgs.gov/.

Acknowledgements

This work was funded with a NASA Earth Ventures Instrument (EVI) supporting the GEDI mission (contract NNL15AA03C) , and FINESST, grant number NNH21ZDA001N.

Spatial Coverage: Global within a latitude extent of -53 to +54 degrees

Spatial Resolution: ~25 m footprints in diameter

Temporal Coverage: 2019-04-17 to 2023-03-16

Temporal Resolution: One-time estimates

Study Area: Latitude and longitude are given in decimal degrees

Site	Westernmost Longitude	Easternmost Longitude	Northernmost Latitude	Southernmost Latitude
Global	-180	180	54	-53

Data File Information

There are 74,860 data files in HDF5 format (.h5), and one companion file with this dataset. The companion file, GEDI_L4C_WSCI_Data_Dictionary.pdf, provides a data dictionary for the variables in the data files. This data dictionary is also included in this guide document.

Data file naming convention:

The files are named GEDI04_C_YYYYDDDHHMMSS_O[orbit_number]_[granule_number]_T[track_number]_[PPDS_type]_ [release_number]_[production_version]_V[version_number].h5 (e.g., GEDI04_C_2019349001415_O05699_04_T03266_02_001_01_V002.h5), where:

GEDI04_C = product short name representing GEDI Level 4C data,

YYYYDDDHHMMSS = date and time of acquisition in Julian day of year, hours, minutes, and seconds format,

[orbit_number] = orbit number,

[granule_number] = sub-orbit granule (or file) number,

[track_number] = track number,

[PPDS_type] = positioning and pointing determination system (PPDS) type (00 is "predict", 01 is "rapid", 02 and higher is "final"),

[release_number] = release number (001), representing the SOC SDS (software) release used to generate this L4C dataset,

[production_version] = granule production version , e.g., a particular data granule (or file) may have been regenerated multiple times,

[version_number] = L4C dataset production version (002), corresponding to the ORNL DAAC's dataset version number, and

.h5 = file extension, HDF5 format.

Table 1. Data Dictionary

Dimension Variable	Description
MT	Number of shots

 

Group:/
short_name	(Attribute)	GEDI04_C
Group:/METADATA/Dataset Identification
abstract	(Attribute)	The GEDI04_C standard data product contains predictions of the Waveform Structural Complexity Index (WSCI) within each laser footprint
characterSet	(Attribute)	utf8
creationDate	(Attribute)	File creation date
credit	(Attribute)	The software that generates the WSCI product was implemented at the Department of Geographical Sciences at the University of Maryland (UMD)
fileName	(Attribute)	Original file name
language	(Attribute)	Eng
Originator Organization Name	(Attribute)	GEDI Science Data Processing System and University of Maryland
PGEVersion	(Attribute)	Product generating executive SDPS release ID
purpose	(Attribute)	The purpose of the WSCI dataset is to provide an estimate of forest structural complexity, similarly to what is measured by airborne LiDAR in the 3D space, using GEDI waveforms
shortName	(Attribute)	GEDI04_C
spatialRepresentationType	(Attribute)	Along-track
status	(Attribute)	On Going
topicCategory	(Attribute)	Geoscientific Information
uuid	(Attribute)	Universally unique identifier (UUID) for this file
VersionID	(Attribute)	SDPS DAAC release ID
gedi_wsci_githash	(Attribute)	Git commit hash of the software used to create the WSCI file
Group:/BEAMXXXX
Label	Datatype (Dimensions)	long_name	Units source	Description
algorithm_run_flag	UINT8 MT	Algorithm run flag	-	The WSCI algorithm is run if this flag is set to 1. This flag selects data which have sufficient waveform fidelity for WSCI estimation.
beam	UINT16 MT	Beam	- L2A	Beam identifier
channel	UINT8 MT	Channel	- L2A	Channel identifier
degrade_flag	UINT8 MT	Degrade flag	- L2A	Flag indicating degraded state of pointing and/or positioning information
delta_time	FLOAT64 MT	Delta time	s L2A	Time delta since Jan 1 00:00 2018.
elev_lowestmode	FLOAT32 MT	Elevation of the lowest mode	m L2A	Elevation of center of lowest mode relative to reference ellipsoid
elev_outlier_flag	UINT8 MT	Elevation outlier flag	- L4B	Flag indicating shot is from a section of an orbit with L2A canopy elevation metrics classified as large outliers relative to reference data
fhd_normal	FLOAT64 MT	Foliage Height Diversity	m L2B	Foliage height diversity index calculated by vertical foliage profile normalized by total plant area index
l2_quality_flag	UINT8 MT	L2A quality flag	- L2A	Flag identifying the most useful L2A data for WSCI predictions
lat_lowestmode	FLOAT64 MT	Latitude of the lowest mode	degrees L2A	Latitude of center of lowest mode
lon_lowestmode	FLOAT64 MT	Longitude of the lowest mode	degrees L2A	Longitude of center of lowest mode
master_frac	FLOAT64 MT	Fraction component of shot time	s L2A	Master time, fractional part. master_int+master_frac is equivalent to /BEAMXXXX/delta_time.
master_int	UINT32 MT	Integer component of shot time	s L2A	Master time, integer part. Seconds since master_time_epoch. master_int+master_frac is equivalent to /BEAMXXXX/delta_time.
selected_algorithm	UINT8 MT	Selected algorithm setting group	- L2A	Selected algorithm setting group
sensitivity	FLOAT32 MT	Beam sensitivity	- L2A	Maximum canopy cover that can be penetrated considering the SNR of the waveform
shot_number	UINT64 MT	Shot number	- L2A	Shot number
solar_elevation	FLOAT32 MT	Solar elevation	degrees L2A	Solar elevation angle
surface_flag	UINT8 MT	Surface flag	- L2A	Indicates elev_lowestmode is within 300m of Digital Elevation Model (DEM) or Mean Sea Surface (MSS) elevation
wsci	FLOAT32 MT	Waveform Structural Complexity Index	-	Predicted 3D canopy entropy from the corresponding Plant Functional Type (PFT) model
wsci_pi_lower	FLOAT32 MT	Waveform Structural Complexity Index lower prediction interval	-	Lower prediction interval at 95% confidence
wsci_pi_upper	FLOAT32 MT	Waveform Structural Complexity Index upper prediction interval	-	Upper prediction interval at 95% confidence
wsci_quality_flag	UINT8 MT	WSCI quality flag	-	Flag simplifying selection of most useful WSCI predictions
wsci_xy	FLOAT32 MT	Horizontal Structural Complexity	-	Predicted WSCI horizontal term over the XY plane within the footprint
wsci_xy_pi_lower	FLOAT32 MT	Horizontal Structural Complexity lower prediction interval	-	Lower prediction interval at 95% confidence
wsci_xy_pi_upper	FLOAT32 MT	Horizontal Structural Complexity upper prediction interval	-	Upper prediction interval at 95% confidence
wsci_z	FLOAT32 MT	Vertical Structural Complexity	-	Predicted WSCI vertical term along the Z axis within the footprint
wsci_z_pi_lower	FLOAT32 MT	Vertical Structural Complexity lower prediction interval	-	Lower prediction interval at 95% confidence
wsci_z_pi_upper	FLOAT32 MT	Vertical Structural Complexity upper prediction interval	-	Upper prediction interval at 95% confidence
Group:/BEAMXXXX/geolocation
Label	Datatype (Dimensions)	long_name	Units source	Description
elev_lowestmode_aN	FLOAT32 MT	Elevation of the lowest mode	m L2A	Elevation of center of lowest mode relative to reference ellipsoid
lat_lowestmode_aN	FLOAT64 MT	Latitude of the lowest mode	degrees L2A	Latitude of center of lowest mode
lon_lowestmode_aN	FLOAT64 MT	Longitude of the lowest mode	degrees L2A	Longitude of center of lowest mode
sensitivity_aN	FLOAT32 MT	Beam sensitivity	- L2A	Maximum canopy cover that can be penetrated considering the SNR of the waveform
shot_number	UINT64 MT	Shot number	- L2A	Shot number
stale_return_flag	UINT8 MT	Stale return flag	- L2A	Flag from digitizer indicating the real-time pulse detection algorithm did not detect a return signal above its detection threshold within the entire 10 km search window. The pulse location of the previous shot was used to select the telemetered waveform.
Group:/BEAMXXXX/land_cover_data
Label	Datatype (Dimensions)	long_name	Units source	Description
landsat_treecover	FLOAT64 MT	Landsat tree canopy cover	percent L2A	Tree cover in the year 2010, defined as canopy closure for all vegetation taller than 5m in height (Hansen et al., 2013). Encoded as a percentage per output grid cell.
landsat_water_persistence	UINT8 MT	25 m Landsat water persistence	percent L2A	The percent UMD GLAD Landsat observations with classified surface water between 2018 and 2019. Values > 80 usually represent permanent water while values < 10 represent permanent land.
leaf_off_doy	INT16	1 km VIIRS leaf-off day-of-year	days L4A	GEDI 1 km EASE 2.0 grid leaf-off start day-of-year derived from the NPP VIIRS Global Land Surface Phenology Product.
leaf_off_flag	UINT8 MT	1 km VIIRS leaf-off flag	- L4A	GEDI 1 km EASE 2.0 grid flag derived from leaf_off_doy, leaf_on_doy and pft_class, indicating if the observation was recorded during leaf-off conditions in deciduous needleleaf or broadleaf forests and woodlands. 1 = leaf-off and 0 = leaf-on.
leaf_on_cycle	UINT8	1 km VIIRS leaf-on cycle number	- L4A	Flag that indicates the vegetation growing cycle for leaf-on observations. Values are 0 (leaf-off conditions), 1 (cycle 1) or 2 (cycle 2).
leaf_on_doy	INT16	1 km VIIRS leaf-on day-of-year	- L4A	GEDI 1 km EASE 2.0 grid leaf-on start day-of-year derived from the NPP VIIRS Global Land Surface Phenology Product.
pft_class	UINT8	1 km MODIS Plant Functional Type class	- L2A	GEDI 1 km EASE 2.0 grid Plant Functional Type (PFT) derived from the MODIS MCD12Q1v006 Product. Values follow the Land Cover Type 5 Classification scheme.
region_class	UINT8 MT	1 km geographic region class	- L2A	GEDI 1 km EASE 2.0 grid world continental regions (0: Water, 1: Europe, 2: North Asia, 3: Australasia, 4: Africa, 5: South Asia, 6: South America, 7: North America).
shot_number	UINT64 MT	Shot number	- L2A	Shot number
urban_focal_window_size	UINT8 MT	Urban focal window size	pixels L4A	The focal window size used to calculate urban_proportion. Values are 3 (3x3 pixel window size) or 5 (5x5 pixel window size).
urban_proportion	UINT8 MT	25 m TDX urban percentage	percent L4A	The percentage proportion of land area within a focal area surrounding each shot that is urban land cover. Urban land cover is derived from the DLR 12 m resolution TanDEM-X Global Urban Footprint Product.
worldcover_class	INT32 MT	ESA landcover class	-	Land cover class from the European Space Agency WorldCover v200 product
Group:/BEAMXXXX/wsci_prediction
Label	Datatype (Dimensions)	long_name	Units source	Description
algorithm_run_flag_aN	UINT8 MT	Algorithm run flag	-	The algorithm is run if this flag is set to 1. This flag selects data which have sufficient waveform fidelity for WSCI estimation.
l2_quality_flag_aN	UINT8 MT	Level 2 quality flag	- L2A	Flag identifying the most useful L2A data for WSCI predictions
shot_number	UINT64 MT	Shot number	- L2A	Shot number
wsci_aN	FLOAT32 MT	Waveform Structural Complexity Index	-	Waveform Structural Complexity Index
wsci_pi_lower_aN	FLOAT32 MT	Waveform Structural Complexity Index lower prediction interval	-	Lower prediction interval at 95% confidence
wsci_pi_upper_aN	FLOAT32 MT	Waveform Structural Complexity Index upper prediction interval	-	Upper prediction interval at 95% confidence
wsci_quality_flag_aN	UINT8 MT	WSCI quality flag	-	Flag simplifying selection of most useful WSCI predictions
wsci_xy_aN	FLOAT32 MT	Horizontal Structural Complexity	-	Horizontal Structural Complexity
wsci_xy_pi_lower_aN	FLOAT32 MT	Horizontal Structural Complexity lower prediction interval	-	Lower prediction interval at 95% confidence
wsci_xy_pi_upper_aN	FLOAT32 MT	Horizontal Structural Complexity upper prediction interval	-	Upper prediction interval at 95% confidence
wsci_z_aN	FLOAT32 MT	Vertical Structural Complexity	-	Vertical Structural Complexity
wsci_z_pi_lower_aN	FLOAT32 MT	Vertical Structural Complexity lower prediction interval	-	Lower prediction interval at 95% confidence
wsci_z_pi_upper_aN	FLOAT32 MT	Vertical Structural Complexity upper prediction interval	-	Upper prediction interval at 95% confidence

The WSCI is the first product aiming at describing 3D structural complexity consistently on a near-global scale. Recent studies mapping forest structural complexity from lidar were limited to sites where airborne or terrestrial laser scanning point clouds were available. To expand estimates of 3D complexity to a global scale we developed relationships between GEDI and over 800,000 collocated ALS point cloud samples across the globe where 3D complexity was measured. Those paired GEDI/ALS samples were used to develop 4 global models to predict structural complexity on a PFT basis, using all GEDI RH percentiles from each footprint to predict 3D canopy entropy (CExyz), a surrogate for structural complexity measured from ALS point clouds, using XGBoost regression. The modeling framework relied on a rigorous grid search spatial cross-validation regime to ensure (1) optimal hyperparameter tuning, (2) model generalization (i.e. robustness against overfitting) and (3) geographical transferability of all models.

We used 816,276 collocated GEDI/ALS samples from 229 ALS surveys distributed around the world. Those sites were split into training and calibration sets, containing the samples from 80% and 20% of the sites, respectively. The training set was used to fit the WSCI models through XGBoost regression, applying grid search hyperparameter tuning with 5-fold spatial cross-validation. The best model was chosen based on the optimal combination of hyperparameters that minimized the root mean squared error (RMSE) loss function in the validation folds, while maintaining a difference of less than 5% between training and validation folds in terms of RMSE and R2, thus enforcing model generalization. Since each fold contained data from different geographical locations, the validation folds measured the model performance on sites unseen during model training, granting geographical transferability of the selected models. The calibration set was used to train conformal predictors (Vovk et al., 2005) for each WSCI model, enabling uncertainty estimation for every model prediction based on paired GEDI/ALS samples never seen during training of the XGBoost models. Uncertainty estimates are provided as prediction intervals at 95% confidence for every footprint.

The GEDI instrument is aboard the International Space Station (ISS) and its mission aims to characterize ecosystem structure and dynamics to enable improved quantification and understanding of the Earth’s carbon cycle and biodiversity. GEDI is led by the University of Maryland in collaboration with NASA Goddard Space Flight Center. GEDI science data algorithms and products are created by the GEDI Science Team.

The GEDI instrument produces high-resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI was launched on December 5, 2018, and is attached to the ISS. GEDI collects data globally at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. The GEDI instrument consists of 3 lasers producing a total of 8 beam ground transects, which consist of ~25 m footprint samples spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth’s surface in the cross-track direction, for an across-track width of ~4.2 km.

The GEDI WSCI models were trained on a database of 816,276 collocated GEDI/ALS samples over from all major continental regions, with a fraction of 24%, 32%, 24% and 20% of those footprints registered in DBT, EBT, ENT and GSW, respectively. The ALS data used in the model development are part of the GEDI Calibration and Validation (Cal/Val) database and were provided by research partners or downloaded from open data initiatives, such as the NEON (NEON, 2023) in the United States, the INPE-EBA (Ometto et al., 2023) in Brazil. With more ALS data sets becoming available allied to the GEDI mission extension, expected to stay operational until the end of the life cycle of the International Space Station, we plan to expand the WSCI training database and keep updating its underlying models.

Prior to training the models, the GEDI footprints were collocated to ALS point clouds to correct for geolocation error in the Cal/Val processing, ensuring that both datasets were matched as precisely as possible and represented the exact same plot over the land surface. 3D Canopy Entropy (CExyz) measurements were then extracted from 25-m diameter ALS point clouds, matching the GEDI footprints in position and area. The CExyz metric was developed by Liu et al. (2022), and leverages entropy planes on the horizontal and vertical directions of a forest plot to account for 3D structural complexity of the forest canopy, capturing variations in features related to tree density and vertical layering simultaneously.

Footprint WSCI is derived from extreme gradient boosted trees (XGBoost) regression models (Chen and Guestrin, 2016) relating GEDI L2A relative height metrics to CExyz measured from ALS point clouds. The XGBoost models were trained on a PFT basis, assigned to each GEDI footprint in the L2A product and extracted from an error-corrected and infilled 1-km grid derived from the Type 5 classification in the MODIS MCD12Q1 V006 data product (Friedl et al., 2010). WSCI estimates are derived from four different models trained on footprints registered over deciduous broadleaf trees (DBT), evergreen broadleaf trees (EBT), evergreen needleleaf trees (ENT), and combinations of woodlands, grasslands, and shrubs (GSW).

We used all available GEDI RH metrics as input in the regression models, allowing the XGBoost algorithm to choose the best predictors of CExyz in a data driven fashion. RH percentiles within close ranges from each other are highly correlated, and although the XGBoost is an efficient machine learning method robust to overfitting, we applied a rigorous regime of hyperparameter tuning and spatial cross-validation to guarantee model generalization and geographical transferability.

On-orbit predictions of WSCI are made using the GEDI02_A elevation and height metric data product as input. The algorithms used by GEDI for generating these are described in the ATBD for GEDI Transmit and Receive Waveform Processing for L1 and L2 Products (Hofton and Blair, 2020). The L4C product contains estimates from all algorithm group settings available in L2A, which apply slightly different methods to calculate the RH metrics, thus affecting WSCI estimates, accompanied by prediction intervals and quality flags for every footprint.

How are the GEDI04_C estimates geolocated?

The GEDI04_C product uses the ground position as the location of each shot and WSCI estimate (elev_lowestmode, lat_lowestmode, lon_lowestmode). Additional waveform ranging points are available in the GEDI02_A product (e.g., elev_highestreturn, lat_highestreturn, lon_highestreturn) and may be joined to GEDI04_C using the shot_number dataset.

What quality metrics and flags should I use to filter the data?

WSCI is predicted for every shot where GEDI02_A algorithm was successfully run and RH metrics are provided, as indicated by the algorithm_run_flag dataset. The GEDI04_C product provides multiple quality flags and metrics that may be used to subset the predictions to the most useful observations for a particular application or region. The l2_quality_flag encapsulates a number of GEDI02_A quality metrics to identify land surface shots with waveforms of high fidelity for WSCI estimation. The wsci_quality_flag applies more strict filters on top of l2_quality_flag to identify shots with a higher likelihood of occurring over tree covered land surfaces, matching the domain for which the WSCI models were developed. More specifically, the wsci_quality_flag applied more strict filters for water and urban surfaces, higher sensitivity thresholds, targeting higher penetration rate on denser forest canopies, and only included footprints flagged as tree cover class in the ESA worldcover v200 product (Zanaga et al., 2022), provided as the worldcover_class dataset in the land_cover_data group of each laser beam in the L4C product.

What are the units and range for the WSCI estimates?

The WSCI estimates are equivalent to CExyz measured on ALS point clouds of 25-m diameter, matching the GEDI’s footprint size. The WSCI estimates 3D entropy, which does not have a physically meaningful unit, but rather ranges from low to high values. Globally, WSCI ranges approximately from 7 to 11.4 on forest landscapes, with WSCI < 8 corresponding to barely vegetated areas, where little to no forest structural complexity is observed. On the other extreme, WSCI > 10.5 represents high structural complexity, such as tall, dense, undisturbed forests in the tropics. Again, these values are given on a global context, and the WSCI ranges and their interpretations may change on local contexts or when comparing forest types of similar structure (e.g. within a given canopy height interval). Therefore, which WSCI values represent low or high structural complexity depends on the local context of each study and should be assessed accordingly.

Chen, T., and C. Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785

De Conto, T., R.O. Dubayah, J. Armston et al. Characterizing the structural complexity of the Earth's forests with spaceborne lidar, 21 May 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4445870/v1]

Friedl, M.,and D. Sulla-Menashe. 2022. MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD12Q1.061

Hofton, M.A., and J.B. Blair. 2020. Algorithm Theoretical Basis Document (ATBD) for GEDI Transmit and Receive Waveform Processing for L1 and L2 Products. Goddard Space Flight Center, Greenbelt, MD. https://doi.org/10.5067/DOC/GEDI/GEDI_WF_ATBD.001

Liu, X., Q. Ma, X. Wu, T. Hu, Z. Liu, L. Liu, Q. Guo, and Y. Su. 2022. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds. Remote Sensing of Environment, 282, 113280. https://doi.org/10.1016/j.rse.2022.113280

NEON (National Ecological Observatory Network). 2023. Discrete return LiDAR point cloud. NEON (National Ecological Observatory Network). https://doi.org/10.48443/xxby-5a18

Ometto, J., E.B. Gorgens, F.R. de Sousa Pereira, L. Sato, M.L.R. Assis, R. Cantinho, M. Longo, A.D. Jacon, and M. Keller. 2023. L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Roraima e Amapá) (Version v20230301). Zenodo. https://doi.org/10.5281/zenodo.7689693

Vovk, V., A. Gammerman, and G. Shafer (Eds.). 2005. Conformal prediction. In Algorithmic Learning in a Random World (pp. 17–51). Springer US. https://doi.org/10.1007/0-387-25061-1_2

Zanaga, D., R. Van De Kerchove, D. Daems, W. De Keersmaecker, C. Brockmann, G. Kirches, J. Wevers, O. Cartus, M. Santoro, S. Fritz, M. Lesiv, M. Herold, N.-E Tsendbazar, P. Xu, F. Ramoino, and O. Arino. 2022. ESA WorldCover 10 m 2021 v200 (Version v200). Zenodo. https://doi.org/10.5281/ZENODO.7254221