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 the 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. This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean ground elevation, and mean canopy height per 1 km x 1 km grid cells globally within -52 and 52 degrees latitude. These L3 gridded products were derived from Level 2 (L2) geolocated laser footprint return profile metrics from the GEDI instrument onboard the International Space Station (ISS). Ground elevation is provided as the mean elevation (in meters) of the center of the lowest waveform mode relative to the WGS84 reference ellipsoid. Canopy height is provided as the mean height (in meters) above the ground of the received waveform signal that was the first reflection off the top of the canopy (RH100). L3 gridded products can be used to characterize important carbon and water cycling processes, biodiversity, habitat and can also be of immense value for climate modeling, forest management, snow and glacier monitoring, and the generation of digital elevation models. This dataset version uses Version 2 of the input L2 data, which includes improved geolocation of the footprints as well as a modified method to predict an optimum algorithm setting group. Note: The version of the GEDI data hosted on OpenTopography has been reprojected into the World Geodetic System 1984 (EPSG:4326) For more details on the GEDI project go to the GEDI homepage For more details on the GEDI L3 Gridded Products, go to the NASA ORNL DAAC

Data set from Schleich Anouk, Durrieu Sylvie, Maxime Soma and Cédric Vega, "Improving GEDI Footprint Geolocation using a High Resolution Digital Elevation Model" We proposed a geolocation correction method for Global Ecosystem Dynamics Investigation (GEDI) data. The method, called GeoGEDI, is only based on high-resolution digital elevation models (DEMs) and GEDI derived ground elevations. The method does not need any other input, except for a DEM and GEDI Level 2A data. For each footprint, an error map between GEDI ground estimates and reference DEM is computed, and a flow accumulation algorithm is used to retrieve the optimal footprint position. GeoGEDI was tested on footprints in Landes and Vosges, two french forests. The method was applied to GEDI versions 1 (v1) and 2 (v2), by either a single or four full-power laser beam tracks. This dataset is composed of 157 977 GEDI footprints whith positions of unchanged GEDI v1 and v2 releases and the optimal position calculated with our GeoGEDI algorithm.

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. 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 mission weeks 19, 32, 34 and 38 (a.k.a. Golden Weeks). These weeks cover the range of instrument operating conditions important for calibration and validation of geolocation algorithms, and also include GEDI orbits that are coincident with underflights acquired by the LVIS (Land, Vegetation, and Ice Sensor) airborne lidar instrument. 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 AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFT) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).

The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.Known Issues Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8). Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers. Known Issues: Section 8 of the User Guide provides additional information on known issues.Improvements/Changes from Previous Versions Metadata has been updated to include spatial coordinates. Granule size has been reduced from one full ISS orbit (~5.83 GB) to four segments per orbit (~1.48 GB). Filename has been updated to include segment number and version number. Improved geolocation for an orbital segment. Added elevation from the SRTM digital elevation model for comparison. Modified the method to predict an optimum algorithm setting group per laser shot. Added additional land cover datasets related to phenology, urban infrastructure, and water persistence. Added selected_mode_flag dataset to root beam group using selected algorithm. Removed shots when the laser is not firing.* Modified file name to include segment number and dataset version.

This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely on GEDI lidar, and validated with independent data. The GEDI sensor, mounted on the International Space Station (ISS), uses eight laser beams spaced by 60 m along-track and 600 m across-track on the Earth surface to measure ground elevation and vegetation structure between approximately 52 degrees North and South latitude. Between April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7 billion quality waveforms suitable for measuring ground elevation and vegetation structure, respectively. This dataset provides GEDI shot metrics aggregated into raster grids at three spatial resolutions: 1 km, 6 km, and 12 km. In addition to many of the standard L2 and L4A shot metrics, several additional metrics have been derived which may be particularly useful for applications in carbon and water cycling processes in earth system models, as well as forest management, biodiversity modeling, and habitat assessment. Variables include canopy height, canopy cover, plant area index, foliage height diversity, and plant area volume density at 5 m strata. Eight statistics are included for each GEDI shot metric: mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, 95th percentile, Shannon's diversity index, and shot count. Quality shot filtering methodology that aligns with the GEDI L4B Gridded Aboveground Biomass Density, Version 2.1 was used. In comparison to the current GEDI L3 dataset, this dataset provides additional gridded metrics at multiple spatial resolutions and over several temporal periods (annual and the full mission duration). Files are provided in cloud optimized GeoTIFF format.

GEDI's Level 2A Geolocated Elevation and Height Metrics Product (GEDI02_A) is primarily composed of 100 Relative Height (RH) metrics, which collectively describe the waveform collected by GEDI. The original GEDI02_A product is a table of point with a spatial resolution (average footprint) of 25 meters. The dataset LARSE/GEDI/GEDI02_A_002_MONTHLY is a …

This dataset provides country-level estimates of land surface mean aboveground biomass density (AGBD), total aboveground biomass (AGB) stocks, and the associated standard errors of the mean calculated using different versions of the Global Ecosystem Dynamics Investigation (GEDI) Level-4B (L4B) product. The GEDI L4B product provides gridded (1 km x 1 km) estimates of AGBD within the GEDI orbital extent (between 51.6 degrees N and 51.6 degrees S). For comparison purposes, this dataset also includes national-scale National Forest Inventory (NFI) estimates of AGBD from the 2020 Global Forest Resources Assessment (FRA) published by the Food and Agriculture Organization (FAO, 2020) of the United Nations. The GEDI instrument produces high-resolution laser ranging observations of the 3-dimensional structure of the Earth's surface. GEDI was launched on December 5, 2018, and is attached to the International Space Station (ISS). The GEDI instrument consists of three lasers producing a total of eight 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 data are provided in comma-separated value (CSV) format.

The GEDI Spatial Querying and Subsetting Quick Guide (https://lpdaac.usgs.gov/documents/635/GEDI_Quick_Guide.pdf) provides instructions on how to find granules for a region of interest and how to perform spatial and/or layer subsetting of GEDI data.

The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date.

The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.

The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (describing canopy vertical structure, for example), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.

Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S. The map is based on the first four months of L1B Version 1 data (April-July 2019). The sparse footprint level predictions are averaged at 0.5 degree resolution (approx. 55 km raster cells at the equator) to obtain a dense map. We refer to the original research article below for further information, especially on how the predictions were filtered before the aggregation.

The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. The file load_pred_RH98_files.py contains more information on how to parse and load the prediction orbit files.

GEDI mission website: https://gedi.umd.edu/.

Citation: Use of these data require citation of this dataset and the original research article. These citations are as follows:

Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760.

Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2019 (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5704852

The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.The GEDI Level 1B Geolocated Waveforms product (GEDI01_B) provides geolocated corrected and smoothed waveforms, geolocation parameters, and geophysical corrections for each laser shot for all eight GEDI beams. GEDI01_B data are created by geolocating the GEDI01_A raw waveform data. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI01_B data product contains 85 layers for each of the eight beams including the geolocated corrected and smoothed waveform datasets and parameters and the accompanying ancillary, geolocation, and geophysical correction. Additional information can be found in the GEDI L1B Product Data Dictionary.Known Issues Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8). Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers. Known Issues: Section 8 of the User Guide provides additional information on known issues.Improvements/Changes from Previous Versions Metadata has been updated to include spatial coordinates. Granule size has been reduced from one full ISS orbit (~7.99 GB) to four segments per orbit (~2.00 GB). Filename has been updated to include segment number and version number. Improved geolocation for an orbital segment. Added elevation from the SRTM digital elevation model for comparison. Modified the method to predict an optimum algorithm setting group per laser shot. Added additional land cover datasets related to phenology, urban infrastructure, and water persistence. Added selected_mode_flag dataset to root beam group using selected algorithm. Removed shots when the laser is not firing.* Modified file name to include segment number and dataset version.

SpatioTemporal Asset Catalog (STAC) Item - usa_neonsrer_2019_NEON_D14_SRER_DP1_L090-1_2019091314_unclassified_point_cloud_0000002 in GEDI_CalVal_Lidar_Data

Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data for April-July 2019 and 2020. We refer to the original research article below for further information. The footprint data were filtered with respect to predictive uncertainty and MODIS non-vegetated probability. The unfiltered data organized in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data is available here: April-July 2019: https://doi.org/10.5281/zenodo.5704852 April-July 2020: https://doi.org/10.5281/zenodo.7737869 GEDI mission website: https://gedi.umd.edu/. Citation: Use of these data require citation of this dataset: Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737946 Original research article: Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760. This filtered dataset (2019 and 2020) was used to develop the global canopy height model fusing Sentinel-2 and GEDI that is presented in: Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, https://doi.org/10.1038/s41559-023-02206-6

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).The algorithm setting group selection used for GEDI02_A Version 2 has been modified for evergreen broadleaf trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. Please see User Guide for more information. The Global Ecosystem Dynamics Investigation GEDI mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth's carbon cycle and biodiversity. The GEDI instrument, attached to the International Space Station (ISS), collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of the 3-dimensional structure of the Earth. 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. ProductDescriptionL2A VectorLARSE/GEDI/GEDI02_A_002L2A Monthly rasterLARSE/GEDI/GEDI02_A_002_MONTHLYL2A table indexLARSE/GEDI/GEDI02_A_002_INDEXL2B VectorLARSE/GEDI/GEDI02_B_002L2B Monthly rasterLARSE/GEDI/GEDI02_B_002_MONTHLYL2B table indexLARSE/GEDI/GEDI02_B_002_INDEXL4A Biomass VectorLARSE/GEDI/GEDI04_A_002L4A Monthly rasterLARSE/GEDI/GEDI04_A_002_MONTHLYL4A table indexLARSE/GEDI/GEDI04_A_002_INDEXL4B BiomassLARSE/GEDI/GEDI04_B_002

Here, we provide an overview of the use of light detection and ranging (lidar) for tropical ecosystem applications, with a particular focus on the Global Ecosystem Dynamics Investigation (GEDI). We summarize how data from GEDI measures vegetation vertical structure and give a step-by-step description of how to obtain spatially-subset GEDI Level 2A data from the NASA EarthData Search web portal. We then provide an example of how to characterize the structure of various vegetation classes in Ucayali, Peru. These vegetation classes include: (1) old-growth lowland forest, (2) young lowland vegetation regrowth (‘Purma’)”, (3) secondary lowland forest, (4) mature oil palm plantations, and (5) cacao plantations (monocrop and agroforestry). We interpret the structural height metrics from GEDI among each of these vegetation classes, identifying edge effects as a possible influence on our results. To address this issue, we conducted a final analysis of the data with an area of 35m diameter footprint (25m of the original diameter area of the beam, and 10m as a conservative additional buffer) and excluded any observations that did not completely overlap with each land cover polygon. When we removed edge effects, no observations remained in the cacao data set and fewer observations remained in the forest stage data set. Nonetheless, the overall structural patterns shown in the relative heights of each forest stage remained very similar. We recommend that future projects utilizing spaceborne lidar for tropical ecosystems consider adopting the techniques and best practices we describe here, including refined noise filtering and explicit consideration of edge effects.

Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data from April-July 2020. The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data.

See also the repository for the data from April-July 2019: https://doi.org/10.5281/zenodo.5704852. This repository also contains the file load_pred_RH98_files.py with more information on how to parse and load the prediction orbit files.

GEDI mission website: https://gedi.umd.edu/.

Citation:

Use of these data require citation of this dataset:

Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737869

Original research article:

Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760.

Remote sensing is an important tool for monitoring species habitat spatially and temporally. Species distribution models (SDM) often rely on remotely-sensed geospatial datasets to predict probability of occurrence and infer habitat preferences. Lidar measurements from the Global Ecosystem Dynamics Investigation (GEDI) are shedding light on three dimensional forest structure in regions of the world where this aspect of species habitat has previously been poorly quantified. Here we combine a large camera trap dataset of mammal species in Borneo and Sumatra with a diverse set of geospatial data to predict the probability of occurrence of 47 species. Multi-temporal GEDI predictors were created through fusion with Landsat time series, extending back to the year 2001. The availability of these GEDI-based forest structure predictors and other temporally-resolved predictor variables enabled temporal matching of species occurrences and hindcast predictions of species probability of occurrence at years 2001 and 2021. Our GEDI-Landsat fusion approach worked well for forest structure metrics related to canopy height (relative height of the 95th percentile of returned energy R2 = 0.62 and relative RMSE = 41%) but, not surprisingly, was less accurate for metrics related to interior canopy vegetation structure (e.g., plant area volume density from 0 to 5 m above the ground R2 = 0.05 and relative RMSE = 85%). For the SDM analyses, we tested several combinations of predictor sets and found that when considering a large pool of multiscale predictors, the exact composition, and whether GEDI Fusion predictors were included, didn’t have a large impact on generalized linear modeling (GLM) and Random Forest (RF) model performance. Adding GEDI Fusion predictors to a baseline set only meaningfully improved performance for some species (n = 4 for RF and n = 3 for GLM). However, when GEDI Fusion predictors were used in a smaller predictor set that is more suitable for hindcasting species probability of occurrence, more SDMs showed meaningful performance improvements relative to the baseline model (n = 9 for RF and n = 4 for GLM) and the relative importance of GEDI-based canopy structure predictors increased relative to when they were combined with the baseline predictor set. Moreover, as we examined predictor importance and partial dependence, the utility of GEDI Fusion predictors in hindcast models was evident in regards to ecological interpretability. We produced a catalog of probability of occurrence maps for all 47 mammals species at 90 m spatial resolution for years 2001 and 2021, enabling subsequent ecological interpretation and conservation analyses.

This dataset provides Global Ecosystem Dynamics Investigation (GEDI) Level 3 (L3) gridded mean canopy height, standard deviation of canopy height, mean ground elevation, standard deviation of ground elevation, and counts of laser footprints per 1-km x 1-km grid cells globally within -52 and 52 degrees latitude. These L3 gridded products were derived from Level 2 (L2) geolocated laser footprint return profile metrics from the GEDI instrument onboard the International Space Station (ISS). Canopy height is provided as the mean height (in meters) above the ground of the received waveform signal that was the first reflection off the top of the canopy (RH100). Ground elevation is provided as the mean elevation (in meters) of the center of the lowest waveform mode relative to the WGS84 reference ellipsoid. L3 gridded products can be used to characterize important carbon and water cycling processes, biodiversity, habitat and can also be of immense value for climate modeling, forest management, snow and glacier monitoring, and the generation of digital elevation models. This dataset version uses Version 2 of the input L2 data, which includes improved geolocation of the footprints as well as a modified method to predict an optimum algorithm setting group.

Dataset for "Non fire-adapted dry forest of Northwestern Madagascar: escalating and devastating trends revealed by Landsat timeseries and GEDI lidar data" by Percival et al. 2024 (PLOS ONE)This dataset includes:ndvi_pnts.csv: The NDVI time series for each pixel in the study area for BFAST analysis. This was used with the NBR time series to identify potential fire events. nbr_pnts.csv: The NBR time series for each pixel in the study area which was used with the NDVI time series to identify potential fire events.YYYY_train.geojson: The fire record training data for 2014, 2017, 2018, 2019, and 2021. Each training file (geojson) consisted of a binary fire-non-fire class for training the land cover classification algorithm.fire_YYYY.gpkg: The fire history maps that were created using the training data and Planet Imagery (downloadable from planet.com). These included a map of fires for the years 2014, 2017, 2018, 2019, and 2021.aggregated_fires.gpkg: A map of aggregated fires for the study site.gedi_fires.gpkg: The GEDI L2B data combined with the fire history data for the main set analyses on the effects of fire on forest structure.Metadata.

The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.

Remote sensing is an important tool for monitoring species habitat spatially and temporally. Species distribution models (SDM) often rely on remotely-sensed geospatial datasets to predict probability of occurrence and infer habitat preferences. Lidar measurements from the Global Ecosystem Dynamics Investigation (GEDI) are shedding light on three dimensional forest structure in regions of the world where this aspect of species habitat has previously been poorly quantified. Here we combine a large camera trap dataset of mammal species in Borneo and Sumatra with a diverse set of geospatial data to predict the probability of occurrence of 47 species. Multi-temporal GEDI predictors were created through fusion with Landsat time series, extending back to the year 2001. The availability of these GEDI-based forest structure predictors and other temporally-resolved predictor variables enabled temporal matching of species occurrences and hindcast predictions of species probability of occurrence at years 2001 and 2021. Our GEDI-Landsat fusion approach worked well for forest structure metrics related to canopy height (relative height of the 95th percentile of returned energy R2 = 0.62 and relative RMSE = 41%) but, not surprisingly, was less accurate for metrics related to interior canopy vegetation structure (e.g., plant area volume density from 0 to 5 m above the ground R2 = 0.05 and relative RMSE = 85%). For the SDM analyses, we tested several combinations of predictor sets and found that when considering a large pool of multiscale predictors, the exact composition, and whether GEDI Fusion predictors were included, didn’t have a large impact on generalized linear modeling (GLM) and Random Forest (RF) model performance. Adding GEDI Fusion predictors to a baseline set only meaningfully improved performance for some species (n = 4 for RF and n = 3 for GLM). However, when GEDI Fusion predictors were used in a smaller predictor set that is more suitable for hindcasting species probability of occurrence, more SDMs showed meaningful performance improvements relative to the baseline model (n = 9 for RF and n = 4 for GLM) and the relative importance of GEDI-based canopy structure predictors increased relative to when they were combined with the baseline predictor set. Moreover, as we examined predictor importance and partial dependence, the utility of GEDI Fusion predictors in hindcast models was evident in regards to ecological interpretability. We produced a catalog of probability of occurrence maps for all 47 mammals species at 90 m spatial resolution for years 2001 and 2021, enabling subsequent ecological interpretation and conservation analyses.