Spatially continuous canopy height maps of forested ecosystems of Canada

doi: 10.4121/21363081.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21363081
Datacite citation style:
Camile Sothe; Alemu Gonsamo; James Snider; Ricardo B. Lourenço; Werner A. Kurz (2022): Spatially continuous canopy height maps of forested ecosystems of Canada. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21363081.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
usage stats
1244
views
321
downloads
categories
geolocation
Canada
time coverage
2020
licence
cc-by.png logo CC BY 4.0

  This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution — one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada — the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI. The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.

history
  • 2022-10-20 first online, published, posted
publisher
4TU.ResearchData
format
tif
funding
  • NSERC Alliance Grant (ALLRP 566310-21)
  • NSERC Discovery Grant (RGPIN-2020-05,708)
  • WWF Canada
  • Canada Research Chairs Program
organizations
McMaster University
WWF Canada

DATA

files (2)