TY - DATA
T1 - Carbon stock map and uncertainty in plants of forested areas of Canada, 250m spatial resolution
PY - 2022/02/04
AU - Camile Sothe
AU - Alemu Gonsamu
AU - James Snider
AU - Joyce Arabian
AU - Werner A. Kurz
AU - Sarah Finkelstein
UR - https://data.4tu.nl/articles/dataset/Carbon_map_and_uncertainty_in_forested_areas_of_Canada_250m_spatial_resolution/14572929/2
DO - 10.4121/14572929.v2
KW - Carbon Stock Estimates
KW - forest carbon data
KW - terrestrial ecosystem
KW - Forest engineering
N2 - This project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with total C stored in plants of forested areas in Canada (AGB, BGB and dead plants) in kg/m² and C stock uncertainty. To estimate the C stored in plants of forest areas, we used 47,967 ground
measurements of AGB measures and 58 covariates mainly composed of optical data, terrain parameters, structural parameters
(e.g., SAR data, clump index, canopy heights – generated from satellite LiDAR- <i>included in the other dataset</i>),
soil type map and radiation flux data. Different models were trained using a recursive feature
elimination, random forest scheme and a 5-fold
cross-validation assessment. The model with higher R² and lowest root mean
square error (RMSE) was used for spatial prediction of AGB in forest areas
while 1<sup>st</sup> and 3<sup>rd</sup> quantiles of RF quantile regression were used to build the uncertainty map. <a>After generating the AGB map, the root biomass
of forest areas </a>was computed by its relationship to AGB according to forest
type. The dead plant materials were computed by
a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as
well as dead plant materials and BGB were multiplied by 0.5 to provide
the maps in kg C m<sup>-2</sup>.
ER -