Spatial distribution of maximum canopy height and heights percentiles in Canada at 250m spatial resolution

The canopy height maps were built to be included as covariates in the model to predict AGB in forest areas of Canada. We created wall-to-wall height metrics using ATL08 LiDAR products from the ICESat-2 satellite. The data was download for one-year period (October 2018 to October 2019). Points were filtered regarding solar background noise and atmospheric scattering, totaling 49,959 points distributed over the entire Canada. These points were associated with 10 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, annual PALSAR-2 HH and HV polarization, annual clumping index, and also the MODIS NDVI summer season. Afterwards, the random forest algorithm was used to extrapolate ATL08 parameters and develop regression models with the abovementioned spatially continuous variables. The maximum height and height percentiles (h85 and h95) were estimated with an R2 of approximately 0.61.<br

Carbon map and uncertainty in forested areas of Canada, 250m spatial resolution

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- included in the other dataset), 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 1st and 3rd quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas 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-2.</div

Soil organic carbon stock and uncertainties, 1m depth, at 250m spatial resolution in Canada

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 the soil organic carbon (SOC) in kg/m² for entire Canada in 1m depth, and the uncertainty in SOC predictions. The SOC stock map was produced using 6,533 ground soil samples, long-term climate data, remote sensing observations and a machine learning model. The soil samples containing the x and y coordinates, depth and SOC (in g/kg) information were overlaid with the stacked covariates (soil forming factors) to compose the regression matrix. Random forest models were trained using a recursive feature elimination scheme and a cross-validation assessment. The best model was used for spatial prediction of SOC over Canada in intermediate depths between 0 and 1 m. Afterwards, the SOC content maps were corrected with bulk density and coarse fragment information to compute the total carbon stock for each horizon. The horizons have been added to compose the 0-1m depth interval multiplied by root depths fraction to discount shallow soils. Water and ice/snow areas were removed using a mask based on the Land Cover of Canada map. The SOC stock uncertainty map was built using the first and third quantiles of RF quantile regression approach.</div