Direct upscaling of national forest inventory aboveground biomass of Canada with Sentinel and ALOS PALSAR observations
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This study aims at mapping wall-to-wall forest aboveground biomass (AGB) of Canada by directly upscaling the national forest inventory (NFI) plot measurements with machine learning method and satellite observations. We used the geolocated ground plots provided by NFI project from 10 provinces over the period 1992 to 2018. This dataset contained ground plots with measurements that were performed up to three times since 1992. We cleaned the data based on age and historical disturbance information to retain as many plots as possible for model training, while ensuring that the AGB in the used plots did not vary greatly or affected by disturbance from the date of measurement up to 2020. Finally, if there were repeat measurements in the remaining plots, we only kept the latest measurement records. The input features for estimation model were extracted from seasonal composited Sentinel 1 spectral images, Sentinel 2 L band SAR images and ALOS PALSAR yearly mosaic data. The Machine learning method - Random Forest Regression was used for AGB estimation. We trained the RF model locally and uploaded the model to the GEE platform to predict a wall-to-wall AGB map for Canada. To train and select the best performing model, we employed three categories of training and validation methods including random split (RS, repeated 100 times), simple 10-fold cross-validation (S10C, repeated 10 times) and stratified 10-fold cross-validation (ST10C, repeated 10 times). The prediction uncertainty of the model was determined by the Quantile Regression (QR at 5%,50% and 95%) equations between the mean bias and the mean prediction of 100 model. The bias of the model showed a characteristic V-shape pattern when compared to the predicted AGB values, which showed the range of bias value widened as the predicted AGB values increased. This distribution of bias can be described by the 5%, 50% and 95% QR line equation response to the lower, median and upper bounds of model prediction bias. With those equations, we can generate bias variation range for all predicted pixels.
