A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series

Accurate and fine-scale forest data are essential to improve natural resource management, particularly in the face of climate change. Here, we present SCANFI, the Spatialized CAnadian National Forest Inventory, which provides coherent, 30 m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, and main species composition). These maps were developed using the NFI photo-plot dataset, a systematic regular sample grid of photo-interpreted high-resolution imagery covering all of Canada's non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a multiresponse k-nearest neighbours and random forest imputation method. This tile-level approach revealed the importance of radiometric variables in predicting vegetation attributes, namely winter radiometry, as the large-scale climate gradients were controlled at the tile-level. SCANFI was validated with rigorous cross-validation analyses, which revealed robust model performance for structural attributes (biomass R 2 = 0.76; crown closure R 2 = 0.82; height R 2 = 0.78) and tree species cover (e.g., Douglas fir R 2 = 0.60). SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover (e.g., black spruce R 2 = 0.53) to satellite LiDAR height data products (e.g., crown closure R 2 = 0.71). SCANFI total aboveground biomass trends also followed those published by other studies. The methodology presented herein can be used to map time series of these attributes, identify the original training points used to make any given prediction, as well as map additional variables associated with the NFI photo-plots that are challenging to map using traditional remote sensing approaches.