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

Abstract

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, 30m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, main species composition). These maps were developed using the National Forest Inventory (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 imagery along with hundreds of tile-level regional models based on a multi-response k-nearest neighbours and random forest imputation method. This 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. Cross-validation analyses were done, which revealed robust model performance for structural attributes (biomass R2=0.76; crown closure R2=0.82; height R2=0.78) and tree species cover. SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover to satellite LiDAR height. The methodology can be used to map time series of these attributes and all other additional variables associated with the NFI photo-plots.