Search

Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe an optimized multivariate k-NN implementation of a 151,700 km2 area in southern NWT that included ca. 2007 Landsat and dual-polarized Phased Array type L-band SAR (PALSAR) data on board the Advanced Land Observing Satellite (ALOS). Five forest attributes were mapped at 30 m cells: stand height, crown closure, stand/total volume and aboveground biomass (AGB). We assessed accuracy gains compared to Landsat-based maps. To circumvent the scarcity of FI plots, we used 3600 footprints from the Geoscience Laser Altimeter System (GLAS) as surrogate FI plots, where forest attributes were estimated using Light Detection and Ranging (LiDAR) metrics as predictors. After optimization, k-NN predicted forest attribute values for each pixel as the average of the 4 nearest (k = 4) surrogate FI plots within the Euclidian space of 9 best features (selected among 6 PALSAR, 10 Landsat, and 6 environmental features). Accuracy comparisons were based on 31 National Forest Inventory ground plots and over 1 million airborne LiDAR plots. Maps that included PALSAR HV backscatter resulted in forest attribute predictions with higher goodness of fit (adj. R2), lower percent mean error (ME%), and percent root mean square error (RMSE%), and lower underestimation for larger attribute values. Predictions were most accurate for conifer stand height (RMSE% = 32.1%, adj. R2 = 0.58) and AGB (RMSE% = 47.8%, adj. R2 = 0.74), which is much more abundant in the area than mixedwood or broadleaf. Our study demonstrates that optimizing k-NN parameters and feature space, including PALSAR, Landsat, and environmental variables, is a viable approach for inventory mapping of the northern boreal forest regions of Canada.

Abstract A recent global study reported a net difference between areas of forest cover loss and of forest cover gain of about 3.6% of total forest area across the boreal biome, and of 5.6% for Canada, over a 12‐yr period. Net losses of this magnitude should be of concern given the importance of this biome in global biogeochemical cycles linked to climate change. Our analysis for Canada fails to support these results and suggests that post‐harvest recovery of tree cover is generally strong, while post‐fire recovery of tree cover is weaker but nevertheless prevalent. We find that current large area remote sensing methodologies can fail to properly recognize post‐disturbance recovery from non‐forest to forest status in low‐productivity boreal forests when using short time series. With climate change and human impacts intensifying around the world, it is urgently important to be able to reliably distinguish temporary forest cover loss followed by naturally slow recovery from forest decline requiring policy action. The analysis was in large part based on the new Canada Landsat Disturbance product in which fires and harvest since 1984 are mapped at 30‐m resolution (https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad).