Canadian Forest Service Publications
A forest attribute mapping framework: a pilot study in a northern boreal forest, Northwest Territories, Canada. 2018. Mahoney, C.; Hall, R.J.; Hopkinson, C.; Filiatrault, M.; Beaudoin, A.; Chen, Q. Remote Sensing 10(9):1338.
Issued by: Northern Forestry Centre
Catalog ID: 39243
CFS Availability: PDF (download)
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A methods framework is presented that utilizes field plots, airborne light detection and ranging (LiDAR), and spaceborne Geoscience Laser Altimeter System (GLAS) data to estimate forest attributes over a 20 Mha area in Northern Canada. The framework was implemented to scale up forest attribute models from field data to intersecting airborne LiDAR data, and then to GLAS footprints. GLAS data were sequentially filtered and submitted to the k-nearest neighbour (k-NN) imputation algorithm to yield regional estimates of stand height and crown closure at a 30mresolution. Resulting outputs were assessed against independent airborne LiDAR data to evaluate regional estimates of stand height (mean difference = -1 m, RMSE = 5 m) and crown closure (mean difference = -5%, RMSE = 9%). Additional assessments were performed as a function of dominant vegetation type and ecoregion to further evaluate regional products. These attributes form the primary descriptive structure attributes that are typical of forest inventory mapping programs, and provide insight into how they can be derived in northern boreal regions where field information and physical access is often limited.
Plain Language Summary
Forest fires are becoming more frequent in northern regions and permafrost is thawing. Management strategies are necessary to ensure the sustainability of northern boreal forests as the climate continues to change. If these strategies are to be effective, they need to be based on good data about the present status of forests. The main way to collect this type of data has been through the interpretation of aerial photographs, but the cost would be prohibitive to complete a seamless inventory of the vast, largely inaccessible forested areas in northern boreal regions. We present a methodological framework we designed to minimize the challenge of quantifying forest attributes at a large scale in remote regions. We used data from field plots, airborne laser scanning, and the satellite-based Geoscience Laser Altimeter System to predict forest attributes over a 200 000 km2 area in northern Canada. Our framework provides a viable approach for measuring the quantity of forest attributes over large geographic areas, and this approach can be used as a foundation as innovations in data collection and modeling become available.