4.7 Article

The Multisource Vegetation Inventory (MVI): A Satellite-Based Forest Inventory for the Northwest Territories Taiga Plains

Journal

REMOTE SENSING
Volume 14, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/rs14051108

Keywords

forest vegetation inventory; LiDAR; GLAS; Landsat; PALSAR-1; k-NN; boreal forest; Northwest Territories

Funding

  1. Canadian Space Agency Government Related Initiatives Program (GRIP) [IMOU 15MOA41001]
  2. Government of NWT [GNWTCRA R00893]
  3. Natural Resources Canada-Canadian Forest Service
  4. Northern Oil and Gas Research Initiative

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Sustainable forest management requires detailed information on the spatial distribution, composition, and structure of forests. However, in regions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, this information is often lacking. The Multisource Vegetation Inventory (MVI) project used a combination of field data and remote sensing data from multiple sources to create a large area forest inventory map that could support strategic forest management in the NWT. This project demonstrated that a reasonably accurate forest inventory map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost.
Sustainable forest management requires information on the spatial distribution, composition, and structure of forests. However, jurisdictions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, often lack detailed forest information across their land base. The goal of the Multisource Vegetation Inventory (MVI) project was to create a large area forest inventory (FI) map that could support strategic forest management in the NWT using optical, radar, and light detection and ranging (LiDAR) satellite remote sensing anchored on limited field plots and airborne LiDAR data. A new landcover map based on Landsat imagery was the first step to stratify forestland into broad forest types. A modelling chain linking FI plots to airborne and spaceborne LiDAR was then developed to circumvent the scarcity of field data in the region. The developed models allowed the estimation of forest attributes in thousands of surrogate FI plots corresponding to spaceborne LiDAR footprints distributed across the project area. The surrogate plots were used as a reference dataset for estimating each forest attribute in each 30 m forest cell within the project area. The estimation was based on the k-nearest neighbour (k-NN) algorithm, where the selection of the four most similar surrogate FI plots to each cell was based on satellite, topographic, and climatic data. Wall-to-wall 30 m raster maps of broad forest type, stand height, crown closure, stand volume, total volume, aboveground biomass, and stand age were created for a similar to 400,000 km(2) area, validated with independent data, and generalized into a polygon GIS layer resembling a traditional FI map. The MVI project showed that a reasonably accurate FI map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost by combining limited field data with remote sensing data from multiple sources.

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