The Multisource Vegetation Inventory (MVI): A Satellite-Based Forest Inventory for the Northwest Territories Taiga Plains
Bibliographic record
Abstract
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 ~400,000 km2 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".