Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS
Bibliographic record
Abstract
Forest inventory data often provide the required base data to enable the largearea mapping of biomass over a range of scales. However, spatially explicit estimates ofabove-ground biomass (AGB) over large areas may be limited by the spatial extent of theforest inventory relative to the area of interest (i.e., inventories not spatially exhaustive), orby the omission of inventory attributes required for biomass estimation. These spatial andattributional gaps in the forest inventory may result in an underestimation of large areaAGB. The continuous nature and synoptic coverage of remotely sensed data have led totheir increased application for AGB estimation over large areas, although the use of thesedata remains challenging in complex forest environments. In this paper, we present anapproach to generating spatially explicit estimates of large area AGB by integrating AGBestimates from multiple data sources; 1. using a lookup table of conversion factors appliedto a non-spatially exhaustive forest inventory dataset (R² = 0.64; RMSE = 16.95 t/ha), 2.applying a lookup table to unique combinations of land cover and vegetation densityoutputs derived from remotely sensed data (R² = 0.52; RMSE = 19.97 t/ha), and 3. hybridmapping by augmenting forest inventory AGB estimates with remotely sensed AGB estimates where there are spatial or attributional gaps in the forest inventory data. Over our714,852 ha study area in central Saskatchewan, Canada, the AGB estimate generated fromthe forest inventory was approximately 40 Mega tonnes (Mt); however, the inventoryestimate represents only 51% of the total study area. The AGB estimate generated from theremotely sensed outputs that overlap those made from the forest inventory based approachdiffer by only 2 %; however in total, the remotely sensed estimate is 30 % greater (58 Mt)than the estimate generated from the forest inventory when the entire study area isaccounted for. Finally, using the hybrid approach, whereby the remotely sensed inputswere used to fill spatial gaps in the forest inventory, the total AGB for the study area wasestimated at 62 Mt. In the example presented, data integration facilitates comprehensiveand spatially explicit estimation of AGB for the entire study area.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".