Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators
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Bibliographic record
Abstract
Abstract A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r 2 ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in root-mean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha−1, respectively. Keywords: Above ground forest biomassairborne laser scanningforest structurelaser altimetryLIDARquantile estimatorsremote sensing Acknowledgments The authors gratefully acknowledge the financial support of the Centre for Research in Earth and Space Technologies (CRESTech), an Ontario Centre of Excellence, and Geomatics for Informed Decisions (GEOIDE), a Canadian National Centre of Excellence. Mr Lim acknowledges the support from the Natural Sciences and Engineering Research Council (NSERC) of Canada through a PGS-B scholarship and the Ontario Government through an Ontario Graduate Scholarship in Science and Technology. Dr Treitz would also like to acknowledge support of the Natural Sciences and Engineering Research Council (NSERC) for financial support through research grants. B. Prenzel, C. Sheriff and V. Thomas are thanked for their assistance with data collection. K. Baldwin and I. Morrison from the Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada are thanked for providing data for the Turkey Lakes watershed study area. The authors gratefully acknowledge Optech Inc. and LaserMap Image Plus for their support in acquiring and processing the LIDAR data for the Turkey Lakes watershed. Notes Lim, K. S. and Treitz, P. M. (Department of Geography, Faculty of Arts and Science, Queen's University, Kingston, Ontario, Canada, K7L 3N6). Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Additional informationNotes on contributorsKevin S. Lim Lim, K. S. and Treitz, P. M. (Department of Geography, Faculty of Arts and Science, Queen's University, Kingston, Ontario, Canada, K7L 3N6). Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it