Aboveground biomass estimation using spaceborne LiDAR in managed conifer forests in south central British Columbia
Bibliographic record
Abstract
In the context of growing concerns regarding global climatic change, developing methods to assess the carbon storage of various ecosystems has become important. This research attempts to develop low or no cost methods to estimate carbon stock in forests using satellite-based data. More specifically, this research explores the utility of spaceborne Light Detection and Ranging (LiDAR) data for forest canopy height and aboveground biomass estimation. High-resolution (sub meter) airborne LiDAR data were collected and validated for a 75 000 ha area near Clearwater, British Columbia. Airborne LiDAR has been widely demonstrated to yield accurate aboveground biomass estimates. 110 temporally coincident Geospatial Laser Altimeter System (GLAS) waveforms from the study site were used in this research. First, I demonstrate that airborne LiDAR can be manipulated to represent waveform curves with a high degree of similarity to GLAS waveform curves. Based on the relationship between the GLAS and simulated waveforms I am able to visualize the ground contribution to GLAS waveforms. Second, I calculate a suite of novel GLAS waveform metrics and develop models of terrain relief, canopy height, and terrain adjusted canopy height. These models compare favourably to other GLAS studies (terrain relief R2=0.76, canopy height R2= 0.75-0.88) and indicate that terrain relief should be included in GLAS derived canopy height models. Third, I attempt to extrapolate the spatially discrete GLAS estimates to spatially continuous estimates using Landsat TM data. Landsat data have been used extensively for AGBM estimation, although they are known to have limitations for studies in high biomass or structurally complex forests. I develop models to predict GLAS AGBM estimates from Landsat bands and indices (R2=0.6). I then use an airborne LiDAR derived AGBM map to generate a map of over and under prediction of AGBM, and evaluate the success of the model in areas of differing forest species and structure. I conclude that GLAS data is appropriate for AGBM estimation in forests over a wide range of biomass values, but that GLAS and Landsat integration for AGBM estimation should only be conducted in forests with less than approximately 120 Mg/ha of AGBM, 60 years of age, or 60% canopy cover.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".