A Workflow to Quantify the Carbon Storage in Urban Trees Using Multispectral ALS Data
Bibliographic record
Abstract
City wide, vegetation contributes the largest proportion of carbon storage, which reduces climate warming and urban heat island effects by sequestering CO 2 and storing carbon in biomass. The carbon content stored in individual trees can be estimated by dendrometric parameters, such as the diameter at breast height (DBH), using allometry-based models. With the development of airborne laser scanning (ALS) technology, ALS data and very-high-resolution multispectral imagery have proven to be promising tools for deriving dendrometric parameters in forestry. This chapter describes a workflow that can quantify the carbon storage in urban trees using multispectral ALS data. The workflow consists of four steps: multispectral ALS data processing, vegetation isolation, dendrometric parameters estimation, and carbon storage modeling. Results suggest that ALS-based dendrometric parameter estimation and allometric models can yield consistent performance and accurate estimation. Citywide carbon storage estimation is derived in this chapter for the Town of Whitchurch-Stouffville, Canada, by extrapolating the values within the study area to the entire city based on the specific proportion of each land cover type. The proposed workflow reveals the potential of multispectral ALS data in land cover mapping and carbon storage estimation at the individual tree level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.025 |
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".