Estimation of Above Ground Biomass and Carbon Stock using UAV images
Bibliographic record
Abstract
Forests have a vital role in maintaining global climate stability by removing greenhouse gases like carbon dioxide from environment. Estimation of carbon stock is crucial in quantifying the amount of carbon that is present in the forest. The estimation of forest biomass and carbon stock through field measurements is a challenging and timeconsuming task. Here in this scenario, our study aims to estimate carbon stock in a forest area using the hybrid technique i.e., aerial survey and ground survey. We used low-altitude remote sensing data acquired by UAV to estimate biomass and carbon stock in an efficient way compared to the traditional techniques. We developed an orthomosaic from the collected aerial imageries and manually delineated tree crowns to obtain crown projection area (CPA) for the entire study area using GIS tools. Our study area contained a mixed species with Pinus Wallichiana to be the dominant species while other species are negligible. Using field-measured tree height and diameter at breast height (DBH) as input, we estimated above-ground biomass (AGB) with an allometric equation and then used a factor value to estimate carbon stock or aboveground carbon (AGC) for six sample plots. Next, we developed a relationship between CPA and carbon stock and validated it by comparing the carbon stock values obtained from the allometric equation for the remaining four sample plots. Among the various developed model, 4th order Polynomial model was chosen due to its highest coefficient of correlation. After the model validation was done the AGC of whole study area was obtained by using the CPA delineated manually from the orthomosaic image. The total AGC and AGB obtained for our study area which was about 7 hectare was 210.7480 tons and 448.4 tons respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".