Hyperspectral remote sensing investigations of vegetation in Northern Peatlands
Bibliographic record
Abstract
Imaging sensors designed to remotely observe the Earth can be used for land-cover studies of peatlands and other areas that provide carbon storage or other important ecosystem services. In order to improve our knowledge of remote sensing and peatland research, this study employed multiple types of remotely sensed data for the evaluation and testing of methodologies that aimed to map different characteristics of peatland vegetation. The study was conducted in two peatland ecosystems: 1) near James Bay Quebec, which is an important site for hydropower generation, and 2) Mer Bleue, an ombrotrophic bog near Ottawa, Ontario. For the first site and study, high-resolution aerial photographs and aerial hyperspectral imagery were used to generate land-cover maps over an area of proximately 145 km2. This study is based on a novel approach that integrates object-oriented classification (GEOBIA) for producing a classification/validation dataset as a base to classify hyperspectral imagery from two different airborne sensors. This methodology demonstrated the feasibility of using such an approach to distinguish between seven vegetation classes commonly found in peatlands. The maps exhibit mapping accuracies higher than 80 % and have a high level of confidence, for example the map produced with the CASI sensor has an overall accuracy of 88.18, 95 % CI [87.3, 89.02]. My findings provide a baseline for developing a more detailed research focus on topics related to land-use change and the stratification of the landscape to study the individual contributions of different plant functional types to the carbon cycle.For the second study, a field experiment was conducted aiming to characterize and map biophysical and biochemical characteristics of vegetation at high spatial scales (< 1 m). Through the implementation of neural network models, the upscaling of ground measurements to airborne imagery was investigated. Results show that maps of the four vegetation characteristics evaluated (i.e. vascular plant height, moss fractional cover and moss carbon and nitrogen content) have good spatial consistency. However they only have a moderate performance in upscaling.My overall research implemented methods that can be used by researchers and industry to map different types of vegetation and their characteristics, contributing to a better understanding of the carbon cycle in peatlands.
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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 | 0.001 | 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) | 0.000 | 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 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".