Heterogeneity characteristics of an inland wetland environment through spatio-spectral analysis
Bibliographic record
Abstract
To manage wetlands, resource managers require information on the size, shape, condition, and spatial distribution of vegetation types at both the community and species levels. Remote sensing methods currently used for generating maps of wetland ecosystems are largely based on spectral transformations of image data, preferably using high spatial resolution data. However, airborne hyperspectral imagery contains not only high spectral content but also considerable spatial information. To date, use of the spatial information in such imagery, particularly to determine biodiversity, has not been fully exploited. In this study, spatial heterogeneity in the reflectance values of hyperspectral imagery recorded over an inland wetland complex in South Dumfries Township, Ontario, Canada, is determined on a per-pixel basis by measuring the degree of local spatial association (local Moran’s Ii) within a 3 × 3 neighbourhood of pixels. These new spatio-spectral Moran’s Ii bands are then classified into heterogeneity categories using a modified version of the spectral angle mapper algorithm. Results show that the extraction of spatial information from hyperspectral data contributes to wetland management initiatives by providing quantitative information on the spatial arrangement of measured biological elements. The use of Moran’s Ii, a local measure of spatial association, also provides a statistical basis on which to map heterogeneity characteristics of elements within a wetland environment and to determine how these characteristics change over time.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".