EFFECT OF TEXTURAL FEATURES FOR LANDCOVER CLASSIFICATION OF UAV MULTISPECTRAL IMAGERY OF A SALT MARSH RESTORATION SITE
Bibliographic record
Abstract
Abstract. Salt marshes are intertidal ecosystems valuable for services including coastal protection and carbon sequestration. Restoration of salt marshes is popular in this era of climate change and sea-level rise, especially in areas where marshes have been historically altered, including in the Bay of Fundy. Salt marsh restoration involves landcover change through time as a community of halophytic vegetation develops in the study area. Restoration sites are difficult to survey using traditional on-foot methods, and developing remote sensing methods to survey them would increase efficiency of monitoring. The purpose of our study was to assess the capability of UAV multispectral imagery to map landcovers in a salt marsh restoration site in the Musquash Estuary, New Brunswick, Canada. We used the Random Forests (RF) supervised classifier and validated our maps using field data. We also evaluated the importance of textural features by running two classifications, with and without textural features. The classification omitting textural features had lower classification and validation accuracies (96.29 % and 91.23 %, respectively) than the classification and validation accuracies obtained by including textural features (99.56 % and 96.84 %, respectively). Additional work is required to test our method in different locations and seasons.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".