Mapping Arctic Coastal Ecosystems with High Resolution Optical Satellite Imagery Using a Hybrid Classification Approach
Bibliographic record
Abstract
Most mapping methods for Arctic land cover are pixel-based techniques for low resolution data, and have limitations in mapping land cover heterogeneity over complex Arctic polygonal tundra terrain. In this study, we developed a hybrid object-based approach for Arctic coastal tundra mapping using very high resolution optical satellite imagery by combining results from semi-automatic water/land separation, texture analysis based on local binary pattern (LBP), and image classification via Random Forests (RF). The method was applied for coastal land cover mapping in a study site in Tuktoyaktuk, Northwest Territories, Canada using Pleiades satellite data. Results from pixel-based Maximum Likelihood Classifier (MLC), segment-based MLC, pixel-based RF, and segment-based RF were compared with the proposed method. The hybrid method outperformed other approaches and achieved an overall accuracy of 88% for 9 classes. In particular, it has successfully identified unique land cover types of Ice-Wedge Polygons, Wetland (inundated low-lying tundra and marsh with water ponds), with both producer's and user's accuracy over 91%. Results from this study indicate that the developed hybrid method is suitable to be applied for mapping Arctic coastal ecosystems, and confirms the feasibility of proper use of LBP at segment level for mapping complex environment.
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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 | 0.001 | 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 | 0.001 | 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".