Remote sensing of large wood in high‐resolution satellite imagery: Design of an automated classification work‐flow for multiple wood deposit types
Bibliographic record
Abstract
Abstract Wood researchers increasingly rely on remote‐sensing products to augment field information about wood deposits in river corridors. The availability of very high‐resolution (<1 m) satellite imagery makes capturing wood over greater spatial extents possible, but previous studies have found difficulty in automatically extracting wood deposits due to the challenge in distinguishing wood from spectrally similar corridor features such as sand. We also lack knowledge on the spectral properties of different wood deposit types in multiple depositional environments. In this work, we explore image classification work‐flows for four wood deposit types in three North American environments: in‐channel jams deposited in the Tatshenshini River in Alaska, USA; a wood raft on the Slave River in Northwest Territories, Canada; and wood deposited along a lakeshore and coastal embayment in the Mackenzie River Delta in Northwest Territories, Canada. We compare classification results of object‐based and pixel‐based image analysis with supervised [support vector machine (SVM)] and unsupervised (ISO clustering) classifiers. We evaluate several accuracy assessment parameters and achieve overall classification accuracies of 65–99%, showing automated image classification is a possible approach for analysing wood across larger areas. We also find that wood sensitivity in the classification ranged from 0 to 95%, indicating that some techniques are better suited to wood capture than others. We find that supervised classification produced more accurate wood maps, though there is large variation in classification outcomes across environments related to spatial arrangement of wood in the landscape. We discuss the influence of depositional environment on classification and provide recommendations for designing a wood classification work‐flow.
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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".