Use of uncrewed aircraft systems (UAS) and regression tree modeling to calculate fractional shrub cover for greater sage-grouse microhabitat
Bibliographic record
Abstract
Accurate maps of habitat availability for greater sage-grouse ( Centrocercus urophasianus ) across broad extents are of paramount importance to conservation efforts in sagebrush ecosystems across the Great Basin, particularly for habitat assessments and mitigation efforts. However, the ability to manage sage-grouse microhabitat is constrained by the spatial and spectral resolution of most remotely sensed vegetation products. Fractional approaches that yield estimates of percent cover at relatively coarse resolution (e.g., 30 ×30 m pixels) are well suited for regional and local estimates at relatively broad spatial scales (e.g., third-order macrohabitat selection and availability). However, precision at individual pixels that could represent finer selection patterns by sage-grouse (e.g., fourth-order microhabitat selection) is often poor. Here, results are presented from a study in central Nevada where previous fractional mapping approaches are advanced by applying regression tree (Cubist) modeling techniques at finer spatial resolutions, with estimates of shrub cover in training plots derived from ultra-high-resolution (< 3 cm) imagery collected with uncrewed aircraft systems (UAS) being applied to multi-spectral WorldView-2 scenes. The approach yields fractional estimates of shrub cover at a 2 m resolution . Compared to other fractional products, this product more closely correlates with actual field measurements that reflect finer selection patterns of sage-grouse. An advantage of the UAS-regression tree approach is that large volumes of training data can be collected rapidly from UAS compared to traditional ground-based vegetation surveys, which could ultimately yield previously lacking estimates of microhabitat availability across macrohabitat or landscape level extents. Our results provide high-resolution maps of shrub cover with multiple conservation applications, including the development of continuous microhabitat maps to inform sage-grouse habitat restoration efforts and incorporation into planning tools that simulate outcomes of multiple sagebrush management decisions. • Managers require microhabitat maps for wildlife monitoring and habitat restoration. • Current fractional cover products cannot accurately quantify microhabitat. • We collected ultra-high-resolution imagery using uncrewed aircraft systems. • We applied the imagery to WorldView scenes using regression tree models. • We developed high accuracy 2-m resolution fractional cover maps of shrub cover.
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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".