Estimates of landscape composition from terrestrial oblique photographs suggest homogenization of Rocky Mountain landscapes over the last century
Bibliographic record
Abstract
Abstract While orthogonal (i.e., aerial or satellite) imagery has become the more conventional source of land cover data because it can yield spatially accurate land cover maps, terrestrial oblique photographs present a valuable, relatively untapped source of raw optical data for studies of land cover change. We present a case study contrasting how these two types of imagery sample landscape composition and using repeat oblique photographs to evaluate long‐term land cover change in a remote region of the Canadian Rocky Mountains. We classified 46 historical oblique photographs and their corresponding modern repeats using the same discrete land cover classes employed in a Landsat‐based map of the same area. We compared landscape‐level composition estimates from both sources and regressed the land cover proportions from Landsat against the modern oblique images, hypothesizing a linear relationship for most classes. We found that the two sources sampled the landscape in broadly similar ways, with near‐concordance for dominant land cover classes, yet that oblique photographs more frequently detected narrow landscape features and estimated higher proportions of rock compared to satellite imagery, possibly due to the higher spatial resolution of the oblique photographs, and to their angle of incidence against steep slopes. We then evaluated land cover change from corresponding historical and repeat photographs and found that the landscape has homogenized over the past century via increased coniferous forest cover. Our work shows that terrestrial oblique photographs can be used to estimate landscape composition, particularly in mountain environments. This is helpful for analyzing past landscape conditions in historical photographs, monitoring decadal‐span landscape change and assessing habitat to model biodiversity through time.
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 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".