Spatiotemporal analysis of ecosystem change and landscape connectivity using satellite imagery in west-central British Columbia, Canada
Bibliographic record
Abstract
Environmental change poses a significant threat to landscape connectivity and threatened wildlife populations. There is a growing need to understand the impacts of broad-scale change across dynamic landscapes on wildlife movement to inform conservation strategies and landscape management plans. As the availability of satellite imagery time series increases, there are new opportunities to monitor broad-scale changes in ecosystems and landscape connectivity. The objective of this thesis is to apply novel methodological approaches using a time series of Landsat imagery to quantify spatiotemporal changes in ecosystem cover and landscape connectivity from 1997 to 2019 in west-central British Columbia, Canada. Using Time-Weighted Dynamic Time Warping (TWDTW) and Landsat imagery, spatiotemporal changes in biogeoclimatic ecosystem classification (BEC) zones were quantified to summarize complex patterns of change that reflect the influence of landscape disturbance. The TWDTW classification showed a transition of the IDF Dry ecosystem to MS Dry and SPBS Dry in the north and northeast of the study area in response to large wildfires in the region. Reduced IDF Dry cover signifies a loss in ungulate habitat, variation in the “green-up” date of vegetation, and increased low productivity forest cover. Omniscape, a circuit theory approach for omni-directional landscape connectivity modelling, was then used to quantify and map landscape connectivity for moose (Alces alces) populations in 1997, 2009, and 2019 and assess the impacts of ecosystem change on potential wildlife movement. Overall landscape connectivity for moose reduced by 70.23% between 1997 and 2019, during which time broad-scale disturbance resulted in ecosystem change. Results represent a novel spatiotemporal analysis of landscape connectivity, reveal variation in overall connectivity for moose across the region in response to disturbance, and predict the location of potential movement corridors. In summary, this thesis demonstrates the application of the TWDTW approach to classify spatiotemporal changes in ecosystem cover across heterogeneous landscapes and the suitability of the Omniscape method for quantifying changing patterns of potential landscape connectivity in the context of ongoing ecosystem change. The combination of time series ecosystem change monitoring and connectivity modelling provides the opportunity to examine the important spatiotemporal relationship between ecosystem cover, disturbance, and wildlife movement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".