Predicted Tree Canopy Cover (1972 - 2020) from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape
Bibliographic record
Abstract
Paper Abstract: Canadian urban and adjacent landscapes have undergone dynamic changes over the last 50 years due to land management practices, land cover alternations, climate change, and various disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics at a local scale. Here, we explore how Landsat, including the often-forgotten MSS sensor, can be used to investigate tree canopy cover (CC) change over nearly 50 years (1972-2020) in a Canadian urban-rural landscape. We built a CC time-series by training random forest models using visually interpreted CC from reference high-resolution imagery. Predictors included topographic and yearly LandTrendr-fitted tasseled cap indices. Harmonized tasseled cap indices were available throughout the full Landsat archive by applying LandsatLinkr. Yearly binary tree canopy maps were also built to mask consistent non-canopy areas and limit noise. To increase confidence in observed CC change in the absence of historical reference imagery, we consider multiple temporal validation options. We explore changes across broad landscape types and build connections with different influential drivers (e.g., agricultural reforestation, housing development, emerald ash borer, ice storms). Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change, providing important information for local organizations. Dataset details: See paper. See code on GitHub: ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.003 | 0.001 |
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".