How Land Cover Spatial Resolution Affects Mapping of Urban Ecosystem Service Flows
Bibliographic record
Abstract
In urban areas, estimating the effect of land cover (LC) data spatial resolution on ecosystem services (ES) mapping remains a challenge. In particular, mapping spatial flows of ES, from greenspaces to beneficiaries, may be more sensitive to LC data resolution than mapping potential supply or demand separately. Our objectives were to compare the sensitivity of global- and local-flow ES maps to LC data resolution, and to assess the effect of LC data resolution within different types of urban land uses. A case study was conducted in the city of Laval, Canada. Carbon storage (a global-flow ES), urban cooling and pollination (two local-flow ES) were mapped using LC data aggregated from 1 m to 15 m. Results were analyzed for districts (comprising various types of urban land uses), and for 480 x 480 m residential and commercial zones. Greenspace cover was generally underestimated at coarser spatial resolutions; as a result, so were ES potential supply and flow. For urban cooling and pollination, the effect of LC data spatial resolution on ES flow also depended on changes in the spatial configuration of ES potential supply relative to ES demand. The magnitude of the effect differed among land use types. However, the effect was also highly variable between similar landscapes, suggesting that it is very sensitive to LC structure. To adequately map the ES provided by the small greenspaces scattered throughout the urban matrix, using land cover data with a spatial resolution of 5 m or finer is recommended, especially for local-flow ES.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".