Evaluating the Relationships of Phenological and Inter-Annual Landscape Dynamics with Farmland Biodiversity using Multi-Spatial and Multi-Temporal Remote Sensing Data
Bibliographic record
Abstract
Agricultural landscapes are highly variable ecosystems and are home to many species.Farmland spatial heterogeneity and phenological and inter-annual agricultural landscape dynamics has been shown to be related to species diversity.Remote sensing provides data that enable monitoring landscape changes at multiple temporal and spatial scales.The goal of this research was to determine the response of biodiversity to phenological and inter-annual landscape dynamics.The study area is the predominantly agricultural region of eastern Ontario.Ninety-three sample landscapes were selected prior to this research.Biodiversity data were collected during the summers of 2011 and 2012 within a 1 × 1 km area at each landscape.This extent and 3 × 3 km were selected for this research to analyze the impacts of spatial scale on biodiversity response.Relationships between biodiversity and vegetation phenology were modelled using MODIS NDVI, while relationships between biodiversity and long term inter-annual vegetation changes were modelled using Landsat NDVI and Tasseled Cap components.Random Forest Regression was used to determine relative variable importance over the many biodiversity models produced.The most important variables were identified and subsequently used in step-wise regression to determine model significance, the landscape variables entered, and the direction of their relationship with biodiversity.Results demonstrated that phenological and inter-annual changes in vegetation dynamics were related to biodiversity.For MODIS, most 3 × 3 km models were significant, whereas most 1 × 1 km models were not.For Landsat, model performance was not consistently different for the two extents, indicating that model performance can depend on landscape extent when coarse spatial resolution data are used.Plant diversity was lower when the time of onset of greenness
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".