Medium-resolution Dynamic Habitat Indices from Landsat and Sentinel-2 satellite imagery
Bibliographic record
Abstract
• We derived Dynamic Habitat Indices (DHIs) from Sentinel-2, Landsat, and MODIS data. • Sentinel-2 and Landsat DHIs matched MODIS DHIs well at broad scales. • Sentinel-2 and Landsat DHIs outperformed MODIS in modeling bird species richness. • Medium-resolution DHIs capture heterogeneous landscapes better. Biodiversity science requires effective tools to predict patterns of species diversity at multiple temporal and spatial scales. The Dynamic Habitat Indices (DHIs) are remotely sensed indices that summarize aboveground vegetation productivity in a way that is ecologically relevant for biodiversity assessments. Existing global DHIs, derived from MODIS at 1-km resolution, predict species richness at broad scales well, but that resolution is coarse relative to the grain at which many species perceive their habitat. With the much finer spatial resolution of Sentinel-2 and Landsat data, plus Landsat’s longer data record, it is possible to track potential changes of vegetation and its impacts on biodiversity at a finer grain over longer periods. Here, our main goals were to derive the DHIs from 10-m Sentinel-2, 30-m Landsat, and 250-m MODIS data for the conterminous US and compare all DHIs at two spatial extents, and to evaluate the ability of these DHIs to predict bird species richness in 25 National Ecological Observatory Network terrestrial sites. In addition, we derived the Landsat DHIs for 1991–2000 and investigated how they changed by 2011–2020. We found that the Sentinel-2, Landsat, and MODIS DHIs were highly correlated when summarized by ecoregion (Spearman correlation ranging from 0.89 to 0.99), indicating good agreement between them and that we were able to overcome the lower temporal resolution of Sentinel-2 and Landsat. Sentinel-2 and Landsat DHIs outperformed MODIS in modeling species richness for all bird guilds, explaining up to 49% of variance of grassland affiliates in linear regression models. Furthermore medium-resolution DHIs (10–30 m resolution) captured spatial heterogeneity much better than MODIS DHIs. We observed considerable changes in Landsat DHIs from 1991–2000 to 2011–2020, such as increased cumulative DHI along the West Coast, in mountain ranges, and in the South, but lower cumulative DHI in the Midwest. Our newly derived DHIs for the conterminous US have great potential for use in biodiversity science and conservation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".