Deciphering the many maps of the Xingu – an assessment of land cover classifications at multiple scales
Bibliographic record
Abstract
Abstract Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics, as well as considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (1989–2018) for ∼1.3 million km 2 encompassing the Xingu river basin, Brazil, from published, freely accessible remotely sensed classifications. While all datasets showed a decrease in forest extent over time, we found a large range in the total area reported by each product for all time periods. The greatest differences ranged from 9% (year 2000) to 17% of the total area (2014-2018 period). We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. We further show the importance of choosing surface water datasets carefully because they differ greatly in location and amount of surface water mapped between sources. In several of the datasets illustrating the land cover following operationalization of the Belo Monte dam, the large reservoirs are notably absent. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g. Riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate dataset for conservation, taxonomy or policy-making. The differences in forest cover between the datasets examined here are not a failure of the technology, but due to different interpretations of ‘forest’ and characteristics of the input data (e.g. spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed datasets and the need for users to familiarize themselves with the characteristics and limitations of each chosen data set.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".