Multi-Criteria Prioritization of Watersheds for Post-Fire Restoration Using GIS Tools and Google Earth Engine: A Case Study from the Department of Santa Cruz, Bolivia
Bibliographic record
Abstract
The Santa Cruz department in Bolivia is characterized by a wide range of ecosystems and by its richness in water resources. In recent years, extended drought caused by climate change has led to extensive fire events. Combined with deforestation, this is resulting in the degradation of the region’s ecosystems and water resources. To address restoration needs from both a land- and water-management perspective, this study proposes to prioritize restoration areas by applying a multi-criteria analysis (MCA) based on two main principles: (1) using the watershed as the main study unit and (2) involving stakeholders in the definition of priority watersheds. Local stakeholders selected criteria representing water resources, biophysical characteristics, land management, productive areas, and fire disaster threats, and reclassified the spatial information based on perceived importance. Different prioritization scenarios were developed and compared in a Google Earth Engine (GEE) application. Priority restoration areas largely depend on the weighting scheme. Focusing solely on past fires leads to prioritizing the south-east basins, while the conservation of the western watersheds becomes more important when increasing the weight of the water resources criteria. This study represents the first step in developing a participatory MCA tool at the watershed scale in Santa Cruz. Highlighting the impact of different prioritization criteria can support collective decision-making around land and watershed restoration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".