Integrating participatory GIS, remote sensing, and explainable machine learning to assess forest provisioning services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forests play a vital role in supporting rural livelihoods by providing essential resources such as food, fuelwood, and medicine. Ensuring the sustainable utilization of these resources while balancing environmental protection requires a data-driven approach that integrates advanced technologies and local knowledge to inform forest management. This study synthesizes data from Participatory Geographic Information System (PGIS) of 66 forest plots and 1864 trees, multisource remote sensing (i.e., radar and optical) and explainable machine learning to assess forest provisioning supply for community forests management. Key findings from the inventory include the multifunctional roles of trees for medicinal, food and culinary uses. Vegetation Indices such as Transformed Soil Adjusted Vegetation Index (TSAVI) and Normalized Difference Index 45 (NDI45) were identified as useful predictors of forest provisioning supply, capturing essential attributes of vegetation dynamics using random forest (R 2 = 0.76, RMSE = 4.51). Radar-derived texture metrics were equally relevant and can be especially useful under challenging climatic conditions, such as persistent cloud covers. The Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) revealed threshold relationships between Sentinel-2 indices and forest provisioning, with notable thresholds observed at NDI45 = 0.3 and TSAVI = 0.59. These thresholds signal possible ecological tipping points associated with forest health and productivity. Also, Independent Conditional Expectations (ICE) and Locally Interpretable Model-agnostic Explanations (LIME) provided site-specific explanations on the association between remote sensing indices and forests provisioning capacity, underscoring the spatial heterogeneity of forest ecosystems. The study fills an important research gap by providing a framework that integrates interpretable and explainable modelling with participatory geospatial methods, aiming to inform community-based forests management and support data-driven landscape and site-specific forest ecosystem management in the Miombo woodlands. • A participatory geospatial and explainable machine learning framework for assessing forest services. • Participatory GIS is used to identify the multifunctional role of forests/trees in agrarian communities. • Radar and optical remote sensing metrics play complementary roles in predicting forest provisioning services. • Global and local model interpretations can inform both landscape-scale and site-specific forest management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it