Understanding forest insect outbreak dynamics: a comparative analysis of machine learning techniques
Accurate modeling and simulation of forest land cover change resulting from epidemic insect outbreaks play a crucial role in equipping scientists and forest managers with essential insights. These insights enable proactive planning and the formulation of effective strategies to mitigate the impact o...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-07-01
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| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2529992 |
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| Summary: | Accurate modeling and simulation of forest land cover change resulting from epidemic insect outbreaks play a crucial role in equipping scientists and forest managers with essential insights. These insights enable proactive planning and the formulation of effective strategies to mitigate the impact of such disturbances. By employing advanced modeling techniques, researchers and managers can anticipate the evolving dynamics of forest ecosystems, thereby facilitating timely interventions and sustainable management practices. In this study, we applied sixteen machine-learning models, plus two ensemble averaging procedures, to Mountain Pine Beetle (Dendroctonus ponderosae) infestation data in British Columbia, to calculate projections of insect-induced deforestation. Model drivers included topographic, climatic and adjacency variables. We verified the results of the simulations by randomly splitting datasets between training and test subsets (aka Validation assessment), as well as by comparing future projections with observations (aka Prediction assessment). All calculations were carried out for different mountain pine beetle map sets and time differences, and we employed up to seven performance metrics (six threshold-dependent and one threshold-independent) and four error metrics to assess goodness of prediction. ANCOVA tests were then run on metric results to test differences between Validation and Prediction assessments. In addition, we computed Friedman rankings for all simulation and metrics. Our results showed that validation assessments were, most of the time, significantly more optimistic than prediction assessments. We also noted that different conclusions could be reached for different performance metrics. We conclude that, for prediction purposes, error metrics and components of the confusion table were most helpful in understanding the ability and limitations of Mountain Pine Beetle predictive maps. These results also suggest that, in general, care must be taken in assessing prediction performance of machine-learning models based solely on validation tests. |
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| ISSN: | 1009-5020 1993-5153 |