Integrating Neighborhood Effect and Supervised Machine Learning Techniques to Model and Simulate Forest Insect Outbreaks in British Columbia, Canada
| Autor: | Saeed Harati, Liliana Perez, Roberto Molowny-Horas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Generalized linear model 010504 meteorology & atmospheric sciences Forest management Land cover Machine learning computer.software_genre 010603 evolutionary biology 01 natural sciences insect outbreaks Deforestation complex systems Spatial analysis 0105 earth and related environmental sciences land cover change model calibration biology business.industry Elevation Forestry lcsh:QK900-989 biology.organism_classification Random forest machine learning lcsh:Plant ecology Environmental science regression Artificial intelligence business computer Mountain pine beetle random forest |
| Zdroj: | Forests, Vol 11, Iss 1215, p 1215 (2020) Forests Volume 11 Issue 11 |
| ISSN: | 1999-4907 |
| Popis: | Background and Objectives: Modelling and simulation of forest land cover change due to epidemic insect outbreaks are powerful tools that can be used in planning and preparing strategies for forest management. In this study, we propose an integrative approach to model land cover changes at a provincial level, using as a study case the simulation of the spatiotemporal dynamics of mountain pine beetle (MPB) infestation over the lodgepole pine forest of British Columbia (BC), Canada. This paper aims to simulate land cover change by applying supervised machine learning techniques to maps of MPB-driven deforestation. Materials and Methods: We used a 16-year series (1999&ndash 2014) of spatial information on annual mortality of pine trees due to MPB attacks, provided by the BC Ministry of Forests. We used elevation, aspect, slope, ruggedness, and weighted neighborhood of infestation as predictors. We implemented a) generalized linear regression (GLM), and b) random forest (RF) algorithms to simulate forestland cover changes due to MPB between 2005 and 2014. To optimize the ability of our models to predict MPB infestation in 2020, a cross-validation procedure was implemented. Results: Simulating infestations from 2008 to 2014, RF algorithms produced less error than GLM. Our simulations for the year 2020 confirmed the predictions from the BC Ministry of Forest by forecasting a slower rate of spread in future MPB infestations in the province. Conclusions: Integrating neighborhood effects as variables in model calibration allows spatiotemporal complexities to be simulated. |
| Databáze: | OpenAIRE |
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