Eucalyptus suitability in Guangxi based on machine learning algorithms

Abstract

Objective The aim is to provide a new idea for tree species suitability evaluation, provide a support for scientific afforestation, and explore the relationship between site factors and tree suitability. Method Take a Eucalyptus plantation in Guangxi as the research object, 1 883 forest resource sub-compartment survey data of Guangxi state-owned Gaofeng Forest Farm were selected. Then, Naive Bayesian, Support Vector Machine, and Random Forest algorithm were used to evaluate the suitability of tree species and to construct a suitability classification model for Eucalyptus. Eleven site factors, namely, landform type, elevation, aspect, slope position, slope, litter thickness, humus layer thickness, soil layer thickness, gravel content, parent material, and soil type were input with the output being Eucalyptus suitability. Result The fitting accuracy of the three models was 63.18% for Naive Bayesian, 69.73% for Support Vector Machine, and 78.03% for Random Forest algorithm with a generalization accuracy of 64.33% for Naive Bayesian, 67.93% for Support Vector Machine, and 78.18% for Random Forest algorithm. The order of importance for site factors was elevation > soil layer thickness > aspect > slope > gravel content > litter thickness > slope position > humus layer thickness > soil type > landform type > parent material. Overall, Eucalyptus was more suitable for growth in areas of 200-350 m altitude and 80-100 cm soil layer thickness. Conclusion Thus, machine learning classification algorithms could be used to fit the non-linear relationship between tree species suitability and site factors.