Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula (original) (raw)

NASA/ADS

;

• Blazek, Radim ;
• Neteler, Markus ;
• Dios, Rut Sánchez de ;
• Ollero, Helios Sainz ;
• Furlanello, Cesare

Abstract

We present a modelling framework for predicting forest areas. The framework is obtained by integrating a machine learning software suite within the GRASS Geographical Information System (GIS) and by providing additional methods for predictive habitat modelling. Three machine learning techniques (Tree-Based Classification, Neural Networks and Random Forest) are available in parallel for modelling from climatic and topographic variables. Model evaluation and parameter selection are measured by sensitivity-specificity ROC analysis, while the final presence and absence maps are obtained through maximisation of the kappa statistic. The modelling framework is applied at a resolution of 1 km with Iberian subpopulations of Pinus sylvestris L. forests. For this data set, the most accurate algorithm is Breiman's random forest, an ensemble method which provides automatic combination of tree-classifiers trained on bootstrapped subsamples and randomised variable sets. All models show a potential area of P. sylvestris for the Iberian Peninsula which is larger than the present one, a result corroborated by regional pollen analyses.

Publication:

Ecological Modelling

Pub Date:

January 2006

DOI:

10.1016/j.ecolmodel.2006.03.015

Bibcode:

2006EcMod.197..383G

Keywords:

• Machine learning;
• Random forest;
• Neural networks;
• Classification and regression trees;
• AUC;
• Kappa;
• Iberian Peninsula;
• Pinus sylvestris L .;
• Habitat suitability