Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

Abstract

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data. We explore and compare two approaches for modelling this type of data: to learn a model for each score separately (single-target approach, a regression tree), or to learn one model for all scores simultaneously (multi-target approach, a multi-target regression tree). In order to lift the predictive performance, we also employ ensembles (bagging and random forests) of regression trees and multi-target regression trees. Our results demonstrate the advantages of a multi-target over a single-target modelling approach. While there is no statistically significant difference between the multi-target and single-target models in terms of model performance, the multi-target models are smaller and faster to learn than the single-target ones. Ensembles of multi-target models, also, improve the spatial prediction of condition. The usefulness of models of vegetation condition is twofold. First, they provide an enhanced knowledge and understanding of the condition of different indigenous vegetation types, and identify possible biophysical and landscape attributes that may contribute to vegetation decline. Second, these models may be used to map the condition of indigenous vegetation, in support of biodiversity planning, management and investment decisions.