Analysis of stable states in global savannas: is the CART pulling the horse? – a comment

Abstract

In their recent paper, H anan et al. (Global Ecology and Biogeography, 2014, 23, 259–263) argue that the use of classification and regression trees (CARTs) to calibrate global remote sensing datasets, including the MODIS VCF tree‐cover dataset, makes these data inappropriate for analysing the frequency distribution of tree cover. While we agree with their most general point – that the use of remote sensing products should be informed and deliberate – their analysis overlooks a few key aspects of the use of CARTs in generating global tree‐cover data. Firstly, while their presentation of flaws in the use of CARTs is compelling, their use of hypothetical data obscures the reasons why CARTs are a useful tool. Secondly, they do not actually examine the error distributions of the MODIS VCF tree‐cover data. Such an analysis, which we perform, revealed the following: (1) the MODIS VCF product may not be useful for differentiating over small ranges of tree cover (less than c. 10%); (2) that the bimodality of low and high tree cover, with a frequency minimum at intermediate tree cover, is not attributable to bias in MODIS VCF tree‐cover calibrations; and (3) that the MODIS VCF is not well‐resolved below c. 20–30% tree cover, such that MODIS cannot be used with any confidence to evaluate multimodality in tree cover in that range. Further validation and calibration are likely to be helpful and, at low tree cover, necessary for improving MODIS VCF tree‐cover estimates. However, the MODIS VCF – which has facilitated major steps in our ability to examine ecological phenomena at global scales – remains a useful tool for well‐informed ecological analysis.