MODIS VCF should not be used to detect discontinuities in tree cover due to binning bias. A comment on Hanan et al. (2014) and Staver and Hansen (2015). Issue 7 (23rd May 2017)

• Record Type: Journal Article
• Authors: Gerard, France; Hooftman, Danny; van Langevelde, Frank; Veenendaal, Elmar; White, Steven M.; Lloyd, Jon
• Other Names: Gillespie Thomas checker.
• Is Part Of: Global ecology & biogeography. Volume 26:Issue 7(2017)
• Page Start: 854
• Page End: 859
• Publication Date: 2017-05-23
• Subjects: alternative stable states -- forest -- frequency distribution -- MODIS VCF -- remote sensing -- savanna -- tree cover; Ecology -- Periodicals; Biogeography -- Periodicals; Biodiversity -- Periodicals; Macroevolution -- Periodicals; 577
• DOI: 10.1111/geb.12592
• Languages: English
• ISSNs: 1466-822X

Abstract

Abstract: In their recent paper, Staver and Hansen ( Global Ecology and Biogeography, 2015, 24, 985–987) refute the case made by Hanan et al. ( Global Ecology and Biogeography, 2014, 23, 259–263) that the use of classification and regression trees (CARTs) to predict tree cover from remotely sensed imagery (MODIS VCF) inherently introduces biases, thus making the resulting tree cover unsuitable for showing alternative stable states through tree cover frequency distribution analyses. Here we provide a new and equally fundamental argument for why the published frequency distributions should not be used for such purposes. We show that the practice of pre‐average binning of tree cover values used to derive cover values to train the CART model will also introduce errors in the frequency distributions of the final product. We demonstrate that the frequency minima found at tree covers of 8–18%, 33–45% and 55–75% can be attributed to numerical biases introduced when training samples are derived from landscapes containing asymmetric tree cover distributions and/or a tree cover gradient. So it is highly likely that the CART, used to produce MODIS VCF, delivers tree cover frequency distributions that do not reflect the real world situation.