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Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential

Title: Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential
Authors: Hengl, T
Walsh, MG
Sanderman, J
Wheeler, I
Harrison, SP
Prentice, IC
Item Type: Journal Article
Abstract: Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing machine learning algorithms—neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist—for operational mapping of PNV. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8,057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly fraction of absorbed photosynthetically active radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief, and lithologic variables, were used as explanatory variables. The overall results indicate that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial cross-validation) and 68% (simple random sub-setting), with the most important predictors being total annual precipitation, monthly temperatures, and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures, and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with the most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers, and month of the year, respectively. Further developments of PNV mapping could include using all GBIF records to map the global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at one km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.
Issue Date: 22-Aug-2018
Date of Acceptance: 27-Jul-2018
URI: http://hdl.handle.net/10044/1/62802
DOI: https://dx.doi.org/10.7717/peerj.5457
ISSN: 2167-8359
Publisher: PEERJ INC
Journal / Book Title: PEERJ
Volume: 6
Copyright Statement: © 2018 Hengl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Natural vegetation
Random forest
Global
Biome
Machine learning
Forest species
FAPAR
Land potential
GLACIAL-MAXIMUM
RANDOM FORESTS
PREDICTION
RESOLUTION
RECONSTRUCTIONS
MIDHOLOCENE
AUSTRALIA
PACKAGE
MODELS
SYSTEM
Publication Status: Published
Article Number: ARTN e5457
Appears in Collections:Department of Life Sciences
Faculty of Natural Sciences