Global mapping of potential natural vegetation: an assessment of Machine Learning algorithms for estimating land potential

• Published in: PeerJ preprints
• Main Authors: Hengl, Tomislav, Walsh, Markus G, Sanderman, Jonathan, Wheeler, Ichsani, Harrison, Sandy P, Prentice, Iain C
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ, Inc 27.07.2018
• Subjects: Algorithms; Artificial intelligence; Bioclimatology; Ecosystems; Learning algorithms; Mapping; Natural vegetation; Neural networks; Precipitation; Regression analysis; Spatial discrimination; Species
• DOI: 10.7287/peerj.preprints.26811v2

Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location non-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 (MLA) for operational mapping of Potential Natural Vegetation (PNV). The following MLA were considered: neural networks (nnet package), random forest (ranger), gradient boosting (gmb), K-nearest neighborhood (class) and cubist. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8057 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 show that random forest gives the overall best performance. he highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial Cross Validation) and 68% (simple random subsetting), 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 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 GBIF records to map 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 1 km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.