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

PeerJ
Tomislav HenglIain C Prentice

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 sp...Continue Reading

References

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Citations

Jan 11, 2020·Conservation Biology : the Journal of the Society for Conservation Biology·Juan Gallego-ZamoranoAafke M Schipper
Aug 6, 2021·Global Change Biology·Andrés F Suárez-CastroOve Hoegh-Guldberg
Apr 29, 2020·Environmental Science & Technology·Laura SchererPeter M van Bodegom
Nov 30, 2021·The Science of the Total Environment·Łukasz Pawlik, Sandy P Harrison

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Software Mentioned

DigitalGlobe
multinom
R package nnet
mlr
gbm
nnet
ROCR
ranger
caret
SIRGAS

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