Imperial College London
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Professor Iain Colin Prentice

Faculty of Natural Sciences, Department of Life Sciences (Silwood Park)

Chair in Biosphere and Climate Impacts
 
 
 
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Contact

 

+44 (0)20 7594 2482c.prentice

 
 
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Location

 

2.3Centre for Population BiologySilwood Park

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Summary

 

Publications

Citation

BibTex format

@article{Hengl:2018:10.7717/peerj.5457,
author = {Hengl, T and Walsh, MG and Sanderman, J and Wheeler, I and Harrison, SP and Prentice, IC},
doi = {10.7717/peerj.5457},
journal = {PEERJ},
title = {Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential},
url = {http://dx.doi.org/10.7717/peerj.5457},
volume = {6},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 leve
AU  - Hengl,T
AU  - Walsh,MG
AU  - Sanderman,J
AU  - Wheeler,I
AU  - Harrison,SP
AU  - Prentice,IC
DO  - 10.7717/peerj.5457
PY  - 2018///
SN  - 2167-8359
TI  - Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential
T2  - PEERJ
UR  - http://dx.doi.org/10.7717/peerj.5457
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000443305000005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/62802
VL  - 6
ER  -
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