scholarly journals Peer Review #4 of "Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential (v0.1)"

2018 ◽  
Author(s):  
J Maestre Vidal
Keyword(s):  
Machine Learning ◽  
Peer Review ◽  
Learning Algorithms ◽  
Natural Vegetation ◽  
Machine Learning Algorithms ◽  
Potential Natural Vegetation ◽  
Global Mapping

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

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Markus G Walsh ◽  
Jonathan Sanderman ◽  
Ichsani Wheeler ◽  
Sandy P Harrison ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Tree Species ◽  
Learning Algorithms ◽  
Forest Tree ◽  
Cloud Fraction ◽  
Natural Vegetation ◽  
Machine Learning Algorithms ◽  
Global Distribution ◽  
Potential Natural Vegetation

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. The highest accuracy for predicting BIOME 6000 classes (20) was estimated at 68% (33% with spatial Cross Validation) 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.

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

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Markus G Walsh ◽  
Jonathan Sanderman ◽  
Ichsani Wheeler ◽  
Sandy P Harrison ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Tree Species ◽  
Learning Algorithms ◽  
Forest Tree ◽  
Cloud Fraction ◽  
Natural Vegetation ◽  
Machine Learning Algorithms ◽  
Global Distribution ◽  
Potential Natural Vegetation

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.

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

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Markus G Walsh ◽  
Jonathan Sanderman ◽  
Ichsani Wheeler ◽  
Sandy P Harrison ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Tree Species ◽  
Learning Algorithms ◽  
Forest Tree ◽  
Cloud Fraction ◽  
Natural Vegetation ◽  
Machine Learning Algorithms ◽  
Global Distribution ◽  
Potential Natural Vegetation

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.

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

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5457 ◽  
Author(s):  
Tomislav Hengl ◽  
Markus G. Walsh ◽  
Jonathan Sanderman ◽  
Ichsani Wheeler ◽  
Sandy P. Harrison ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Tree Species ◽  
Learning Algorithms ◽  
Forest Tree ◽  
Cloud Fraction ◽  
Machine Learning Algorithms ◽  
Global Distribution ◽  
Annual Precipitation ◽  
Potential Natural Vegetation

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.

Sign in / Sign up

Export Citation Format

Share Document