Nonparametric machine learning for mapping forest cover and exploring influential factors

[11] Breiman L(2014)Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information Comput Geosci 63 22-33

[12] Breiman L(2007)Random forests for classification in ecology Ecology 88 2783-2792

[13] Friedman J(2015)Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling Ecosyst Serv 15 63-74

[14] Olshen R(2013)Collinearity: a review of methods to deal with it and a simulation study evaluating their performance Ecography 36 27-46

[15] Stone C(2018)A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area Int J Geograph Inf Sci 32 757-782

[16] Brereton RG(2005)Working set selection using second order information for training support vector machines J Mach Learn Res 6 1889-1918

[17] Lloyd GR(2005)Global consequences of land use Science 309 570-574

[18] Bryan BA(2015)Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance Can J For Res 46 323-339

[19] Chang C-C(2010)Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest For Ecol Manag 259 410-417

[20] Lin C-J(1996)Schapire RE Experiments with a new boosting algorithm Icml 96 148-156

[11] Breiman L(2014)Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information Comput Geosci 63 22-33

[12] Breiman L(2007)Random forests for classification in ecology Ecology 88 2783-2792

[13] Friedman J(2015)Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling Ecosyst Serv 15 63-74

[14] Olshen R(2013)Collinearity: a review of methods to deal with it and a simulation study evaluating their performance Ecography 36 27-46

[15] Stone C(2018)A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area Int J Geograph Inf Sci 32 757-782

[16] Brereton RG(2005)Working set selection using second order information for training support vector machines J Mach Learn Res 6 1889-1918

[17] Lloyd GR(2005)Global consequences of land use Science 309 570-574

[18] Bryan BA(2015)Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance Can J For Res 46 323-339

[19] Chang C-C(2010)Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest For Ecol Manag 259 410-417

[20] Lin C-J(1996)Schapire RE Experiments with a new boosting algorithm Icml 96 148-156