Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance

Modeling Percent Tree Canopy Cover

(2015) John W. Coulston et al.   PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING

Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling

(2014) İnci Güneralp et al.   International Journal of Applied Earth Observation and Geoinformation

Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting

(2014) Anthony M. Filippi et al.   Remote Sensing Letters

Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery

(2013) G. Chirici et al.   International Journal of Applied Earth Observation and Geoinformation

Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches

(2010) Scott L. Powell et al.   REMOTE SENSING OF ENVIRONMENT

Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals

(2009) Simon J. Pittman et al.   JOURNAL OF COASTAL RESEARCH

Gradient modeling of conifer species using random forests

(2009) Jeffrey S. Evans et al.   LANDSCAPE ECOLOGY

A first map of tropical Africa’s above-ground biomass derived from satellite imagery

(2008) A Baccini et al.   Environmental Research Letters

A working guide to boosted regression trees

(2008) J. Elith et al.   JOURNAL OF ANIMAL ECOLOGY

Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery

(2008) Jonathan Cheung-Wai Chan et al.   REMOTE SENSING OF ENVIRONMENT

Woody cover in African savannas: the role of resources, fire and herbivory

(2007) Mahesh Sankaran et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY