Related references
Note: Only part of the references are listed.Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling
Inci Gueneralp et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2014)
Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting
Anthony M. Filippi et al.
REMOTE SENSING LETTERS (2014)
Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery
G. Chirici et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2013)
Modeling Percent Tree Canopy Cover: A Pilot Study
John W. Coulston et al.
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING (2012)
COMPARISON OF VALUES OF PEARSON'S AND SPEARMAN'S CORRELATION COEFFICIENTS ON THE SAME SETS OF DATA
Jan Hauke et al.
QUAESTIONES GEOGRAPHICAE (2011)
Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches
Scott L. Powell et al.
REMOTE SENSING OF ENVIRONMENT (2010)
Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals
Simon J. Pittman et al.
JOURNAL OF COASTAL RESEARCH (2009)
Gradient modeling of conifer species using random forests
Jeffrey S. Evans et al.
LANDSCAPE ECOLOGY (2009)
A first map of tropical Africa's above-ground biomass derived from satellite imagery
A. Baccini et al.
ENVIRONMENTAL RESEARCH LETTERS (2008)
Woody cover in African savannas: the role of resources, fire and herbivory
Mahesh Sankaran et al.
GLOBAL ECOLOGY AND BIOGEOGRAPHY (2008)
A working guide to boosted regression trees
J. Elith et al.
JOURNAL OF ANIMAL ECOLOGY (2008)
Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
Jonathan Cheung-Wai Chan et al.
REMOTE SENSING OF ENVIRONMENT (2008)
Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods
Gretchen G. Moisen et al.
ECOLOGICAL MODELLING (2006)
Newer classification and regression tree techniques: Bagging and random forests for ecological prediction
AM Prasad et al.
ECOSYSTEMS (2006)
Random Forests for land cover classification
PO Gislason et al.
PATTERN RECOGNITION LETTERS (2006)
Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest)
RL Lawrence et al.
REMOTE SENSING OF ENVIRONMENT (2006)
Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees
J. R. Leathwick et al.
MARINE ECOLOGY PROGRESS SERIES (2006)
Afforestation and stream temperature in a temperate maritime environment
BW Webb et al.
HYDROLOGICAL PROCESSES (2006)
Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service
RE McRoberts et al.
CANADIAN JOURNAL OF FOREST RESEARCH (2005)
Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis
R Lawrence et al.
REMOTE SENSING OF ENVIRONMENT (2004)
Development of a 2001 National Land-Cover Database for the United States
C Homer et al.
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING (2004)
Comparing five modelling techniques for predicting forest characteristics
GG Moisen et al.
ECOLOGICAL MODELLING (2002)
Stochastic gradient boosting
JH Friedman
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)
Additive logistic regression: A statistical view of boosting
J Friedman et al.
ANNALS OF STATISTICS (2000)
Share Paper
