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Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2621

Title: Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery
Authors: Chirici, Gherardo
Scotti, Roberto
Montaghi, Alessandro
Barbati, Anna
Cartisano, Rosaria
Lopez, Giovanni
Marchetti, Marco
McRoberts, Ronald
Olsson, Hakan
Corona, Piermaria
Keywords: Airborne laser scanning
IRS LISS-III imagery
Forest fuel type mapping
Forest fires
Mediterranean forests
Classification and regression trees
Issue Date: 2013
Publisher: Elsevier
Citation: Chirici, G. et al. 2013. Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery. "International journal of applied earth observation and geoinformation" 25: 87–97
Abstract: This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km2 and 487 km2 in Sicily (Italy),16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork.
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DOI: 10.1016/j.jag.2013.04.006
URI: http://www.sciencedirect.com/science/article/pii/S0303243413000494
http://hdl.handle.net/2067/2621
ISSN: 0303-2434
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