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

Chirici, Gherardo and Scotti, Roberto and Montaghi, Alessandro and Barbati, Anna and Cartisano, Rosaria and Lopez, G. and Marchetti, Marco and McRoberts, Ron E. and Olsson, Håkan and Corona, Piermaria (2013) Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery. Interrnational Journal of Applied Earth Observation and Geoinformation, Vol. 25 , p. 87-97. ISSN 1569-8432. eISSN 1872-826X. Article.

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DOI: 10.1016/j.jag.2013.04.006

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. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.

Item Type:Article
ID Code:9862
Status:Published
Refereed:Yes
Uncontrolled Keywords:Airborne laser scanning, IRS LISS-III imagery, forest fuel type mapping, forest fires, Mediterranean forests, classification and regression trees
Subjects:Area 07 - Scienze agrarie e veterinarie > AGR/05 Assestamento forestale e selvicoltura
Divisions:001 Università di Sassari > 01-a Nuovi Dipartimenti dal 2012 > Agraria
Publisher:Elsevier
ISSN:1569-8432
eISSN:1872-826X
Deposited On:10 Jun 2014 11:36

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