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|Title:||Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery||Authors:||Chirici, Gherardo
|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||Source:||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. 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.
|Appears in Collections:||DiSAFRi - Archivio della produzione scientifica|
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checked on Oct 23, 2020
checked on Oct 23, 2020
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