globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2013.04.006
Scopus记录号: 2-s2.0-84888254842
论文题名:
Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery
作者: Chirici G; , Scotti R; , Montaghi A; , Barbati A; , Cartisano R; , Lopez G; , Marchetti M; , Mcroberts R; E; , Olsson H; , Corona P
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 25, 期:1
起始页码: 87
结束页码: 97
语种: 英语
英文关键词: Airborne laser scanning ; Classification and regression trees ; Forest fires ; Forest fuel type mapping ; IRS LISS-III imagery ; Mediterranean forests
Scopus关键词: fire management ; forest canopy ; forest cover ; forest fire ; laser method ; mapping ; stochasticity ; tree ; Italy ; Mediterranean Region ; Sicily
英文摘要: 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. © 2013 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79855
Appears in Collections:气候变化事实与影响

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作者单位: Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, C.da F.te Lappone, snc, 86090 Pesche, Isernia, Italy; Dipartimento di Agraria (Nuoro Forestry School), Università degli Studi di Sassari, Via Cristoforo Colombo 1, 08100 Nuoro, Italy; Department of Forest Resource Management, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden; Dipartimento per la Innovazione nei Sistemi Biologici, Agroalimentari e Forestali (DIBAF), Università degli Studi della Tuscia, via San Camillo de Lellis, snc, 01100 Viterbo, Italy; Northern Research Station, U.S. Forest Service, Saint Paul, MN, United States

Recommended Citation:
Chirici G,, Scotti R,, Montaghi A,et al. Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,25(1)
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