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Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data


Koetz, B; Morsdorf, Felix; van der Linde, S; Curt, T; Allgöwer, Britta (2008). Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. Forest Ecology and Management, 256(3):263-271.

Abstract

Forest fire management practices are highly dependent on the proper monitoring of the spatial distribution of the natural and man-made fuel complexes at landscape level. Spatial patterns of fuel types as well as the three-dimensional structure and state of the vegetation are essential for the assessment and prediction of forest fire risk and fire behaviour. A combination of the two remote sensing systems, imaging spectrometry and light detection and ranging (LiDAR), is well suited to map fuel types and
properties, especially within the complex wildland–urban interface. LiDAR observations sample the spatial information dimension providing explicit geometric information about the structure of the Earth’s surface and super-imposed objects. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of surface types. As a non-parametric classifier support vector machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple sources as proposed in this work. The presented approach achieves an improved land cover
mapping adapted to forest firemanagement needs. The map is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.

Abstract

Forest fire management practices are highly dependent on the proper monitoring of the spatial distribution of the natural and man-made fuel complexes at landscape level. Spatial patterns of fuel types as well as the three-dimensional structure and state of the vegetation are essential for the assessment and prediction of forest fire risk and fire behaviour. A combination of the two remote sensing systems, imaging spectrometry and light detection and ranging (LiDAR), is well suited to map fuel types and
properties, especially within the complex wildland–urban interface. LiDAR observations sample the spatial information dimension providing explicit geometric information about the structure of the Earth’s surface and super-imposed objects. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of surface types. As a non-parametric classifier support vector machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple sources as proposed in this work. The presented approach achieves an improved land cover
mapping adapted to forest firemanagement needs. The map is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:Forest fire management Land cover classification Hyperspectral LiDAR Support vector machines Multi-sensor fusion
Language:English
Date:April 2008
Deposited On:21 Jan 2009 13:28
Last Modified:05 Apr 2016 12:39
Publisher:Elsevier
ISSN:0378-1127
Publisher DOI:https://doi.org/10.1016/j.foreco.2008.04.025

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