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

• Published in: Forest ecology and management 2008-07, Vol.256 (3), p.263-271
• Main Authors: Koetz, B., Morsdorf, F., van der Linden, S., Curt, T., Allgöwer, B.
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; classification; Environmental Sciences; Forest and land fires; Forest fire management; fuels (fire ecology); Fundamental and applied biological sciences. Psychology; Hyperspectral; hyperspectral imagery; image analysis; Land cover classification; LiDAR; Multi-sensor fusion; Phytopathology. Animal pests. Plant and forest protection; remote sensing; spatial data; spectral analysis; Support vector machines; Synecology; Terrestrial ecosystems; vegetation cover; Weather damages. Fires
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2008.04.025

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 fire management needs. The map is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.