Articles | Volume 10, issue 2
https://doi.org/10.5194/nhess-10-305-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-10-305-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
17 Feb 2010
Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007
G. P. Petropoulos
University of Bristol, Department of Earth Sciences, Wills Memorial Building, Queens Road, BS8 1RJ, Bristol, UK
W. Knorr
University of Bristol, Department of Earth Sciences, Wills Memorial Building, Queens Road, BS8 1RJ, Bristol, UK
M. Scholze
University of Bristol, Department of Earth Sciences, Wills Memorial Building, Queens Road, BS8 1RJ, Bristol, UK
L. Boschetti
Department of Geography, University of Maryland, 2181 LeFrak Hall, College Park, MD 20740, USA
G. Karantounias
Agricultural University of Athens, Department of Natural Resources Development and Agricultural Engineering, 75, Iera Odos St., 118 55, Athens, Greece
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Cited
34 citations as recorded by crossref.
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- Evaluation of Multi-Spectral Band Efficacy for Mapping Wildland Fire Burn Severity from PlanetScope Imagery D. Hamilton et al. https://doi.org/10.3390/rs15215196
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- An intercomparison of burnt area estimates derived from key operational products: the Greek wildland fires of 2005–2007 D. Kalivas et al. https://doi.org/10.5194/npg-20-397-2013
- Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada G. Ireland & G. Petropoulos https://doi.org/10.1016/j.apgeog.2014.11.016
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- Burned area mapping in Mediterranean environment using medium-resolution multi-spectral data and a neuro-fuzzy classifier N. Mitrakis et al. https://doi.org/10.1080/19479832.2011.635604
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- Performance Analyzing of High Resolution Pan-sharpening Techniques: Increasing Image Quality for Classification using Supervised Kernel Support Vector Machine Y. . et al. https://doi.org/10.3923/rjit.2011.12.23
- Land Cover Dynamics in Beni Chougrane Mountains, North West of Algeria, Using Remote Sensing . Tayeb Si Tayeb & . Benabdeli Kheloufi https://doi.org/10.6000/1927-5129.2014.10.34
- GIS-based forest fire susceptibility modeling in Pauri Garhwal, India: a comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques A. Tiwari et al. https://doi.org/10.1007/s11069-020-04351-8
- Selection of classification techniques for land use/land cover change investigation P. Srivastava et al. https://doi.org/10.1016/j.asr.2012.06.032
- Determination of burn severity models ranging from regional to national scales for the conterminous United States J. Picotte et al. https://doi.org/10.1016/j.rse.2021.112569
- Crop type discrimination using Geo-Stat Endmember extraction and machine learning algorithms P. Singh et al. https://doi.org/10.1016/j.asr.2022.08.031
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- Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data G. Mallinis & N. Koutsias https://doi.org/10.1080/01431161.2011.648284
- Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing R. Banerjee & P. Srivastava https://doi.org/10.1016/j.landusepol.2013.03.005
- Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters S. Yousefi et al. https://doi.org/10.3390/land11070993
- A review of machine learning applications in wildfire science and management P. Jain et al. https://doi.org/10.1139/er-2020-0019
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