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International Journal of Image and Data Fusion Volume 3, 2012 - Issue 4
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Research Articles

Burned area mapping in Mediterranean environment using medium-resolution multi-spectral data and a neuro-fuzzy classifier

Nikolaos E. Mitrakis Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece; European Commission, Joint Research Centre, Institute for Protection and Security of the Citizen, Maritime Affairs Unit G.04, TP 051, 21027 Ispra (VA), Italy
,
Giorgos Mallinis School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Box 248, GR-54124, Thessaloniki, GreeceCorrespondence[email protected]
,
Nikos Koutsias Department of Environmental and Natural Resources Management, University of Ioannina, 30100, Agrinio, Greece
&
John B. Theocharis Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
Pages 299-318 | Received 21 Apr 2011, Accepted 12 Oct 2011, Published online: 30 Nov 2011

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Rui Zhang, John J. Qu, Yongqiang Liu, Xianjun Hao, Chengquan Huang & Xiwu Zhan. (2015) Detection of burned areas from mega-fires using daily and historical MODIS surface reflectance. International Journal of Remote Sensing 36:4, pages 1167-1187.
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Georgia Galidaki & Ioannis Gitas. (2015) Mediterranean forest species mapping using classification of Hyperion imagery. Geocarto International 30:1, pages 48-61.
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Articles from other publishers (9)

Jesús N.S. Rubí, Paulo H.P. de Carvalho & Paulo R.L. Gondim. (2023) Application of machine learning models in the behavioral study of forest fires in the Brazilian Federal District region. Engineering Applications of Artificial Intelligence 118, pages 105649.
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Janine Florath & Sina Keller. (2022) Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area. Remote Sensing 14:3, pages 657.
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Xikun Hu, Yifang Ban & Andrea Nascetti. (2021) Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning. Remote Sensing 13:8, pages 1509.
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Piyush JainSean C.P. CooganSriram Ganapathi SubramanianMark CrowleySteve TaylorMike D. Flannigan. (2020) A review of machine learning applications in wildfire science and management. Environmental Reviews 28:4, pages 478-505.
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Resul ÇÖMERT, Dilek Küçük MATCI & Uğur AVDAN. (2019) Object based burned area mapping with random forest algorithm. International Journal of Engineering and Geosciences 4:2, pages 78-87.
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S Costafreda-Aumedes, A Cardil, DM Molina, SN Daniel, R Mavsar & C Vega-Garcia. (2016) Analysis of factors influencing deployment of fire suppression resources in Spain using artificial neural networks. iForest - Biogeosciences and Forestry 9:1, pages 138-145.
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Eleni Dragozi, Ioannis Gitas, Dimitris Stavrakoudis & John Theocharis. (2014) Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery. Remote Sensing 6:12, pages 12005-12036.
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Chaoyang Wu, David Gaumont-Guay, T. Andrew Black, Rachhpal S. Jassal, Shiguang Xu, Jing M. Chen & Alemu Gonsamo. (2014) Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan, Canada. ISPRS Journal of Photogrammetry and Remote Sensing 94, pages 80-90.
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Pei Zhou, Jinliang Huang, Robert PontiusJr.Jr. & Huasheng Hong. (2014) Land Classification and Change Intensity Analysis in a Coastal Watershed of Southeast China. Sensors 14:7, pages 11640-11658.
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