FUZZY LOGIC BASED BURNED SEVERITY CLASSIFICATION AND MAPPING WITH LANDSAT-8 DATA

Wildfire has a strong effect on both land use and land cover so that every year, thousands of hectares of forests, farms, and urban infrastructure are destroyed. Mapping and estimation of damages are crucial for planning and decision making. The aim of this study is classification and mapping burn s...

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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-2/W16; pp. 259 - 265
Main Authors Valipour Shokouhi, B., Eslami, M.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 17.09.2019
Copernicus Publications
Subjects
Classification
Decision making
Feature recognition
Fuzzy logic
Land cover
Land use
Landsat
Landsat satellites
Mapping
Remote sensing
Satellite imagery
Training
Urban agriculture
Wildfires
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Summary:Wildfire has a strong effect on both land use and land cover so that every year, thousands of hectares of forests, farms, and urban infrastructure are destroyed. Mapping and estimation of damages are crucial for planning and decision making. The aim of this study is classification and mapping burn severity using multi-temporal Landsat data and well-known burn severity indices including Normalized Burn Ratio (NBR) and Burned Area Index (BAI) calculated for pre- and post- Landsat 8 images. Subtracted images such as dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio) and dBAI (Difference Burned Area Index) were produced on the bases of indices as classification input. Among classification methods, the fuzzy supervised classification was utilized with three classes. The result shows the strong performance of the Fuzzy Logic system (FLS) in the detection of the area with the limited number of training data so that the average accuracy of the classes is 85%; plus, from the human logic perspective, the result was meaningful so that recognition of the features and changes visually were understandable.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-2-W16-259-2019