Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/1343
Title: COMPARISON OF TREE-BASED CLASSIFICATION ALGORITHMS IN MAPPING BURNED FOREST AREAS
Authors: Matcı, Dilek Küçük
Cömert, Resul
Avdan, Uğur
Keywords: tree-based algorithm
machine learning
remote sensing
dupervised vlassification
Landsat
Issue Date: 2020
Publisher: Assoc Surveyors Slovenia
Abstract: In this study, we compared the performance of tree-based classification algorithms - Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant.
URI: https://doi.org/10.15292/geodetski-vestnik.2020.03.348-360
https://hdl.handle.net/20.500.13087/1343
ISSN: 0351-0271
1581-1328
Appears in Collections:Havacılık Yönetimi Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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