TY  - JOUR
TI  - OBJECT BASED BURNED AREA MAPPING WITH RANDOM FOREST ALGORITHM
AB  - It is very important to map the burned forest areas economically, quickly and with the high accuracy ofissues such as damage assessment studies, fire risk analysis, and management of forest regeneration processes. Remotesensing methods give advantages such as fast, easy-to-use and high accuracy for burned area mapping. Recent yearsmachine learning algorithms have become more popular in satellite image classification, due to the effective solutions forthe analysis of complex datasets which have a large number of variables. In this study, the success of object based randomforest algorithm was investigated for burned forest area mapping. For this purpose, Object based image analysis (OBIA)was performed using Landsat 8 image of the Adrasan and Kumluca fires which occurred in 24 – 27 June 2016. The studyconsisted of five steps. In the first step, the multi-resolution image segmentation was performed for obtaining image objectsfrom Landsat 8 spectral bands. In the second step, the image object metrics such as spectral index and layer values werecalculated for all image objects. In the third step, a random forest classifier model was developed. Then, the developedmodel applied to the test site for classification of the burned area. Finally, the obtained results evaluated with confusionmatrix based on the randomly sampled points. According to the results, we obtained 0.089 commission error (CE) with0.014 omission error (OE). An overall accuracy was obtained as 0.99. The results show that this approach is very useful tobe used to determine burned forest areas.
AU  - Avdan, Ugur
AU  - KÜÇÜK MATCI, DİLEK
AU  - Comert, Resul
DO  - 10.26833/ijeg.455595
PY  - 2019
JO  - International Journal of Engineering and Geosciences
VL  - 4
IS  - 2
SN  - 2548-0960
SP  - 78
EP  - 87
DB  - TRDizin
UR  - http://search/yayin/detay/334330
ER  -