It is very important to map the burned forest areas economically, quickly and with the high accuracy of issues such as damage assessment studies, fire risk analysis, and management of forest regeneration processes. Remote sensing methods give advantages such as fast, easy-to-use and high accuracy for burned area mapping. Recent years machine learning algorithms have become more popular in satellite image classification, due to the effective solutions for the analysis of complex datasets which have a large number of variables. In this study, the success of object based random forest 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 study consisted of five steps. In the first step, the multi-resolution image segmentation was performed for obtaining image objects from Landsat 8 spectral bands. In the second step, the image object metrics such as spectral index and layer values were calculated for all image objects. In the third step, a random forest classifier model was developed. Then, the developed model applied to the test site for classification of the burned area. Finally, the obtained results evaluated with confusion matrix based on the randomly sampled points. According to the results, we obtained 0.089 commission error (CE) with 0.014 omission error (OE). An overall accuracy was obtained as 0.99. The results show that this approach is very useful to be used to determine burned forest areas.
Buildings are most affected the objects by earthquake disaster. Detection of collapsed buildings after an earthquake is important both for determining the current situation and quick response. Unmanned aerial vehicles that have evolved in recent years, can provide very high resolution images of the earth surface using camera systems attached to them. Information for the intended purpose can be obtained through the products produced from these images.In this study, collapsed buildings were detected in the area where high-resolution images were obtained whit unmanned aerial vehicle in 2015 and 2014. Building detection process was made based on a scenario events. In this context, 2015 images were taken before the earthquake and 2014 images were taken after the earthquake. The images of both years were processed separately to produce the digital elevation model and orthophoto image of the study area. building of the study area were obtained by applying the object-based classification process to the generated data. 11 buildings which were available in the area in 2015 and not available in the area in 2014, were detected successfully comparison of building classes of two years.
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