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Journals / International Journal of Engineering and Geosciences / 2019 / Cilt: 4 - Sayı: 2

Object based burned area mapping with random forest algorithm

Dilek KÜÇÜK MATCI
Resul CÖMERT
Uğur AVDAN
Pages
78–87
Publication Date
2022-10-19
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results, we obtained 0.089 commission error (CE) with 0.014 omission error (OE).

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:According to the results, we obtained 0.089 commission error (CE) with 0.014 omission error (OE).
🔬Design:Modeling & Simulation
📍Scope:2016 Verileri
⚙️Variables:Random forest · Burned area mapping · Object based images analysis

Abstract

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.

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Cite This Article

MATCI, D. K., CÖMERT, R., AVDAN, U. (2022). Object based burned area mapping with random forest algorithm. International Journal of Engineering and Geosciences, Cilt: 4 - Sayı: 2, 78–87. https://ajindex.com/articles/540722

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