DOI: 10.5937/jaes18-25495
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.
Volume 18 article 678 pages: 207 - 215
Forest or land fire is a disaster that commonly occurred in Indonesia mainly in Kalimantan and Sumatera. Optical
remote sensing satellite becomes a promising technology that can be utilized to identify the burned area in quick time
for disaster management response.This study evaluated the use of supervised machine learning, such as Support
Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN) to classify burned area in the Central
Kalimantan province on June and August 2019 as pre-fire event and post-fire event using Sentinel-2 imageries.
An imbalanced and a balanced dataset with varying hyper-parameter were used on those classifiers. Hotspot data
derived from MODIS and Suomi NPP data are also used as training and testing dataset. Based on the study, the
imbalanced dataset influences precision and recall values, as well as the accuracy of SVM and DNN classifiers, but
not as much in RF. RF classifier outperforms SVM and DNN in terms of precision, recall, and accuracy for both a
balanced dataset and an imbalanced dataset with the accuracy ranged from 98.2 -99.3%. The accuracy of SVM classifier is ranged from 94.7-98.1% for an imbalanced dataset and 90.4 % - 98.2 % for a balanced dataset. Although the
high accuracy is still can be achieved in DNN classifier, there is a changing accuracy from 98.5-98.8 % in a balanced
dataset to 95.5-95.7% in an imbalanced dataset. These fi ndings imply that the high accuracy is still can be achieved
by SVM, RF, and DNN classifiers with an imbalanced or a balanced dataset.
This research is funded by Universitas Indonesia under
the PITQQ Grant number NKB-00321/UN2.R3.1/
HKP.05.00/2019. Also, the authors thank the Disaster
Division, Remote Sensing Application Center - Indonesian
National Institute of Aeronautics and Space for giving
valuable advice to this research.
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