Machine learning approaches for burned area identification using sentinel-2 in central kalimantan

Anugrah Indah Lestari, Dyah Lalita Luhurkinanti, Hajar Indah Fitriasari, Ruki Harwahyu, Riri Fitri Sari

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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 findings imply that the high accuracy is still can be achieved by SVM, RF, and DNN classifiers with an imbalanced or a balanced dataset.

Original languageEnglish
Pages (from-to)207-215
Number of pages9
JournalJournal of Applied Engineering Science
Volume18
Issue number2
DOIs
Publication statusPublished - 2020

Keywords

  • Burned area
  • Classification
  • Deep neural network
  • Machine learning
  • Random forest
  • Remote sensing satellite
  • Support vector machine

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