A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data

Dodi Sudiana, Anugrah Indah Lestari, Indra Riyanto, Mia Rizkinia, Rahmat Arief, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data.

Original languageEnglish
Article number728
JournalRemote Sensing
Volume15
Issue number3
DOIs
Publication statusPublished - Feb 2023

Keywords

  • burned area
  • convolutional neural network (CNN)
  • random forest (RF)
  • remote sensing
  • Sentinel-1
  • Sentinel-2
  • synthetic aperture radar (SAR)

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