A new machine learning approach in detecting the oil palm plantations using remote sensing data

Research output: Contribution to journalArticle

Abstract

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.

Details

Authors
  • Kaibin Xu
  • Jing Qian
  • Zengyun Hu
  • Zheng Duan
  • Chaoliang Chen
  • Jun Liu
  • Jiayu Sun
  • Shujie Wei
  • Xiuwei Xing
Organisations
External organisations
  • Jiangxi University of Science and Technology
  • Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences
  • TripleSAI Technology
  • Shenzhen Institutes of Advanced Technology, CAS
  • CAS Research Center for Ecology and Environment of Central Asia (RCEECA)
  • University of the Chinese Academy of Sciences
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Remote Sensing

Keywords

  • Land cover classification, Landsat, Oil palm detection, Random forest, Sentinel
Original languageEnglish
Article number236
Pages (from-to)1-17
Number of pages17
JournalRemote Sensing
Volume13
Issue number2
Publication statusPublished - 2021
Publication categoryResearch
Peer-reviewedYes