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photo of Zheng Duan on Lund webpage

Zheng Duan

Associate senior lecturer

photo of Zheng Duan on Lund webpage

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

Author

  • Kaibin Xu
  • Jing Qian
  • Zengyun Hu
  • Zheng Duan
  • Chaoliang Chen
  • Jun Liu
  • Jiayu Sun
  • Shujie Wei
  • Xiuwei Xing

Summary, in English

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.

Department/s

  • Dept of Physical Geography and Ecosystem Science

Publishing year

2021

Language

English

Pages

1-17

Publication/Series

Remote Sensing

Volume

13

Issue

2

Document type

Journal article

Publisher

MDPI AG

Topic

  • Remote Sensing

Keywords

  • Land cover classification
  • Landsat
  • Oil palm detection
  • Random forest
  • Sentinel

Status

Published

ISBN/ISSN/Other

  • ISSN: 2072-4292