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

Xu, Kaibin ; Qian, Jing ; Hu, Zengyun ; Duan, Zheng LU ; Chen, Chaoliang ; Liu, Jun ; Sun, Jiayu ; Wei, Shujie and Xing, Xiuwei (2021) In Remote Sensing 13(2). p.1-17
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... (More)

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.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Land cover classification, Landsat, Oil palm detection, Random forest, Sentinel
in
Remote Sensing
volume
13
issue
2
article number
236
pages
17 pages
publisher
MDPI AG
external identifiers
  • scopus:85099201626
ISSN
2072-4292
DOI
10.3390/rs13020236
language
English
LU publication?
yes
id
6d2ed3e1-0a9b-48fb-8e1f-0a775c81d7a0
date added to LUP
2021-01-25 10:27:48
date last changed
2023-02-21 10:38:15
@article{6d2ed3e1-0a9b-48fb-8e1f-0a775c81d7a0,
  abstract     = {{<p>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.</p>}},
  author       = {{Xu, Kaibin and Qian, Jing and Hu, Zengyun and Duan, Zheng and Chen, Chaoliang and Liu, Jun and Sun, Jiayu and Wei, Shujie and Xing, Xiuwei}},
  issn         = {{2072-4292}},
  keywords     = {{Land cover classification; Landsat; Oil palm detection; Random forest; Sentinel}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{1--17}},
  publisher    = {{MDPI AG}},
  series       = {{Remote Sensing}},
  title        = {{A new machine learning approach in detecting the oil palm plantations using remote sensing data}},
  url          = {{http://dx.doi.org/10.3390/rs13020236}},
  doi          = {{10.3390/rs13020236}},
  volume       = {{13}},
  year         = {{2021}},
}