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

Research output: Contribution to journal › Article

Standard

A new machine learning approach in detecting the oil palm plantations using remote sensing data. / Xu, Kaibin; Qian, Jing; Hu, Zengyun; Duan, Zheng; Chen, Chaoliang; Liu, Jun; Sun, Jiayu; Wei, Shujie; Xing, Xiuwei.

In: Remote Sensing, Vol. 13, No. 2, 236, 2021, p. 1-17.

Research output: Contribution to journal › Article

Harvard

Xu, K, Qian, J, Hu, Z, Duan, Z, Chen, C, Liu, J, Sun, J, Wei, S & Xing, X 2021, 'A new machine learning approach in detecting the oil palm plantations using remote sensing data', Remote Sensing, vol. 13, no. 2, 236, pp. 1-17. https://doi.org/10.3390/rs13020236

APA

Xu, K., Qian, J., Hu, Z., Duan, Z., Chen, C., Liu, J., Sun, J., Wei, S., & Xing, X. (2021). A new machine learning approach in detecting the oil palm plantations using remote sensing data. Remote Sensing, 13(2), 1-17. [236]. https://doi.org/10.3390/rs13020236

CBE

Xu K, Qian J, Hu Z, Duan Z, Chen C, Liu J, Sun J, Wei S, Xing X. 2021. A new machine learning approach in detecting the oil palm plantations using remote sensing data. Remote Sensing. 13(2):1-17. https://doi.org/10.3390/rs13020236

MLA

Xu, Kaibin et al. "A new machine learning approach in detecting the oil palm plantations using remote sensing data". Remote Sensing. 2021, 13(2). 1-17. https://doi.org/10.3390/rs13020236

Vancouver

Xu K, Qian J, Hu Z, Duan Z, Chen C, Liu J et al. A new machine learning approach in detecting the oil palm plantations using remote sensing data. Remote Sensing. 2021;13(2):1-17. 236. https://doi.org/10.3390/rs13020236

Author

Xu, Kaibin ; Qian, Jing ; Hu, Zengyun ; Duan, Zheng ; Chen, Chaoliang ; Liu, Jun ; Sun, Jiayu ; Wei, Shujie ; Xing, Xiuwei. / A new machine learning approach in detecting the oil palm plantations using remote sensing data. In: Remote Sensing. 2021 ; Vol. 13, No. 2. pp. 1-17.

RIS

TY - JOUR

T1 - A new machine learning approach in detecting the oil palm plantations using remote sensing data

AU - Xu, Kaibin

AU - Qian, Jing

AU - Hu, Zengyun

AU - Duan, Zheng

AU - Chen, Chaoliang

AU - Liu, Jun

AU - Sun, Jiayu

AU - Wei, Shujie

AU - Xing, Xiuwei

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Land cover classification

KW - Landsat

KW - Oil palm detection

KW - Random forest

KW - Sentinel

U2 - 10.3390/rs13020236

DO - 10.3390/rs13020236

M3 - Article

AN - SCOPUS:85099201626

VL - 13

SP - 1

EP - 17

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 2

M1 - 236

ER -