A New Machine Learning Approach in Detecting the Oil Palm Plantations Using Remote Sensing Data
| Autor: | Jiayu Sun, Kaibin Xu, Jun Liu, Shujie Wei, Zengyun Hu, Chaoliang Chen, Jing Qian, Xiuwei Xing, Zheng Duan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Synthetic aperture radar
Science Vegetation Ecosystem services Random forest Statistical classification land cover classification Feature (computer vision) Deforestation Hyperparameter optimization General Earth and Planetary Sciences Environmental science oil palm detection Sentinel Landsat random forest Remote sensing |
| Zdroj: | Remote Sensing Volume 13 Issue 2 Pages: 236 Remote Sensing, Vol 13, Iss 236, p 236 (2021) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13020236 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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