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Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images

School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222002, China
School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
National Astronomical Observatories, Key Lab of Lunar Science and Deep-Space Exploration, Chinese Academy of Science, Beijing 100101, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1927;
Received: 30 June 2019 / Revised: 9 August 2019 / Accepted: 13 August 2019 / Published: 17 August 2019
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Wetlands are one of the world’s most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang—Worldview-2 and Landsat-8 images—were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are. View Full-Text
Keywords: coastal wetland; classification; RF algorithm coastal wetland; classification; RF algorithm
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MDPI and ACS Style

Wang, X.; Gao, X.; Zhang, Y.; Fei, X.; Chen, Z.; Wang, J.; Zhang, Y.; Lu, X.; Zhao, H. Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images. Remote Sens. 2019, 11, 1927.

AMA Style

Wang X, Gao X, Zhang Y, Fei X, Chen Z, Wang J, Zhang Y, Lu X, Zhao H. Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images. Remote Sensing. 2019; 11(16):1927.

Chicago/Turabian Style

Wang, Xiaoxue, Xiangwei Gao, Yuanzhi Zhang, Xianyun Fei, Zhou Chen, Jian Wang, Yayi Zhang, Xia Lu, and Huimin Zhao. 2019. "Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images" Remote Sensing 11, no. 16: 1927.

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