Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.
Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.
Key Laboratory of remote sensing monitoring of geographic environment, College of Heilongjiang Province, Harbin Normal University, No. 1 Shida Road, Limin Economic Development Zone, Harbin, Heilongjiang, 150025, People's Republic of China.
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Key Laboratory of remote sensing monitoring of geographic environment, College of Heilongjiang Province, Harbin Normal University, No. 1 Shida Road, Limin Economic Development Zone, Harbin, Heilongjiang, 150025, People's Republic of China. zsy6311@163.com.
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Zang
Department of Geography, University of Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI, 53201-0413, USA.
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Department of Geography, University of Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI, 53201-0413, USA.
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D000383:Agriculture; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D011825:Radar; D045483:Rivers / Q000737:chemistry*; D053833:Wetlands
Forested wetland; Object-based; Random forest; Remote sensing; SAR imagery