Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Dan Chen, Xianyun Fei (), Zhen Wang, Yajun Gao, Xiaowei Shen, Tingting Han and Yuanzhi Zhang
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Dan Chen: School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222002, China
Xianyun Fei: School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222002, China
Zhen Wang: Lianyungang Forestry Technical Guidance Station, Lianyungang 222005, China
Yajun Gao: Lianyungang Forestry Technical Guidance Station, Lianyungang 222005, China
Xiaowei Shen: School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222002, China
Tingting Han: School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222002, China
Yuanzhi Zhang: Key Laboratory of Lunar and Deep Space Exploration, National Astronomical Observatory, Chinese Academy of Sciences, Beijing 100101, China

Sustainability, 2022, vol. 14, issue 20, 1-13

Abstract: This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms.

Keywords: vegetation types; image classification; fused images; mountainous areas (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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