Integration of ZiYuan-3 multispectral and stereo imagery for mapping urban vegetation using the hierarchy-based classifier

ZiYuan-3 Environmental sciences Physical geography Hierarchy-based classifier 11. Sustainability 0401 agriculture, forestry, and fisheries GE1-350 04 agricultural and veterinary sciences 15. Life on land Urban vegetation GB3-5030
DOI: 10.1016/j.jag.2021.102594 Publication Date: 2021-10-29T07:28:36Z
ABSTRACT
Urban vegetation has important impacts on urban heat island, human living environments and even quality of life. The areal increase of urban vegetation has great contribution in achieving Sustainable Development Goals (SDGs) of United Nations. It is needed to accurately extract different urban vegetation types using high spatial resolution images, but the limitation of remotely sensed data and complexity of urban landscapes make it challenging. This research aims to explore the integration of multispectral and stereo imagery with high spatial resolution for vegetation classification in the urban landscape in East China. A hierarchy-based classifier based on optimization of selected variables in each tree node is developed to conduct urban vegetation classification through incorporation of canopy height features into spectral and textural data. The results show that use of canopy height features improved overall classification accuracy of 4.6% comparing with the dataset without use of canopy height features. The proposed hierarchy-based classifier can further improve the vegetation classification accuracy by 3% comparing with random forest. This research indicates that selection of proper variables from different source data, especially canopy height features, plays important roles in improving urban vegetation classification. This research provides a new insight for accurate urban vegetation classification using a hierarchy-based classification approach based on integration of spectral, spatial and canopy features.
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