Mapping vegetation functional types in urban areas with WorldView-2 imagery: Integrating object-based classification with phenology

《Mapping vegetation functional types in urban areas with WorldView-2 imagery: Integrating object-based classification with phenology》

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关键字: Urban land cover; Vegetation phenology; Vegetation functional type; WorldView-2 imagery; Multi-seasonal images; LAND-COVER CLASSIFICATION; RESOLUTION SATELLITE IMAGERY; TREE SPECIES CLASSIFICATION; SURFACE PHENOLOGY; IKONOS IMAGERY; TIME-SERIES; LIDAR DAT

作者单位: [Yan, Jingli; Zhou, Weiqi; Han, Lijian; Qian, Yuguo] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China. [Yan, Jingli; Zhou, Weiqi; Han, Lijian; Qian, Yuguo] Univ Chinese Acad Sci, Beijing 100049, Peoples R China. Zhou, WQ (reprint author), Shuangqing Rd 18, Beijing 100085, Peoples R China. E-Mail: wzhou@rcees.ac.cn

摘要: Mapping urban vegetation is a prerequisite to accurately understanding landscape patterns and ecological services provided by urban vegetation. However, the uncertainties in fine-scale vegetation biodiversity mapping still exist in capturing vegetation functional types efficiently at fine scale. To facilitate the application of fine-scale vegetation spatial configuration used for urban landscape planning and ecosystem service valuation, we present an approach integrating object-based classification with vegetation phenology for fine-scale vegetation functional type mapping in compact city of Beijing, China. The phenological information derived from two WorldView-2 imagery scenes, acquired on 14 September 2012 and 26 November 2012, was used to aid in the classification of tree functional types and grass. Then we further compared the approach to that of using only one WorldView imagery. We found WorldView-2 imagery can be successfully applied to map functional types of urban vegetation with its high spatial resolution and relatively high spectral resolution. The application of the vegetation phenology into classification greatly improved the overall accuracy of classification from 82.3% to 91.1%. In particular, the accuracies of vegetation types was improved by from 10% to 13.26%. The approach integrating vegetation phenology with high-resolution remote sensed images provides an efficient tool to incorporate multi-temporal data into fine-scale urban classification.