文献类型：期刊文献

英文题名：Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data

作者：Liu, Luxia[1,4] Coops, Nicholas C.[2] Aven, Neal W.[3] Pang, Yong[4]

第一作者：Liu, Luxia

通信作者：Liu, LX[1]

机构：[1]Anhui Agr Univ, Sch Forestry & Landscape Architecture, Hefei 230036, Anhui, Peoples R China;[2]Univ British Columbia, Fac Forestry, Dept Forest Resources Management, Vancouver, BC, Canada;[3]Pk Div, Urban Forestry, City Of Surrey, BC, Canada;[4]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2017

卷号：200

起止页码：170-182

外文期刊名：REMOTE SENSING OF ENVIRONMENT

收录：;EI(收录号：20173704146407);Scopus(收录号：2-s2.0-85028948905);WOS:【SSCI(收录号:WOS:000412607600013)，SCI-EXPANDED(收录号:WOS:000412607600013)】；

基金：Funding for this project was provided by an NSERC Discovery (RGPIN 311926-13) grant to Coops and NSERC Engage and Engage + and CRD (EGP 462042-13, EGP2 476350-14 and CRDPJ 488240-15 respectively) collaboration grants with Surrey City Energy. We thank Andrew Plowright, Txomin Hermosilla, Xuan Guo, Curtis Chance and Henry Flanagan for research insights and editorial assistance. We also thank the anonymous reviewers for helpful suggestions.

语种：英文

外文关键词：Urban tree species classification; Airborne LiDAR; Airborne hyperspectral; Random forest; British Columbia, Canada

摘要：Mapping tree species within urban areas is essential for sustainable urban planning as well as to improve our understanding of the role of urban vegetation as an ecological service. Urban trees contribute significantly in mitigating the urban heat island effect and supporting biodiversity. However, accurate and up-to-date mapping of urban tree species is difficult because of the time-consuming nature of field sampling, fine-scale spatial variation, and potentially high species diversity. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) with high pulse density (25 point/m(2)) and hyperspectral imagery offer two different yet complementary approaches to estimating crown structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, we evaluate the potential of these technologies to map 15 common urban tree species using a Random Forest (RF) classifier in the City of Surrey, British Columbia, Canada. LiDAR-derived crown structural information was combined with hyper spectral-derived spectral vegetation indices for species classification. Results indicate an overall accuracy of 51.1%, 61.0%, and 70.0% using hyperspectral, LiDAR and the combined data respectively. The overall accuracy for the two most important and iconic native coniferous species improved markedly from 78.3% up to 91% using the combined data. The results of this research highlight that (1) the combination of structural and spectral information provided an improved classification accuracy than when used separately, and variables derived from LiDAR data contributed more to the accurate prediction of species than hyperspectral features; (2) higher classification accuracies were observed for evergreen species, species with distinguishable crown structure, and species undergoing flowering; (3) and finally the anthocyanin content index and photochemical reflectance index were the most important hyperspectral features for the discrimination of tree species in the spring bud burst stage.