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Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data

School of Land Sciences and Technology, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
Remote Sensing and Geospatial Analysis Laboratory, Precision Forestry Cooperative, School of Environnemental and Forest Sciences, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA
Bing Maps, Microsoft, 555 110th Ave NE, Bellevue, WA 98004, USA
Department of Geosciences and Natural Resources, College of Arts and Sciences, Western Carolina University, Cullowhee, NC 28723, USA
Author to whom correspondence should be addressed.
Academic Editors: Chris Hopkinson, Laura Chasmer and Craig Mahoney
Forests 2016, 7(6), 122;
Received: 15 April 2016 / Revised: 3 June 2016 / Accepted: 4 June 2016 / Published: 11 June 2016
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
In precision forestry, tree species identification is key to evaluating the role of forest ecosystems in the provision of ecosystem services, such as carbon sequestration and assessing their effects on climate regulation and climate change. In this study, we investigated the effectiveness of tree species classification of urban forests using aerial-based HyMap hyperspectral imagery and light detection and ranging (LiDAR) data. First, we conducted an object-based image analysis (OBIA) to segment individual tree crowns present in LiDAR-derived Canopy Height Models (CHMs). Then, hyperspectral values for individual trees were extracted from HyMap data for band reduction through Minimum Noise Fraction (MNF) transformation which allowed us to reduce the data to 20 significant bands out of 118 bands acquired. Finally, we compared several different classifications using Random Forest (RF) and Multi Class Classifier (MCC) methods. Seven tree species were classified using all 118 bands which resulted in 46.3% overall classification accuracy for RF versus 79.6% for MCC. Using only the 20 optimal bands extracted through MNF, both RF and MCC achieved an increase in overall accuracy to 87.0% and 88.9%, respectively. Thus, the MNF band selection process is a preferable approach for tree species classification when using hyperspectral data. Further, our work also suggests that RF is heavily disadvantaged by the high-dimensionality and noise present in hyperspectral data, while MCC is more robust when handling high-dimensional datasets with small sample sizes. Our overall results indicated that individual tree species identification in urban forests can be accomplished with the fusion of object-based LiDAR segmentation of crowns and hyperspectral characterization. View Full-Text
Keywords: LiDAR; hyperspectral; tree species classification; urban forests LiDAR; hyperspectral; tree species classification; urban forests
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MDPI and ACS Style

Zhang, Z.; Kazakova, A.; Moskal, L.M.; Styers, D.M. Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data. Forests 2016, 7, 122.

AMA Style

Zhang Z, Kazakova A, Moskal LM, Styers DM. Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data. Forests. 2016; 7(6):122.

Chicago/Turabian Style

Zhang, Zhongya, Alexandra Kazakova, Ludmila M. Moskal, and Diane M. Styers 2016. "Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data" Forests 7, no. 6: 122.

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