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Erratum: Kromer, R.A.; et al. A 4D Filtering and Calibration Technique for Small-Scale Point Cloud Change Detection with a Terrestrial Laser Scanner. Remote Sensing 2015, 7, 13029–13052
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Remote Sens. 2015, 7(12), 16917-16937; doi:10.3390/rs71215861

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

Beijing Laboratory of Water Resource Security, State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, 105 West Third Ring Road, Haidian District, Beijing 100048, China
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Academic Editors: Randolph H. Wynne and Prasad S. Thenkabail
Received: 16 October 2015 / Revised: 28 November 2015 / Accepted: 10 December 2015 / Published: 15 December 2015
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Abstract

Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes—classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images—were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%–82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%–20.2% for the CNU area and 4.7%–12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples. View Full-Text
Keywords: urban tree species classification; bi-temporal images; object-based method; support vector machine; random forest urban tree species classification; bi-temporal images; object-based method; support vector machine; random forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, D.; Ke, Y.; Gong, H.; Li, X. Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images. Remote Sens. 2015, 7, 16917-16937.

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