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Remote Sens. 2015, 7(12), 15917-15932; doi:10.3390/rs71215811

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

1
Department of Geography, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada
2
Forest Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, 1235 Queen Street East, Sault Ste Marie, ON, P6A 2E5, Canada
3
Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada
4
Department of Geography, University of Toronto Mississauga, 3359 Mississauga Rd North, Mississauga, ON L5L 1C6, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Compton Tucker, Xiaofeng Li and Prasad S. Thenkabail
Received: 5 October 2015 / Revised: 4 November 2015 / Accepted: 19 November 2015 / Published: 27 November 2015
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [2358 KB, uploaded 27 November 2015]   |  

Abstract

Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28% ± 6.16% vs. 68.54% ± 9.03% and 64.51% ± 11.32%, separately). High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications. View Full-Text
Keywords: canopy gap segmentation; classification; Object-Based Image Analysis (OBIA); high spatial resolution; multispectral image; LiDAR; data integration canopy gap segmentation; classification; Object-Based Image Analysis (OBIA); high spatial resolution; multispectral image; LiDAR; data integration
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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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MDPI and ACS Style

Yang, J.; Jones, T.; Caspersen, J.; He, Y. Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data. Remote Sens. 2015, 7, 15917-15932.

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