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

Remote Sensing

Jian Yang³  Trevor Jones⁴  John Caspersen¹  Yuhong He²  Sangram Ganguly⁵  Compton Tucker⁵  Xiaofeng Li⁵ 

[1] Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada;Department of Geography, University of Toronto Mississauga, 3359 Mississauga Rd North, Mississauga, ON L5L 1C6, Canada;;Department of Geography, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, CanadaForest Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, 1235 Queen Street East, Sault Ste Marie, ON, P6A 2E5, Canada;;id="af1-remotesensing-07-15811">1Department of Geography, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Cana

关键词: canopy gap segmentation;    classification;    Object-Based Image Analysis (OBIA);    high spatial resolution;    multispectral image;    LiDAR;    data integration;   

DOI  :  10.3390/rs71215811

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.