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.
Highlights
IntroductionA canopy gap is defined as a small opening within a continuous and relatively mature canopy, where trees are absent (i.e., non-forest gaps) or much smaller than their immediate neighbors (i.e., forest gaps) [1]
A canopy gap is defined as a small opening within a continuous and relatively mature canopy, where trees are absent or much smaller than their immediate neighbors [1]
This study focuses on answering the following three research questions: (1) how does the synergistic use of optical and light detection and ranging (LiDAR) data influence the quality of canopy gap segmentation; (2) what are the advantages of the synergistic use of optical and LiDAR data in the process of object-based canopy gap classification; and (3) to what extent can the quality of canopy gap segmentation affect the accuracy of object-based gap classification?
Summary
A canopy gap is defined as a small opening within a continuous and relatively mature canopy, where trees are absent (i.e., non-forest gaps) or much smaller than their immediate neighbors (i.e., forest gaps) [1]. Canopy gaps play an important role in forest regeneration, turnover, and overall dynamics of forest ecosystems [1]. In northern hardwood forests, for example, the size of gaps plays a critical role in controlling the regeneration of tree species that are not tolerant of deep shade [4]. In boreal mixedwood forests, Vepakomma et al [5] found that canopy gaps increased availability of abiotic resources within canopy gaps as well as up to 30 m into the surrounding forest
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