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Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta.
- Source :
- Remote Sensing; Aug2019, Vol. 11 Issue 16, p1919-1919, 1p
- Publication Year :
- 2019
-
Abstract
- Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km<superscript>2</superscript> expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m<superscript>2</superscript>, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m<superscript>2</superscript> and 159,848 m<superscript>2</superscript>, respectively) compared to LiDAR (245,920 m<superscript>2</superscript>). Our results illustrate DAP's sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology's capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 11
- Issue :
- 16
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 138317974
- Full Text :
- https://doi.org/10.3390/rs11161919