Introducing Close-Range Photogrammetry for Characterizing Forest Understory Plant Diversity and Surface Fuel Structure at Fine Scales. Issue 5 (2nd September 2016)

• Record Type: Journal Article
• Authors: Bright, Benjamin C.; Loudermilk, E. Louise; Pokswinski, Scott M.; Hudak, Andrew T.; O'Brien, Joseph J.
• Is Part Of: Canadian journal of remote sensing. Volume 42:Issue 5(2016)
• Page Start: 460
• Page End: 472
• Publication Date: 2016-09-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2016.1229598
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: Methods characterizing fine-scale fuels and plant diversity can advance understanding of plant-fire interactions across scales and help in efforts to monitor important ecosystems such as longleaf pine ( Pinus palustris Mill.) forests of the southeastern United States. Here, we evaluate the utility of close-range photogrammetry for measuring fuels and plant diversity at fine scales (submeter) in a longleaf pine forest. We gathered point-intercept data of understory plants and fuels on nine 3-m 2 plots at a 10-cm resolution. For these same plots, we used close-range photogrammetry to derive 3-dimensional (3D) point clouds representing understory plant height and color. Point clouds were summarized into distributional height and density metrics. We grouped 100 cm 2 cells into fuel types, using cluster analysis. Comparison of photogrammetry heights with point-intercept measurements showed that photogrammetry points were weakly to moderately correlated to plant and fuel heights (r = 0.19–0.53). Mann–Whitney pairwise tests evaluating separability of fuel types, species, and plant types in terms of photogrammetry metrics were significant 44%, 41%, and 54% of the time, respectively. Overall accuracies using photogrammetry metrics to classify fuel types, species, and plant types were 44%, 39%, and 44%, respectively. This research introduces a new methodology for characterizing fine-scale 3D surface vegetation and fuels.