abstract
- Microform is important in understanding wetland functions and processes. But collecting imagery of and mapping the physical structure of peatlands is often expensive and requires specialized equipment. We assessed the utility of coupling computer vision-based structure from motion with multiview stereo photogrammetry (SfM-MVS) and ground-based photos to map peatland topography. The SfM-MVS technique was tested on an alpine peatland in Banff National Park, Canada, and guidance was provided on minimizing errors. We found that coupling SfM-MVS with ground-based photos taken with a point and shoot camera is a viable and competitive technique for generating ultrahigh-resolution elevations (i.e., <0.01 m, mean absolute error of 0.083 m). In evaluating 100+ viable SfM-MVS data collection and processing scenarios, vegetation was found to considerably influence accuracy. Vegetation class, when accounted for, reduced absolute error by as much as 50%. The logistic flexibility of ground-based SfM-MVS paired with its high resolution, low error, and low cost makes it a research area worth developing as well as a useful addition to the wetland scientists' toolkit.