Abstract
We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon PowerShot A800 digital camera mounted on an unmanned aerial system. Image mosaicking and dense point matching were accomplished using Agisoft PhotoScan. The resulting point cloud was classified according to ground/non-ground classes and used to interpolate a high resolution DTM which was both compared to a DTM from an existing lidar dataset and used to model vegetation height for fifteen 20 × 20 m plots. Differences in the interpolated DTM surfaces demonstrate that the SfM surface overestimates lidar-modeled ground height with a mean difference of 0.19 m and standard deviation of 0.66 m. Height estimates obtained solely from SfM products were successful with R2 values of 0.91, 0.90, and 0.89 for mean, median, and maximum canopy height, respectively. Use of the lidar DTM in the analyses resulted in R2 values of 0.90, 0.89, and 0.89 for mean, median, and maximum canopy height. Our results suggest that SfM-derived point cloud products function as well as lidar data for estimating vegetation canopy height for our specific study area.
Highlights
Over the past 15 years, our ability to characterize and quantify vegetation structure has been greatly augmented by the use of three dimensional data obtained from light detection and ranging sensors
This study presents evidence of the utility of image-based point clouds products obtained from a low-cost unmanned aerial vehicle system to provide a suitable representation of the bare earth surface under vegetation canopy as well as robust estimates (i.e., R2 ě 0.89) of simple plot-level canopy heights
This study is unique and significant in that our analysis provides a comparison of canopy height regression estimates derived from height metrics calculated using both a lidar-derived digital terrain model and a terrain model created from an image-based point cloud within a woodland ecosystem
Summary
Over the past 15 years, our ability to characterize and quantify vegetation structure has been greatly augmented by the use of three dimensional data obtained from light detection and ranging (lidar) sensors. Lidar data have allowed for significant improvements in many areas of topographic representations and vegetation structure modeling, especially when compared to the sole application of passive multispectral sensors [6,11,12,13]. The cost of lidar data can be prohibitive for many applications, especially when the study area is relatively small or requires repeat acquisitions to monitor vegetation change. SfM is a computer vision technique that can generate high density three dimensional point clouds from high resolution imagery acquired from multiple perspectives [14]. These images are acquired from relatively low-cost unmanned aerial system (UAS) platforms. SfM is similar to traditional photogrammetry in that the technique uses overlapping images to construct dense
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