A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment

Abstract

offered throughout this process. When I expressed interest in field work as a career goal, I was offered an opportunity to work in the field as part of a collaborative research effort and they shared their expertise. Both made themselves available to workshop methodological issues and academic decisions in the context of my work and larger goals. Through their mentorship, and that of the Geography Department as a whole, I have achieved well beyond what I initially intended as a student and am encouraged to carry the momentum forward. Dr. Carol Hanchette also deserves mention by name for inspiring me as a non-traditional undergraduate to pursue an advanced degree. Of course, no achievement would be possible without the dedicated partnership of Micki Scarborough and our loving dogs, providing more support than I probably deserve.

Using Very-High-Resolution Multispectral Classification to Estimate Savanna Fractional Vegetation Components

“…Despite significantly lower point cloud densities, NIR point clouds produced in that study consistently showed better representation of the canopy structure than denser RGB point clouds, and the same finding was noted elsewhere [43]. For more details regarding the processing parameters, please see [37].…”

“…To produce canopy height models (CHMs), we subtracted the DTM from the corresponding DSM, which included the above-ground vegetation structure as generated by the SfM-MVS algorithm. We chose to use NIR-derived CHMs exclusively for this analysis as previous work determined that increased spectral detail in vegetation improves canopy height estimates even at the expense of coarser spatial resolution [37]. Despite significantly lower point cloud densities, NIR point clouds produced in that study consistently showed better representation of the canopy structure than denser RGB point clouds, and the same finding was noted elsewhere [43].…”

“…However, the RF classifier still demands input data and a set of ancillary data layers to train the model. An alternative is a straightforward thresholding that uses UAS imagery and photogrammetric techniques for extracting densified point clouds that translate into height-related information to best characterize the FVC of a landscape [37].…”

Comparison of Different Analytical Strategies for Classifying Invasive Wetland Vegetation in Imagery from Unpiloted Aerial Systems (UAS)

“…We subsetted each dataset using .exif data on height of captured image and geolocation tags to calculate the width of each image footprint (via the exifr package in R) [37]. We then used a distance of seven meters to create a buffered polygon of all the GPS point locations of each photo and used the convex hull setting in the Minimum Bounding tool in ArcPy [38]. The resulting polygon indicates the area of the flight footprint with substantive image overlap and thereby excludes distorted margins of orthomosaics [38].…”

“…We then used a distance of seven meters to create a buffered polygon of all the GPS point locations of each photo and used the convex hull setting in the Minimum Bounding tool in ArcPy [38]. The resulting polygon indicates the area of the flight footprint with substantive image overlap and thereby excludes distorted margins of orthomosaics [38].…”