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

Authors: Nicholas Kolarik*, , Narcisa Pricope, University of North Carolina Wilmington, Kyle Woodward, University of North Carolina Wilmington, Andrea Gaughan, University of Louisville, Forrest Stevens, University of Louisville
Topics: Remote Sensing, Arid Regions, Land Use and Land Cover Change
Keywords: Unmanned Aerial Systems, UAS, UAV, land cover, drone, vegetation, remote sensing
Session Type: Paper
Day: 4/4/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Harding, Marriott, Mezzanine Level
Presentation File: No File Uploaded

Unmanned Aerial Systems (UAS) have emerged as a capable platform for measuring vegetation health, structure and productivity. Products derived from UAS imagery have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail. This study uses UAS-derived point clouds and imagery collected in the Chobe Enclave of northern Botswana across a gradient of savanna sites classified as either grass-, shrub-, or tree-dominated. We compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. Sensor types are also compared, determining whether multispectral data improves estimates of vegetation structure at the expense of spatial resolution. We found that leveraging height, texture, and where applicable, color or multispectral reflectance information aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. Comparisons are made between various crown delineation techniques, and the efficacy of each technique within savanna environments is discussed. The methods presented hold potential to inform field sampling protocols and UAS-based techniques for autonomous crown delineation in future dryland systems research. These findings advance research for field and remote sensing analyses assessing degradation in heterogeneous landscapes where varying levels of vegetation structure have implications on land use and land functions.

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