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

Unmanned Aerial Systems (UAS) represent an important niche platform for measuring vegetation health, structure, and productivity; metrics that directly inform sustainable conservation and development initiatives in rural African savannas. 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, an advantage especially useful for challenging environments such as dryland savannas. This study uses UAS-captured imagery to assess the efficacy of UAS for monitoring structural characteristics of vegetation in a mixed savanna woodland. The main objective was to compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. We compare different sensor types to determine whether multispectral data improve estimates of vegetation structure at the expense of spatial resolution. Results indicate that leveraging multispectral reflectance information, particularly in the near-infrared portion of the spectrum, aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. We also compare two image segmentation techniques for crown delineation and found that all techniques perform best in grassy savanna sites where trees and shrubs are easily distinguishable. Overall, a region-growing technique consistently exhibits highest levels of agreement with in situ height and crown area measurements, while a simple height threshold is best for determining fractional coverage of structural classes present. Findings from this work contribute to the advancement for applying high spatial resolution, UAS-derived methods in remote sensing analyses with specific consideration towards autonomous crown delineation and resource management initiatives in dryland systems. Lastly, data-informed analyses, as presented here, provide robust scientific evidence that contribute to informing environmental management decisions when considering the use of UAS technology in conservation and wildlife management across Africa.