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Describing seasonal differences in tree crown delineation using multispectral UAS data and structure from motion

ABSTRACT Unmanned Aerial Systems (UAS) have emerged as a platform capable of providing valuable data on vegetation structure, health, and productivity. The platform provides maximum temporal flexibility in data collection and customizability of spatial footprints which is important for mapping and monitoring vegetation conditions especially for high spectrally and structurally heterogeneous landscapes. In this letter, we investigate the potential improvement in structural information when using sensor payloads that include infrared bands (730–810 nm) in addition to the typical visible bands of many UAS sensor payloads. Specifically, we use derived point clouds and imagery collected from UAS-mounted sensors at a test site in Louisville, Kentucky, USA to assess whether multispectral sensors improve estimates of vegetation structure for plant greenness (leaf-on) and senescence (leaf-off) periods. Results indicate that including detailed multispectral reflectance information beyond the visible portion of the electromagnetic spectrum aids in estimating structural characteristics of woody vegetation, regardless of season. While both leaf-on and leaf-off periods result in reasonable crown height estimates, leaf-on conditions also improve radial estimates, likely due to continuous canopy surfaces. These findings advance research for remote sensing analyses assessing structural composition in heterogeneous landscapes where varying levels of vegetation structure have implications on land use and land function.