Preprint Article Version 1 This version is not peer-reviewed

Spatial Variation in Canopy Structure across Forest Landscapes

Version 1 : Received: 21 June 2018 / Approved: 22 June 2018 / Online: 22 June 2018 (06:38:03 CEST)

A peer-reviewed article of this Preprint also exists.

Hardiman, B.S.; LaRue, E.A.; Atkins, J.W.; Fahey, R.T.; Wagner, F.W.; Gough, C.M. Spatial Variation in Canopy Structure across Forest Landscapes. Forests 2018, 9, 474. Hardiman, B.S.; LaRue, E.A.; Atkins, J.W.; Fahey, R.T.; Wagner, F.W.; Gough, C.M. Spatial Variation in Canopy Structure across Forest Landscapes. Forests 2018, 9, 474.

Journal reference: Forests 2018, 9, 474
DOI: 10.3390/f9080474

Abstract

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy lidar system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10’s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales.

Subject Areas

forest structure; macrosystems biology; portable canopy LiDAR; rugosity; transect spatial autocorrelation

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