Coupling Fine-Scale Root and Canopy Structure Using Ground-Based Remote Sensing

Abstract

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above-and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy LiDAR (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence (i.e., correlation in the frequency of spatial variation) at multiple spatial scales ≤10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more becoming even vertically as forests aged. In all sites, above-and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 m, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing LiDAR and radar technologies to quantitatively couple above-and belowground ecosystem structure.Keywords: canopy; root; biomass; spatial wavelet coherence; radar; LiDAR Highlights •Canopy and root biomass are examined across a forest chronosequence.• Colocated ground-based LiDAR and ground-penetrating radar data were collected.• Spatial wavelet analysis reveals coherence in canopy height and root biomass. •All ages exhibited coherence between canopy and root structures at a scale of 3-4 m; oldest stands demonstrated coherence at 8 m. •We demonstrate methods to quantify fine-scale patterns of root-canopy structure.

Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling

“…The synergy between high-resolution radar, optical (e.g. Sentinel-1 and Sentinel-2) and LIDAR data could also be used to inform on important aspects of habitat structure and function such as structural complexity of the canopy [ 112 ], or soil moisture mapping [ 113 ], which could help to improve model accuracy. Furthermore, the continuous updating of current climatic and land-cover datasets would complement the information provided by the integrative response of satellite-derived ecosystem functional variables (EFAs) [ 32 , 114 ].…”

Community and structural constraints on the complexity of eastern North American forests

“…Maximum canopy height (m) was the vertical distance of the tallest vegetated 1‐m‐wide column within a plot. The multi‐step derivation of canopy rugosity ( R c , m) used here is detailed in Appendix S1 of Atkins, Bohrer, et al (2018) and was applied to several prior studies of R c and ecosystem functioning, disturbance, or above/belowground structure– R c relationships (Atkins, Fahey, et al, 2018; Fahey et al, 2019; Gough et al, 2019; Hardiman, Bohrer, et al, 2013; Hardiman, Gough, et al, 2013; Hardiman et al, 2017; 2018; Hickey et al, 2019; Scheuermann et al, 2018); for continuity and consistency, we retain in Equation 1 the nomenclature and abbreviations used in these studies. We note that our mathematical definition of R c differs from its conceptual formula described by Hardiman et al (2011), and Atkins, Bohrer, et al (2018) should be consulted for the full mathematical derivation and ecological principles underlying Equation 1.…”

Quantifying vegetation and canopy structural complexity from terrestrial LiDARdata using theforestr rpackage

“…The characterization of forest structure is a long‐standing (Watt, ; Whittaker & Woodwell, ) research area fundamental to interpreting, modelling, and improving the understanding of ecosystem functions. Light detection and ranging (LiDAR) has been used to characterize ecosystem structural features relevant to biogeochemical cycling (Antonarakis, Saatchi, Chazdon, & Moorcroft, ; Hardiman, Bohrer, Gough, Vogel, & Curtis, ; Hardiman et al., ), growth and carbon uptake (Stark et al., ), habitat suitability (Vierling, Vierling, Gould, Martinuzzi, & Clawges, ), food web stability (Barbosa et al., ), plant and canopy physiology (Asner & Mascaro, ; Atkins, Bohrer, et al., ; Atkins, Fahey, Hardiman, & Gough, ), ecosystem metaproperties (Paynter et al., ), forest relationships with hydrological networks (Detto, Muller‐Landau, Mascaro, & Asner, ), fire susceptibility (Hiers, O'Brien, Mitchell, Grego, & Loudermilk, ; Skowronski, Clark, Duveneck, & Hom, ) and community dynamics and competition (Rodríguez‐Ronderos, Bohrer, Sanchez‐Azofeifa, Powers, & Schnitzer, ). In terrestrial ecosystems, ground‐based, commercially available, and field portable LiDAR systems are revolutionizing the collection of quantitative ecosystem physical structural information, and providing an unprecedented view of structure (Asner et al., ; Calders, Armston, Newnham, Herold, & Goodwin, ; Calders et al, ; Eitel et al., ; Newnham et al., ; Paynter et al., ; Wilkes et al., ).…”