Characterizing vegetation structural and topographic characteristics sampled by eddy covariance within two mature aspen stands using lidar and a flux footprint model: Scaling to MODIS

Abstract

In this study, a Boolean classification was applied using novel methods to 3‐D vegetation structural and topographic attributes found within flux footprint source/sink areas measured by eddy covariance instrumentation. The purpose was to determine if the spatial frequency of 3‐D attributes, such as canopy height, effective leaf area index, etc., found within 1 km resolution Moderate Resolution Imaging Spectroradiometer (MODIS) pixels were significantly different from or similar to attributes sampled by flux footprints originating from prevailing wind directions. A Kolmogorov‐Smirnov test was used for the first time to apply confidence limits to individual MODIS pixels based on (1) the spatial distribution of cumulative frequencies of attributes representative of those sampled by eddy covariance and (2) temporal representation of MODIS pixels related to area sampling frequency by eddy covariance based on wind direction. Structural and topographic attributes at homogeneous Southern Old Aspen and heterogeneous Upland Aspen sites are representative of 56% and 69% of a 1 km radius area surrounding the tower and 21% and 47% of a 4 × 4 km area. Attributes found within the MODIS “tower” pixel compare well with attributes most frequently sampled by eddy covariance instruments at both sites. By classifying pixels using the Boolean approach, correspondence between MODIS pixels and eddy covariance estimates of gross primary production (GPP) explain up to 13% more variance than using pixels proximal to the tower. This study, therefore, provides a method for choosing MODIS pixels that have similar attributes to those found within footprints most frequently sampled by eddy covariance.

Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data

“…A rasterbased footprint parameterization based on Kljun et al (2004) was used to map EC-sampled source/sink areas per half-hour period. Following the methods of Chasmer et al (2011), the half-hourly footprints were then totalised over entire years to determine appropriate grid-cell weightings of ΔTLC ALS around each of the EC towers. Footprint weighting provides the most spatially comparable estimate of ΔTLC ALS to EC-based estimates of GPP and NEP.…”

“…ALS is already a recognized method for better characterising flux tower site canopy structural variability (Chasmer et al, 2008b) within approximately 44 international Fluxnet sites, with at least 8 of these sites containing two or more temporal ALS datasets (Beland et al, 2015). Furthermore, the work of Chasmer et al (2008aChasmer et al ( , 2008bChasmer et al ( and 2011 has provided the platform for such a framework by developing and refining methods of ALS forest canopy attribute integration with EC CO 2 flux data.…”

Effects of harvesting and drought on CO2and H2O fluxes in an aspen-dominated western boreal plain forest: early chronosequence recovery

“…The footprint is defined as the probability of contribution by CO 2 and water fluxes per unit area upwind of the eddy-covariance system. Although most inputs were obtained from eddy covariance and wind direction, roughness length (z0m) and zero plane displacement (d) of vegetation were mapped at 1 m resolution within 10°wind sectors based on canopy height from airborne light detection and ranging (LiDAR) data (z0m = 1/10 height; d = 2/3 height of trees) (Chasmer et al 2011). Wind sectors were used to constrain the iterative footprint model, which was then accumulated over the growing season.…”

“…A 50% overlap of scan lines was adopted to reduce laser "shadowing" by canopies and to ensure sampling of both sides of trees. LiDAR data were also collected in 2002 and were used to briefly describe preharvest stand structural characteristics (Hopkinson et al 2005;Chasmer et al 2011).…”

“…Several gridded products were derived following Chasmer et al (2011), including a digital elevation model (DEM, m), a digital surface model (DSM, m) of canopy heights plus topography, and a canopy height model (CHM (m) = DSM -DEM). The difference between the 2008 and 2011 CHMs quantify growth characteristics of regenerating species throughout the two sites ( Fig.…”