Radiative Transfer Modeling of a Coniferous Canopy Characterized by Airborne Remote Sensing

Abstract

Solar radiation beneath a forest canopy can have large spatial variations, but this is frequently neglected in radiative transfer models for large-scale applications. To explicitly model spatial variations in subcanopy radiation, maps of canopy structure are required. Aerial photography and airborne laser scanning are used to map tree locations, heights, and crown diameters for a lodgepole pine forest in Colorado as inputs to a spatially explicit radiative transfer model. Statistics of subcanopy radiation simulated by the model are compared with measurements from radiometer arrays, and scaling of spatial statistics with temporal averaging and array size is discussed. Efficient parameterizations for spatial averages and standard deviations of subcanopy radiation are developed using parameters that can be obtained from the model or hemispherical photography.

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

“…Under-canopy snow distribution is governed by multiple factors that affect the energy environment, as observed by melting (Essery et al, 2008;Gelfan et al, 2004) and accumulation rates Schmidt and Gluns, 1991;Teti, 2003). Our results show different responses when comparing the snow-depth difference between open and canopycovered areas between study sites (Fig.…”

“…Thus a sigmoidal function was used to characterize the snow-depth difference changes with elevation, excluding topographic interactions. The interactions between topographic variables and vegetation are most likely attributable to the under-canopy snowpack being less sensitive to solar radiation versus snowpack in the open area (Courbaud et al, 2003;Dubayah, 1994;Essery et al, 2008;Musselman et al, 2008Musselman et al, , 2012. In spite of filtering the topographic effect, there is still about a 20 cm magnitude of fluctuation in the snow-depth difference, which might be attributed to various clearing sizes of open area at different locations and various vegetation types in forests Pomeroy et al, 2002;Schmidt and Gluns, 1991); however, we were not able to explore these features of the sites from the current lidar data set.…”

Laser vision: lidar as a transformative tool to advance critical zone science

“…Improved terrain representation has helped characterize hysteretic relationships between water storage and contributing area in large wetland complexes within parameterized runoff models (Shook et al, 2013), improved mapping in and along river channels to parameterize network-level structure and flood inundation models (French, 2003;Kinzel et al, 2007;Snyder, 2009;Bates, 2012), and expanded investigation of geomorphological change in floodplains (Thoma et al, 2005;Jones et al, 2007). Lidar provides vertical information that permits the direct retrieval of forest attributes such as tree height and canopy structure (Hyyppä et al, 2012;Vosselman and Maas, 2010) that can be used to model canopy volume (Palminteri et al, 2012), biomass (Zhao et al, 2009), and the transmittance of solar radiation (Essery et al, 2008;Musselman et al, 2013;von Bode et al, 2014). Lidar has also proven to be instrumental in the verification of model states.…”

“…Lidar has also proven to be instrumental in the verification of model states. For example, lidar data sets have been used to verify physically based models, including landscape evolution models (Pelletier et al, 2014;Pelletier and Perron, 2012;Rengers and Tucker, 2014), aeolian models Pelletier, 2013), physiological models , snowpack energy balance models (Essery et al, 2008, Broxton et al, 2014, and an ecosystem dynamics model (Antonarakis et al, 2014). Simpler, empirical models have also been developed using lidar-derived estimates of soil erosion (Pelletier and Orem, 2014) and snow accumulation and ablation (Varhola et al, 2014).…”

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

“…The potential applications of a reliable DTM include habitat assessment, forest succession, snowmelt simulation, hydrologic modeling, carbon sequestration, glacial monitoring, and floodplain assessments [1][2][3][4][5][6]. Prior to the introduction of Light Detection and Ranging (LiDAR), traditional methods such as photogrammetry and field surveys were conducted to produce DTMs.…”