2014
Bode, Collin; Limm, Michael; Power, Mary; Finlay, Jacques
Subcanopy Solar Radiation model: Predicting solar radiation across a heavily vegetated landscape using LiDAR and GIS solar radiation models Journal Article
In: Remote Sensing of Environment, vol. 154, no. SI, pp. 387-397, 2014, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: ERCZO, insolation, LiDAR, solar model, subcanopy, vegetation shading
@article{Bode2014,
title = {Subcanopy Solar Radiation model: Predicting solar radiation across a heavily vegetated landscape using LiDAR and GIS solar radiation models},
author = {Collin Bode and Michael Limm and Mary Power and Jacques Finlay},
url = {https://angelo.berkeley.edu/wp-content/uploads/sites/59/2015/01/Bode_SSR_Methods_RSE2013.pdf},
doi = {DOI:10.1016/j.rse.2014.01.028},
issn = {0034-4257},
year = {2014},
date = {2014-11-14},
journal = {Remote Sensing of Environment},
volume = {154},
number = {SI},
pages = {387-397},
abstract = {Solar radiation flux, irradiance, is a fundamental driver of almost all hydrological and biological processes. Ecological models of these processes often require data at the watershed scale. GIS-based solar models that predict insolation at the watershed scale take topographic shading into account, but do not account for vegetative shading. Most methods that quantify subcanopy insolation do so only at a single point. Further, subcanopy model calibration requires significant field effort and knowledge of characteristics (species composition, leaf area index & mean leaf angle for each species), and upscaling to watersheds is a significant source of uncertainty.
We propose an approach to modeling insolation that uses airborne LiDAR data to estimate canopy openness as a Light Penetration Index (LPI). We couple LPI with the GRASS GIS r.sun solar model to produce the Subcanopy Solar Radiation model (SSR). SSR accounts for both topographic shading and vegetative shading at a landscape scale.
After calibrating the r.sun model to a weather station at our study site, we compare SSR model predictions to black thermopile pyranometer field measurements and to hemispherical photographs using Gap Light Analyzer software, a standard method for point estimation of subcanopy radiation. Both SSR and hemispherical models exhibit a similar linear relationship with pyranometer data, and the models predict similar total solar radiation flux across the range of canopy openness. This approach allows prediction of light regimes at watershed scales with resolution that was previously possible only for local point measurements.},
keywords = {ERCZO, insolation, LiDAR, solar model, subcanopy, vegetation shading},
pubstate = {published},
tppubtype = {article}
}
Solar radiation flux, irradiance, is a fundamental driver of almost all hydrological and biological processes. Ecological models of these processes often require data at the watershed scale. GIS-based solar models that predict insolation at the watershed scale take topographic shading into account, but do not account for vegetative shading. Most methods that quantify subcanopy insolation do so only at a single point. Further, subcanopy model calibration requires significant field effort and knowledge of characteristics (species composition, leaf area index & mean leaf angle for each species), and upscaling to watersheds is a significant source of uncertainty.
We propose an approach to modeling insolation that uses airborne LiDAR data to estimate canopy openness as a Light Penetration Index (LPI). We couple LPI with the GRASS GIS r.sun solar model to produce the Subcanopy Solar Radiation model (SSR). SSR accounts for both topographic shading and vegetative shading at a landscape scale.
After calibrating the r.sun model to a weather station at our study site, we compare SSR model predictions to black thermopile pyranometer field measurements and to hemispherical photographs using Gap Light Analyzer software, a standard method for point estimation of subcanopy radiation. Both SSR and hemispherical models exhibit a similar linear relationship with pyranometer data, and the models predict similar total solar radiation flux across the range of canopy openness. This approach allows prediction of light regimes at watershed scales with resolution that was previously possible only for local point measurements.
We propose an approach to modeling insolation that uses airborne LiDAR data to estimate canopy openness as a Light Penetration Index (LPI). We couple LPI with the GRASS GIS r.sun solar model to produce the Subcanopy Solar Radiation model (SSR). SSR accounts for both topographic shading and vegetative shading at a landscape scale.
After calibrating the r.sun model to a weather station at our study site, we compare SSR model predictions to black thermopile pyranometer field measurements and to hemispherical photographs using Gap Light Analyzer software, a standard method for point estimation of subcanopy radiation. Both SSR and hemispherical models exhibit a similar linear relationship with pyranometer data, and the models predict similar total solar radiation flux across the range of canopy openness. This approach allows prediction of light regimes at watershed scales with resolution that was previously possible only for local point measurements.