Lidar-based approaches for estimating solar insolation in heavily forested streams

<p>Methods to quantify solar insolation in riparian landscapes are needed due to the importance of stream temperature to aquatic biota. We have tested three lidar predictors using two approaches developed for other applications of estimating solar insolation from airborne lidar using field dat...

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Main Authors: J. J. Richardson, C. E. Torgersen, L. M. Moskal
Format: Article
Language:English
Published: Copernicus Publications 2019-07-01
Series:Hydrology and Earth System Sciences
Online Access:https://www.hydrol-earth-syst-sci.net/23/2813/2019/hess-23-2813-2019.pdf
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spelling doaj-c0c24b85c07c4eb68125c5c78286b6742020-11-25T01:40:38ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382019-07-01232813282210.5194/hess-23-2813-2019Lidar-based approaches for estimating solar insolation in heavily forested streamsJ. J. Richardson0C. E. Torgersen1L. M. Moskal2Sterling College, Craftsbury Common, VT, USAUS Geological Survey, Forest and Rangeland Ecosystem Science Center, Cascadia Field Station, University of Washington, Seattle, WA, USAPrecision Forestry Cooperative, School of Environmental and Forest Science, University of Washington, Seattle, WA, USA<p>Methods to quantify solar insolation in riparian landscapes are needed due to the importance of stream temperature to aquatic biota. We have tested three lidar predictors using two approaches developed for other applications of estimating solar insolation from airborne lidar using field data collected in a heavily forested narrow stream in western Oregon, USA. We show that a raster methodology based on the light penetration index (LPI) and a synthetic hemispherical photograph approach both accurately predict solar insolation, explaining more than 73&thinsp;% of the variability observed in pyranometers placed in the stream channel. We apply the LPI-based model to predict solar insolation for an entire riparian system and demonstrate that no field-based calibration is necessary to produce an unbiased prediction of solar insolation using airborne lidar alone.</p>https://www.hydrol-earth-syst-sci.net/23/2813/2019/hess-23-2813-2019.pdf
collection DOAJ
language English
format Article
sources DOAJ
author J. J. Richardson
C. E. Torgersen
L. M. Moskal
spellingShingle J. J. Richardson
C. E. Torgersen
L. M. Moskal
Lidar-based approaches for estimating solar insolation in heavily forested streams
Hydrology and Earth System Sciences
author_facet J. J. Richardson
C. E. Torgersen
L. M. Moskal
author_sort J. J. Richardson
title Lidar-based approaches for estimating solar insolation in heavily forested streams
title_short Lidar-based approaches for estimating solar insolation in heavily forested streams
title_full Lidar-based approaches for estimating solar insolation in heavily forested streams
title_fullStr Lidar-based approaches for estimating solar insolation in heavily forested streams
title_full_unstemmed Lidar-based approaches for estimating solar insolation in heavily forested streams
title_sort lidar-based approaches for estimating solar insolation in heavily forested streams
publisher Copernicus Publications
series Hydrology and Earth System Sciences
issn 1027-5606
1607-7938
publishDate 2019-07-01
description <p>Methods to quantify solar insolation in riparian landscapes are needed due to the importance of stream temperature to aquatic biota. We have tested three lidar predictors using two approaches developed for other applications of estimating solar insolation from airborne lidar using field data collected in a heavily forested narrow stream in western Oregon, USA. We show that a raster methodology based on the light penetration index (LPI) and a synthetic hemispherical photograph approach both accurately predict solar insolation, explaining more than 73&thinsp;% of the variability observed in pyranometers placed in the stream channel. We apply the LPI-based model to predict solar insolation for an entire riparian system and demonstrate that no field-based calibration is necessary to produce an unbiased prediction of solar insolation using airborne lidar alone.</p>
url https://www.hydrol-earth-syst-sci.net/23/2813/2019/hess-23-2813-2019.pdf
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