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Remote Sens. 2016, 8(8), 658;

Estimating Understory Temperatures Using MODIS LST in Mixed Cordilleran Forests

Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada
Author to whom correspondence should be addressed.
Academic Editors: Zhaoliang Li, Clement Atzberger and Prasad S. Thenkabail
Received: 26 April 2016 / Revised: 19 July 2016 / Accepted: 9 August 2016 / Published: 15 August 2016
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Satellite remote sensing provides a rapid and broad-scale means for monitoring vegetation phenology and its relationship with fluctuations in air temperature. Investigating the response of plant communities to climate change is needed to gain insight into the potentially detrimental effects on ecosystem processes. While many studies have used satellite-derived land surface temperature (LST) as a proxy for air temperature, few studies have attempted to create and validate models of forest understory temperature (Tust), as it is obscured from these space-borne observations. This study worked to predict instantaneous values of Tust using daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST data over a 99,000 km2 study area located in the Rocky Mountains of western Alberta, Canada. Specifically, we aimed to identify the forest characteristics that improve estimates of Tust over using LST alone. Our top model predicted Tust to within a mean absolute error (MAE) of 1.4 °C with an overall model fit of R2 = 0.89 over two growing seasons. Canopy closure and the LiDAR-derived standard deviation of canopy height metric were found to significantly improve estimations of Tust over MODIS LST alone. These findings demonstrate that canopy structure and forest stand-type function to differentiate understory air temperatures from ambient canopy temperature as seen by the sensor overhead. View Full-Text
Keywords: MODIS; land surface temperature; understory air temperature; phenology; climate change; LiDAR MODIS; land surface temperature; understory air temperature; phenology; climate change; LiDAR

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Laskin, D.N.; Montaghi, A.; Nielsen, S.E.; McDermid, G.J. Estimating Understory Temperatures Using MODIS LST in Mixed Cordilleran Forests. Remote Sens. 2016, 8, 658.

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