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LiDAR-derived topography and forest structure predict fine-scale variation in daily surface temperatures in oak savanna and conifer forest landscapes

Authors :
Nicholas W. Synes
Geoffrey A. Fricker
Alan L. Flint
Ian M. McCullough
Josep M. Serra-Diaz
Janet Franklin
Frank W. Davis
University of California [Santa Barbara] (UCSB)
University of California
Arizona State University [Tempe] (ASU)
University of California [Riverside] (UCR)
California Polytechnic State University [San Luis Obispo] (CAL POLY)
Michigan State University [East Lansing]
Michigan State University System
SILVA (SILVA)
Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-AgroParisTech
Aarhus University [Aarhus]
California Water Science Center
Partenaires INRAE
United States Geological Survey (USGS)
Source :
Agricultural and Forest Meteorology, Agricultural and Forest Meteorology, Elsevier Masson, 2019, 269-270, pp.192-202. ⟨10.1016/j.agrformet.2019.02.015⟩, Davis, F W, Synes, N W, Fricker, G A, McCullough, I M, Serra-Diaz, J M, Franklin, J & Flint, A L 2019, ' LiDAR-derived topography and forest structure predict fine-scale variation in daily surface temperatures in oak savanna and conifer forest landscapes ', Agricultural and Forest Meteorology, vol. 269-270, pp. 192-202 . https://doi.org/10.1016/j.agrformet.2019.02.015
Publication Year :
2019
Publisher :
eScholarship, University of California, 2019.

Abstract

International audience; In mountain landscapes, surface temperatures vary over short distances due to interacting influences of topography and overstory vegetation on local energy and water balances. At two study landscapes in the Sierra Nevada of California, characterized by foothill oak savanna at 276–481 m elevation and montane conifer forest at 1977–2135 m, we deployed 288 near-surface (5 cm above the surface) temperature sensors to sample site-scale (30 m) temperature variation related to hillslope orientation and vegetation structure and microsite-scale (2–10 m) variation related to microtopography and tree overstory. Daily near-surface maximum and minimum temperatures for the 2013 calendar year were related to topographic factors and vegetation overstory characterized using small footprint LiDAR imagery acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). At both landscapes we recorded large site and microsite spatial variation in daily maximum temperatures, and less absolute variation in daily minimum temperatures. Generalized boosted regression trees were estimated to measure the influence of tree canopy density, understory solar radiation, cold-air drainage and pooling, ground cover and microtopography on daily maximum and minimum temperatures at site and microsite scales. Site-scale models based on indices of understory solar radiation and landscape position explained an average of 61–65% of daily variation in maximum temperature; site-scale models based on tree canopy density and landscape position explained 65–83% of variation in minimum temperatures. Models explained

Details

ISSN :
01681923
Database :
OpenAIRE
Journal :
Agricultural and Forest Meteorology, Agricultural and Forest Meteorology, Elsevier Masson, 2019, 269-270, pp.192-202. ⟨10.1016/j.agrformet.2019.02.015⟩, Davis, F W, Synes, N W, Fricker, G A, McCullough, I M, Serra-Diaz, J M, Franklin, J & Flint, A L 2019, ' LiDAR-derived topography and forest structure predict fine-scale variation in daily surface temperatures in oak savanna and conifer forest landscapes ', Agricultural and Forest Meteorology, vol. 269-270, pp. 192-202 . https://doi.org/10.1016/j.agrformet.2019.02.015
Accession number :
edsair.doi.dedup.....137aefe12116924b227b305460c59954
Full Text :
https://doi.org/10.1016/j.agrformet.2019.02.015⟩