Comparing canopy leaf temperature of three Central European tree species based on simultaneous confidence bands for penalized splines View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-09

AUTHORS

Manuel Vonrüti, Aleksandar Spasojevic, Nils Nölke, Thomas Kneib, Christoph Kleinn

ABSTRACT

Temperature is an important physical factor that is known to strongly affect biodiversity as well as ecosystems and their functioning. However, research in this area is still relatively limited; this may also be attributed to the multitude of influencing factors and the complexity of the statistics involved. This study analyzes the differences between the surface temperature of three Central European broadleaf tree species. A better understanding of these differences may help to elucidate the role of microclimate in biodiversity. We consider a time series of high-resolution thermal images taken from a meteorological observation tower and calculate mean canopy leaf temperatures for beech, ash and maple (Fagus silvatica, Fraxinus excelsior and Acer pseudoplatanus). In a first step, comparable image areas are extracted from the thermal image sections of the crown of each tree species avoiding shadow areas, branches, etc. We used an automatic segmentation technique, the Otsu thresholding. Extracted canopy leaf temperature values were then processed and the resulting temperature profiles estimated by O’Sullivan penalized splines. For comparing the differences in canopy leaf temperature over time, we propose the construction of simultaneous confidence bands. The analyses show that there are significant—though small—differences in canopy surface temperature between the three tree species. More... »

PAGES

385-398

References to SciGraph publications

Journal

TITLE

Environmental and Ecological Statistics

ISSUE

3

VOLUME

24

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10651-017-0375-1

DOI

http://dx.doi.org/10.1007/s10651-017-0375-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085710433


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