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Lookup NU author(s): Dr Rachel GaultonORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Very few spatially explicit tree models have so far been constructed with a view to project remotesensingdata directly. To fill this gap, we introduced the prototype of the CanopyShotNoise model,an individual-based model specifically designed for projecting airborne laser scanning (ALS) data.Given the nature of ALS data the model focusses on the dynamics of individual-tree canopies inforest ecosystems, i.e. spatial tree interaction and resulting growth, birth- and death processes. Inthis study, CanopyShotNoise was used to analyse the long-term effects of the processes crownplasticity (C) and superorganism formation (S) on spatial tree canopy patterns that are likely toplay an important role in ongoing climate change. We designed a replicated computer experimentinvolving the four scenarios C0S0, C1S0, C0S1 and C1S1 where 0 and 1 imply that the precedingprocess was switched off and on, respectively. We hypothesised that C and S are antagonisticprocesses, specifically that C would lead to increasing regularity of tree locations and S wouldresult in clustering. Our simulation results confirmed that in the long run intertree distancesdecreased and canopy gap size increased when superorganisms were encouraged to form. At thesame time the overlap and packing of tree crowns increased. The long-term effect of crownplasticity increased the regularity of tree locations, however, this effect was much weaker than thatof superorganism formation. As a result gap patterns remained more or less unaffected by crownplasticity. In scenario C1S1, both processes interestingly interacted in such a way that crownplasticity even increased the effect of superorganism formation. Our simulation results are likelyto prove helpful in recognising patterns of facilitation with ongoing climate change.
Author(s): Pommerening A, Gaulton R, Magdon P, Myllymaki M
Publication type: Article
Publication status: Published
Journal: International Journal of Remote Sensing
Year: 2021
Volume: 42
Issue: 18
Pages: 6837-6865
Online publication date: 27/07/2021
Acceptance date: 28/04/2021
Date deposited: 05/06/2021
ISSN (print): 0143-1161
ISSN (electronic): 1366-5901
Publisher: Taylor and Francis
URL: https://doi.org/10.1080/01431161.2021.1944695
DOI: 10.1080/01431161.2021.1944695
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