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CanopyShotNoise – An individual-based tree canopy modelling framework for projecting remote-sensing data and ecological sensitivity analysis

Lookup NU author(s): Dr Rachel Gaulton

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

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.


Publication metadata

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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