Abstract

Very few spatially explicit tree models have so far been constructed with a view to project remote-sensing data 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 focuses on the dynamics of individual-tree canopies in forest ecosystems, that is, spatial tree interaction and resulting growth, birth and death processes. In this study, CanopyShotNoise was used to analyse the long-term effects of the processes crown plasticity (C) and superorganism formation (S) on spatial tree canopy patterns that are likely to play an important role in ongoing climate change. We designed a replicated computer experiment involving the four scenarios C0S0, C1S0, C0S1 and C1S1 where 0 and 1 imply that the preceding process was switched off and on, respectively. We hypothesized that C and S are antagonistic processes, specifically that C would lead to increasing regularity of tree locations and S would result in clustering. Our simulation results confirmed that in the long run intertree distances decreased and canopy gap size increased when superorganisms were encouraged to form. At the same time, the overlap and packing of tree crowns increased. The long-term effect of crown plasticity increased the regularity of tree locations; however, this effect was much weaker than that of superorganism formation. As a result, gap patterns remained more or less unaffected by crown plasticity. In scenario C1S1, both processes interestingly interacted in such a way that crown plasticity even increased the effect of superorganism formation. Our simulation results are likely to prove helpful in recognizing patterns of facilitation with ongoing climate change.

Published in

International Journal of Remote Sensing
2021, Volume: 42, number: 18, pages: 6837-6865
Publisher: TAYLOR AND FRANCIS LTD

SLU Authors

• Pommerening, Arne

  • Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Remote Sensing


Publication identifier

DOI: https://doi.org/10.1080/01431161.2021.1944695

Permanent link to this page (URI)

https://res.slu.se/id/publ/113261