Integrating remote sensing and demography for more efficient and effective assessment of changing mountain forest distribution

Morley, Peter J.; Donoghue, Daniel N.M.; Chen, Jan-Chang; Jump, Alistair S.

Abstract

Species range shifts have been well studied in light of rising global temperatures and the role climate plays in restricting species distribution. In mountain regions, global trends show upward elevational shifts of altitudinal treelines. However, there is significant variation in response between geographic locations driven by climatic and habitat heterogeneity and biotic interactions. Accurate estimation of treeline shifts requires fine-scale patterns of forest structure to be discriminated across mountain ranges. Satellite remote sensing allows detailed information on forest structure to be extrapolated across mountain ranges, however, variation in methodology combined with a lack of information on accuracy and repeatability has led to high uncertainty in the utility of remotely sensed data in studies of mountain treelines. We unite three themes; suitability of remote sensing products, ecological relevance of classifications and effectiveness of the training and validation process in relation to the study of mountain treeline ecotones. We identify needs for further research comparing the utility of different remotely sensed data sets, better characterisation of treeline structure and incorporation of accuracy assessment. Collectively, the improvements we describe will significantly improve the utility of remote sensing by facilitating a more consistent approach to defining geographic variation in treeline structure, improving our ability to link processes from stand to regional scale and the accuracy of range shift assessments. Ultimately, this advance will enable better monitoring of mountain treeline shifts and estimation of the associated to biodiversity and ecosystem function.

Citation

Morley, P. J., Donoghue, D. N., Chen, J., & Jump, A. S. (2018). Integrating remote sensing and demography for more efficient and effective assessment of changing mountain forest distribution. Ecological Informatics, 43, 106-115. https://doi.org/10.1016/j.ecoinf.2017.12.002

• Journal Article Type: Article
• Acceptance Date: Dec 6, 2017
• Online Publication Date: Dec 7, 2017
• Publication Date: 2018-01
• Deposit Date: Jan 3, 2018
• Publicly Available Date: Jan 3, 2018
• Journal: Ecological Informatics
• Print ISSN: 1574-9541
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 43
• Pages: 106-115
• DOI: https://doi.org/10.1016/j.ecoinf.2017.12.002
• Public URL: https://durham-repository.worktribe.com/output/1342520