Treeline remote sensing: from tracking treeline shifts to multi‐dimensional monitoring of ecotonal change

• Published in: Remote Sensing in Ecology and Conservation
• Main Authors: Matteo Garbarino, Donato Morresi, Nicolò Anselmetto, Peter J. Weisberg
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: Climate change; ecotones; meta‐analysis; remote sensing; spatial pattern; treeline; Technology; T; Ecology; QH540-549.5; Arctic
• Online Access: https://doi.org/10.1002/rse2.351; https://doaj.org/article/376695da49b64f6893fc73e765db7b34

Abstract Remote sensing applications have a long history in treeline research. Recent reviews have examined the topic mainly from a methodological point of view. Here, we propose a question‐oriented review of remote sensing in treeline ecology to relate remote sensing methodologies to key ecological metrics and identify knowledge gaps and promising areas for future research. We performed a meta‐analysis to assess the role of remote sensing as a tool for measuring spatial patterns and dynamics of alpine and Arctic treeline ecotone globally. We assessed the geographic distribution, scale of analysis, and relationships between remote sensing techniques and treeline ecological metrics through co‐occurrence mapping and multivariate statistics. Our analysis revealed that only 10% of treeline ecology studies applied remote sensing tools, often associated with the keyword ‘climate change’. Monitoring studies adopted coarser spatial resolutions over longer temporal extents in comparison with other treeline studies. A multiscale and multi‐sensor spatial approach was implemented in just 19% of papers. Long‐term research commonly relied on aerial and oblique photography to measure treeline shifts through photointerpretation within a multidisciplinary framework. More recent treeline dynamics were often quantified using greenness trends derived from the pixel‐based classification of satellite images. Many recent short‐term studies focused on delineating tree scale metrics derived from the object‐based classification of uncrewed aerial vehicle (UAV) images or LiDAR data. Over the past decade, high‐resolution and low‐cost UAV remote sensing has emerged as an interesting opportunity to fill the gap between local‐scale ecological patterns and coarse‐resolution satellite sensors. Additionally, treeline remote sensing applications would strongly benefit from multidisciplinary frameworks that integrate field studies in ecology and environmental science. The multi‐dimensional structural complexity of treelines typically responds to ...