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 environmental drivers over multiple scales and thus is best described with multiscale and multi-sensor approaches.

中文翻译：

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林线遥感：从跟踪林线变化到生态变化的多维监测

遥感应用在林线研究方面有着悠久的历史。最近的评论主要从方法论的角度审视了该主题。在这里，我们提出对林线生态学遥感进行面向问题的审查，将遥感方法与关键生态指标联系起来，并确定知识差距和未来研究的有希望的领域。我们进行了一项荟萃分析，以评估遥感作为测量全球高山和北极林线交错带空间格局和动态的工具的作用。我们通过共现制图和多元统计评估了地理分布、分析规模以及遥感技术和林线生态指标之间的关系。我们的分析显示，只有 10% 的林线生态学研究应用了遥感工具，这些工具通常与关键词“气候变化”相关。与其他林线研究相比，监测研究在较长的时间范围内采用了更粗糙的空间分辨率。仅 19% 的论文采用了多尺度和多传感器空间方法。长期研究通常依赖于航空和倾斜摄影，通过多学科框架内的照片解释来测量林线变化。最近的林线动态通常使用从卫星图像的基于像素的分类得出的绿色趋势来量化。最近的许多短期研究侧重于描绘从无人飞行器（UAV）图像或激光雷达数据的基于对象的分类中得出的树木规模指标。在过去的十年中，高分辨率和低成本的无人机遥感已经成为填补局部生态模式和粗分辨率卫星传感器之间空白的一个有趣的机会。此外，林线遥感应用将极大地受益于整合生态学和环境科学实地研究的多学科框架。林线的多维结构复杂性通常会响应多个尺度的环境驱动因素，因此最好用多尺度和多传感器方法来描述。