Abstract Human-mediated climate change over the past century has resulted in significant impacts to global ecosystems and biodiversity including accelerating redistribution of the geographic ranges of species. In mountainous regions, the transition zone from continuous closed-canopy montane forests to treeless alpine tundra areas at higher elevations is commonly referred to as the “alpine treeline ecotone” (ATE). Globally, warming climate is expected to drive ATEs upslope, which could lead to negative impacts on local biodiversity and changes in ecosystem function. However, existing studies rely primarily on field-based data which are difficult and time consuming to collect. In this study, we define an ATE-detection index (ATEI) to automatically identify the ATE positions from 2009 to 2011 in the western United States using geospatial tools and remotely sensed datasets provided by Google Earth Engine. A binomial logistic regression model was fitted between standardized ATEI components and a binary variable of pixel status of 141 sampled Landsat pixels manually classified with high-resolution imagery in Google Earth. The average model accuracy was around 0.713 (±0.111) and the average Kappa coefficient was approximately 0.426 (±0.221) based on a 100-time repeated 10-fold cross-validation. Furthermore, the ATEI-estimated elevation is highly correlated (Pearson's r = 0.98) with a published set of field-collected ATE elevations at 22 sampling sites across the region. The detection metric developed in this study facilitates monitoring the geographic location and potential shifts of ATEs as well as the general impact of climate change in mountainous areas during recent decades. We also expect this approach to be useful in monitoring other ecosystem boundaries.

中文翻译：

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美国西部高山林线交错带的空间检测

摘要 在过去的一个世纪里，人类介导的气候变化对全球生态系统和生物多样性产生了重大影响，包括加速物种地理范围的重新分布。在山区，从连续的封闭林冠山地森林到海拔较高的无树高山苔原地区的过渡带通常被称为“高山林线交错带”（ATE）。在全球范围内，气候变暖预计将推动 ATEs 上升，这可能对当地生物多样性和生态系统功能的变化产生负面影响。然而，现有的研究主要依赖于实地数据，这些数据收集起来既困难又耗时。在这项研究中，我们定义了一个 ATE 检测指数 (ATEI)，以使用 Google Earth Engine 提供的地理空间工具和遥感数据集自动识别 2009 年至 2011 年在美国西部的 ATE 位置。在标准化的 ATEI 组件和 141 个采样 Landsat 像素的像素状态二进制变量之间拟合了二项逻辑回归模型，这些像素状态是在 Google 地球中使用高分辨率图像手动分类的。基于 100 次重复的 10 倍交叉验证，平均模型准确度约为 0.713 (±0.111)，平均 Kappa 系数约为 0.426 (±0.221)。此外，ATEI 估计的海拔高度与该地区 22 个采样点公布的一组现场收集的 ATE 海拔高度相关（Pearson's r = 0.98）。本研究中开发的检测指标有助于监测 ATE 的地理位置和潜在变化，以及近几十年来山区气候变化的一般影响。我们还希望这种方法有助于监测其他生态系统边界。