The northern forest-tundra ecotone is one of the fastest warming regions of the globe. Models of vegetation change generally predict a northward advance of boreal forests and corresponding retreat of the tundra. Previous satellite remote sensing analyses in this region have focused on mapping vegetation greenness and tree cover derived from optical multi-spectral sensors. Changes in vegetation structure relating to height and biomass are less frequently investigated due to limited availability of lidar data over space and time in comparison with optical platforms. As such, there is an opportunity to combine lidar and optical remote sensing products for continuous mapping of vegetation structure at high-latitudes, with an emphasis on the forest-tundra transition. In this study, we used lidar data from the Ice, Cloud and land Elevation Satellite (ICESat-2) to classify canopy presence/absence, and predict canopy height across 120 million hectares of the Canadian forest-tundra ecotone at 30 m spatial resolution. Spatially continuous predictors derived from the Landsat satellite archive (2012−2021) and the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) Digital Elevation Model were used to extrapolate 98th percentile canopy height from the ICESat-2 Land and Vegetation Height (ATL08) product using Random Forests models developed in R (version 4.2.2). Model accuracy was assessed using data from the Land, Vegetation and Ice Sensor (LVIS), a large-footprint airborne lidar system. The overall accuracy of the canopy presence classification was 89%, and canopy presence was detected with 88% accuracy. Models of vegetation height showed an overall R of 0.54 and RMSE of 2.09 m. Finally, we used these methods to map the limit of continuous 3 m forest across Canada and compared our model outputs with forest cover from the MODIS and Landsat Vegetation Continuous Fields datasets. This work demonstrates the challenges and potential for mapping horizontal and vertical vegetation structure within sparse, high latitude forests using both lidar and optical remote sensing data.

中文翻译：

---

使用 ICESat-2、陆地卫星时间序列和地形数据绘制植被高度并确定加拿大北部森林界限

北部森林-苔原交错带是全球变暖最快的地区之一。植被变化模型通常预测北方森林向北推进以及苔原相应的后退。此前对该地区的卫星遥感分析主要集中于利用光学多光谱传感器绘制植被绿度和树木覆盖情况。由于与光学平台相比，激光雷达数据在空间和时间上的可用性有限，因此与高度和生物量相关的植被结构变化的研究较少。因此，有机会将激光雷达和光学遥感产品结合起来，对高纬度植被结构进行连续测绘，重点关注森林-苔原过渡。在这项研究中，我们使用来自冰、云和陆地高程卫星 (ICESat-2) 的激光雷达数据对树冠存在/不存在进行分类，并以 30 m 空间分辨率预测加拿大森林-苔原交错带 1.2 亿公顷的树冠高度。使用来自 Landsat 卫星档案（2012−2021 年）和 ASTER（先进星载热发射和反射辐射计）数字高程模型的空间连续预测器，根据 ICESat-2 土地和植被高度 (ATL08) 产品推断第 98 个百分位的冠层高度使用 R（版本 4.2.2）开发的随机森林模型。使用来自陆地、植被和冰传感器 (LVIS)（一种大型机载激光雷达系统）的数据来评估模型精度。冠层存在分类的总体准确率为 89%，检测冠层存在的准确度为 88%。植被高度模型显示总体 R 为 0.54，RMSE 为 2.09 m。最后，我们使用这些方法绘制了加拿大连续 3 m 森林的界限，并将我们的模型输出与 MODIS 和 Landsat Vegetation Continuous Fields 数据集的森林覆盖率进行了比较。这项工作展示了使用激光雷达和光学遥感数据绘制稀疏高纬度森林中水平和垂直植被结构地图的挑战和潜力。