Worldwide, trees are colonizing rangelands with high conservation value. The introduction of trees into grasslands and shrublands causes large-scale changes in ecosystem structure and function, which have cascading impacts on ecosystem services, biodiversity, and agricultural economies. Satellites are increasingly being used to track tree cover at continental to global scales, but these methods can only provide reliable estimates of change over recent decades. Given the slow pace of tree cover expansion, remote sensing techniques that can extend this historical record provide critical insights into the magnitude of environmental change. Here, we estimate conifer expansion in rangelands of the northern Great Plains, United States, North America, using historical aerial imagery from the mid-20th century and modern aerial imagery. We analyzed 19.3 million hectares of rangelands in Montana, USA, using a convolutional neural network (U-Net architecture) and cloud computing to detect tree features and tree cover change. Our bias-corrected results estimate 3.0 ± 0.2 million hectares of conifer tree cover expansion in Montana rangelands, which accounts for 15.4% of the total study area. Overall accuracy was >91%, but the producer's accuracy was lower than the user's accuracy (0.60 vs. 0.88) for areas of tree cover expansion. Nonetheless, the omission errors were not spatially clustered, suggesting that the method is reliable for identifying the regions of Montana where substantial tree expansion has occurred. Using the model results in conjunction with historical and modern imagery allows for effective communication of the scale of tree expansion while overcoming the recency effect caused by shifting environmental baselines.

中文翻译：

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使用高分辨率历史航空图像绘制美国蒙大拿州牧场的树木覆盖扩张情况

在世界范围内，树木正在占领具有高保护价值的牧场。草原和灌木丛中树木的引入导致生态系统结构和功能发生大规模变化，对生态系统服务、生物多样性和农业经济产生连锁影响。卫星越来越多地用于跟踪大陆到全球范围内的树木覆盖，但这些方法只能提供近几十年来变化的可靠估计。鉴于树木覆盖扩张速度缓慢，可以扩展这一历史记录的遥感技术为了解环境变化的严重程度提供了重要的见解。在这里，我们利用 20 世纪中叶的历史航空图像和现代航空图像来估计美国和北美大平原北部牧场的针叶树扩张情况。我们分析了19. 美国蒙大拿州 300 万公顷牧场，使用卷积神经网络（U-Net 架构）和云计算来检测树木特征和树木覆盖变化。我们的偏差校正结果估计蒙大拿州牧场的针叶树覆盖面积扩大了 3.0 ± 20 万公顷，占研究总面积的 15.4%。总体准确度 >91%，但对于树木覆盖范围扩大的区域，生产者的准确度低于用户的准确度（0.60 与 0.88）。尽管如此，遗漏错误并未在空间上聚集，这表明该方法对于识别蒙大拿州发生大量树木扩张的地区是可靠的。