Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for >20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 × 1 (30 × 30 m) and 3 × 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R² of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichens.

尽管在植被研究中通常很少受到关注，但地衣（地衣）是许多高纬度生态系统中整体碳和氮循环、反照率、生物多样性和生物量的主要贡献者。席子形成的苍白地衣生物量的变化有可能影响植被、动物群、气候和包括驯鹿饲养在内的人类活动。地衣具有复杂的光谱特征，而地衣的生长高度有限，通常与较高的植被混合生长。到目前为止，这阻碍了遥感技术的发展，以准确评估地衣生物量，这将成为生态系统和生态研究以及牧场管理的有力工具。我们提出了一个使用深度神经网络开发的基于 Landsat 的遥感模型，训练有超过 20 年收集的 8914 份地衣体积现场记录。与早期提出的地衣机器学习和回归方法相比，我们的模型利用了神经网络处理混合空间分辨率输入的能力。我们使用基于 7 个反射波段和 3 个索引的 1 × 1 (30 × 30 m) 和 3 × 3 Landsat 像素的输入，结合 10 m 空间分辨率数字高程模型来训练候选模型。我们对每个图的局部高程数据进行了归一化，以消除特定区域的变化，同时保持地形的信息性局部变化。最终模型预测了评估集中的地衣体积（我们的模型利用了神经网络处理混合空间分辨率输入的能力。我们使用基于 7 个反射波段和 3 个索引的 1 × 1 (30 × 30 m) 和 3 × 3 Landsat 像素的输入，结合 10 m 空间分辨率数字高程模型来训练候选模型。我们对每个图的局部高程数据进行了归一化，以消除特定区域的变化，同时保持地形的信息性局部变化。最终模型预测了评估集中的地衣体积（我们的模型利用了神经网络处理混合空间分辨率输入的能力。我们使用基于 7 个反射波段和 3 个索引的 1 × 1 (30 × 30 m) 和 3 × 3 Landsat 像素的输入，结合 10 m 空间分辨率数字高程模型来训练候选模型。我们对每个图的局部高程数据进行了归一化，以消除特定区域的变化，同时保持地形的信息性局部变化。最终模型预测了评估集中的地衣体积（同时保持地形信息丰富的局部变化。最终模型预测了评估集中的地衣体积（同时保持地形信息丰富的局部变化。最终模型预测了评估集中的地衣体积（n  = 159) 达到 R ²0.57。NDVI 和海拔是最重要的预测因子，其次是绿色波段。即使树木覆盖密度适中，该模型也很有效，与基于特定反射率的早期方法相比，提供了相当大的改进。该模型原则上是根据斯堪的纳维亚半岛的数据进行训练的，但当应用于北美和俄罗斯的地点时，该模型的预测与我们对地衣丰度的视觉解释非常吻合。我们还准确地量化了挪威北部地衣丰度最近的历史性（35 年）变化。这种新方法能够进一步研究与多个研究问题以及牧场管理和经济和文化生态系统服务相关的地衣生物量变化和变化的空间和时间。结合气候等驱动因素变化的信息，