Abstract

Reindeer lichens (Cladonia spp.) are an important food source for woodland and barren ground caribou herds. In this study, we assessed Cladonia classification accuracy in a rocky, Canadian Shield landscape near Yellowknife, Northwest Territories using both Unmanned Aerial Vehicle (UAV) sensors and high-resolution satellite sensors. At the UAV scale, random forest classifications derived from a multispectral, visible-near infrared sensor (Micasense Altum) had an average 5% higher accuracy for mapping Cladonia (i.e., 95.5%) than when using a conventional color RGB camera (DJI Phantom 4 RTK). We aggregated Altum lichen classifications from three 5 ha study sites to train random forest regression models of fractional lichen cover using predictor features from WorldView-3 and Planet CubeSat satellite imagery. WorldView models at 6 m resolution had an average 6.8% RMSE (R² = 0.61) when tested at independent study sites and outperformed the 6 m Planet models, which had a 9.9% RMSE (R² = 0.34). These satellite results are comparable to previous lichen mapping studies focusing on woodlands, but the small cover of Cladonia in our study area (11.6% or 16.8% within the barren portions) results in a high relative RMSE (62.2%) expressed as a proportion of mean lichen cover.

摘要

驯鹿地衣（Cladonia spp.）是林地和贫瘠地面驯鹿群的重要食物来源。在这项研究中，我们使用无人驾驶飞行器 (UAV) 传感器和高分辨率卫星传感器在西北地区耶洛奈夫附近的加拿大地盾岩石景观中评估了 Cladonia分类的准确性。在无人机尺度上，来自多光谱可见近红外传感器 (Micasense Altum) 的随机森林分类在绘制Cladonia地图时的准确度平均提高了 5%（即 95.5%）比使用传统彩色 RGB 相机（DJI Phantom 4 RTK）时。我们汇总了三个 5 公顷研究地点的 Altum 地衣分类，使用 WorldView-3 和 Planet CubeSat 卫星图像的预测特征来训练分数地衣覆盖的随机森林回归模型。在独立研究地点进行测试时，分辨率为 6 m 的 WorldView 模型的平均 RMSE 为 6.8% ( R ² = 0.61)，优于 6 m 的 Planet 模型，后者的 RMSE 为 9.9% ( R ² = 0.34)。这些卫星结果与之前关注林地的地衣测绘研究相当，但Cladonia的小覆盖在我们的研究区域（贫瘠部分为 11.6% 或 16.8%）导致相对 RMSE 较高（62.2%），表示为平均地衣覆盖率的比例。