Context The amount and composition of phytochemicals in forage plants influences habitat quality for wild herbivores. However, evaluating forage quality at fine resolutions across broad spatial extents (i.e., foodscapes) is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging this gap in spatial scale. Objectives We evaluated the potential for UAS technology to accurately predict nutritional quality of sagebrush ( Artemisia spp.) across landscapes. We mapped seasonal forage quality across two sites in Idaho, USA, with different mixtures of species but similar structural morphotypes of sagebrush. Methods We classified the sagebrush at both study sites using structural features of shrubs with object-based image analysis and machine learning and linked this classification to field measurements of phytochemicals to interpolate a foodscape for each phytochemical with regression kriging. We compared fine-scale landscape patterns of phytochemicals between sites and seasons. Results Classification accuracy for morphotypes was high at both study sites (81–87%). Forage quality was highly variable both within and among sagebrush morphotypes. Coumarins were the most accurately mapped (r 2 = 0.57–0.81), whereas monoterpenes were the most variable and least explained. Patches with higher crude protein were larger and more connected in summer than in winter. Conclusions UAS allowed for a rapid collection of imagery for mapping foodscapes based on the phytochemical composition of sagebrush at fine scales but relatively broad extents. However, results suggest that a more advanced sensor (e.g., hyperspectral camera) is needed to map mixed species of sagebrush or to directly measure forage quality.

中文翻译：

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使用无人机系统绘制多种食草动物的食物景观和山艾树形态类型

背景草料植物中植物化学物质的数量和组成影响野生食草动物的栖息地质量。然而，在广泛的空间范围（即食物景观）中以高分辨率评估草料质量具有挑战性。无人机系统 (UAS) 为弥合空间尺度上的这一差距提供了途径。目标我们评估了 UAS 技术在准确预测不同景观中山艾树（蒿属）营养质量方面的潜力。我们绘制了美国爱达荷州两个地点的季节性草料质量图，这些地点具有不同的物种混合物，但具有相似的山艾树结构形态。方法我们使用灌木的结构特征和基于对象的图像分析和机器学习对两个研究地点的山艾树进行分类，并将这种分类与植物化学物质的现场测量联系起来，以使用回归克里金法为每种植物化学物质插入食物景观。我们比较了不同地点和季节之间植物化学物质的精细景观模式。结果 两个研究地点的形态类型分类准确度都很高（81-87%）。草料质量在山艾树形态类型内部和之间变化很大。香豆素的映射最准确（r 2 = 0.57–0.81），而单萜是变化最大且解释最少的。与冬季相比，具有较高粗蛋白的斑块在夏季更大且连接更紧密。结论 UAS 允许基于山艾树的植物化学成分在精细尺度但相对广泛的范围内快速收集用于绘制食物景观的图像。然而，结果表明需要更先进的传感器（例如，高光谱相机）来绘制混合的山艾树物种或直接测量草料质量。