Data from: Assessing accuracy of GAP and LANDFIRE land cover datasets in winter habitats used by greater sage-grouse in Idaho and Wyoming, USA

Remotely sensed land cover datasets have been increasingly employed in studies of wildlife habitat use. However, meaningful interpretation of these datasets is dependent on how accurately they estimate habitat features that are important to wildlife. We evaluated the accuracy of the GAP dataset, which is commonly used to classify broad cover categories (e.g., vegetation communities) and LANDFIRE datasets, which are classify narrower cover categories (e.g., plant species) and structural features of vegetation. To evaluate accuracy, we compared classification of cover types and estimates of percent cover and height of sagebrush (Artemisia spp.) derived from GAP and LANDFIRE datasets to field-collected data in winter habitats used by greater sage-grouse (Centrocercus urophasianus). Accuracy was dependent on the type of dataset used as well as the spatial scale (point, 100-m, 1-km and 5-km) and biological level (community versus dominant species) investigated. GAP datasets had the highest overall classification accuracy of broad sagebrush cover types (46.1%) compared to LANDFIRE datasets for narrower cover types (43.8% community-level; 42.6% species-level). Percent cover and height were not accurately estimated in the LANDFIRE dataset. Our results suggest that researchers must be cautious when applying GAP or LANDFIRE datasets to classify narrow categories of land cover types or to predict percent cover or height of sagebrush within sagebrush-dominated landscapes. We conclude that ground-truthing is critical for successful application of land cover datasets in landscape-scale evaluations and management planning, particularly when wildlife use relatively rare habitat types compared to what is available.

遥感土地覆盖数据集在野生动物栖息地利用研究中的应用日益广泛。然而，对这些数据集的合理解读取决于其对野生动物重要栖息地特征的估计精度。本研究评估了GAP数据集的准确性，该数据集常用于对广泛覆盖类别（例如植被群落）进行分类，以及LANDFIRE数据集，该数据集用于对较窄覆盖类别（例如植物物种）和植被结构特征进行分类。为了评估准确性，我们将GAP和LANDFIRE数据集对覆盖类型、百分覆盖率和蒿属（Artemisia spp.）高度的分类与在冬季栖息地中由大草鸡（Centrocercus urophasianus）使用的实地收集数据进行比较。准确性取决于所使用的数据集类型、空间尺度（点、100米、1公里和5公里）以及生物水平（群落与优势物种）的探究。与LANDFIRE数据集对较窄覆盖类型（社区水平为43.8%，物种水平为42.6%）相比，GAP数据集在广泛蒿属覆盖类型的整体分类精度最高，达到46.1%。在LANDFIRE数据集中，百分覆盖率和高度估计并不准确。我们的结果表明，研究人员在将GAP或LANDFIRE数据集应用于对土地覆盖类型的窄分类或预测蒿属在蒿属占主导的景观中的百分覆盖率和高度时必须谨慎。我们得出结论，实地验证对于在景观尺度评估和管理规划中成功应用土地覆盖数据集至关重要，尤其是当野生动物相对于可获得的栖息地类型使用相对罕见的栖息地类型时。