Department of Biological Sciences, Boise State University, 1910 University Drive, Boise, ID, 83725, USA.
Department of Biological Sciences, Boise State University, 1910 University Drive, Boise, ID, 83725, USA.
J Environ Manage. 2021 Feb 15;280:111720. doi: 10.1016/j.jenvman.2020.111720. Epub 2020 Dec 11.
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 classifies 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, 500-m, and 1-km) and biological level (community versus dominant species) investigated. GAP datasets had the highest overall classification accuracy of broad sagebrush cover types (49.8%) compared to LANDFIRE datasets for narrower cover types (39.1% community-level; 31.9% 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生境利用研究中得到了越来越多的应用。然而，这些数据集的意义解释取决于它们对野生动物重要的生境特征的估计的准确性。我们评估了 GAP 数据集的准确性，该数据集通常用于对广泛的覆盖类别（如植被群落）进行分类，而 LANDFIRE 数据集则对较窄的覆盖类别（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 数据集对覆盖类型的分类以及对 sagebrush（Artemisia spp.）的覆盖率和高度的估计与冬季栖息地使用的大角羊（Centrocercus urophasianus）的野外数据进行了比较。准确性取决于所使用的数据集类型以及所研究的空间尺度（点、500 米和 1 公里）和生物水平（群落与优势物种）。与 LANDFIRE 数据集相比，GAP 数据集对广泛的 sagebrush 覆盖类型的总体分类准确性最高（49.8%），而 LANDFIRE 数据集对较窄的覆盖类型的分类准确性较低（群落水平为 39.1%；物种水平为 31.9%）。LANDFIRE 数据集中的覆盖率和高度无法准确估计。我们的结果表明，研究人员在应用 GAP 或 LANDFIRE 数据集对狭窄类别的土地覆盖类型进行分类或预测 sagebrush 的覆盖率或高度时必须谨慎。我们得出结论，地面实况对于成功应用土地覆盖数据集进行景观尺度评估和管理规划至关重要，特别是当野生动物使用的生境类型相对较少时，而这些生境类型相对于可用的生境类型是相对较少的。
