Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

中文翻译：

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基于多时相Landsat影像的栖息地指标，用于绘制大型哺乳动物栖息地

最新和精细的栖息地信息对于野生动植物的管理和保护至关重要。评估野生动植物栖息地的研究通常依靠分类土地覆盖图作为栖息地模型的预测因子。但是，广泛的土地覆盖类别通常无法充分捕捉关键的栖息地特征，因此生成可靠的土地覆盖图既困难又费力。直接从卫星图像获得的连续变量为捕获生境模型中的土地覆盖特征提供了一种替代方法。改进的数据可用性和处理能力现在允许以合成方法集成来自中分辨率传感器的所有可用图像，这些合成方法可在像素级别导出频谱时间指标，从而汇总随时间变化的频谱响应。在这个研究中，我们评估了从Landsat影像中获得的此类度量标准对于绘制野生动植物栖息地的有用性。我们将频谱时间指标分类为生境指标描述野生动植物栖息地的不同方面。将这些指标与山猫，马鹿和ro的栖息地模型中分类土地覆盖图的性能进行比较，我们发现，使用栖息地指标的模型始终优于基于分类土地覆盖图的模型，模型平均提高了13.7％ AUC和连续博伊斯指数的9.7％。对于季节性栖息地模型，性能提升更大，这表明栖息地指标比栖息地图更好地捕捉了栖息地条件下的年内变化。将适用性地图与辅助数据进行比较，进一步表明，我们的栖息地指标对与森林结构相关的栖息地的精细异质性敏感。总体，我们的研究强调了基于Landsat的光谱时间指标在评估野生动植物栖息地方面的巨大潜力。鉴于这些指标可以直接从全球免费获得的Landsat影像中以自动化方式获得，它们为监视时空动态提供了新的可能性。