A significant limitation in biodiversity conservation has been the effective implementation of laws and regulations that protect species’ habitats from degradation. Flexible, efficient, and effective monitoring and enforcement methods are needed to help conservation policies realize their full benefit. As remote sensing data become more numerous and accessible, they can be used to identify and quantify land-cover changes and habitat loss. However, these data remain underused for systematic conservation monitoring in part because of a lack of simple tools. We adapted 2 algorithms that automatically identify differences between pairs of images. We used free, publicly available satellite data to evaluate their ability to rapidly detect land-cover changes in a variety of land-cover types. We compared algorithm predictions with ground-truthed results at 100 sites of known change in the United States. We also compared algorithm predictions to manually created polygons delineating anthropogenic change in 4 case studies involving imperiled species’ habitat: oil and gas development in the range of the Greater Sage Grouse (Centrocercus urophasianus); sand mining operations in the range of the dunes sagebrush lizard (Sceloporus arenicolus); loss of Piping Plover (Charadrius melodus) coastal habitat after Hurricane Michael (2018); and residential development in St. Andrew beach mouse (Peromyscus polionotus peninsularis) habitat. Both algorithms effectively discriminated between pixels corresponding to land-cover change and unchanged pixels as indicated by area under a receiver operating characteristic curve >0.90. The algorithm that was most effective differed among the case-study habitat types, and both effectively delineated habitat loss as indicated by low omission (min. = 0.0) and commission (min. = 0.0) rates, and moderate polygon overlap (max. = 47%). Our results showed how these algorithms can be used to help close the implementation gap of monitoring and enforcement in biodiversity conservation. We provide a free online tool that can be used to run these analyses (https://conservationist.io/habitatpatrol).

中文翻译：

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通过 Google Earth Engine 中的自动变化检测支持栖息地保护

生物多样性保护的一个重大限制是有效实施保护物种栖息地免遭退化的法律和法规。需要灵活、高效和有效的监测和执法方法来帮助保护政策实现其全部利益。随着遥感数据变得越来越多和越来越容易获取，它们可用于识别和量化土地覆盖变化和栖息地丧失。然而，这些数据在系统保护监测方面仍未得到充分利用，部分原因是缺乏简单的工具。我们采用了 2 种算法来自动识别图像对之间的差异。我们使用免费的、公开可用的卫星数据来评估它们快速检测各种土地覆盖类型的土地覆盖变化的能力。我们将算法预测与美国 100 个已知变化地点的真实结果进行了比较。在涉及濒危物种栖息地的 4 个案例研究中，我们还将算法预测与手动创建的描绘人为变化的多边形进行了比较：Greater Sage Grouse 范围内的石油和天然气开发（Centrocercus urophasianus ); 在沙丘山艾树蜥蜴（Sceloporus arenicolus）范围内进行采砂作业；飓风迈克尔（2018 年）之后管道鸻（Charadrius melodus）沿海栖息地的丧失；圣安德鲁海滩鼠（Peromyscus polionotus peninsularis）的住宅开发） 栖息地。两种算法都有效地区分了对应于土地覆盖变化的像素和不变的像素，如接收器操作特征曲线下的面积 > 0.90。最有效的算法在案例研究栖息地类型之间有所不同，并且都有效地描绘了栖息地丧失，如低遗漏（最小 = 0.0）和佣金（最小 = 0.0）率以及中等多边形重叠（最大 = 47%）。我们的结果显示了如何使用这些算法来帮助缩小生物多样性保护监测和执法的实施差距。我们提供了一个免费的在线工具，可用于运行这些分析 (https://conservationist.io/habitatpatrol)。