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利用谷歌地球引擎中的自动变化检测来支持生境保护。

Supporting habitat conservation with automated change detection in Google Earth Engine.

机构信息

Department of Environmental Science and Policy, George Mason University, 4400 University Dr., Fairfax, DC, 20030, U.S.A.

出版信息

Conserv Biol. 2021 Aug;35(4):1151-1161. doi: 10.1111/cobi.13680. Epub 2021 Jan 13.

Abstract

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).

摘要

生物多样性保护的一个重大局限在于,如何有效执行保护物种栖息地免受退化的法律法规。需要灵活、高效和有效的监测与执行手段,以帮助保护政策充分发挥效益。随着遥感数据的日益增多和普及,它们可用于识别和量化土地覆盖变化和生境丧失。然而,这些数据在系统保护监测方面的应用仍然不足,部分原因是缺乏简单的工具。我们改编了 2 种自动识别图像对之间差异的算法。我们使用免费的、公开可用的卫星数据,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。我们在 100 个美国已知变化点进行了实地验证,并将算法预测与实地数据进行了比较。我们还将算法预测与手动创建的多边形进行了比较,这些多边形划定了 4 个涉及濒危物种栖息地的人为变化案例研究的范围:大角羊(Centrocercus urophasianus)分布区的石油和天然气开发;沙丘岩蜥蜴(Sceloporus arenicolus)分布区的采砂作业;飓风迈克尔(2018 年)后滨鹬(Charadrius melodus)沿海栖息地的丧失;以及圣安德鲁海滩鼠(Peromyscus polionotus peninsularis)栖息地的住宅开发。两种算法均能有效地将与土地覆盖变化相对应的像素与未变化像素区分开来,其接收者操作特征曲线下的面积>0.90。在案例研究的生境类型中,最有效的算法有所不同,并且这两种算法都能有效地划定生境丧失的范围,其遗漏率(min. = 0.0)和误报率(min. = 0.0)较低,多边形重叠度适中(max. = 47%)。我们的研究结果展示了这些算法如何帮助缩小生物多样性保护中监测和执行方面的实施差距。我们提供了一个免费的在线工具,可以用来运行这些分析(https://conservationist.io/habitatpatrol)。

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