A Participatory Science Approach to Expanding Instream Infrastructure Inventories
A Participatory Science Approach to Expanding Instream Infrastructure Inventories
复制标题
扩大河内基础设施库存的参与性科学方法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
T. Pavelsky
中科院分区：
文献类型：
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作者：
Aaron Whittemore;M. Ross;W. Dolan;T. Langhorst;Xiao Yang;Sayali K. Pawar;Michiel W. P. Jorissen;Eric Lawton;S. Januchowski‐Hartley;T. Pavelsky
在过去的十年中，遥感数据的分辨率得到了提高，并且变得更加广泛，为其在环境科学和保护领域的应用带来了新的机遇。一项潜在的应用是识别和绘制世界各地的河流基础设施图，这对渔业、水文、洪水等具有重要影响。迄今为止，河内基础设施数据库主要关注带有水库的大型水坝，这些水库相对容易通过遥感图像检测到。尽管小型基础设施对淡水生态系统有影响，但它们常常被忽视。为了克服这些挑战，我们需要更系统的方法，例如此处介绍的全球河流阻塞数据库 (GROD)，来绘制河流基础设施图。我们提出了一种参与式方法来识别、绘制和验证基础设施，并为美国本土 (n = 4,197) 提供初始数据集。我们强调了包括公众在内的参与式方法的价值，并提出了将其与机器学习融合以用于未来应用的建议。
Over the past decade, remote sensing data have improved in resolution and become more widely available, bringing new opportunities for its use in environmental science and conservation. One potential application is to identify and map instream infrastructure across the world, with important implications for fisheries, hydrology, flooding, and more. To date, databases of instream infrastructure focus on larger dams with reservoirs that are comparatively easy to detect with remotely sensed imagery. Despite their impact on freshwater ecosystems, smaller infrastructure is often overlooked. To overcome these challenges, we require more systematic approaches, such as the Global River Obstruction Database (GROD) presented here, to map instream infrastructure. We present a participatory approach to identify, map, and validate infrastructure and provide an initial data set for the contiguous United States (n = 4,197). We highlight the value of participatory methods that include the public and suggest ways they could be fused with machine learning for future applications.