Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, Colorado, 80523, USA.
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, 80523, USA.
Ecology. 2017 Apr;98(4):920-932. doi: 10.1002/ecy.1730. Epub 2017 Mar 20.
Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.
Landsat 数据越来越多地被用于生态监测和研究。这些数据在进行分析之前通常需要进行预处理，以考虑传感器、太阳、大气和地形的影响。然而，使用这些数据的生态学家面临着一个文献中包含术语不一致、方法过时以及大量方法相互矛盾的建议的问题。这些问题最多只能使确定正确的预处理工作流程变得困难和耗时，最坏的情况则会导致错误的结果。我们通过提供 Landsat 任务和传感器的简明概述，并澄清经常混淆的术语和方法来解决这些问题。我们区分并解释了通常应用于 Landsat 数据的预处理步骤，包括地理参考和配准、辐射率转换、太阳校正、大气校正、地形校正和相对校正。然后，我们通过展示工作流程和决策树来综合这些信息，根据生态研究问题确定适当的图像预处理级别，同时强调需要根据研究地点和手头的问题来调整每个工作流程。我们建议采用一种简洁的 Landsat 预处理方法，避免不必要的步骤，并推荐经过充分测试、易于获取且文档齐全的方法和数据产品。我们的重点是 Landsat 数据的生态应用，但讨论的许多概念和建议也适用于其他学科和遥感平台。
