ABSTRACT. Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics and considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (1989–2018) for ∼1.3 million km2 encompassing the Xingu River Basin, Brazil, from published, freely accessible remotely sensed land cover classifications. While all showed a decrease in forest extent over time, the total deforested area reported by each ranged widely for all time periods. The greatest differences ranged from 9% to 17% (116,958 to 219,778 km2) deforestation of the total area for year 2000 and 2014–2018 time period, respectively. We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. Surface water classifications must be chosen carefully because sources differ greatly in location and mapped area of surface water. After operationalization of the Belo Monte dam complex, the large reservoirs are notably absent from several of the classifications illustrating land cover. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g., riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate classification for ecological studies, conservation, or policy making. The differences between the classifications examined here are not a failure of the technology, but due to different interpretations of ‘forest cover’ and characteristics of the input data (e.g., spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed classifications and the need for users to familiarize themselves with the characteristics and limitations of each data set.

中文翻译：

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解读新谷河流域的多幅地图——多尺度土地覆盖分类评估

摘要。遥感是一种非常宝贵的工具，可以客观地说明世界范围内栖息地范围的迅速减少。许多可操作的地球观测平台为生成土地覆盖图提供了选项，每个平台都具有独特的特征和类定义中的相当大的语义差异。因此，基线估计的差异是不可避免的。在这里，我们根据已发布的、可免费访问的遥感土地覆盖分类，比较了跨越三个十年（1989-2018 年）的四个时间段内大约 130 万平方公里的森林覆盖和地表水估计，其中包括巴西新古河流域。虽然随着时间的推移，所有的森林面积都在减少，但每个报告的森林砍伐总面积在所有时间段都相差很大。最大的差异在 9% 到 17% 之间（116,958 到 219，778 平方公里）在 2000 年和 2014-2018 年期间总面积的森林砍伐，分别。我们还展示了森林碎片化指标（熵和前景面积密度）对数据质量和空间分辨率的高敏感性，云层覆盖和传感器人工制品会导致错误。地表水分类必须谨慎选择，因为来源在地表水的位置和映射区域有很大差异。在 Belo Monte 大坝综合设施投入运营后，在说明土地覆盖的几个分类中明显没有大型水库。淡水生态系统健康受水体周围土地覆盖（例如，河岸带）的影响。了解许多遥感基线之间的差异对于避免信息滥用至关重要，并客观地为生态研究、保护或政策制定选择最合适的分类。这里检查的分类之间的差异不是技术的失败，而是由于对“森林覆盖”的不同解释和输入数据的特征（例如，空间分辨率）。我们的发现证明了遥感分类生成过程中透明度的重要性，以及用户需要熟悉每个数据集的特征和局限性。