Unprecedented amounts of analysis-ready Earth Observation (EO) data, combined with increasing computational power and new algorithms, offer novel opportunities for analysing ecosystem dynamics across large geographic extents, and to support conservation planning and action. Much research effort has gone into developing global EO-based land-cover and land-use datasets, including tree cover, crop types, and surface water dynamics. Yet there are inherent trade-offs between regional and global EO products pertaining to class legends, availability of training/validation data, and accuracy. Acknowledging and understanding these trade-offs is paramount for both developing EO products and for answering science questions relevant for ecology or conservation studies based on these data. Here we provide context on the development of global EO-based land-cover and land-use datasets, and outline advantages and disadvantages of both regional and global datasets. We argue that both types of EO-derived land-cover datasets can be preferable, with regional data providing the context-specificity that is often required for policy making and implementation (e.g., land-use and management, conservation planning, payment schemes for ecosystem services), making use of regional knowledge, particularly important when moving from land cover to actors. Ensuring that global and regional land-cover and land-use products derived based on EO data are compatible and nested, both in terms of class legends and accuracy assessment, should be a key consideration when developing such data. Open access high-quality training and validation data derived as part of such efforts are of utmost importance. Likewise, global efforts to generate sets of essential variables for climate change, biodiversity, or eventually land use, which often require land-cover maps as inputs, should consider regionalized, hierarchical approaches to not sacrifice regional context. Global change impacts manifest in regions, and so must the policy and planning responses to these challenges. EO data should embrace that regions matter, perhaps more than ever, in an age of global data availability and processing.

中文翻译：

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区域问题：关于全球变化时期区域土地覆盖数据集的有用性

史无前例的分析就绪地球观测 (EO) 数据，加上不断提高的计算能力和新算法，为分析大范围地理范围内的生态系统动态提供了新的机会，并支持保护规划和行动。许多研究工作已投入到开发基于 EO 的全球土地覆盖和土地利用数据集，包括树木覆盖、作物类型和地表水动态。然而，区域和全球 EO 产品之间存在与类图例、培训/验证数据的可用性和准确性有关的固有权衡。承认和理解这些权衡对于开发 EO 产品和回答与基于这些数据的生态学或保护研究相关的科学问题至关重要。在这里，我们提供了全球基于 EO 的土地覆盖和土地利用数据集的开发背景，并概述了区域和全球数据集的优缺点。我们认为，两种类型的 EO 衍生的土地覆盖数据集可能更可取，区域数据提供了政策制定和实施通常需要的特定背景（例如，土地使用和管理、保护规划、生态系统支付计划）服务），利用区域知识，这在从土地覆盖转移到行动者时尤其重要。确保基于 EO 数据得出的全球和区域土地覆盖和土地利用产品在类别图例和准确性评估方面兼容和嵌套，应该是开发此类数据时的一个关键考虑因素。作为此类努力的一部分，开放获取高质量的培训和验证数据至关重要。同样，为气候变化、生物多样性或最终土地利用生成一组基本变量（通常需要土地覆盖图作为输入）的全球努力应考虑区域化、分层的方法，而不牺牲区域背景。全球变化的影响体现在地区，因此应对这些挑战的政策和规划也必须如此。在全球数据可用性和处理的时代，EO 数据应该包含区域问题，也许比以往任何时候都重要。不牺牲区域背景的分层方法。全球变化的影响体现在地区，因此应对这些挑战的政策和规划也必须如此。在全球数据可用性和处理的时代，EO 数据应该包含区域问题，也许比以往任何时候都重要。不牺牲区域背景的分层方法。全球变化的影响体现在地区，因此应对这些挑战的政策和规划也必须如此。在全球数据可用性和处理的时代，EO 数据应该包含区域问题，也许比以往任何时候都重要。