Human activities alter the land surface of the Earth (Haberl et al., 2007; Hurtt et al., 2006), and one key questionwithin geography is, therefore, the assessment of land use/land cover change (LULCC) and the analysis and understanding of the underlying drivers and effects of LULCC (Brandt, Primdahl, & Reenberg, 1999; Bürgi, Hersperger, & Schneeberger, 2004; Plieninger et al., 2016). The last decades have seen amassive andwidespread digitalization of spatial and non-spatial information, which can be linked (and hence geo-referred) to specific spatial units or entities (Fuchs, Verburg, Clevers, & Herold, 2015; Goldberg, Olivares, Li, & Klein, 2014). Digitalization came relatively early to the field of land change science, mainly due to the use of geographic information systems to handle spatial data in the form of maps and remotely sensed data (Haines-Young, Green, & Cousins, 1993). It can, therefore, be argued that a long digital tradition exists within land change science, which has been open to pursue the possibilities of new data sources and methods as these have emerged. Recent years have seen an unprecedented growth in the amount of digital data as well as rapid advances in new digital methods, tools and research infrastructures across academia (Kong, 2015). This includes easy access to spatially specific information from other domains and academic fields, such as register data from government agencies, data from aerial and terrestrial sensors, and a wide range of historical data repositories. Furthermore, as many data sets are available for whole regions, countries or even at global scale, analyses of land change with large geographical coverage and at the same time at high spatial resolutions are increasingly made possible. It can be argued that a new era of digital data has opened up for transdisciplinary interactions for land change research. Yet, increased access to digital data also entails challenges related to the validity of data and the need for new approaches to integrate multiple digital data sources. All five articles in this special issue concern the application of digital data in land change research. In the following sections, we present the articles and discuss them in the wider context of: (1) the sound understanding, interpretation and thus application of digital data and (2) the development ofmethodologies to integratemultiple digital data in a transdisciplinary manner.

中文翻译：

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土地变化科学中的数字跨学科——整合多种类型的数字数据

人类活动改变了地球的陆地表面（Haberl 等人，2007 年；Hurtt 等人，2006 年），因此地理学中的一个关键问题是土地利用/土地覆盖变化 (LULCC) 评估以及分析和了解 LULCC 的潜在驱动因素和影响（Brandt、Primdahl 和 Reenberg，1999 年；Bürgi、Hersperger 和 Schneeberger，2004 年；Plieninger 等人，2016 年）。在过去的几十年里，空间和非空间信息发生了大量和广泛的数字化，这些信息可以链接（并因此被地理参考）到特定的空间单位或实体（Fuchs、Verburg、Clevers 和 Herold，2015 年；Goldberg、Olivares、Li , & 克莱因, 2014)。数字化在土地变化科学领域出现得相对较早，主要是由于使用地理信息系统以地图和遥感数据的形式处理空间数据 (Haines-Young, Green, & Cousins, 1993)。因此，可以说，土地变化科学中存在着悠久的数字传统，随着这些新数据源和方法的出现，它一直在寻求新数据源和方法的可能性。近年来，数字数据量空前增长，新数字方法、工具和研究基础设施在学术界迅速发展（Kong，2015）。这包括轻松访问来自其他领域和学术领域的空间特定信息，例如来自政府机构的注册数据、来自航空和地面传感器的数据以及广泛的历史数据存储库。此外，由于整个区域、国家甚至全球范围内都有许多数据集可用，因此越来越有可能对具有较大地理覆盖范围且同时具有高空间分辨率的土地变化进行分析。可以说，数字数据的新时代为土地变化研究的跨学科互动开辟了道路。然而，增加对数字数据的访问也带来了与数据有效性相关的挑战，以及对集成多个数字数据源的新方法的需求。本期特刊中的所有五篇文章都涉及数字数据在土地变化研究中的应用。在以下部分中，我们将介绍这些文章并在更广泛的背景下讨论它们：(1) 合理的理解，