Aarhus University , Department of Agroecology, Blichers Allé 20, 8830 Tjele, Denmark.
Aalborg University , Department of Planning, Skibbrogade 3, 9000 Aalborg, Denmark.
Environ Sci Technol. 2017 Jul 18;51(14):7954-7962. doi: 10.1021/acs.est.6b04684. Epub 2017 Jun 28.
Land use and land-use changes (LULUC) information is essential to determine the environmental impacts of anthropogenic land-use and conversion. However, existing data sets are either local-scale or they quantify land occupation per land-use type rather than providing information on land-use changes. Here we combined the strengths of the remotely sensed MODIS land cover data set and FAOSTAT land-use data to obtain a database including a collection of 231 country-specific LULUC matrixes, as suggested by the IPCC. We produced two versions of each matrix: version 1, identifying forestland based on canopy cover criteria; version 2, distinguishing primary, secondary, planted forests and permanent crops. The outcome was a first country-based, consistent set of spatially explicit LULUC matrixes. The database facilitates a more holistic assessment of land-use changes, quantifying changes that occur between land classes from 2001 to 2012, providing crucial information for assessing environmental impacts caused by LULUC. The data allow global-scale land-use change analyses, requiring a distinction between land types based not only on land cover but also on land uses. The spatially explicit data set may also serve as a starting point for further studies aiming at determining the drivers of land-use change supported by spatial statistical modeling.
土地利用和土地利用变化（LULUC）信息对于确定人为土地利用和转换对环境的影响至关重要。然而，现有的数据集要么是局部规模的，要么是量化每种土地利用类型的土地占用量，而不是提供土地利用变化的信息。在这里，我们结合了 MODIS 土地覆盖遥感数据集和 FAOSTAT 土地利用数据的优势，获得了一个包含 231 个国家特定 LULUC 矩阵的数据库，这些矩阵是根据 IPCC 的建议收集的。我们为每个矩阵制作了两个版本：版本 1 根据树冠覆盖标准识别林地；版本 2 区分原生林、次生林、人工林和永久作物。其结果是一套基于国家的、一致的、具有空间明确性的 LULUC 矩阵。该数据库促进了对土地利用变化的更全面评估，量化了 2001 年至 2012 年期间不同土地类别之间发生的变化，为评估 LULUC 造成的环境影响提供了关键信息。这些数据允许进行全球规模的土地利用变化分析，要求不仅基于土地覆盖，而且还基于土地用途来区分土地类型。该空间明确数据集也可以作为进一步研究的起点，旨在通过空间统计建模确定土地利用变化的驱动因素。
