Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Key Laboratory of Environment Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China.
Int J Environ Res Public Health. 2020 Jan 22;17(3):707. doi: 10.3390/ijerph17030707.
Modern global cropland products have been widely used to assess the impact of land use and cover change (LUCC) on carbon budgets, climate change, terrestrial ecosystems, etc. However, each product has its own uncertainty, and inconsistencies exist among different products. Understanding the reliability of these datasets is essential for knowing the uncertainties that exist in the study of global change impact forced by cropland reclamation. In this paper, we propose a set of coincidence assessments to identify where reliable cropland distribution is by overlaying ten widely used global land cover/cropland datasets around 2000 AD. A quantitative assessment for different spatial units is also performed. We further discuss the spatial distribution characteristics of different coincidence degrees and explain the reasons. The results show that the high-coincidence proportion is only 40.5% around the world, and the moderate-coincidence and low-coincidence proportion is 18.4% and 41.1%, respectively. The coincidence degrees among different continents and countries have large discrepancies. The coincidence is relatively higher in Europe, South Asia and North America, while it is very poor in Latin America and Africa. The spatial distribution of high and moderate coincidence roughly corresponds to the regions with suitable agricultural conditions and intensive reclamation. In addition to the random factors such as the product's quality and the year it represented, the low coincidence is mainly caused by the inconsistent land cover classification systems and the recognition capability of cropland pixels with low fractions in different products.
现代全球耕地产品已被广泛用于评估土地利用和覆被变化 (LUCC) 对碳预算、气候变化、陆地生态系统等的影响。然而，每种产品都有其自身的不确定性，不同产品之间存在不一致性。了解这些数据集的可靠性对于了解耕地开垦对全球变化影响研究中存在的不确定性至关重要。在本文中，我们提出了一套一致性评估方法，通过叠加十套广泛使用的全球土地覆盖/耕地数据集，确定可靠的耕地分布位置，时间范围为公元 2000 年左右。我们还对不同空间单元进行了定量评估。我们进一步讨论了不同一致性程度的空间分布特征，并解释了原因。结果表明，全球范围内高一致性比例仅为 40.5%，中一致性和低一致性比例分别为 18.4%和 41.1%。不同大陆和国家之间的一致性程度存在很大差异。欧洲、南亚和北美地区的一致性相对较高，而拉丁美洲和非洲地区的一致性则非常差。高一致性和中一致性的空间分布大致对应于农业条件适宜和开垦强度较大的地区。除了产品质量和代表年份等随机因素外，低一致性主要是由于不一致的土地覆盖分类系统和不同产品中低分数耕地像素的识别能力造成的。
