论文标题
协调卢卡斯（Lucas）的原位数据和土地覆盖的照片，并在欧盟的5次三年级调查中使用
Harmonised LUCAS in-situ data and photos on land cover and use from 5 tri-annual surveys in the European Union
论文作者
论文摘要
准确地表征土地表面随着地球观测的变化需要地理位置的地面真相。在欧盟（EU）中，自2006年以来，根据土地使用/覆盖面积调查（Lucas）收集了一项经过三年一次的土地覆盖和土地使用样本。在五次卢卡斯调查中收集了651,780个独特位置的1,351,293个观测值，共117个变量以及540万张照片。到目前为止，这些数据从未被协调到一个数据库中，从而限制了对信息的全面开发。本文介绍了卢卡斯点采样/测量方法，包括收集标准变量，例如土地覆盖，环境参数以及完整的分辨率格局和点照片，然后描述了协调过程。由此产生的统一数据库是土地覆盖物上最全面的原位数据集，并在欧盟使用。该数据库对于土地使用和土地覆盖变化的地理空间和统计分析很有价值。此外，最新的计算进步（例如深度学习）将增强其提供多个时间内原地数据的潜力。
Accurately characterizing land surface changes with Earth Observation requires geo-localized ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1,351,293 observations at 651,780 unique locations for 117 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.
