ABSTRACT

Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data. To assure consistency, multiple images are used, interpreters are trained, sites are interpreted by several individuals, or the procedure includes a review. But little is known about important factors influencing the quality of visually interpreted data. We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers. Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project. Four cycles of visual interpretation were conducted, each was followed by review and feedback. Each interpreted site element was labelled according to dominant land cover type. We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal, training, and environmental categories. Variable importance was assessed using random forest regression. Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts, while the environmental variables complexity and image availability had least impact. Feedback loops reduced updating and hence improved consistency of the interpretations. Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data.

摘要

大型土地覆盖图的参考数据通常是通过对遥感数据的视觉解释来获取的。为了确保一致性，使用了多个图像，对口译员进行了培训，几个人对现场进行了口译，或者该过程包括了审核。但是对于影响视觉解释数据质量的重要因素知之甚少。我们评估了口译员和审稿人之间多个变量对土地覆盖物类别协议的影响。我们的分析涉及为验证哥白尼全球土地服务项目中的全球土地覆盖图而收集的数据。进行了四个视觉解释循环，每个循环之后均进行了回顾和反馈。每个解释的地点元素均根据主要的土地覆盖类型进行标记。我们评估了反馈后的解释更新次数与按个人，培训和环境类别分组的变量之间的关系。使用随机森林回归评估变量重要性。发现个人变量解释器标识符和训练变量时间戳是更新计数的最强预测因子，而环境变量的复杂性和图像可用性影响最小。反馈循环减少了更新，因此提高了解释的一致性。在可视化解释的数据集中实施反馈循环可提高获取的土地覆盖参考数据的一致性。发现个人变量解释器标识符和训练变量时间戳是更新计数的最强预测因子，而环境变量的复杂性和图像可用性影响最小。反馈循环减少了更新，因此提高了解释的一致性。在可视化解释的数据集中实施反馈循环可提高获取的土地覆盖参考数据的一致性。发现个人变量解释器标识符和训练变量时间戳是更新计数的最强预测因子，而环境变量的复杂性和图像可用性影响最小。反馈循环减少了更新，因此提高了解释的一致性。在可视化解释的数据集中实施反馈循环可提高获取的土地覆盖参考数据的一致性。