ABSTRACT Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas.

中文翻译：

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使用潜在狄利克雷分配进行土地覆盖协调

摘要 制作大面积土地覆盖图以满足不同的信息需求。具有部分或完全空间和/或时间重叠、不同图例以及相似类别不同精度的土地覆盖图越来越普遍。为了解决这些问题并结合两个 30 米分辨率的土地覆盖产品，我们使用潜在狄利克雷分配 (LDA) 模型实施了协调程序。LDA 模型使用来自多个地图的区域化类共现，通过从类共现中统计表征土地属性，为每个像素生成统一的类标签。我们评估了多种协调方法：单独使用 LDA 模型并结合更常用的信息源进行协调（即误差矩阵和语义亲和度分数）。将结果与使用简单图例人行横道生成的基准地图进行比较，并表明使用带有误差矩阵的 LDA 输出表现更好，并且将源地图之间不一致区域的协调地图整体精度提高了 6-19%。我们的结果揭示了误差矩阵对协调的重要性，因为排除误差矩阵会使整体准确度降低 4-20%。本文中展示的基于 LDA 的协调方法是定量的、透明的、可移植的，并且可以有效地利用大面积多个土地覆盖图的优势。我们的结果揭示了误差矩阵对协调的重要性，因为排除误差矩阵会使整体准确度降低 4-20%。本文中展示的基于 LDA 的协调方法是定量的、透明的、可移植的，并且可以有效地利用大面积多个土地覆盖图的优势。我们的结果揭示了误差矩阵对协调的重要性，因为排除误差矩阵会使整体准确度降低 4-20%。本文中展示的基于 LDA 的协调方法是定量的、透明的、可移植的，并且可以有效地利用大面积多个土地覆盖图的优势。