Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature's fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel's class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research.

中文翻译：

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让你的地图变得模糊！——分类概率和植物区系梯度作为植被遥感清晰映射的替代方案

基于遥感数据将植被映射为硬类是一种常用的方法，尽管这种清晰的分类表示不符合自然的模糊性。因此，在生成的地图中很难描述交错带中植物物种组成的逐渐转变以及不同斑块之间微弱的组成差异。几个概念有望提供更好的植被图。这些包括（1）模糊分类（又名软分类），它考虑了图像像素的类成员的概率和（2）基于排序的梯度映射，它将植物物种组成描述为植物区系连续体并避免对植被进行分类描述模式。迄今为止，还没有对这些方法进行系统和全面的比较。因此，本文概述了模糊分类和梯度映射的最新技术，并在案例研究中比较了这些方法。讨论了这些方法的优缺点，并将它们的性能与硬分类（又名清晰或布尔分类）进行了比较。梯度映射最好地保存了原始数据中的信息，并且不需要先验分类。模糊分类在信息丢失方面接近，同样保留了植被的连续性，然而，仍然依赖于先验分类。先验的需要分类可能是一个缺点，或者在其他情况下是一个优点，因为它允许使用分类输入数据而不是排序所需的详细植被记录。这两种方法都支持空间显式精度分析，这进一步提高了输出地图的实用性。梯度映射和模糊分类相对于硬分类具有多种优势，始终可以转换为清晰的映射，并且通常适用于各种数据结构。因此，我们建议在生态研究中使用这些方法而不是硬分类。