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Enhancing an unsupervised clustering algorithm with a spatial contiguity constraint for river habitat analysis
Ecohydrology ( IF 2.6 ) Pub Date : 2021-02-16 , DOI: 10.1002/eco.2285
Erik Rooijen 1 , Davide Vanzo 1 , David F. Vetsch 1 , Robert M. Boes 1 , Annunziato Siviglia 2
Affiliation  

The spread of two-dimensional numerical hydrodynamic tools for ecohydraulic applications allowed for the development of automatic habitat detection methods, adopted as predicting tools for river habitat analysis. These automatic approaches differ for the employed identification rules, such as preference curves, fuzzy rules and clustering methods. Previous research has shown promising results in the automatic identification of mesoscale habitat patches by using clustering algorithms together with numerical hydrodynamic model results. These algorithms attempt to implement and simulate some of the expert-based requirements adopted in the field to delineate habitat patches. Spatial contiguity is one of such expert-based requirements that has not been enforced and exploited in automatic mesohabitat identification so far. In this work, we propose a novel tool (BASEmeso) based on an agglomerative hierarchical clustering algorithm where we enforced a spatial contiguity criteria. We compare our approach with a more established method without spatial constraints, considering a synthetic river reach where the composition of mesohabitat patches is known a priori, and on three experimental river reaches, to investigate the effects of different river morphologies. Our results show that when employing a contiguity constraint, a patch's extent is better captured, different patches can be distinguished better and the distribution of patch characteristics is smoother. This holds for all investigated morphologies. Together, it suggests that including a spatial contiguity constraint can improve the automatic delineation of river mesohabitat patches. The proposed methodology could positively contribute in the development of automatic, objective and predictive meso-scale habitat assessment workflows.

中文翻译:

增强具有空间邻接约束的无监督聚类算法,用于河流栖息地分析

用于生态水力应用的二维数值水动力工具的传播允许开发自动栖息地检测方法,用作河流栖息地分析的预测工具。这些自动方法因采用的识别规则而异,例如偏好曲线、模糊规则和聚类方法。先前的研究表明,通过使用聚类算法和数值水动力模型结果,在自动识别中尺度栖息地斑块方面取得了可喜的成果。这些算法试图实现和模拟该领域采用的一些基于专家的要求,以描绘栖息地斑块。空间连续性是这种基于专家的要求之一,迄今为止尚未在自动中生境识别中得到实施和利用。在这项工作中,我们提出了一种基于凝聚层次聚类算法的新工具 (BASEmeso),我们在其中强制执行了空间连续性标准。我们将我们的方法与没有空间限制的更成熟的方法进行比较,考虑到先验已知中生境斑块组成的合成河段,并在三个实验河段上研究不同河流形态的影响。我们的结果表明,当采用邻接约束时,可以更好地捕获补丁的范围,可以更好地区分不同的补丁,补丁特征的分布更平滑。这适用于所有研究的形态。总之,它表明包括空间邻接约束可以改善河流中生境斑块的自动划分。
更新日期:2021-02-16
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