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Classification of salt marsh vegetation using edaphic and remote sensing-derived variables

机译：利用蒸发和遥感变量对盐沼植被进行分类

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Salt marsh plant communities are known for their striking patterns of vertical zonation. Two of the most important edaphic parameters that affect species distribution patterns are soil salinity and waterlogging, both of which are related to topographical variations and distance to the water. The primary objective of this study was to evaluate whether information on elevation and distance derived through remote sensing could be used to predict plant distributions in a southeastern United States salt marsh. We classified four marsh vegetation classes (tall Spartina alterniflora, medium S. altemiflora/short S. alterniflora, marsh meadow, and Borrichia frutescens/Juncus memerianus) based on landscape metrics obtained from a light detection and ranging (LIDAR)-derived digital elevation model (DEM) and compared results to a classification based on field-collected edaphic variables. Our secondary objective was to compare the performance of linear discriminant analysis (LDA) with non-parametric classification and regression trees (CART) for these classifications. Models based on the edaphic variables soil water content, salinity, and redox potential attained accuracies of 0.62 and 0.71 with LDA and CART, respectively. When the remote sensing-derived variables DEM elevation, slope, distance to the mean high water line, and distance to upland area were used, classification accuracies improved to 0.78 for LDA and 0.79 for CART. Our results suggest that remote sensing-derived metrics can capture edaphic gradients effectively, which makes them especially suited to landscape level analyses of salt marsh plant habitats, with potential application for predicting the effects of sea level rise on salt marsh plant distribution.
机译：盐沼植物群落以垂直分区的醒目的模式而闻名。影响物种分布方式的两个最重要的土壤参数是土壤盐度和涝渍，这两个因素都与地形变化和距水的距离有关。这项研究的主要目的是评估通过遥感获得的有关海拔和距离的信息是否可用于预测美国东南部盐沼的植物分布。我们根据从光检测和测距（LIDAR）得出的数字高程模型获得的景观指标，对四种沼泽植被类别进行了分类（高互花米草，中等链霉菌/短链霉菌，短生链霉菌，沼泽草甸和红景天/芥菜）。 （DEM），然后将结果与基于现场收集的教育变量的分类进行比较。我们的次要目标是将线性判别分析（LDA）与非参数分类和回归树（CART）的性能进行比较。基于前瞻性变量的模型使用LDA和CART分别获得0.62和0.71的准确度，土壤水分，盐度和氧化还原电位。当使用遥感变量DEM高程，坡度，到平均高水位线的距离以及到高地区域的距离时，LDA和CART的分类精度分别提高到0.78和0.79。我们的结果表明，基于遥感的度量标准可以有效捕获深层梯度，这使其特别适用于盐沼植物栖息地的景观水平分析，并有可能用于预测海平面上升对盐沼植物分布的影响。

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