Hollister Jeffrey, Stachelek Joseph
US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, RI, USA.
Michigan State University, Department of Fisheries and Wildlife, Natural Resources Building, East Lansing, MI, USA.
F1000Res. 2017 Sep 21;6:1718. doi: 10.12688/f1000research.12512.1. eCollection 2017.
Metrics describing the shape and size of lakes, known as lake morphometry metrics, are important for any limnological study. In cases where a lake has long been the subject of study these data are often already collected and are openly available. Many other lakes have these data collected, but access is challenging as it is often stored on individual computers (or worse, in filing cabinets) and is available only to the primary investigators. The vast majority of lakes fall into a third category in which the data are not available. This makes broad scale modelling of lake ecology a challenge as some of the key information about in-lake processes are unavailable. While this valuable information may be difficult to obtain, several national datasets exist that may be used to model and estimate lake morphometry. In particular, digital elevation models and hydrography have been shown to be predictive of several lake morphometry metrics. The R package has been developed to utilize these data and estimate the following morphometry metrics: surface area, shoreline length, major axis length, minor axis length, major and minor axis length ratio, shoreline development, maximum depth, mean depth, volume, maximum lake length, mean lake width, maximum lake width, and fetch. In this software tool article we describe the motivation behind developing , discuss the implementation in R, and describe the use of with an example of a typical use case.
描述湖泊形状和大小的指标，即湖泊形态测量指标，对任何湖沼学研究都很重要。对于长期以来一直是研究对象的湖泊，这些数据通常已经收集并可公开获取。许多其他湖泊也收集了这些数据，但获取数据具有挑战性，因为这些数据通常存储在个人计算机上（或者更糟糕的是，存放在文件柜中），并且只有主要研究人员才能获取。绝大多数湖泊属于第三类，即没有这些数据。这使得湖泊生态的大规模建模成为一项挑战，因为一些关于湖内过程的关键信息无法获取。虽然这些有价值的信息可能难以获得，但存在几个国家数据集可用于建模和估计湖泊形态测量。特别是，数字高程模型和水文地理已被证明可预测几个湖泊形态测量指标。已经开发了R包来利用这些数据并估计以下形态测量指标：表面积、岸线长度、长轴长度、短轴长度、长轴与短轴长度比、岸线发育、最大深度、平均深度、体积、最大湖泊长度、平均湖泊宽度、最大湖泊宽度和吹程。在这篇软件工具文章中，我们描述了开发该软件包的动机，讨论了在R中的实现，并通过一个典型用例示例描述了该软件包的使用。
