Tuck Sean L, Phillips Helen Rp, Hintzen Rogier E, Scharlemann Jörn Pw, Purvis Andy, Hudson Lawrence N
Department of Plant Sciences, University of Oxford Oxford, OX1 3RB, U.K.
Department of Life Sciences, Imperial College London, Silwood Park Buckhurst Road, Ascot, Berkshire, SL5 7PY, U.K ; Department of Life Sciences, Natural History Museum Cromwell Road, London, SW7 5BD, U.K.
Ecol Evol. 2014 Dec;4(24):4658-68. doi: 10.1002/ece3.1273. Epub 2014 Dec 2.
Remotely sensed data - available at medium to high resolution across global spatial and temporal scales - are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R (2) values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools).
在全球空间和时间尺度上可获取的中等到高分辨率的遥感数据,是生态学家的宝贵资源。特别是美国国家航空航天局(NASA)的中分辨率成像光谱仪(MODIS)所提供的产品,能实现全球每日两次覆盖,已被广泛用于生态应用。我们展示了MODISTools,这是一个R软件包,旨在改进对遥感MODIS数据的访问、下载和处理。MODISTools能自动完成从任意数量的地点、时间段和MODIS产品进行数据下载和处理的过程。这种自动化降低了人为错误的风险,与逐个地点手动下载相比,也减少了研究人员所需的工作量。该软件包对于包括多个地点的生态研究(如荟萃分析、观测网络和全球分布的实验)将特别有用。我们给出了MODISTools所提供的简单、可重复工作流程以及在此过程中进行的检查的示例。最终产品是适合进行统计建模的格式。我们分析了在温带森林的526个地点观测到的多个高等分类群的物种丰富度与植被指数(地上净初级生产力的度量)之间的关系。我们为每个已采样物种丰富度的地点下载了MODIS衍生的植被指数时间序列,并将数据汇总为三个度量:时间序列最大值、时间平均值和时间变异性。平均而言,物种丰富度与我们的植被指数度量呈正相关。不同的高等分类群与植被指数呈现出不同的正相关关系。模型具有较高的R²值,表明高等分类群身份和植被指数梯度共同解释了我们数据中物种丰富度的大部分变异。MODISTools可在Windows、Mac和Linux平台上使用,可从CRAN和GitHub(https://github.com/seantuck12/MODISTools)获取。