West Amanda M, Evangelista Paul H, Jarnevich Catherine S, Young Nicholas E, Stohlgren Thomas J, Talbert Colin, Talbert Marian, Morisette Jeffrey, Anderson Ryan
Natural Resource Ecology Laboratory, Colorado State University;
Natural Resource Ecology Laboratory, Colorado State University.
J Vis Exp. 2016 Oct 11(116):54578. doi: 10.3791/54578.
Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.
早期发现入侵植物物种对于自然资源管理和生态系统过程保护至关重要。利用卫星遥感绘制入侵植物分布的做法正变得越来越普遍，然而传统的成像软件和分类方法已被证明不可靠。在本研究中，我们测试并评估了五种物种分布模型技术与卫星遥感数据相结合，用于绘制科罗拉多州东南部阿肯色河沿岸入侵柽柳（Tamarix spp.）分布的情况。所测试的模型包括增强回归树（BRT）、随机森林（RF）、多元自适应回归样条（MARS）、广义线性模型（GLM）和最大熵模型（Maxent）。这些分析使用了一个新开发的名为辅助栖息地建模软件（SAHM）的软件包进行。所有模型均使用499个存在点、10000个伪不存在点以及在八个月期间从陆地卫星5专题制图仪（TM）传感器获取的预测变量进行训练，通过检测物候差异来区分柽柳与本地河岸植被。从陆地卫星影像中，我们使用了各个波段，并计算了归一化植被指数（NDVI）、土壤调节植被指数（SAVI）和缨帽变换。基于独立位置数据的阈值独立和阈值依赖评估指标，所有五个模型都成功识别了景观中当前柽柳的分布。为了考虑模型的特定差异，我们生成了所有五个模型的集合，地图输出突出显示了一致区域和不确定区域。我们的结果证明了物种分布模型在分析遥感数据方面的有用性以及集合制图的效用，并展示了SAHM在预处理和执行多个复杂模型方面的能力。
