NASA Ames Research Center, University of California-Berkeley, Moffett Field, CA 94035, USA.
J Environ Qual. 2010 Apr 13;39(3):955-63. doi: 10.2134/jeq2009.0158. Print 2010 May-Jun.
The western United States is under invasion from cheatgrass (Bromus tectorum L.), an annual grass that alters the pattern of phenology in the ecosystems it infests. This study was conducted to investigate methods for monitoring this invasion. As a result of its annual phenology, cheatgrass is not only an extremely competitive invader, it is also detectible from time series of remotely sensed data. Using the MODerate resolution imaging spectro-radiometer (MODIS) normalized difference vegetation index (NDVI) and spatially interpolated precipitation data, we fit splines to monthly observations to generate time series of NDVI and precipitation from 2001 to 2005 in the state of Utah. We generated a variety of existing metrics of phenology and developed several metrics to describe the relationship between the NDVI and the precipitation time series. These metrics not only describe the pattern of response to precipitation in ecosystems of various infestation levels, but they are predictive of cheatgrass infestation. We tested several popular data mining algorithms to investigate the predictive ability of the time series-based metrics. Our results show that presence-absence can be predicted with 90% accuracy, and four categorical levels of infestation can be predicted with 71% accuracy. The results show that time series-based metrics are effective in prediction of cheatgrass abundance levels, are more effective than metrics based only on NDVI, and provide more information that existing approaches to cheatgrass mapping using phenology. These results are important for designing strategies to monitor ecosystem health over long periods of time at a landscape scale.
美国西部正遭受毒草（Bromus tectorum L.）的侵袭，这种一年生草本植物改变了其侵害的生态系统的物候模式。本研究旨在探讨监测这种入侵的方法。由于其一年生的物候，毒草不仅是一种极具竞争力的入侵物种，而且还可以从遥感数据的时间序列中检测到。本研究使用中分辨率成像光谱仪（MODIS）归一化差异植被指数（NDVI）和空间插值降水数据，通过拟合样条函数，生成了 2001 年至 2005 年犹他州的 NDVI 和降水时间序列。本研究生成了多种现有的物候指标，并开发了几种描述 NDVI 和降水时间序列之间关系的指标。这些指标不仅描述了不同受侵水平的生态系统对降水的响应模式，而且还可以预测毒草的受侵情况。本研究测试了几种流行的数据挖掘算法，以研究基于时间序列的指标的预测能力。研究结果表明，存在/不存在可以以 90%的准确率进行预测，并且可以以 71%的准确率预测四种不同程度的受侵情况。结果表明，基于时间序列的指标在预测毒草丰度水平方面是有效的，比仅基于 NDVI 的指标更有效，并提供了更多有关使用物候学进行毒草制图的现有方法的信息。这些结果对于设计在景观尺度上长时间监测生态系统健康状况的策略非常重要。
