Benefits of hyperspectral remote sensing for tracking plant invasions

Biological invasions are viewed as a significant component of global change and have become a serious threat to natural ecosystems. Concerns for the implications and consequences of successful invasions have stimulated a considerable amount of research aimed at understanding the mechanisms of invasion and providing guidelines for control and management efforts. In this paper, we aim to report what remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. We review the utility of hyperspectral remote sensing for detecting, mapping, and predicting the spatial spread of invasive species. A range of topics are discussed, including the tradeoff between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. We found that hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. The information collected by sensors on the spatial distribution of invasive species can help land managers to make long-term constructive conservation plans for protecting and maintaining natural ecosystems