Malmstrom Carolyn M, Butterfield H Scott, Planck Laura, Long Christopher W, Eviner Valerie T
Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America.
Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan, United States of America.
PLoS One. 2017 Oct 9;12(10):e0181665. doi: 10.1371/journal.pone.0181665. eCollection 2017.
Invasive weeds threaten the biodiversity and forage productivity of grasslands worldwide. However, management of these weeds is constrained by the practical difficulty of detecting small-scale infestations across large landscapes and by limits in understanding of landscape-scale invasion dynamics, including mechanisms that enable patches to expand, contract, or remain stable. While high-end hyperspectral remote sensing systems can effectively map vegetation cover, these systems are currently too costly and limited in availability for most land managers. We demonstrate application of a more accessible and cost-effective remote sensing approach, based on simple aerial imagery, for quantifying weed cover dynamics over time. In California annual grasslands, the target communities of interest include invasive weedy grasses (Aegilops triuncialis and Elymus caput-medusae) and desirable forage grass species (primarily Avena spp. and Bromus spp.). Detecting invasion of annual grasses into an annual-dominated community is particularly challenging, but we were able to consistently characterize these two communities based on their phenological differences in peak growth and senescence using maximum likelihood supervised classification of imagery acquired twice per year (in mid- and end-of season). This approach permitted us to map weed-dominated cover at a 1-m scale (correctly detecting 93% of weed patches across the landscape) and to evaluate weed cover change over time. We found that weed cover was more pervasive and persistent in management units that had no significant grazing for several years than in those that were grazed, whereas forage cover was more abundant and stable in the grazed units. This application demonstrates the power of this method for assessing fine-scale vegetation transitions across heterogeneous landscapes. It thus provides means for small-scale early detection of invasive species and for testing fundamental questions about landscape dynamics.
入侵杂草威胁着全球草原的生物多样性和牧草生产力。然而，这些杂草的管理受到诸多限制，一方面难以在大片区域检测小规模的侵扰，另一方面对景观尺度的入侵动态理解有限，包括斑块扩张、收缩或保持稳定的机制。虽然高端高光谱遥感系统能够有效绘制植被覆盖图，但目前对于大多数土地管理者来说，这些系统成本过高且可用性有限。我们展示了一种基于简单航空影像的更易获取且成本效益更高的遥感方法在量化杂草覆盖随时间变化方面的应用。在加利福尼亚一年生草原，感兴趣的目标群落包括入侵杂草（节节麦和螺旋黑麦草）和优质牧草物种（主要是燕麦属和雀麦属）。检测一年生禾本科杂草入侵以一年生植物为主的群落尤其具有挑战性，但我们能够利用每年两次（生长季中期和末期）获取的影像进行最大似然监督分类，基于它们在生长高峰期和衰老期的物候差异，持续地对这两个群落进行特征描述。这种方法使我们能够以1米的尺度绘制杂草主导的覆盖图（正确检测出景观中93%的杂草斑块），并评估杂草覆盖随时间的变化。我们发现，在数年没有显著放牧的管理单元中，杂草覆盖比有放牧的单元更普遍且持久，而在放牧单元中，牧草覆盖更丰富且稳定。该应用展示了这种方法在评估异质景观中精细尺度植被变化方面的能力。因此，它为入侵物种的小规模早期检测以及检验关于景观动态的基本问题提供了手段。
