Invasive alien plants are considered a major driver of global biodiversity loss. Therefore, there is a huge demand of spatial and temporal data on their distribution for investigating possible drivers of species invasions and for predictions of future distributions. We use Google Street View imagery (GSV) as a new source of spatial and temporal data. GSV provides millions of panoramic views along road networks worldwide allowing for the identification of many plant species, including invasive ones. Thus, GSV has a great potential to support ecological research in documenting species distribution, but reliable validation of its precision and accuracy is lacking. Here, we describe and evaluate an approach using GSV to visually track the spread of invasive alien plants, the North American goldenrods (Solidago canadensis and S. gigantea) occurring abundantly along road network in Poland (Central Europe). We determined presence/absence of the species along 160 randomly selected transects of a length of 500 m by visual inspection of GSV images and compared it with field surveys at the same transects. We show that the occurrence of goldenrods in GSV is a reliable predictor of their occurrence in the wild. Sampling parameters, like road width, season when GSV pictures were taken and number of months elapsed since taking the GSV pictures, did not change the correlation between outputs of the two methods (GSV and field sampling). Furthermore, both the occurrence of goldenrods observed in the field and their occurrence in GSV have similar relations to habitat characteristics investigated (the same direction of relationship and similar effect size). We suggest Google Street View images may be an additional tool to be used in the detection and tracking of the spread of invasive alien plants along roadsides. The approach may be useful in assessing temporal changes in roadside vegetation and managing problematic plant species across large spatial scales and may contribute to the further development of more efficient sampling methods in ecological studies.

外来入侵植物被认为是全球生物多样性丧失的主要驱动力。因此，对空间和时间数据的分布有巨大的需求，以调查物种入侵的可能驱动因素和对未来分布的预测。我们将Google街景图像（GSV）用作空间和时间数据的新来源。GSV可在全球道路网络上提供数百万个全景，从而可以识别许多植物物种，包括入侵物种。因此，GSV在记录物种分布的生态学研究方面具有巨大的潜力，但缺乏对其准确性和准确性的可靠验证。在这里，我们描述并评估了一种使用GSV直观地跟踪外来入侵植物北美golden（Solidago canadensis和S. gigantea）沿波兰（中欧）的公路网络大量出现。我们通过目视检查GSV图像，确定了沿160个随机选择的长度为500 m的样带的物种存在与否，并将其与相同样带的野外调查进行了比较。我们表明，GSV中金毛病的发生是在野外发生的可靠预测因子。采样参数（例如道路宽度，拍摄GSV照片的季节以及自拍摄GSV照片以来经过的月数）不会改变两种方法（GSV和现场采样）的输出之间的相关性。此外，在野外观察到的金毛发生及其在GSV中的发生都与所研究的生境特征具有相似的关系（相同的方向和相似的影响大小）。我们建议Google街景图像可能是检测和跟踪外来入侵植物在路边扩散情况的附加工具。该方法可能有助于评估路边植被的时空变化，并在较大的空间尺度上管理有问题的植物物种，并且可能有助于生态学研究中更有效采样方法的进一步发展。