Roads and roadsides provide dispersal channels for non-native invasive alien plants (IAP), many of which hold devastating impacts in the economy, human health, biodiversity and ecosystem functionality. Remote sensing is an essential tool for efficiently assessing and monitoring the dynamics of IAP along roads. In this study, we explore the potentialities of object based image analysis (OBIA) approach to map several invasive plant species along roads using very high spatial resolution imagery. We compared the performance of OBIA approaches implemented in one open source software (OTB/Monteverdi) against those available in two proprietary programs (eCognition and ArcGIS). We analysed the images by two sequential processes. First, we obtained a land-cover map for 15 study sites by segmenting the images with the algorithms Mean Shift Segmentation (MSS) and Multiresolution Segmentation (MRS), and by classifying the segmented images with the algorithms Support Vector Machine (SVM), Nearest Neighbour Classifier (NNC) and Maximum Likelihood Classifier (MLC). We created a mask using the polygons classified as non-vegetation to crop the images of the 15 study sites. Second, we repeated the previous segmentation and classification steps over the 15 masked images of vegetated areas using the same algorithms. OTB/Monteverdi, with MSS and SVM algorithms, showed to be a good software for land-cover mapping (OA = 87.0%), as well as ArcGIS, with MSS and MLC algorithms (OA = 84.3%). However, these two programs, using the same segmentation algorithms, did not achieve good accuracy results when mapping IAP species (OA_{OTB/Monteverdi} = 63.3%; OA_ArcGIS = 45.7%). eCognition, with MRS and NNC algorithms, reached better classification results in both land-cover and IAP maps (OA_Land-cover = 95.7%; OA_{Invasive-plant} = 92.8%). ’Bare soil’ and ‘Road’, and ‘A. donax’ were the classes with best and worst overall accuracy, respectively, when mapping land-cover classes in the three programs. ‘Other trees’ was the class with the most accurate and significant differences in the three programs when mapping IAP species. The separation of each invasive species should be improved with a phenology-based design of field surveys. This study demonstrates the effectiveness of sequential segmentation and classification of RS data for mapping and monitoring plant invasions along linear infrastructures, which allows to reduce the time, cost and hazard of extensive field campaigns along roadsides.

道路和路旁为非本地外来入侵植物（IAP）提供了传播渠道，其中许多对经济，人类健康，生物多样性和生态系统功能具有破坏性影响。遥感是有效评估和监测道路沿线IAP动态的重要工具。在这项研究中，我们探索使用基于对象的图像分析（OBIA）方法使用非常高的空间分辨率图像在道路上绘制几种入侵植物物种的潜力。我们将在一种开源软件（OTB / Monteverdi）中实现的OBIA方法的性能与在两个专有程序（eCognition和ArcGIS）中可用的OBIA方法的性能进行了比较。我们通过两个顺序的过程来分析图像。第一，通过使用均值漂移分割（MSS）和多分辨率分割（MRS）算法对图像进行分割，并通过支持向量机（SVM），最近邻分类器算法对分割后的图像进行分类，我们获得了15个研究地点的土地覆盖图（NNC）和最大似然分类器（MLC）。我们使用分类为非植被的多边形创建了一个蒙版，以裁剪15个研究站点的图像。其次，我们使用相同的算法对15个植被区域的蒙版图像重复了先前的分割和分类步骤。具有MSS和SVM算法的OTB / Monteverdi被证明是用于土地覆盖制图（OA = 87.0％）的优秀软件，以及具有MSS和MLC算法（OA = 84.3％）的ArcGIS。但是，这两个程序使用相同的细分算法，_{OTB / Monteverdi}  = 63.3％；OA _ArcGIS  = 45.7％）。借助MRS和NNC算法，eCognition在土地覆盖和IAP地图中均达到了更好的分类结果（OA_土地覆盖 = 95.7％； OA_入侵植物 = 92.8％）。“裸土”和“道路”，以及“ A. donax”在这三个程序中绘制土地覆被类别时，分别是总体准确性最高和最差的类别。在绘制IAP物种时，“其他树木”是这三个程序中最准确，最重要的区别。应当通过基于物候学的现场调查设计来改善每种入侵物种的分离。这项研究证明了对RS数据进行顺序分割和分类对于绘制和监视线性基础设施上的植物入侵的有效性，从而可以减少沿路边进行大规模野战的时间，成本和危害。