Abstract The ability to accurately detect and quantify the presence of invasive plants is integral in their management, treatment, and removal. Remotely piloted aircraft systems (RPASs) are becoming an important remote sensing tool for mapping invasive plants. Spotted knapweed (Centaurea maculosa) is highly invasive in North America. This study developed and evaluated a novel method for analysis of multispectral data to map the relative cover of spotted knapweed in a heterogeneous grassland community. The method developed in this work, termed metapixel-based image analysis, segments the image into a grid of metapixels for which grey level co-occurrence matrix (GLCM)-based statistics can be computed as descriptive features. Using RPAS-acquired multispectral imagery and plant species inventories performed on 1m2 quadrats, a random forest classifier was trained to predict the qualitative degree of spotted knapweed ground cover within each metapixel. The best mean cross-validation score achieved was 71.3% when describing relative ground cover of spotted knapweed, with an accuracy of 66.0% when applied to an independent validation dataset. Analysis of the performance of metapixel-based image analysis on this study site suggests that feature optimization, including feature subset selection, and the use of GLCM-based texture features is of critical importance for achieving an accurate classification.

中文翻译：

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用 RPAS 获得的多光谱图像监测斑竹 (Centaurea maculosa) 对草原的入侵

摘要 准确检测和量化入侵植物存在的能力是其管理、处理和移除不可或缺的一部分。遥控飞机系统 (RPAS) 正在成为绘制入侵植物图的重要遥感工具。斑点鼠尾草（Centaurea maculosa）在北美具有高度入侵性。本研究开发并评估了一种用于分析多光谱数据的新方法，以绘制异质草原群落中斑点紫罗兰的相对覆盖率。在这项工作中开发的方法，称为基于元像素的图像分析，将图像分割成元像素网格，其基于灰度共生矩阵 (GLCM) 的统计数据可以计算为描述性特征。使用 RPAS 获得的多光谱图像和在 1 平方米样方上进行的植物物种清单，训练随机森林分类器来预测每个元像素内斑点紫荆地面覆盖的定性程度。在描述斑紫罗兰的相对地面覆盖时，获得的最佳平均交叉验证分数为 71.3%，应用于独立验证数据集时的准确率为 66.0%。对本研究站点上基于元像素的图像分析性能的分析表明，特征优化，包括特征子集选择，以及基于 GLCM 的纹理特征的使用对于实现准确分类至关重要。应用于独立验证数据集时为 0%。对本研究站点上基于元像素的图像分析性能的分析表明，特征优化，包括特征子集选择，以及基于 GLCM 的纹理特征的使用对于实现准确分类至关重要。应用于独立验证数据集时为 0%。对本研究站点上基于元像素的图像分析性能的分析表明，特征优化，包括特征子集选择，以及基于 GLCM 的纹理特征的使用对于实现准确分类至关重要。