美国科罗拉多州西北部叶类植物(Euphorbia esula, L.)的远程制图

Chloe M. Mattilio, Daniel R. Tekiela, U. Norton
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摘要

叶芽菜(Euphorbia esula L.)被引入美国科罗拉多州西北部的扬帕河已有40多年的历史,洪水和径流事件将叶芽菜的繁殖体转移到邻近的景观中。叶状花序在河道以外的蔓延尚未被绘制和记录,本研究旨在绘制扬帕河谷叶状花序的发生情况。2019年至2021年夏季进行了重要的利益相关者测绘工作,获得了关于主通道叶草存在和缺失的优秀空间数据。2019年夏季,利用多光谱SPOT七卫星图像、利益相关者地面测绘工作和明亮的黄绿色叶菜花苞片来解释图像,识别密集、未被遮挡的叶菜花斑块,并将其数字化。然后使用来自叶菜和其他土地覆盖类别(概括为“非叶菜”)的训练样本的光谱特征来训练随机森林机器学习分类。在2021年夏天,将生成的分类地图与多光谱卫星图像和利益相关者地面绘制的叶草存在情况进行了比较。发现了不匹配,并确定了271个验证位置,导航到并评估了叶状茎的存在。叶芽菜训练样本分类准确率达96%。正确分类的阔叶花序位置比未分类的阔叶花序位置具有更高的覆盖度和更低的冠层。生长在灌木冠层下或作为单株沿河岸生长的叶状花序更容易被遗漏。对在验证地点发现的其他植物物种的频率分析确定,光雀麦(Bromus inermis Leyss.),蒲公英(Taraxacum officinale L.)和柳树(Salix sp.)最常被错误归类为叶状花。综上所述,卫星多光谱影像可用于阔叶阔叶植物密集覆盖区阔叶阔叶植物的遥感检测,但在稀疏阔叶阔叶植物和弥散阔叶阔叶植物侵染的识别方面还需要做更多的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Remote mapping of leafy spurge (Euphorbia esula, L.) in Northwestern Colorado
Leafy spurge (Euphorbia esula L.) has been introduced to the Yampa River in Northwestern Colorado for over 40 years and flood and runoff events transport leafy spurge propagules onto adjacent landscapes. The spread of leafy spurge beyond the river channels has yet to be mapped and recorded, and this research was conducted to map leafy spurge occurrence in the Yampa River Valley. Significant stakeholder mapping efforts took place in the summer of 2019–2021, leading to excellent spatial data on leafy spurge presence and absence along the main channel. In summer 2019, multispectral SPOT seven satellite imagery, stakeholder ground mapping efforts, and bright yellow-green leafy spurge bracts were used to interpret imagery, identify dense, unobscured patches of leafy spurge, and digitize them. Spectral signatures from training samples for leafy spurge and other land cover classes (generalized as “not leafy spurge”) were then used to train a Random Forest machine learning classification. In the summer of 2021, generated classification maps were compared to multispectral satellite imagery and stakeholder ground mapped leafy spurge presence. Mismatches were identified, and 271 validation locations were identified, navigated to, and evaluated for leafy spurge presence. Leafy spurge training samples were classified with 96% accuracy. Correctly classified leafy spurge locations had higher leafy spurge coverage and lower overstory canopy than missed leafy spurge locations. Leafy spurge growing beneath shrub canopy or growing as individual plants along the riverbanks were more likely to be missed. A frequency analysis for other plant species found at validation locations determined that smooth brome (Bromus inermis Leyss.), dandelion (Taraxacum officinale L.), and willow (Salix sp.) were most frequently misclassified as leafy spurge. In conclusion, multispectral satellite imagery was useful at remote detection of leafy spurge in open areas with dense leafy spurge coverage, but more work must be done for identification of sparse and diffuse leafy spurge infestations.
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