A Multi-Prong Approach for Monitoring Hydrilla [Hydrilla verticillate (L. fil.) Royle] in Lakes and Reservoirs

IF 1.1 Q3 FISHERIES
Jackson C. Glomb, Roger C. Lowe III, James L. Shelton, Martin J. Hamel
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Abstract

Hydrilla verticillatata is an invasive aquatic macrophyte that has negatively impacted freshwater ecosystems in areas around the world. As a result, lake managers often seek ways to manage hydrilla and mitigate its spread. Having effective methods for assessing hydrilla abundance in a system is imperative, but traditional assessment methods are expensive, time consuming, and may be inaccurate. Contemporary remote sensing techniques have the potential to provide a faster and more effective means for obtaining hydrilla coverage estimates. Therefore, we set out to determine what methodologies provide the most efficient and effective approach for assessing hydrilla in a large reservoir. We used spectral indices on satellite imagery and unoccupied aerial vehicle (UAV) imagery to develop an image classification scheme for quantifying hydrilla occurrence. We also used down-scan sonar in conjunction with the BioBase software to generate hydrilla coverage and biovolume estimates. the normalized difference vegetation index derived from 3-m resolution multispectral satellite imagery proved effective for training an image classification, providing a reliable means at quantifying hydrilla colonization through time. The coloration index combined with UAV imagery yielded pixel values for hydrilla that were distinct from other aquatic plant species and can be used to verify results of satellite imagery. However, these methods were ineffective when hydrilla had not yet grown to maximum shoot length. In these conditions, down-scan sonar remained a valuable assessment tool to supplement aerial remote sensing techniques. These results equip lake managers with the knowledge to make more informed decisions, quickly assess hydrilla occurrence, and develop effective management strategies.

Abstract Image

监测湖泊和水库水草的多管齐下方法
水草(Hydrilla verticillatata)是一种入侵性水生大型植物,对世界各地的淡水生态系统造成了负面影响。因此,湖泊管理者经常寻求管理水草和减少其蔓延的方法。必须采用有效的方法来评估水草在一个系统中的丰度,但传统的评估方法成本高、耗时长,而且可能不准确。现代遥感技术有可能提供一种更快、更有效的方法来获取水草覆盖率估计值。因此,我们着手确定哪些方法可为评估大型水库中的水草提供最高效、最有效的方法。我们使用卫星图像和无人飞行器 (UAV) 图像上的光谱指数制定了一套图像分类方案,用于量化水草的出现情况。我们还将下扫声纳与 BioBase 软件结合使用,生成水草覆盖率和生物体积估计值。事实证明,从 3 米分辨率多光谱卫星图像中得出的归一化差异植被指数可有效训练图像分类,为量化水草的定植时间提供可靠的方法。着色指数结合无人机图像得出的水草像素值有别于其他水生植物物种,可用于验证卫星图像的结果。不过,当水草尚未长到最大芽长时,这些方法就不起作用了。在这种情况下,下扫描声纳仍是一种有价值的评估工具,可作为航空遥感技术的补充。这些结果为湖泊管理者提供了知识,使他们能够做出更明智的决定,快速评估水草的发生情况,并制定有效的管理策略。
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