Application of acoustical remote sensing techniques for ecosystem monitoring of a seagrass meadow

这项研究的重点是设计和测试一个能够长期实地部署的系统，该系统使用声音来监测海草生物量和生产力，与传统技术相比，时间分辨率有显着提高。生态系统生产力相关参数的长期连续测量对于生态系统健康评价和可持续管理具有重要意义。用光学传感器测量溶解氧是估算海草光合作用与水下辐照度关系的最广泛使用的方法。氧气的释放对于维持地下海草根和根茎的有氧条件也是至关重要的，海草根和根茎占植物总生物量的60-80%。然而，在夏季强光条件下经常发生的氧饱和条件下，植物不断释放自由气泡。光学传感器无法在这种过饱和条件下检测到气泡，导致海草光合碳生产被低估。由于氧饱和度在春末至初秋最普遍的较高温度和光照条件下更容易发生，因此只有在低光照条件下或清晨才有可能准确测定生产力。由于声音在水中的传播是非常敏感的气泡的存在下，声学方法提供了一种替代措施，真正的光合作用在海草草甸的氧气生产具有高的时间分辨率。研究人员将参与外展活动，包括德克萨斯大学奥斯汀分校（UT）的新生研究计划（FRI），该计划使一年级学生有机会与教师和研究生一起发起和参与现实世界的研究经验。该项目还将通过在拟议研究中收集的长期数据集的基础上开发一个数据金块，使K-16级的人参与进来，这一独立的实地部署系统将使用宽带声学测量来遥感海草生物量和气体沸腾。该系统将由三个主要组成部分组成：1）一个声源发射器和一套接收水听器，2）一个仪器压力容器（IPV），容纳控制声学数据采集和数据存储的电子设备，以及3）一套环境传感器记录器。 拟议的测量系统将被制造得足够紧凑和轻便，以便能够从能够进入德克萨斯州墨西哥湾海岸浅海湾的小型船只上手动部署在海草草甸中。目标部署水深为2-3米，反映了德克萨斯州沿海沃茨海草分布的最大深度。地声推断技术将被应用于量化的气体在海水中的空隙率以及海草组织内存在的气体体积。贝叶斯技术将用于评估参数不确定性并揭示参数相关性。低频声音（3 kHz）是最敏感的海草组织内夹带的气体，和一个有效的介质模型，占海草组织的弹性和结构的海草叶正在开发量化的海草叶，根和根茎内存在的气体体积。中频声音（3.5至35 kHz）对海草光合作用产生的水中自由气泡最敏感。水中气体的空隙率可以通过考虑氧气气泡到海面的垂直运输而与氧气产生相关。该系统的数据将用于在海草状况指标和环境压力之间建立新的联系。这项研究将导致预测海草对气候变化的反应，通过占两个趋势和事件驱动的扰动，影响物理强迫因素，如光传输，水循环，温度和盐度。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This research focuses on the design and testing of a system capable of long-term field deployment that uses sound to monitor seagrass biomass and productivity with significantly improved temporal resolution compared to traditional techniques. Long-term continuous measurements of relevant parameters of ecosystem production are of primary importance for ecosystem health assessment and sustainable management. Dissolved oxygen measurements made with optical sensors are the most widely used method for estimating seagrass photosynthesis in relation to underwater irradiance. Oxygen evolution is also critical in maintaining aerobic conditions for below ground seagrass roots and rhizomes that constitute 60-80% of total plant biomass. However, under oxygen saturation conditions, which occurs frequently in summer under high light conditions, free gas bubbles are continuously released by the plants. Optical sensors are unable to detect bubbles under such supersaturated conditions, leading to underestimates in seagrass photosynthetic carbon production. Since oxygen saturation occurs more readily at higher temperatures and light conditions that are most prevalent from the late spring to early fall months, accurate determinations of productivity are only possible under low light conditions or in the early morning hours. Since sound propagation in water is very sensitive to the presence of bubbles, acoustic methods provide an alternative measure of true photosynthetic oxygen production in seagrass meadows with high temporal resolution. The researchers will engage in outreach activities including the Freshman Research Initiative (FRI) at University of Texas at Austin (UT), which gives first-year students the opportunity to initiate and engage in real-world research experience with faculty and graduate students. The project will also engage grades K-16 through the development of a Data Nugget based on the long-term data set collected through the proposed research.The stand-alone field-deployed system will use broadband acoustic measurements to remotely sense both seagrass biomass and gas ebullition. The system will consist of three main components: 1) an acoustic source projector and a set of receiving hydrophones, 2) an instrumentation pressure vessel (IPV) that houses the electronics controlling the acoustic data acquisition and data storage, and 3) a suite of environmental sensor-loggers. The proposed measurement system will be made compact and lightweight enough that it can be hand-deployed in the seagrass meadow from a small watercraft that is capable of accessing the shallow bays of the Texas Gulf of Mexico coast. The target deployment water depth is 2-3 m, which reflects the maximum depths of seagrass distribution in Texas coastal waters. Geoacoustic inference techniques will be applied to quantify the void fraction of gas in the seawater as well as the gas volume present within the seagrass tissue. Bayesian techniques will be used to assess parameter uncertainties and reveal parameter correlations. Low-frequency sound (3 kHz) is most sensitive to gas entrained within the seagrass tissue, and an effective medium model which accounts for the seagrass tissue elasticity and structure of seagrass leaves is being developed to quantify the gas volume present within the seagrass leaves, roots, and rhizomes. Mid-frequency sound (3.5 to 35 kHz) is most sensitive to free bubbles in the water resulting from seagrass photosynthesis. The void fraction of gas in the water can be related to oxygen production by accounting for vertical transport of oxygen bubbles to the sea surface. The data from this system will be used to build new connections between seagrass condition indicators and environmental stressors. This research will lead to predictions of seagrass responses to climate change by accounting for both trend- and event-driven perturbations that affect physical forcing factors such as light transmission, water circulation, temperature, and salinity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.