SBIR Phase I: Tool to estimate object visibility to fish in complex aquatic environments.

小型企业创新研究(SBIR)第一阶段项目的更广泛影响是开发工具，为国家内陆淡水创建大量水质数据。这些数据可用于直接环境监测和验证其他遥感地理信息系统技术，以测量内陆水质。还将开发算法，利用这些数据，允许垂钓者根据钓鱼的时间和地点定制他们对诱饵的选择。垂钓者的困难是预测鱼最容易看到的诱饵，因为鱼的视力与人类不同。这个项目开发了软件工具和水质传感器，允许垂钓者为任何类型的水域定制他们的钓鱼策略。该产品最终将根据水的特性、诱饵的特性以及鱼在该环境中的视觉能力来推荐适合特定地点的诱饵。拟议的项目将测量湖泊和河流的光学特性。将建造第一代测光仪，以估计三个波长的次表层透射率和光水平，并获取有关水质数据。该项目还提出了新的方法来适应对诱饵上荧光颜料的选择。算法将使用这些数据来估计某些鱼类所感知的视觉对比度，并确定哪些诱饵对该物种的成功概率最高--首先是鲈鱼。这种方法的新奇之处在于，它依赖于估计三个波长段的关键照明参数。传统的方法依赖于光谱仪，光谱仪估计有数千个数据点的光谱，对垂钓者来说是不可行的。该项目直接测试3个间隔良好的数据点是否能够准确估计照明环境、视觉信号及其在不同水体中的传输。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to develop tools that will create vast amounts of water quality data for the nation's inland, fresh waters. The data allow for direct environmental monitoring and for validation of other remote sensing GIS techniques to measure inland water quality. Algorithms will also be developed to use the data to allow anglers to customize their selection of lures based on the time and location where they are fishing. The difficulty for the angler is anticipating which lures are most visible to the fish, because fish see differently than humans. This project develops software tools and water quality sensors that allow anglers to customize their fishing strategies for any type of water. The product will ultimately recommend lures that work well for a given location based on the properties of the water, the properties of the lures, and the visual capabilities of fish in that environment.The proposed project will measure the optical properties of (a) lakes and rivers. First-generation photometers would be constructed to estimate the sub-surface transmission and light-levels at three wavelengths and capture relevant water quality data. The project also proposes new methods to accommodate the selection of fluorescent pigments on lures. Algorithms will use the data to estimate the visual contrasts as perceived by certain species of fish and determine which lures have the highest probability of success for that species - bass in the first instance. The novelty of this approach is that it relies on estimating critical lighting parameters at three wavelength bands. Traditional methods rely on spectrometers that estimate spectra with thousands of data points and are not feasible for anglers. This project directly tests whether 3 well-spaced data points can accurately estimate lighting environments, visual signals, and their transmission in diverse water bodies.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.