Real Time HABs Mapping Using NASA Glenn Hyperspectral Imager

Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach

Hyperspectral remote sensing has tremendous potential for monitoring land cover and water bodies from the rich spatial and spectral information contained in the images. It is a time and resource consuming task to obtain groundtruth data for these images by field sampling. A semi-supervised method for labeling and classification of hyperspectral images is presented. The unsupervised stage consists of image enhancement by feature extraction, followed by clustering for labeling and generating the groundtruth image. The supervised stage for classification consists of a preprocessing stage involving normalization, computation of principal components, and feature extraction. An ensemble of machine learning models takes the extracted features and groundtruth data from the unsupervised stage as input and a decision block then combines the output of the machines to label the image based on majority voting. The ensemble of machine learning methods includes support vector machines, gradient boosting, Gaussian classifier, and linear perceptron. Overall, the gradient boosting method gives the best performance for supervised classification of hyperspectral images. The presented ensemble method is useful for generating labeled data for hyperspectral images that do not have groundtruth information. It gives an overall accuracy of 93.74% for the Jasper hyperspectral image, 100% accuracy for the HSI2 Lake Erie images, and 99.92% for the classification of cyanobacteria or harmful algal blooms and surface scum. The method distinguishes well between blue green algae and surface scum. The full pipeline ensemble method for classifying Lake Erie images in a cloud server runs 24 times faster than a workstation.

Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River

Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling.

A Forecasting Method for Harmful Algal Bloom(HAB)-Prone Regions Allowing Preemptive Countermeasures Based only on Acoustic Doppler Current Profiler Measurements in a Large River

Harmful algal blooms (HABs) have been recognized as a serious problem for aquatic ecosystems and a threat to drinking water systems. The proposed method aimed to develop a practical and rapid countermeasure, enabling preemptive responses to massive algal blooms, through which prior to the algal bloom season we can identify HAB-prone regions based on estimations of where harmful algae initiates and develops significantly. The HAB-prone regions were derived from temperature, depth, flow velocity, and sediment concentration data based only on acoustic Doppler current profilers (ADCPs) without relying further on supplementary data collection, such as the water quality. For HAB-prone regions, we employed hot-spot analysis using K-means clustering and the Getis-Ord G*, in conjunction with the spatial autocorrelation of Moran’s I and the local index of spatial association (LISA). The validation of the derived HAB-prone regions was conducted for ADCP measurements located at the downstream of Nam and Nakdong River confluence, South Korea, which preceded three months of algal bloom season monitored by unmanned aerial vehicles (UAVs). The visual inspection demonstrated that the comparison resulted in an acceptable range of agreement and consistency between the predicted HAB-prone regions and actual UAV-based observations of actual algal blooms.