Classification of Submerged Aquatic Vegetation in Black River Using Hyperspectral Image Analysis

Abstract

Monitoring aquatic vegetation is an important component of water resource management due to the ecological services provided by these habitats. Spectrally-rich hyperspectral imagery can be an efficient tool for mapping and classifying macrophyte communities. Identification of submerged vegetation in aquatic regions is complicated by variations in optical properties of water constituents, sun-water-sensor geometry, water depth and the spectral/structural complexity of the plants. Many studies have attempted to detect aquatic vegetation in coastal waters; however, few studies have targeted shallow, black-water rivers tainted with chromophoric dissolved organic matter (CDOM). This study investigates methods to analyze airborne hyperspectral imagery and detect and classify aquatic vegetation in a black-water riverine system. Images were normalized to account for reflectance from the water surface and varying water depth before being analyzed by the Maximum Likelihood (ML) and three other non-parametric classifiers: Artificial Neural Network (ANN), Support Vector Machine (SVM) and Spectral Angular Mapper (SAM). Quality assessment analysis indicated a general classification and detection accuracy improvement when non-parametric classifiers were applied on the normalized and depth invariant images. A maximum classification accuracy of about 69% was achieved when the ANN classifier was applied on the normalized images, and maximum detection accuracies of 93% and 92% were obtained when the SAM and the SVM classifiers were applied on depth invariant images, respectively.