QUANTITATIVE COMPARISON OF CHANGE-DETECTION ALGORITHMS FOR MONITORINGEELGRASS FROM REMOTELY-SENSED DATA

The eelgrass (Zostera marina L.) population in Great Bay, New Hampshir e has recently undergone dramatic changes. A reoccurrence of the 1930s wasting disease and decreasing water quality due to pollution led to a reduction in the eelgrass population during the late 1980s. Currentl y, the eelgrass populations in Great Bay have experienced a remarkable recovery from the decline in the late 1980s. Eelgrass is important in our estuarine ecosystems because it is utilized as habitat by many co mmercial and non-commercial organisms and is a food source for waterfo wl. In order to monitor the eelgrass populations in Great Bay, a chang e detection analysis was performed to determine the fluctuation in eel grass meadows over time. Change defection is a technique used to deter mine the change between two or more time periods of a particular objec t of study. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution in the population of interest. A large number of change-detection techniques have been dev eloped, but little has been done to quantitatively assess the accuraci es of these techniques. In this study, post-classification, image diff erencing, and principal components change-detection techniques were us ed to determine the change in eelgrass meadows with Landsat Thematic M apper (TM) data. Low altitude (1,000 m), oblique aerial photography co mbined with boat surveys were used as reference data. A proposed chang e-defection error matrix was used to quantitatively assess the accurac y of each change-detection technique. The three differ ent techniques were then compared using standard accuracy assessment procedures. The image differencing change-detection technique performed significantly better than the post-classification and principal components analysis. The overall accuracy of the image differencing change detection was 6 6 percent with a Khat value of 0.43. This study provided an applicatio n of Landsat Thematic Mapper to detect submerged aquatic vegetation an d the methodology for comparing change detection techniques using a pr oposed change detection error matrix and standard accuracy assessment procedures. In addition, this study showed that image differencing was better than the post-classification or principal components technique s for detecting changes in submerged aquatic vegetation.