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Pe'eri, S.; Morrison, J.R.; Short, F.; Mathieson, A., and Lippmann, T., 2016. Eelgrass and macroalgal mapping to develop nutrient criteria in New Hampshire's estuaries using hyperspectral imagery. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 209–218. Coconut Creek (Florida), ISSN 0749-0208. In recent years, mapping of seagrass beds for assessment of water quality has become more common in the United States and around the world. The static location of seagrass on marine sediments and its sensitivity to light make it a good environmental indicator and an alternative to water sampling of suspended particulates and dissolved matter. The New Hampshire (NH) Department of Environmental Services (DES) adopted the assumption that eelgrass survival could be used as the water quality target for nutrient criteria in NH's estuaries. One of the hypotheses put forward regarding eelgrass decline in the Great Bay Estuary (GBE) is that a eutrophication response to nutrient increases caused proliferation of nuisance macroalgae. This paper presents an eelgrass and macroalgae mapping procedure using hyperspectral imagery (HSI) collected by an AISA Eagle sensor. In addition to HSI, an external source bathymetric dataset provided a key dataset in the procedure. The bathymetric dataset was used to correct for light attenuation by the water column for resolving bottom reflectance and to calculate the extinction depth of light in the estuary's water for mapping areas that are optically deep. The procedure was developed in the Environment for Visualizing Images (ENVI) and includes two separate approaches based on the available spectral ranges for mapping vegetation above and below the water. A composite eelgrass and macroalgal map was produced over Great Bay proper. A high level of correlation was found between the eelgrass results to more detailed eelgrass maps (above 30% density) produced from aerial imagery and ground truthing. Little quantitative verification for the macroalgal data was available beyond a visual inspection. The two datasets showed good correlation. Based on the procedural results and long-term eelgrass mapping data, numeric nutrient criteria for NH's estuaries were developed.

Eelgrass and Macroalgal Mapping to Develop Nutrient Criteria in New Hampshire's Estuaries using Hyperspectral Imagery