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The invasive algae in the Mediterranean Caulerpa taxifolia and Caulerpa racemosa var. cylind- racea have gained much interest in the Adriatic Sea due to their fast spread and negative impacts on native seaweed species. Lack of accurate mapping and its overall high costs represent some of the major difficulties for successful detection and subsequent environmental management. Remote sens- ing techniques could provide a quicker, more accurate and more cost efficient approach to mapping these invasive species. In order to allow for classification of remotely sensed data, we have collected and analyzed hyperspectral reflectance profiles of both invasive Caulerpa species in the Adriatic and the native Caulerpa cf. laetevirens species from the southeastern Atlantic coast of the United States. We have measured distinctive spectral features for spectroscopically distinguishing between these species, which is particularly interesting in the areas where these native and invasive species coex- ist. The initial results suggest that it might be possible to distinguish between the species based on their spectral reflectance profile. These results would have to be verified with samples taken from more populations in different phenological phases of the seaweed growth and other environmental conditions.

Landslides are recognized as the number one life-claiming natural disaster worldwide. The Eastern Seaboard of the United States is such a problematic area. We exploit remotely light imaging, detection, and ranging data, as well as sensed hyperspectral image data acquired from a horizontal scanning configuration to gain insights to the drainage, mineralogy, topography, vegetative cover, and anthropogenic elements known to be contributing factors to potential landslide zones.