An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Ecosystem health of Montevideo coastal zone: A multi approach using some different benthic indicators to improve a ten-year-ago assessment

A study on the benthic ecosystem health was performed to assess the environmental quality of Montevideo coastal zone, in view of the construction of a new sanitation system. Data were compared to previous research undertaken 10 years ago, and biochemical composition of organic matter, heavy metals, organic matter, phytopigments, benthic diatoms, macrofauna community structure and a biotic index (AMBI) were used as proxies. Results indicate an environmental quality-gradient, with the worst conditions within the inner stations of Montevideo Bay and an improvement towards the adjacent coastal zone. Higher levels of chromium, lead, phaeopigments, organic biopolymers and poor benthic macrofauna and diatom communities, characterised the hypertrophic innermost portion of Montevideo Bay. Data indicated a clear deterioration of the adjacent coastal zone comparatively to that observed 10 years ago. The complementary use of approaches not applied before (benthic diatoms and organic biopolymers) with those formerly applied improve our assessment of the trophic status and the environmental health of the area. (C) 2010 Elsevier B.V. All rights reserved.