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Daniel Ierodiaconou

Researcher at Deakin University

Publications -  147
Citations -  3452

Daniel Ierodiaconou is an academic researcher from Deakin University. The author has contributed to research in topics: Shore & Geology. The author has an hindex of 30, co-authored 128 publications receiving 2527 citations. Previous affiliations of Daniel Ierodiaconou include Commonwealth Scientific and Industrial Research Organisation.

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Perceived and objectively measured greenness of neighbourhoods: Are they measuring the same thing?

TL;DR: The authors examined agreement between perceived greenness of neighbourhoods and an objective measure of greenness using a normalised vegetation index (NDVI) and found that there was a lack of agreement between the perceived and objective measures of Greenness, suggesting that these measures are capturing different aspects of neighbourhood greenness.
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Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations

TL;DR: This work uses towed video observations and seafloor complexity variables derived from multibeam echosounder bathymetry and backscatter to predict the distribution of 8 dominant benthic biological communities in a 54 km2 site, off the central coast of Victoria, Australia.
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Protected species use of a coastal marine migratory corridor connecting marine protected areas

TL;DR: This study used a geospatial approach to delineate a coastal corridor from 73 adult female flatback postnesting migratory tracks from four rookeries along the north-west coast of Australia, providing valuable information towards proposing new locations requiring protection, as well as identifying high-priority network linkages between existing marine protected areas.
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The consequences of land use change on nutrient exports: a regional scale assessment in south-west Victoria, Australia.

TL;DR: The analysis demonstrates an increase in modelled nitrogen and phosphorus loadings from 1980 to 2002 and demonstrated the enormous potential of using remote sensing and GIS for monitoring regional scale environmental processes.
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Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

TL;DR: This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data from multibeam echo-sounder technology using four different supervised learning methods.