Ned Dwyer

Coastal Informatics: Web Atlas Design and Implementation.

Dawn Wright is a professor of geography and oceanography at Oregon State University, and the director of the Davey Jones’ Locker Seafloor Mapping/Marine GIS Laboratory. Her research interests include geographic information science, coastal web atlases, benthic terrain and habitat characterization, tectonics of mid-ocean ridges, and the processing and interpretation of high-resolution bathymetry and underwater videography/photography. She serves on the editorial boards of the International Journal of Geographical Information Science, Transactions in GIS, Journal of Coastal Conservation, The Professional Geographer, and Geography Compass, as well as on the US National Academy of Sciences’ Ocean Studies Board, Committee on Strategic Directions in the Geographical Sciences for the Next Decade, Committee on an Ocean Infrastructure Strategy for US Ocean Research in 2030, and the Committee on Geophysical and Environmental Data. She serves on the Technical Advisory Board of the Marine Metadata Interoperability project. Dawn’s other books include Arc Marine: GIS for a Blue Planet (with M. Blongewicz, P. Halpin, and J. Breman, ESRI Press, 2007), Place Matters: Geospatial Tools for Marine Science, Conservation, and Management in the Pacific Northwest (with A. Scholz, Oregon State University Press, 2005), Undersea with GIS (ESRI Press, 2002), and Marine and Coastal Geographical Information Systems (with D. Bartlett, Taylor & Francis, 2000). Dawn holds a Ph.D. in Physical Geography and Marine Geology from the University of California at Santa Barbara. She is a fellow of the American Association for the Advancement of Science. Market: This premier publication is essential for all academic and research library reference collections. It is a crucial tool for academicians, researchers, and practitioners and is ideal for classroom use. Dawn Wright (Oregon State University, USA), Ned Dwyer (University College Cork, Ireland) and Valerie Cummins (University College Cork, Ireland)

Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series

More than 80% of agricultural land in Ireland is grassland, providing a major feed source for the pasture based dairy farming and livestock industry. Intensive grass based systems demand high levels of intervention by the farmer, with estimation of pasture cover (biomass) being the most important variable in land use management decisions, as well as playing a vital role in paddock and herd management. Many studies have been undertaken to estimate grassland biomass using satellite remote sensing data, but rarely in systems like Ire-lands intensively managed, small scale pastures, where grass is grazed as well as harvested for winter fodder. The objective of this study is to estimate grassland yield (kgDM/ha) from MODIS derived vegetation indices on a near weekly basis across the entire 300+ day growing season using three different methods (Multiple Linear Regression (MLR), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)). The results show that ANFIS model produced best result (R2 = 0.86) as compare to the ANN (R2 = 0.57) and MLR (R2 = 0.31).

Satellite remote sensing as a tool for monitoring vegetation seasonality

An increase in average air temperature across the island of Ireland has resulted in a change in the seasonality of vegetation. Current ground-based methods of monitoring seasonality are species-specific and limited to a few point locations across the country. Medium resolution satellite data, e.g. MERIS, provide a means of acquiring multi-year time series of imagery that can be used to capture the spatio-temporal dynamics in vegetation seasonality over the whole island. For this study, a geophysical measure of vegetation growth, the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), derived from MERIS Global Vegetation Index (MGVI) data is being used to determine seasonality. Tiles, extracted from a rectangular global grid, covering the island of Ireland have been processed through the European Space Agencys (ESA) Grid Processing on Demand (GPOD) service. Initial analysis of the imagery has consisted of defining an optimal time composite period in order to minimise cloud effects for daily MGVI values using ancillary cloud data from a meteorological observatory. Methods of in-situ observation of seasonality in mixed woodland have also been explored. Initial findings suggest that a 10-day composite period should be optimal for Ireland given the high occurrence of cloud cover.