A UV-visible prime focus camera for the Keck telescopes

Many areas of astronomical research rely on deep blue wide-field imaging. Mauna Kea enjoys the very best UV transparency from the ground and the Keck telescopes with 10 meter f/1.75 primaries are well suited to a prime focus camera with a large angular field. Swinburne University leads a proposal to provide a camera (KWFI, for Keck Wide Field Imager) that is optimized in the UV but works well to 1μm wavelength. Keck has interchangeable top end modules, of which one is now unused and easily capable of housing the required corrector lens and detector enclosure. This paper concentrates on details of the KWFI optical design

A UV-visible prime focus camera for the Keck telescopes

Many areas of astronomical research rely on deep blue wide-field imaging. Mauna Kea enjoys the very best UV transparency from the ground and the Keck telescopes with 10 meter f/1.75 primaries are well suited to a prime focus camera with a large angular field. Swinburne University leads a proposal to provide a camera (KWFI, for Keck Wide Field Imager) that is optimized in the UV but works well to 1μm wavelength. Keck has interchangeable top end modules, of which one is now unused and easily capable of housing the required corrector lens and detector enclosure. This paper concentrates on details of the KWFI optical design

Modeling Uncertainty in Climate Change: A Multi‐Model Comparison

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO 2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets