Optical water type discrimination and tuning remote sensing band-ratio algorithms: Application to retrieval of chlorophyll and Kd(490) in the Irish and Celtic Seas

This paper assesses the feasibility of applying remote sensing algorithms based on blue/green reflectance ratios to Case 2 waters. Two algorithms from the SeaDAS (NASA) image processing package, OC4v4 for surface chlorophyll concentration, Chl, and K(490) for the attenuation coefficient for downward irradiance at 490 nm, K_d(490), were investigated using an extensive set of observations from the Irish and Celtic Seas. In situ data from a profiling radiometer were used as inputs for the algorithms to avoid uncertainties in atmospheric correction procedures, and direct measurements of Chl and K_d490 and were used for validation purposes. The standard versions of the algorithms performed poorly: OC4v4 generally overestimated Chl (with a very low coefficient of determination), and K(490) progressively underestimated K_d490 for values greater than 0.3 m⁻¹. A two-step procedure for level 2 product generation was therefore devised in which the numerical coefficients of OC4v4 and K(490) were tuned for the two optical water types known to occur most frequently in this region (McKee, D., Cunningham, A., 2006. Identification and characterisation of two optical water types in the Irish Sea from in situ inherent optical properties and seawater constituents. Estuarine, Coastal and Shelf Science 68, 305–316) by statistical regression on a data set of 102 stations from the Irish and Celtic Seas. The water types were distinguished by the magnitude of their normalised water leaving radiance signals at 665 nm, nL_w(665), and appropriate versions of the tuned algorithms applied to each water type. When this procedure was tested on an independent data set of 19 stations from the Bristol Channel, Chl values were recovered with an RMS error of 0.36 mg m⁻³ and K_d(490) values with an RMS error of 0.095 m⁻¹. The identification of water types from water-leaving radiance signals, and the application of band-ratio algorithms tuned for specific water types, may therefore provide a simple means of improving the quality of remote sensing products in optically complex shelf seas.     

Learning to trade ethically: Knowledgeable capitalism, retailers and contested commodity chains

This paper explores what happens when corporations engage explicitly in practices of organisational learning not only to become better capitalists by generating ever more innovative ways of maintaining profitability, improving competitiveness and maximising shareholder value, but also to become more responsible corporate citizens in their business practices. In particular, I evaluate the ways in which UK food and clothing retailers are learning to develop their ethical trading programmes in response to political calls for more responsible trading. Thrift’s [Thrift, N., 2005. Knowing Capitalism. Sage, London] notion of ‘knowledgeable, or soft, capitalism’ is adopted to understand the creative and experimental ways in which retailers and their mentors (ethical consultancies, social auditors and multi-stakeholder organisations) are learning to trade ethically. Two specific examples of formal learning spaces experienced by UK food and clothing retailers are examined: (i) training courses on social auditing and (ii) corporate awareness-raising courses on ethical trade. These courses are shown to encompass various participative and affective practices of learning. And while particular limits to the success of these courses are argued to exist, ethical learning practices discussed in this paper are nonetheless suggested to play a role in the making of new, albeit moderate, forms of responsible capitalism.     

Hyper Redundancy for Accuracy Enhancement in Automated Close Range Photogrammetry

A generic network design in close range photogrammetry is one where optimal multi-ray intersection geometry is obtained with as few camera stations as practicable. Hyper redundancy is a concept whereby, once the generic network is in place, many additional images are recorded, with the beneficial impact upon object point precision being equivalent to the presence of multiple exposures at each camera position within the generic network. The effective number of images per station within a hyper redundant network might well be in the range of 10 to 20 or more. As is apparent when it is considered that a hyper redundant network may comprise hundreds of images, the concept is only applicable in practice to fully automatic vision metrology systems, where it proves to be a very effective means of enhancing measurement accuracy at the cost of minimal additional work in the image recording phase. This paper briefly reviews the network design and accuracy aspects of hyper redundancy and illustrates the technique by way of the photogrammetric measurement of surface deformation of a radio telescope of 26 m diameter. This project required an object point measurement accuracy of σ  = 0·065 mm, or 1/400 000 of the diameter of the reflector.     

UK seismology – 2002 and beyond

Alex Brisbourne reports on a one-day discussion meeting organized by SEIS-UK for seismologists to discuss current seismological research undertaken in the UK and to consider how the provision of new seismic equipment and training facility may enhance that work.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Climate change hotspots mapping: what have we learned?

In the past 5 years there has been a proliferation of efforts to map climate change “hotspots” — regions that are particularly vulnerable to current or future climate impacts, and where human security may be at risk. While some are academic exercises, many are produced with the goal of drawing policy maker attention to regions that are particularly susceptible to climate impacts, either to mitigate the risk of humanitarian crises or conflicts or to target adaptation assistance. Hotspots mapping efforts address a range of issues and sectors such as vulnerable populations, humanitarian crises, conflict, agriculture and food security, and water resources. This paper offers a timely assessment of the strengths and weaknesses of current hotspots mapping approaches with the goal of improving future efforts. It also highlights regions that are anticipated, based on combinations of high exposure, high sensitivity and low adaptive capacity, to suffer significant impacts from climate change.