Traceable Radiometry Underpinning Terrestrial- and Helio-Studies (truths)

The Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS) mission offers a novel approach to the provision of key scientific data with unprecedented radiometric accuracy for Earth Observation (EO) and solar studies, which will also establish well-calibrated reference targets/standards to support other EO missions. This paper presents the TRUTHS mission and its objectives. TRUTHS will be the first satellite mission to calibrate its EO instrumentation directly to SI in orbit, overcoming the usual uncertainties associated with drifts of sensor gain and spectral shape by using an electrical rather than an optical standard as the basis of its calibration. The range of instruments flown as part of the payload will also provide accurate input data to improve atmospheric radiative transfer codes by anchoring boundary conditions, through simultaneous measurements of aerosols, particulates and radiances at various heights. Therefore, TRUTHS will significantly improve the performance and accuracy of EO missions with broad global or operational aims, as well as more dedicated missions. The provision of reference standards will also improve synergy between missions by reducing errors due to different calibration biases and offer cost reductions for future missions by reducing the demands for on-board calibration systems. Such improvements are important for the future success of strategies such as Global Monitoring for Environment and Security (GMES) and the implementation and monitoring of international treaties such as the Kyoto Protocol. TRUTHS will achieve these aims by measuring the geophysical variables of solar and lunar irradiance, together with both polarised and unpolarised spectral radiance of the Moon, Earth and its atmosphere.

Assessing the sensitivity of seabed species and biotopes - the Marine Life Information Network (MarLIN).

Science-based approaches to support the conservation of marine biodiversity have been developed in recent years. They include measures of ‘rarity’, ‘diversity’, ‘importance’, biological indicators of water ‘quality’ and measures of ‘sensitivity’. Identifying the sensitivity of species and biotopes, the main topic of this contribution, relies on accessing and interpreting available scientific data in a structured way and then making use of information technology to disseminate suitably presented information to decision makers. The Marine Life Information Network (MarLIN) has achieved that research for a range of environmentally critical species and biotopes over the past four years and has published the reviews on the MarLIN Web site (www.marlin.ac.uk). Now, by linking the sensitivity database and databases of survey information, sensitivity mapping approaches using GIS are being developed. The methods used to assess sensitivity are described and the approach is advocated for wider application in Europe.

History of the Continuous Plankton Recorder Database. Proceedings of 'The Colour of Ocean Data' Symposium, Brussels, 25-27 November 2002.

The Continuous Plankton Recorder Survey has operated in the North Atlantic and North Sea since 1931, providing a unitque multi-decadal dataset of plankton abundance. Over the period since 1931 technology has advanced and the system for storing the CPR data has developed considerably. From 1969 an electronic database was developed to store the results of CPR analysis. Since that time the CPR database has undergone a number of changes due to performance related factors such as processor speed and disk capacity as well as economic factors such as the cost of software. These issues have been overcome and the system for storing and retrieving the data has become more user friendly at every development stage.

The evaluation of time series: their scientific value and contribution to policy needs. Occasional Publication of the Marine Biological Association 22

In 2000 a Review of Current Marine Observations in relation to present and future needs was undertaken by the Inter-Agency Committee for Marine Science and Technology (IACMST). The Marine Environmental Change Network (MECN) was initiated in 2002 as a direct response to the recommendations of the report. A key part of the current phase of the MECN is to ensure that information from the network is provided to policy makers and other end-users to enable them to produce more accurate assessments of ecosystem state and gain a clearer understanding of factors influencing change in marine ecosystems. The MECN holds workshops on an annual basis, bringing together partners maintaining time-series and long-term datasets as well as end-users interested in outputs from the network. It was decided that the first workshop of the MECN continuation phase should consist of an evaluation of the time series and data sets maintained by partners in the MECN with regard to their ‘fit for purpose’ for answering key science questions and informing policy development. This report is based on the outcomes of the workshop. Section one of the report contains a brief introduction to monitoring, time series and long-term datasets. The various terms are defined and the need for MECN type data to complement compliance monitoring programmes is discussed. Outlines are also given of initiatives such as the United Kingdom Marine Monitoring and Assessment Strategy (UKMMAS) and Oceans 2025. Section two contains detailed information for each of the MECN time series / long-term datasets including information on scientific outputs and current objectives. This information is mainly based on the presentations given at the workshop and therefore follows a format whereby the following headings are addressed: Origin of time series including original objectives; current objectives; policy relevance; products (advice, publications, science and society). Section three consists of comments made by the review panel concerning all the time series and the network. Needs or issues highlighted by the panel with regard to the future of long-term datasets and time-series in the UK are shown along with advice and potential solutions where offered. The recommendations are divided into 4 categories; ‘The MECN and end-user requirements’; ‘Procedures & protocols’; ‘Securing data series’ and ‘Future developments’. Ever since marine environmental protection issues really came to the fore in the 1960s, it has been recognised that there is a requirement for a suitable evidence base on environmental change in order to support policy and management for UK waters. Section four gives a brief summary of the development of marine policy in the UK along with comments on the availability and necessity of long-term marine observations for the implementation of this policy. Policy relating to three main areas is discussed; Marine Conservation (protecting biodiversity and marine ecosystems); Marine Pollution and Fisheries. The conclusion of this section is that there has always been a specific requirement for information on long-term change in marine ecosystems around the UK in order to address concerns over pollution, fishing and general conservation. It is now imperative that this need is addressed in order for the UK to be able to fulfil its policy commitments and manage marine ecosystems in the light of climate change and other factors.

The Atlantic Meridional Transect (AMT) data management experience

As the UK's national marine data centre, a key responsibility of the British Oceanographic Data Centre (BODC) is to provide data management support for the scientific activities of complex multi-disciplinary long-term research programmes. Since the initial cruise in 1995, the NERC funded Atlantic Meridional Transect (AMT) project has undertaken 18 north–south transects of the Atlantic Ocean. As the project has evolved there has been a steady growth in the number of participants, the volume of data, its complexity and the demand for data. BODC became involved in AMT in 2002 at the beginning of phase II of this programme and since then has provided continuous support to the AMT and the wider scientific community through the rescue, quality control, processing and access to the data. The data management is carried out by a team of specialists using a sophisticated infrastructure and hardware to manage, integrate and serve physical, biological and chemical data. Here, we discuss the approach adopted, techniques applied and some guiding principles for management of large multi-disciplinary programmes.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.