Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

High-throughput proteomics using matrix-assisted laser desorption/ ionization mass spectrometry

It has become evident that the mystery of life will not be deciphered just by decoding its blueprint, the genetic code. In the life and biomedical sciences, research efforts are now shifting from pure gene analysis to the analysis of all biomolecules involved in the machinery of life. One area of these postgenomic research fields is proteomics. Although proteomics, which basically encompasses the analysis of proteins, is not a new concept, it is far from being a research field that can rely on routine and large-scale analyses. At the time the term proteomics was coined, a gold-rush mentality was created, promising vast and quick riches (i.e., solutions to the immensely complex questions of life and disease). Predictably, the reality has been quite different. The complexity of proteomes and the wide variations in the abundances and chemical properties of their constituents has rendered the use of systematic analytical approaches only partially successful, and biologically meaningful results have been slow to arrive. However, to learn more about how cells and, hence, life works, it is essential to understand the proteins and their complex interactions in their native environment. This is why proteomics will be an important part of the biomedical sciences for the foreseeable future. Therefore, any advances in providing the tools that make protein analysis a more routine and large-scale business, ideally using automated and rapid analytical procedures, are highly sought after. This review will provide some basics, thoughts and ideas on the exploitation of matrix-assisted laser desorption/ ionization in biological mass spectrometry - one of the most commonly used analytical tools in proteomics - for high-throughput analyses.

High-throughput proteomics using matrix-assisted laser desorption/ionization mass spectrometry

It has become evident that the mystery of life will not be deciphered just by decoding its blueprint, the genetic code. In the life and biomedical sciences, research efforts are now shifting from pure gene analysis to the analysis of all biomolecules involved in the machinery of life. One area of these postgenomic research fields is proteomics. Although proteomics, which basically encompasses the analysis of proteins, is not a new concept, it is far from being a research field that can rely on routine and large-scale analyses. At the time the term proteomics was coined, a gold-rush mentality was created, promising vast and quick riches (i.e., solutions to the immensely complex questions of life and disease). Predictably, the reality has been quite different. The complexity of proteomes and the wide variations in the abundances and chemical properties of their constituents has rendered the use of systematic analytical approaches only partially successful, and biologically meaningful results have been slow to arrive. However, to learn more about how cells and, hence, life works, it is essential to understand the proteins and their complex interactions in their native environment. This is why proteomics will be an important part of the biomedical sciences for the foreseeable future. Therefore, any advances in providing the tools that make protein analysis a more routine and large-scale business, ideally using automated and rapid analytical procedures, are highly sought after. This review will provide some basics, thoughts and ideas on the exploitation of matrix-assisted laser desorption/ionization in biological mass spectrometry - one of the most commonly used analytical tools in proteomics - for high-throughput analyses.

Retrievals of thick cloud optical depth from the Geoscience Laser Altimeter System (GLAS) by calibration of solar background signal

Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other spaceborne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (< about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so one must first calibrate the reflected solar radiation received by the photon-counting detectors of the GLAS 532-nm channel, the primary channel for atmospheric products. Solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (i) calibration with coincident airborne and GLAS observations, (ii) calibration with coincident Geostationary Opera- tional Environmental Satellite (GOES) and GLAS observations of deep convective clouds, and (iii) cali- bration from first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retriev- als is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.