Characterising errors in airborne laser altimetry data to extract soil roughness

Airborne laser altimetry has the potential to make frequent detailed observations that are important for many aspects of studying land surface processes. However, the uncertainties inherent in airborne laser altimetry data have rarely been well measured. Uncertainty is often specified as generally as 20cm in elevation, and 40cm planimetric. To better constrain these uncertainties, we present an analysis of several datasets acquired specifically to study the temporal consistency of laser altimetry data, and thus assess its operational value. The error budget has three main components, each with a time regime. For measurements acquired less than 50ms apart, elevations have a local standard deviation in height of 3.5cm, enabling the local measurement of surface roughness of the order of 5cm. Points acquired seconds apart acquire an additional random error due to Differential Geographic Positioning System (DGPS) fluctuation. Measurements made up to an hour apart show an elevation drift of 7cm over a half hour. Over months, this drift gives rise to a random elevation offset between swathes, with an average of 6.4cm. The RMS planimetric error in point location was derived as 37.4cm. We conclude by considering the consequences of these uncertainties on the principle application of laser altimetry in the UK, intertidal zone monitoring.

Extraction of tidal channel networks from aerial photographs alone and combined with laser altimetry

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semiautomatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted.

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.

Scanning LDV for vibration measurement of filiform hairs in crickets in response to induced airflow - art. no. 63450E

Cercal hairs represent in cricket a wind sensitive escape system, able to detect the airflow generated from predating species. These sensors have been studied as a biomimetic concept to allow the development of MEMS for biomedical use. In particular, the behaviour of the hairs, including airflow response, resonant frequency and damping, has been investigated up to a frequency of 20 kHz. The microscopic nature of the hairs, the complex vibrations of excited hairs and the high damping of the system suggested that the use of Laser Doppler vibrometry could possibly improve the test performance. Two types of tests were performed: in the first case the hairs were indirectly excited using the signal obtained from a vibrating aluminium plate, whilst in the second case the hairs were directly excited using a white noise chirp. The results from the first experiment indicated that the hairs move in-phase with the exciting signal up to frequencies in the order of 10 kHz, responding to the vibration modes of the plate with a signal attenuation of 12 to 20 dB. The chirp experiment revealed the presence of rotational resonant modes at 6850 and 11300 Hz. No clear effect of hair length was perceivable on the vibration response of the filiform sensors. The obtained results proved promising to support the mechanical and vibration characterisation of the hairs and suggest that scanning Laser vibrometry can be used extensively on highly dampened biological materials.

Development of a method fon non-destructive testing of fruits using scanning laser vibrometry (SLV)

Quality control on fruits requires reliable methods, able to assess with reasonable accuracy and possibly in a non-destructive way their physical and chemical characteristics. More specifically, a decreased firmness indicates the presence of damage or defects in the fruit or else that the fruit has exceeded its “best before date”, becoming unsuitable for consumption. In high-value exotic fruits, such as mangoes, where firmness cannot be easily measured from a simple observation of texture, colour changes and unevenness of fruits surface, the use of non-destructive techniques is highly recommendable. In particular, the application of Laser vibrometry, based on the Doppler effect, a non-contact technique sensitive to differences in displacements inferior to the nanometre, appears ideal for a possible on-line control on food. Previous results indicated that a phase shift can be in a repeatable way associated with the presence of damage on the fruit, whilst a decreased firmness results in significant differences in the displacement of the fruits under the same excitation signal. In this work, frequency ranges for quality control via the application of a sound chirp are suggested, based on the measurement of the signal coherence. The variations of the average vibration spectrum of a grid of points, or of point-by-point signal velocity allows the go-no go recognition of “firm” and “over-ripe” fruits, with notable success in the particular case of mangoes. The future exploitation of this work will include the application of this method to allow on-line control during conveyor belt distribution of fruits.

Cavity-enhanced absorption: detection of nitrogen dioxide and iodine monoxide using a violet laser diode

We present an application of cavity-enhanced absorption spectroscopy with an off-axis alignment of the cavity formed by two spherical mirrors and with time integration of the cavity-output intensity for detection of nitrogen dioxide (NO2) and iodine monoxide (IO) radicals using a violet laser diode at lambda = 404.278 nm. A noise-equivalent (1sigma = root-mean-square variation of the signal) fractional absorption for one optical pass of 4.5x10(-8) was demonstrated with a mirror reflectivity of similar to0.99925, a cavity length of 0.22 m and a lock-in-amplifier time constant of 3 s. Noise-equivalent detection sensitivities towards nitrogen dioxide of 1.8x10(10) molecule cm(-3) and towards the IO radical of 3.3x10(9) molecule cm(-3) were achieved in flow tubes with an inner diameter of 4 cm for a lock-in-amplifier time constant of 3 s. Alkyl peroxy radicals were detected using chemical titration with excess nitric oxide (RO2 + NO --> RO + NO2). Measurement of oxygen-atom concentrations was accomplished by determining the depletion of NO2 in the reaction NO2 + O --> NO + O-2. Noise-equivalent concentrations of alkyl peroxy radicals and oxygen atoms were 3x10(10) molecule cm(-3) in the discharge-flow-tube experiments.

Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants

Ruminant husbandry is a major source of anthropogenic greenhouse gases (GHG). Filling knowledge gaps and providing expert recommendation are important for defining future research priorities, improving methodologies and establishing science-based GHG mitigation solutions to government and non-governmental organisations, advisory/extension networks, and the ruminant livestock sector. The objectives of this review is to summarize published literature to provide a detailed assessment of the methodologies currently in use for measuring enteric methane (CH4) emission from individual animals under specific conditions, and give recommendations regarding their application. The methods described include respiration chambers and enclosures, sulphur hexafluoride tracer (SF6) technique, and techniques based on short-term measurements of gas concentrations in samples of exhaled air. This includes automated head chambers (e.g. the GreenFeed system), the use of carbon dioxide (CO2) as a marker, and (handheld) laser CH4 detection. Each of the techniques are compared and assessed on their capability and limitations, followed by methodology recommendations. It is concluded that there is no ‘one size fits all’ method for measuring CH4 emission by individual animals. Ultimately, the decision as to which method to use should be based on the experimental objectives and resources available. However, the need for high throughput methodology e.g. for screening large numbers of animals for genomic studies, does not justify the use of methods that are inaccurate. All CH4 measurement techniques are subject to experimental variation and random errors. Many sources of variation must be considered when measuring CH4 concentration in exhaled air samples without a quantitative or at least regular collection rate, or use of a marker to indicate (or adjust) for the proportion of exhaled CH4 sampled. Consideration of the number and timing of measurements relative to diurnal patterns of CH4 emission and respiratory exchange are important, as well as consideration of feeding patterns and associated patterns of rumen fermentation rate and other aspects of animal behaviour. Regardless of the method chosen, appropriate calibrations and recovery tests are required for both method establishment and routine operation. Successful and correct use of methods requires careful attention to detail, rigour, and routine self-assessment of the quality of the data they provide.