Ian J. Davenport

Image processing of airborne scanning laser altimetry data for improved river flood modelling

Airborne scanning laser altimetry (LiDAR) is an important new data source for environmental applications, being able to map topographic height, and the height of surface objects, to high vertical and horizontal accuracy over large areas. This paper describes a range image segmentation system for data from a LiDAR measuring either time of last significant return, or measuring time of both first and last returns. We focus on the application of the segmenter to improving the data required by 2D hydraulic flood models, i.e. maps of topographic height which provide model bathymetry, and vegetation height, which could be converted to distributed floodplain friction coefficients. In addition, the location of river channels and a suitable height contour are used to define the extent of the model domain. An advantage of segmentation is that it allows different topographic and vegetation height extraction algorithms to be used in regions of different cover type. LiDAR data for a reach of the River Severn, UK, is presented. Short vegetation heights (grass and cereal crops) are predicted with a rms error of 14 cm. The topography underlying such cover differs from manually measured spot heights by 17 cm (rms error). The topographic accuracy decreases in the presence of a densely wooded slope. Errors in the vegetation height map, apparent at the overlap regions of adjacent swaths, are reduced by the removal of heights measured at large scan angles.

Accurate and efficient determination of the shoreline in ERS-1 SAR images

Extraction of the shoreline in SAR images is a difficult task to perform using simple image processing operations such as grey-value thresholding, due to the presence of speckle and because the signal returned from the sea surface may be similar to that from the land. A semiautomatic method for detecting the shoreline accurately and efficiently in ERS-1 SAR images is presented. This is aimed primarily at a particular application, namely the construction of a digital elevation model of an intertidal zone using SAR images and hydrodynamic model output, but could be carried over to other applications. A coarse-fine resolution processing approach is employed, in which sea regions are first detected as regions of low edge density in a low resolution image, then image areas near the shoreline are subjected to more elaborate processing at high resolution using an active contour model. Over 90% of the shoreline detected by the automatic delineation process appear visually correct.

Waterline mapping in flooded vegetation from airborne SAR imagery

Multifrequency, polarimetric airborne synthetic aperture radar (SAR) survey of a salt marsh on the east coast of the UK is used to investigate the radar backscattering properties of emergent salt marsh vegetation. Two characteristics of flooded vegetation are observed: backscatter enhanced by approximately 1.2 dB at C-band, and 180° HH-VV phase differences at L-band. Both are indicative of a double bounce backscattering mechanism between the horizontal water surface and upright emergent vegetation. The mapping of inundated vegetation is demonstrated for both these signatures, using a statistical active contour model for the C-band enhanced backscatter, and median filtering and thresholding for the L-band HH-VV phase difference. The two techniques are validated against the waterline derived from tidal elevation measured at the time of overpass intersected with an intertidal DEM derived from airborne laser altimetry. The inclusion of flooded vegetation is found to reduce errors in waterline location by a factor of approximately 2, equivalent to a reduction in waterline location error from 120 to 70 m. The DEM is also used to derive SAR waterline heights, which are observed to underpredict the tidal elevation due to the effects of vegetation. The underprediction can be corrected for vegetation effects using canopy height maps derived from the laser altimetry. This third technique is found to improve the systematic error in waterline heights from 20 to 4 cm, but little improvement in random error is evident, chiefly due to significant noise in the vegetation height map.

Construction of an inter‐tidal digital elevation model by the ‘Water‐Line’ Method

Progress in the development of a method for constructing a Digital Elevation Model (DEM) of an inter-tidal zone using remote sensing and hydrodynamic modelling is described. The method allows the construction of an inter-tidal DEM over large areas relatively quickly as well as frequent subsequent monitoring of the DEM to detect changes, and is particularly suited to SAR satellite data because of the all-weather day-night capability of SAR. The resulting DEM may be used for the development of improved hydrodynamic models, and for studying sediment mass transport in the inter-tidal zone.

Improving bird population models using airborne remote sensing.

This work was undertaken to devise a technique to measure the height of crops in farmland fields through remote sensing. Crop height is a useful spatial variable which, when measured by ground-based manual survey, has proven to be an important predictor of bird species population. An airborne scanning laser system capable of measuring topography to a height accuracy of better than 10 cm was used to acquire height data over a region of farmland near Oxford, UK. A scanning laser was pulsed from an aircraft at the ground, measuring the time between transmission and receipt of the last significant return signal. Differential Geographical Positioning System (GPS) and onboard attitude sensors were combined with these delay times to construct a set of spot heights through the region. Crop height was also measured from the ground. Pulses were returned from mainly within the crop, rather than predominantly the canopy or ground, so an algorithm to measure the variation of the returned height, after detrending the heights for topography, was developed. A simple relationship was found between the mean crop height and the standard deviation of detrended return heights within a field. This relationship could be used to derive crop height from Light Detection and Ranging (LiDAR) data with an accuracy better than 10cm.

Characterizing 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 20 cm in elevation and 40 cm 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 50 ms apart, elevations have a local standard deviation in height of 3.5 cm, enabling the local measurement of surface roughness of the order of 5 cm. Points acquired seconds apart acquire an additional random error due to differential geographic positioning system fluctuation. Measurements made up to an hour apart show an elevation drift of 7 cm over a half hour. Over months, this drift gives rise to a random elevation offset between swathes, with an average of 6.4 cm. The root mean square planimetric error in point location was derived as 37.4 cm. We conclude by considering the consequences of these uncertainties on the principle application of laser altimetry in the U.K. intertidal zone monitoring.

A sensitivity analysis of soil moisture retrieval from the tau-omega microwave emission model

The potential of the /spl tau/--/spl omega/ model for retrieving the volumetric moisture content of bare and vegetated soil from dual-polarization passive microwave data acquired at single and multiple angles is tested. Measurement error and several additional sources of uncertainty will affect the theoretical retrieval accuracy. These include uncertainty in the soil temperature, the vegetation structure, and consequently its microwave single-scattering albedo, and uncertainty in soil microwave emissivity based on its roughness. To test the effects of these uncertainties for simple homogeneous scenes, we attempt to retrieve soil moisture from a number of simulated microwave brightness temperature datasets generated using the /spl tau/--/spl omega/ model. The uncertainties for each influence are estimated and applied to curves generated for typical scenarios, and an inverse model used to retrieve the soil moisture content, vegetation optical depth, and soil temperature. The effect of each influence on the theoretical soil moisture retrieval limit is explored, the likelihood of each sensor configuration meeting user requirements is assessed, and the most effective means of improving moisture retrieval indicated.

Measurement of canopy geometry characteristics using LiDAR laser altimetry: a feasibility study

Airborne scanning laser altimetry offers the potential for extracting high-resolution vegetation structure characteristics for monitoring and modeling the land surface. A unique dataset is used to study the sensitivity of laser interception profiles and laser-derived leaf area index (LAI) to assumptions about the surface structure and the measurement process. To simulate laser interception, one- and three-dimensional (3-D) vegetation structure models have been developed for maize and sunflower crops. Over sunflowers, a simple regression technique has been developed to extract laser-derived LAI, which accounts for measurement and model biases. Over maize, a 3-D structure/interception model that accounts for the effects of the laser inclination angle and detection threshold has enabled the fraction of radiation reaching the ground surface to be modelled to within 0.5% of the observed fraction. Good agreement was found between modelled and measured profiles of laser interception with a vertical resolution of 10 cm.

Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Robust predictive models of the effects of habitat change on species abundance over large geographical areas are a fundamental gap in our understanding of population distributions, yet are urgently required by conservation practitioners. Predictive models based on underpinning relationships between environmental predictors and the individual organism are likely to require measurement of spatially fine-grained predictor variables. Further, models must show spatial generality if they are to be used to predict the consequences of habitat change over large geographical areas. Remote sensing techniques using airborne scanning laser altimetry (LiDAR) and high resolution multi-spectral imagery allow spatially fine-grained predictor variables to be measured over large geographical areas and thus facilitate testing of the spatial generality of organism-habitat models. These techniques are considered using the skylark as an example species. A range image segmentation system for LiDAR data is described which allows measurement of skylark habitat predictor variables such as within-field vegetation height, boundary height and shape for individual fields within the LiDAR image. Additional variables such as field vegetation type and fractional vegetation ground cover may be obtained from co-registered multi-spectral data. These techniques could have wide application in testing the generality of relationships between populations and habitats, and in ecological monitoring of change in habitat structures and the associated effects on wildlife, over large geographical areas.

A digital elevation model of the inter-tidal areas of the Wash, England, produced by the waterline method

Figure 1 (cover) shows a digital elevation model (DEM) of the inter-tidal areas within the Wash, England, produced by the waterline method using ERS-1 SAR images and hydrodynamic modelling. The waterline method (Cracknell et al. 1987, Koopmans and Wang 1995, Mason et al. 1995, Ramsey 1995) involves ® nding the georegistered positions of the shoreline (the land± sea boundary) from a remotely sensed image using image processing techniques, then superimposing the heights of the shoreline relative to mean sea level on the corresponding positions. These heights are predicted using a hydrodynamic tide-surge model run for this area with the atmospheric conditions pertaining at the time of image acquisition. From multiple images obtained over a range of tide and surge elevations, it is possible to build up a set of heighted shorelines within the inter-tidal zone, and from this a gridded DEM may be interpolated. Such a DEM is useful for developing improved tide-surge models, and changes in the DEM over time allow measurement of sediment mass transfers in the inter-tidal zone due to storm or seasonal changes. Figure 2 shows the shorelines used to produce the DEM. These were extracted from thirteen ERS-1 SAR images obtained mainly during the winter months of 1992 ± 1994, using the semi-automatic shoreline delineator described in Mason and Davenport (1996). They were heighted using shoreline elevations generated by a hydrodynamic tide-surge model of the English east coast similar to that of Flather (1994). Model heights were corrected using local tide gauge information as described in Davenport et al. (1996 ). The lowest shoreline present had a mean elevation of O 2 8m ODN, whilst the highest shoreline had a mean elevation of 2 9m ODN. A raster DEM was interpolated from the heighted shorelines using universal