Per-pixel classification algorithms are poorly equipped to monitor urban land use in images acquired by the current generation of high spatial resolution satellite sensors. This is because urban areas commonly comprise a complex spatial assemblage of spectrally distinct land-cover types. In this study, a technique is described that attempts to derive information on urban land use in two stages. The first involves classification of the image into broad land-cover types. In the second stage, referred to as spatial reclassification, the classified pixels are grouped into discrete land-use categories on the basis of both the frequency and the spatial arrangement of the land-cover labels within a square kernel. The application of this technique, known as SPARK (~~~tial Reclassification erne el), is demonstrated using a SPOT-1 HRV m ultispectral image of southeast London, England. Preliminary results indicate that SPARK can be used to distinguish quite subtle differences of land use in urban areas.

Land Cover

Kernel (algebra)

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Citations (217)

Simulating the scale dependence of urban land cover inference for the region-based derivation of fine scale urban land use information

Stuart Barr

Images acquired by very high spatial resolution multispectral satellite sensors are one means by which urban land use information can be derived. It has been argued that this may be achieved using a two- stage region-based approach, where initially derived land cover parcels (regions) are subsequently analysed, in terms of their spatial and morphological structure, to infer land use. Although initial results of the above approach have been promising, little work to date has been conducted into quantifying its scale dependence. This is particularly the case in relation to how inferred region morphology and spatial composition changes as af unction of image spatial resolution. In order to address this issue this paper employs fine scale land cover digital map data to generate simulated classified images at resolutions of between 2-6m. Subsequent region- based structural analysis shows that inferred region morphology, particularly for man-made objects (buildings and roads), is severely degraded at spatial resolutions below 2m, making the inference of certain residential land use types problematic. The implication of these findings are that for certain spatially fragmented residen- tial types, characterised by a relatively large number of small detached houses, region-based structural infer- ence of urban land use using multispectral images acquired by sensors such as IKONOS-1 (4m) is likely to be problematic.

Land Cover

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Automated correction of surface obstruction errors in digital surface models using off‐the‐shelf image processing

The Photogrammetric Record (2006)

T. D. James Stuart Barr Stuart N. Lane

Abstract Airborne topographic data collection requires removal of errors that arise due to surface features that obstruct the ground from the sensor. Typically, this has been based on manual correction and/or automated filtering. To some degree, the latter has provided a method for identifying and removing unwanted surface obstructions in large topographic data‐sets. However, the algorithms used are unintelligent in that they cannot reliably differentiate between the various types of obstructions and the ground. If coincident optical support imagery is available, the use of intelligent correction routines becomes possible. This paper describes an automated approach for removing obstruction errors using optical support imagery and simple image processing routines. Orthorectification and classification of support imagery enable obstruction errors to be identified in the digital surface model (DSM) and corrected intelligently to produce a digital terrain model (DTM). The results show that support imagery can be used with basic image processing routines to remove obstructions intelligently and automatically from large topographic data‐sets. Since the approach can differentiate between types of obstructions, the removal of each type of error can be customised, making this a very flexible approach to topographic data correction.

Orthophoto

Digital surface

Data Processing

10.1111/j.1477-9730.2006.00398.x

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Citations (9)

A multi-scale urban integrated assessment framework for climate change studies: A flooding application

Computers Environment and Urban Systems (2019)

Alistair Ford Stuart Barr Richard Dawson James Virgo Michael Batty

In order to assess the potential future impacts of climate change on urban areas, tools to assist decision-makers to understand future patterns of risk are required. This paper presents a modelling framework to allow the downscaling of national- and regional-scale population and employment projections to local scale land-use changes, providing scenarios of future socio-economic change. A coupled spatial interaction population model and cellular automata land development model produces future urbanisation maps based on planning policy scenarios. The framework is demonstrated on Greater London, UK, with a set of future population and land-use scenarios being tested against flood risk under climate change. The framework is developed in Python using open-source databases and is designed to be transferable to other cities worldwide.

Spatial Planning

10.1016/j.compenvurbsys.2019.02.005

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Citations (38)

Hyperspectral Characterisation of arable crop stress associated with buried gas pipeline earthworks

D. C. White Mathew Williams Stuart Barr

Arable land

Earthworks

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Characterising soil moisture in transport corridor environments using airborne LIDAR and CASI data

Hydrological Processes (2011)

Andrew Hardy Stuart Barr J. P. Mills Pauline E. Miller

Abstract Much of the world's transport networks are located on cutting and embankment earthworks. In the UK, many of these earthwork structures were constructed in the mid‐19th century and are susceptible to slope instability. Instability in transport corridors tends to be triggered by an increase in pore water pressure, which is directly influenced by an increase in soil moisture. This study explores the integration of high spatial resolution airborne (1 m) LIght Detection And Ranging (LIDAR) and Compact Airborne Spectrographic Imager (CASI) imagery to characterize soil moisture distribution for a transport corridor near Haltwhistle, UK. The distribution of soil moisture is estimated using these sensors through techniques that are relatively unaffected by, or make use of, the presence of vegetation, unlike other techniques using thermal, shortwave and microwave sensors. Terrain analysis calculations of potential solar radiation and a topographic wetness index were applied to a DEM interpolated from the LIDAR point data. The CASI imagery was used to map Ellenberg moisture indicator values using partial least squares regression. Individually, the remotely sensed metrics were found to have poor correlations with observed soil moisture. However, improvements could be made using an integrated model which demonstrated a correlation coefficient of 0·68. The resulting integrated model showed soil moisture content to increase on north‐facing earthworks and towards the toe of earthwork slopes. Concentrations of moisture were also predicted in cuttings where water is contributed from areas surrounding the earthworks. Copyright © 2011 John Wiley & Sons, Ltd.

Earthworks

Topographic Wetness Index

10.1002/hyp.8217

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Citations (10)

New Methods for Estimating Sea-Level Rise Contributions From Svalbard Glaciers

AGUFM (2007)

T. D. James Tavi Murray Nicholas E. Barrand Martin King Adrian Luckman

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Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations