Date of submission: 12

3.2 Remote Sensing

Remote sensing has been increasingly used to monitor urban land cover change, with the Landsat data series being widely utilised (Masek et al 2000, McMahon et al 2002, Ji et al 2006, Yuan et al, 2005). Remote sensing in urban areas present many challenges as these areas are heterogeneous and most urban image pixels at the resolution of Landsat comprise a mix of different surfaces. Mixed pixels are problematic for mapping using conventional classification methods because most algorithms are predicated on the assumption of spectral homogeneity within a particular land cover; therefore, the urban mosaic can result in high rates of misclassification between urban and other land cover classes (Martinuzzi et al, 2007). It is for this reason that a detailed differentiation and classification of different land covers and uses is not easy in urban environments (Griffiths et al, 2010). Improvements in spatial and spectral sensor resolutions in the last few years have led to further research in classifying urban areas and narrower wavelength ranges allow the extraction of detailed signatures to better discriminate objects on the ground (Yang, 2002). Whilst not being of a high enough resolution for this, the benefit of Landsat is in the now 40 year archive of data it provides. It is well suited for a synoptic view of urban development and has been widely used to measure urban form and changes from natural to impervious surfaces – which mark a loss of open spaces to urbanisation. Landsat has been used to study the extent of the loss of green spaces in urban areas; Raifee et al (2009) provide a study into the changes and extent of green spaces in Mashad, Iran between 1987 and 2006 and McMahon et al (2002) assess the change in green space over time in two cities in Idaho, USA. There have been a large number of studies using a time-series of Landsat data to measure the change in urban land-cover over time, which are presented in the table below. Many of these are focused on growing US cities.

There is considered to be a difference in US style sprawl and European ‘compact’ urban form, however Landsat has proved effective in identifying changes in both (Huang et al, 2007).

3.3 Change Detection

There are many applications for change detection analysis which may range from short term phenomena such as the measurement of flood water to longer term phenomena such as urban fringe development (Lillesand and Kiefer, 2000). Using multi-date satellite imagery to detect land cover changes dates back to the early 1970’s. It is important that imagery data used for change detection is acquired by the same (or very similar) sensor and be recorded using the same spatial resolution, spectral bands and viewing geometry. It is also important to consider images that have near anniversary dates in order to minimise seasonal variability in vegetation and sun angle. A variety of change detection methods have been employed in studies which generally fall into the pre and post classification methods noted above. The pre-classification techniques apply various algorithms to detect change between images. These include image differencing which was employed by Ridd and Liu (1998) in the Salt Lake Valley, image ratioing, used by Prakash and Gupta (1998) to detect land use changes in the Jharia coalfield in India and neural networks, used by Dai and Khorram (1999) to detect changes in land cover in Wilmington, North Carolina from Landsat imagery. Other pre classification methods include Principal Components Analysis, Change Vector Analysis and vegetation index differencing and all of these methods can be applied to a single or multiple spectral bands and directly to multiple dates of satellite imagery to generate ‘‘change’’ vs. ‘‘no-change’’ maps (Yuan et al, 2005). While the pre-classification methods are able to detect areas of change between images, a draw-back of these methods is that the type of change is not specified. Some studies have proposed change detection techniques for monitoring urban growth changes by using the Normalised Difference Vegetation Index (NDVI). The NDVI is the normalised difference between near-infrared and visible reflectance so can be directly related to the amount of biomass within a pixel (Masek et al, 2000). Urban growth in an area replaces open spaces (higher NDVI) with impervious built up land (low NDVI), so sudden decreases in NDVI have been shown to reflect urban development. Studies by Howarth and Boasson (1983) in Hamilton, Ontario and Masek et al 2000 in Washington DC have found this to be true. It has been noted that using the NDVI differencing method alone can confuse urban growth where agricultural areas are included due to the effects of crop rotation over a period of time. Griffiths (1998) has suggested filtering NDVI maps to remove agricultural noise.

Post classification methods detect land cover change by comparing independently produced classifications of images from different dates. Through the application of this technique a detailed matrix of ‘from and to’ changes can be built up (Rafiee et al, 2009) and the classification of each date of imagery builds a historical series that can be more easily understood and also used for applications other than change detection. In addition, post-classification comparison minimises the problems caused by variation in sensors and atmospheric conditions, as well as vegetation phenology between different dates as the data from different dates are separately classified (Zhou et al, 2008). An issue with post classification methods is that of error propagation – the technique is dependent on the accuracy of each classified map and a poor classification can lead to uncertainty propagated through the change analysis. Despite this, post classification has been widely utilised in change detection of urban environments in the US, with studies including Kansas (Ji et al, 2006), Minnesota (Yuan et al, 2005) and Baltimore (Zhou et al, 2008).

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