Table 1:
|
Urban sprawl studies utilising a time-series of Landsat data……………….
|
18/19
|
Table 2:
|
Landsat Imagery used in the study………………………………………………………
|
31/32
|
Table 3:
|
US census bureau shapefiles……………………………………………………………….
|
32
|
Table 4:
|
City of Austin GIS Data, used as reference data for the study……………..
|
33
|
Table 5:
|
Landsat bands summary information………………………………………………….
|
33/34
|
Table 6:
|
Change detection map calculations……………………………………………………..
|
42
|
Table 7:
|
Cramer’s V results for the two selected explanatory variables……………
|
56
|
Table 8:
|
Amount of NDVI change across the study area for the three change
epochs…………………………………………………………………………………………………
|
61
|
Table 9:
|
Accuracy assessment for decrease in NDVI – amount of speckle experienced………………………………………………………………………………………..
|
62
|
Table 10:
|
Urban growth experienced in Austin…………………………………………………..
|
63
|
Table 11:
|
Mean and maximum distance of urban growth from the CBD…………….
|
66
|
Table 12:
|
Mean distance of urban growth away from a primary or secondary road……………………………………………………………………………………………………..
|
68
|
Table 13:
|
Pearson’s correlation and student’s t-test results - distance of urban growth from roads……………………………………………………………………………….
|
70
|
Table 14:
|
Study Limitations summary………………………………………………………………….
|
82/83
|
Introduction
During the last century there has been a huge increase in the amount of people living in urban areas and an associated growth in the size of these. This process, termed urbanisation, has greatly transformed landscapes throughout the world and continues to be one of the most significant forms of land cover change in cities across the world. As urban areas continue to grow and expand beyond their original boundaries the process becomes known as urban sprawl. A general consensus regarding the definition and impact of urban sprawl has not been achieved as the nature of this can vary in different parts of the world (Huang et al, 2007). However, it is acknowledged that it is generally characterised by an increase in developed land, often at a cost of natural land, which can cause a number of environmental issues, such as an increased risk of flooding (Kaźmierczak and Cavan, 2011) and elevated air and noise pollution (Gairola and Noresla, 2010). However, a loss of open space is but one of the many issues surrounding urban sprawl. Urban sprawl has also been criticised for being a financial and social drain on a city (Geller, 2003). Outlying suburbs often require more costly infrastructure and sprawl can create economic disparities and social fragmentation across a city (Brueckner, 2000). The traditional city centres also pay a price when residents leave for the outlying areas with development outside of the CBD leading to the deterioration of many inner city areas (Brueckner, 2000).
Given the issues it has been known to cause, it is critically important to monitor urban sprawl. There are various methods in capturing change, such as the recording of building/planning permits and simple visual inspections, though it can be difficult to accurately capture and display temporal changes using these methods. Remote sensing has been increasingly used to detect and examine urban land cover changes over time. Whilst it is acknowledged that the complexity in urban environments present many challenges in accurately detecting land cover change, remote sensing is being effectively used to monitor changes and proves an effective tool in policy making for urban planning. The Landsat series, which began in 1972, was designed to operate with the objective of tracking changes in land cover (Williams et al, 2006). This is well suited to provide a synoptic view of urban development and has been widely used to measure urban form and changes from natural to impervious surfaces. Using multi-date satellite imagery such as the Landsat series, it is possible to observe land-cover change over a period of time. Rather than just providing a gross change over a long period, satellite time-series can record the variability of urban development in space and time, thus allowing a comparison with different factors, such as economic and demographic data (Masek et al, 2000). Change detection, which involves the use of multi-temporal data to discriminate areas of land cover change between dates of imaging (Lillesand and Kiefer, 2000) can be performed to measure these changes and act as an efficient means of obtaining information on temporal trends and the spatial distribution of urban areas which are required for understanding, modelling and projecting land cover change (Elvidge et al, 2004). Modelling of potential future land cover change can help to provide an insight into the land use change dynamics and can allow us to quantitatively predict where future change might occur based on previous changes. Urban planners require reliable information to assess the possible consequences of urban sprawl and manage the spatial trajectory of an urban area over time (Griffiths et al, 2010) and models can prove an effective tool in planning for future growth scenarios.
Share with your friends: |
