How we can integrate the statistical method (Relative Shannon's entropy) with remote sensing and GIS to quantify the urban growth patters of mountain towns?.
Urbanization is regarded as one of the most powerful causes of land use and land cover change associated with population and economic expansion. In recent years, the integration of remote sensing (RS) and Geographical Information System (GIS) techniques has proven to be an efficient tool for identifying urban expansion and modeling. Effective planning at the local level is required to sustain a systematic urban growth pattern at the regional or global level.
Relative Shannon's entropy varies from 0 to 1. A value around zero implies a compact or concentrated distribution of urban area, whereas a value near one indicates widespread urban sprawl. As a result, greater entropy implies greater sprawl (Alsharif et al., 2015).
Kindly read through the following articles for further explanations (see attached)
The Shannon’s entropy can be computed in terms of spatial phenomenon, in order to quantify the built up area (impervious area) (urbanization ).Higher value of overall entropy for the whole urban area represents higher dispersion of impervious area, which gives an indication of urban sprawl.
The integration of the Relative Shannon's Entropy (RSE) statistical method with remote sensing and GIS can be used to quantify the urban growth pattern. This can be done by using a combination of satellite imagery and GIS data to identify changes in land use/cover and urban morphology across an area. The RSE can then be used to analyse the changes in land use/cover, urban morphology and quantify the urban growth pattern. In this method, satellite imagery is used to measure changes in land use/cover and urban morphology, while the GIS data is used to identify the boundaries of the urban area. The RSE can then be used to measure the degree of change in the land use/cover and urban morphology over time. This can help in understanding the dynamics of urban growth and provide insight into how the urban area is changing over time.
Maybe this can help: https://www.researchgate.net/publication/317850451_Comparing_two_methods_for_Urban_Complexity_calculation_using_Shannon-Wiener_index
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