155
positive values for the water features, which makes water features free from built-up
area noise on index image. The index will not impact on vegetation due to negative
value for vegetation. Therefore, taking the difference between index highlighting
built-up area and bare land with indices highlighting vegetation and water as shown
in Eq. 1 will result in positive values for built-up and barren pixels and negative
values for vegetation and water.
8.4.1 The Built-Up Indices
8.4.1.1 NDBI
After the urban index, which was formulated in the 2000s (Zha et al. 2003), different Built-Up Indices (BU) have been used to determine the built-up area in urban
areas. NDBI is used in recent years to demarcate the built-up area in the city region.
NDBI is used to detect the change of built-up area, utilising the spectral response of
the built-up surface against other types of land use. As it uses SWIR band, hence it
is hard to differentiate between dry vegetation and NDBI-driven built-up area.
8.4.1.2 NDVI
NDVI is one of the basic indices to know the vegetation health condition within a
particular area. Here, it is used to know the actual vegetation condition of respected
city during the time periods. Different researchers used this index as an opposite
index to figure out the built-up area and its relation with NDVI.
8.4.1.3 EBBI
The Enhanced Built-Up and Bareness Index (EBBI) has been developed by
As-syakur et al. (2012) to differentiate between the bareness ground and built-up
area, and it is the first built-up index which uses three bands, i.e. near-infrared (NIR)
band, 0.83 μm; short-wave infrared (SWIR) band, 1.65 μm; and thermal infrared
(TIR) band, 11.65 μm, of multispectral satellite imagery. And different researchers
suggest using this index as it can differentiate the built-up and bareness index.
According to Weng, the utilisation of TIR channels is very effective for mapping of
built-up areas based on the outgoing radiation or low albedo, which minimises the
effect of shadows and waterbodies, while a high albedo demonstrates built-up and
bare land areas clearly. The TIR channel also exhibits a high level of contrast for
vegetation. The temperature difference between built-up areas and other vegetation
areas sometimes may reach 10–12 degrees; therefore, the combination of the NIR,
SWIR and TIR exhibits the improvement of the mapping of built-up areas and bare
land areas more clearly than indices. The ratio between the subtraction of Band 4
and Band 5 and the summation of Band 5 and Band 6 will assign zero water bodies,
8 Spatio-Temporal Transformation of Urban Built-Up Areas for Sustainable…
positive values for the water features, which makes water features free from built-up
area noise on index image. The index will not impact on vegetation due to negative
value for vegetation. Therefore, taking the difference between index highlighting
built-up area and bare land with indices highlighting vegetation and water as shown
in Eq. 1 will result in positive values for built-up and barren pixels and negative
values for vegetation and water.
8.4.1 The Built-Up Indices
8.4.1.1 NDBI
After the urban index, which was formulated in the 2000s (Zha et al. 2003), different Built-Up Indices (BU) have been used to determine the built-up area in urban
areas. NDBI is used in recent years to demarcate the built-up area in the city region.
NDBI is used to detect the change of built-up area, utilising the spectral response of
the built-up surface against other types of land use. As it uses SWIR band, hence it
is hard to differentiate between dry vegetation and NDBI-driven built-up area.
8.4.1.2 NDVI
NDVI is one of the basic indices to know the vegetation health condition within a
particular area. Here, it is used to know the actual vegetation condition of respected
city during the time periods. Different researchers used this index as an opposite
index to figure out the built-up area and its relation with NDVI.
8.4.1.3 EBBI
The Enhanced Built-Up and Bareness Index (EBBI) has been developed by
As-syakur et al. (2012) to differentiate between the bareness ground and built-up
area, and it is the first built-up index which uses three bands, i.e. near-infrared (NIR)
band, 0.83 μm; short-wave infrared (SWIR) band, 1.65 μm; and thermal infrared
(TIR) band, 11.65 μm, of multispectral satellite imagery. And different researchers
suggest using this index as it can differentiate the built-up and bareness index.
According to Weng, the utilisation of TIR channels is very effective for mapping of
built-up areas based on the outgoing radiation or low albedo, which minimises the
effect of shadows and waterbodies, while a high albedo demonstrates built-up and
bare land areas clearly. The TIR channel also exhibits a high level of contrast for
vegetation. The temperature difference between built-up areas and other vegetation
areas sometimes may reach 10–12 degrees; therefore, the combination of the NIR,
SWIR and TIR exhibits the improvement of the mapping of built-up areas and bare
land areas more clearly than indices. The ratio between the subtraction of Band 4
and Band 5 and the summation of Band 5 and Band 6 will assign zero water bodies,
8 Spatio-Temporal Transformation of Urban Built-Up Areas for Sustainable…