*3.1.2.4 Change analysis techniques*

The post-classification comparison approach is used based on comparing separately the produced classified LULC maps (1972, 1990, and 2000) in order to identify the change in the LULC classes and provide descriptive information about the nature of change that occurred in different dates. Spatial metrics and GIS overlay analysis are used to characterize changes in the urban area class. A total of eight instances of urban change trajectories are produced (**Figure 3**) [8].

**89**

**Table 2.**

*Areas (hectare) of urban areas.*

**Figure 3.**

*Instances of urban change trajectories 72–2000.*

*Modeling the Environment with Remote Sensing and GIS: Applied Case Studies from Diverse…*

Urban areas were extracted using a semi-automatic method including manual editing of boundaries of certain classes based on authors' familiarity with the study area. A value of 1 was assigned to classes that fall in the urban category while a value

Change detection analysis across 1972, 1990, and 2000 was conducted using the post-classification comparison method. The LULC classification results are presented in **Table 1**. The GIS overlay analysis was also applied on the LULC maps,

**LULC classes 1972 1990 2000**

Urban areas 4107.00 5.33 13,965.00 18.14 20,160.00 26.18 Others 72,893.00 94.67 63,035.00 81.86 56,840.00 73.82 Total 77,000 100.00 77,000 100.00 77,000 100.00

**Area (ha) Area** 

**(%)**

**Area (ha) Area** 

**(%)**

**(%)**

**Area (ha) Area** 

) possible combinations of classes over the

Results of the spatial metrics calculation are shown in **Table 3**.

*3.1.2.5.3 Change detection between 1972, 1990, and 2000*

*DOI: http://dx.doi.org/10.5772/intechopen.82024*

of 0 was given to all other classes (**Table 2**).

*3.1.2.5.2 Spatial metrics calculation*

which allowed the creation of 216 (=63

*3.1.2.5.1 Urban class extraction*

*3.1.2.5 Results*

*Modeling the Environment with Remote Sensing and GIS: Applied Case Studies from Diverse… DOI: http://dx.doi.org/10.5772/intechopen.82024*

#### *3.1.2.5 Results*

*Geographic Information Systems and Science*

images and decrease analytical errors.

• Sand and gravel (dark soils)

• Sand dunes (bright sand)

• Water bodies and shadow

*3.1.2.2 Urban class extraction*

*3.1.2.3 Spatial metrics*

• Limestone

• Vegetation (oases, farms, and parks)

is created to include urban and non-urban only.

• Land consumption rate (LCR)

• Land absorption coefficient (LAC)

• Annual urban growth rate (AGR)

*3.1.2.4 Change analysis techniques*

• The percentage of built-up land (PLAND\_U)

• Urban (built-up including roads and buildings)

*3.1.2.1 Classification schema*

*3.1.2 GIS project implementation for characterization of Al Ain urban growth*

A hybrid of unsupervised and supervised classification schema is used. First unsupervised classification is carried out using the ISODATA algorithm. A number of iterations of 67, 80, and 60 for MSS, TM, and ETM+ are reached respectively with the convergence value at 0.990. The maximum likelihood algorithm with training sites carefully selected from the unsupervised classification results is used to run the supervised classification. Furthermore, the classified images were filtered using a 3 × 3 majority filter to remove speckles and to smooth the resulting

The following are six classes representing most land cover types of the study area:

Urban class is defined in this case as 'all manmade features including buildings, roads, and pavements in addition to vegetation covered areas such as oases, farms, parks, and farmed areas within the city boundary'. To extract urban areas, a bitmap

The application of a number of spatial metrics is used to characterize the urban

The post-classification comparison approach is used based on comparing separately the produced classified LULC maps (1972, 1990, and 2000) in order to identify the change in the LULC classes and provide descriptive information about the nature of change that occurred in different dates. Spatial metrics and GIS overlay analysis are used to characterize changes in the urban area class. A total of

eight instances of urban change trajectories are produced (**Figure 3**) [8].

growth of the city. As such, the following spatial metrics are used:

**88**

### *3.1.2.5.1 Urban class extraction*

Urban areas were extracted using a semi-automatic method including manual editing of boundaries of certain classes based on authors' familiarity with the study area. A value of 1 was assigned to classes that fall in the urban category while a value of 0 was given to all other classes (**Table 2**).

#### *3.1.2.5.2 Spatial metrics calculation*

Results of the spatial metrics calculation are shown in **Table 3**.
