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

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, which allowed the creation of 216 (=63 ) possible combinations of classes over the

#### **Figure 3.**

*Instances of urban change trajectories 72–2000.*


#### **Table 2.**

*Areas (hectare) of urban areas.*

study period, hence producing 216 different from-to-to change maps (not shown here). As our main concern was on the change characteristics of urban areas, a map was created using ArcGIS displaying the eight different instances of urban change trajectories throughout the study period (**Figure 4**).

In conclusion, it was found that neither Hafeet Mountain nor the sand dunes have ever formed a barrier to urban growth and will probably not do so in the future. Furthermore, it seems that in the short-term, the city will not witness urban expansion across the sand dunes for reasons explained above. However, it is thought that policy makers and planners will be forced to review their decisions to reverse the expansion from being horizontal, which was the case for the last forty years, to become vertical in order to minimize the necessary expenses of reclaiming more lands from sand dunes to urban areas, and to avoid more investments in kilometers of water pipes, electricity lines, and other infrastructures.


#### **Table 3.**

*Spatial metrics used for urban growth characterization.*

**91**

concession areas.

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

**3.2 Assessing landfill locations for waste management for the city of Abu Dhabi** 

The location of waste treatment and disposal facilities often has an impact on property values owing to noise, dust, pollution, unsightliness, and negative stigma. Hence, proper waste management practices need to be adopted to minimize the

A landfill must be situated and designed so as to meet the necessary conditions for preventing pollution of the soil, ground, and surface water as well as ensuring efficient collection of leachate. Similarly, a landfill site should be kept as far away from densely populated areas as possible, to reduce the impact of pollution on public health. At the same time, the landfill site should be placed as close as possible to existing roads to reduce costs of road development, transportation, and waste collection. Likewise, uneven or steep terrain is not appropriate for hosting landfills. As a result of an extensive literature review, local regulations, and expertise, a set of 19 criteria of an appropriate landfill site was identified and grouped into five parameters (**Table 4**).

The study area of the actual study is delimited by the administrative boundary of Abu Dhabi City Municipality. Datasets representing the set of 19 criteria identified in **Table 4** were collected from Abu Dhabi Municipality, the Environmental Agency of Abu Dhabi, Statistics Center of Abu Dhabi, and the National Center of Meteorology and Seismology. These datasets were clipped using the study area boundary, converted to a common spatial frame (UTM coordinate system, WGS84

The site selection process adopted a multi-criteria evaluation (MCE) approach that produces a suitability map from the criteria listed in **Table 4**. Given the varying importance of the parameters used in the selection process, they are assigned different weights as illustrated in **Table 4**. The overall suitability score (S) is then calculated based on the simple additive weighting (SAW) method as per Eq. (1).

S = Σ wi × xi (1)

where S is suitability, wi is the relative weight of parameter i, and xi is the rank (score) of parameter i. xi is calculated as the sum of ranks of all the attributes belonging to parameter i and then normalized to the range 1–10 with 1 indicating the most favorable condition. The suitability value for each parameter was reclassified into four classes: Highly suitable (1–2), Suitable (3–5), Moderately suitable (6–7), and Unsuitable (8–10). The final suitability (S), calculated using Eq. (1) and reclassified into the four pre-defined classes, is shown in **Figure 5**. The final results indicate that 41% of the study area is considered as highly suitable for siting a landfill. Only 27% of the study area is unsuitable mostly due to proximity to restricted areas and oil fields. The created suitability map can be used to select candidate locations for a new landfill site from available parcels that fall within the most suitable regions, such as the seven sites shown in **Figure 5**, prior to evaluating their environmental impact in order to help in the selection process. Furthermore, the model was used to examine and evaluate the existing "Al Dhafra Landfill" shown in **Figure 5**. It was concluded that this particular site falls within the unsuitable zone, owing to its proximity to oil

datum, and Zone 40N), and added into a geodatabase for analysis.

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

risks to human health and the environment.

*3.2.1 Requirements for landfill location*

*3.2.2 GIS project implementation*

**using GIS**

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

### **3.2 Assessing landfill locations for waste management for the city of Abu Dhabi using GIS**

The location of waste treatment and disposal facilities often has an impact on property values owing to noise, dust, pollution, unsightliness, and negative stigma. Hence, proper waste management practices need to be adopted to minimize the risks to human health and the environment.

#### *3.2.1 Requirements for landfill location*

*Geographic Information Systems and Science*

infrastructures.

**Table 3.**

trajectories throughout the study period (**Figure 4**).

2000 0.29 26.18

*Spatial metrics used for urban growth characterization.*

*Al Ain Urban area trajectory maps in 1972, 1990, and 2000. U, urban; N: non-urban.*

study period, hence producing 216 different from-to-to change maps (not shown here). As our main concern was on the change characteristics of urban areas, a map was created using ArcGIS displaying the eight different instances of urban change

In conclusion, it was found that neither Hafeet Mountain nor the sand dunes have ever formed a barrier to urban growth and will probably not do so in the future. Furthermore, it seems that in the short-term, the city will not witness urban expansion across the sand dunes for reasons explained above. However, it is thought that policy makers and planners will be forced to review their decisions to reverse the expansion from being horizontal, which was the case for the last forty years, to become vertical in order to minimize the necessary expenses of reclaiming more lands from sand dunes to urban areas, and to avoid more investments in kilometers of water pipes, electricity lines, and other

**Year LCR PLAND\_U (%) Period AGR (%)** 1972 1.52 5.33 1972–1990 0.67 1990 0.43 18.14 1990–2000 0.73

**90**

**Figure 4.**

A landfill must be situated and designed so as to meet the necessary conditions for preventing pollution of the soil, ground, and surface water as well as ensuring efficient collection of leachate. Similarly, a landfill site should be kept as far away from densely populated areas as possible, to reduce the impact of pollution on public health. At the same time, the landfill site should be placed as close as possible to existing roads to reduce costs of road development, transportation, and waste collection. Likewise, uneven or steep terrain is not appropriate for hosting landfills. As a result of an extensive literature review, local regulations, and expertise, a set of 19 criteria of an appropriate landfill site was identified and grouped into five parameters (**Table 4**).

### *3.2.2 GIS project implementation*

The study area of the actual study is delimited by the administrative boundary of Abu Dhabi City Municipality. Datasets representing the set of 19 criteria identified in **Table 4** were collected from Abu Dhabi Municipality, the Environmental Agency of Abu Dhabi, Statistics Center of Abu Dhabi, and the National Center of Meteorology and Seismology. These datasets were clipped using the study area boundary, converted to a common spatial frame (UTM coordinate system, WGS84 datum, and Zone 40N), and added into a geodatabase for analysis.

The site selection process adopted a multi-criteria evaluation (MCE) approach that produces a suitability map from the criteria listed in **Table 4**. Given the varying importance of the parameters used in the selection process, they are assigned different weights as illustrated in **Table 4**. The overall suitability score (S) is then calculated based on the simple additive weighting (SAW) method as per Eq. (1).

$$\mathbf{S} = \boldsymbol{\Sigma} \mathbf{w}\_{\mathrm{i}} \times \mathbf{x}\_{\mathrm{i}} \tag{1}$$

where S is suitability, wi is the relative weight of parameter i, and xi is the rank (score) of parameter i. xi is calculated as the sum of ranks of all the attributes belonging to parameter i and then normalized to the range 1–10 with 1 indicating the most favorable condition. The suitability value for each parameter was reclassified into four classes: Highly suitable (1–2), Suitable (3–5), Moderately suitable (6–7), and Unsuitable (8–10). The final suitability (S), calculated using Eq. (1) and reclassified into the four pre-defined classes, is shown in **Figure 5**. The final results indicate that 41% of the study area is considered as highly suitable for siting a landfill. Only 27% of the study area is unsuitable mostly due to proximity to restricted areas and oil fields.

The created suitability map can be used to select candidate locations for a new landfill site from available parcels that fall within the most suitable regions, such as the seven sites shown in **Figure 5**, prior to evaluating their environmental impact in order to help in the selection process. Furthermore, the model was used to examine and evaluate the existing "Al Dhafra Landfill" shown in **Figure 5**. It was concluded that this particular site falls within the unsuitable zone, owing to its proximity to oil concession areas.


**93**

**Figure 5.**

**Table 4.**

*a new landfill (sites 1–7).*

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

**Parameter Weight Layer name Classification Ranking** Restricted areas 20% Airports <3 km 10

Political criteria 10% Admin boundary <1000 m 10

>3 km 1

>3 km 1

>3 km 1

>500 m 1

>1000 m 1

Military area <3 km 10

Oilfields <3 km 10

Protected areas <500 m 10

**3.3 Mapping sand dune fields in Abu Dhabi Emirate over the period 1992–2013**

*The final landfill suitability map with the location of existing Al Dhafra landfill site and of potential sites for* 

The UAE has witnessed rapid economic growth since the discovery of oil in 1968. As a result, major urbanization and farming projects have been undertaken throughout the country including in the heart of the sand sea. Sand dunes and their movements present a serious threat to the country's urban centers as well as its infrastructure. This study focuses on the mapping of sand dune fields and assessing their changes over the period 1992–2013 in the UAE using Landsat imagery.

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

*Landfill site selection criteria with weights and ranks.*

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


#### **Table 4.**

*Geographic Information Systems and Science*

Socio-economic criteria

**Parameter Weight Layer name Classification Ranking** Environment 30% Hydrology <500 m 10

>500 m 1

5–15 m 5 >15 m 1

Other 10

5–15% 1 <5% 5

>500 m 1

>200 m 1

1–5 km 5 5–10 km 1 >10 km 10

1–5 km 5 5–10 km 1 >10 km 10

>2000 m 1 Industrial <200 m 10 >200 m 1 Other < 200 m 10 >200 m 1

>500 m 1

Low 1

Low 1

Low 10

10

1

Groundwater <5 m 10

Geology Sabkha, Eolian 1

Slope Slope: >15% 10

Shoreline <500 m 10

Vegetation <200 m 10

Built-up area <1 km 10

Land use Residential < 2000 m 10

Utilities <500 m 10

<2000 m from densely populated areas

>2000 m from densely populated areas

Rainfall High 10

Temperature High 1

10% Wind speed High 10

Population density

30% Roads <1 km 10

**92**

Climatological criteria

*Landfill site selection criteria with weights and ranks.*

#### **Figure 5.**

*The final landfill suitability map with the location of existing Al Dhafra landfill site and of potential sites for a new landfill (sites 1–7).*

#### **3.3 Mapping sand dune fields in Abu Dhabi Emirate over the period 1992–2013**

The UAE has witnessed rapid economic growth since the discovery of oil in 1968. As a result, major urbanization and farming projects have been undertaken throughout the country including in the heart of the sand sea. Sand dunes and their movements present a serious threat to the country's urban centers as well as its infrastructure. This study focuses on the mapping of sand dune fields and assessing their changes over the period 1992–2013 in the UAE using Landsat imagery.

Availability of similar spatial and spectral characteristics of the Landsat TM and ETM+ sensors during the study period ensured the provision of consistent reliable imagery used in the study.

#### *3.3.1 Requirements for mapping sand dune fields*

Optical sensors have proven very useful in detecting sand over large areas. In particular, the use of discrete bands of multispectral sensors enables reliably to distinguish between sand and other land covers. Since the ultimate goal is to study changes in the sand cover extent, it is important that imagery used to map each representative period be acquired at anniversary dates.

In this study, we develop an approach to detect and map sand fields using Landsat imagery collected in three different years: 1992, 2002, and 2013. This approach is used to create land cover and sand/non-sand maps over the study area for the three different dates. We assess their accuracy using higher resolution imagery and store them in vector format for use in change detection studies. The study area encompasses the whole Emirate of Abu Dhabi extending over 67,000 km<sup>2</sup> . It is characterized by a harsh climate where temperatures reach 48°C and humidity ranges between 80 and 90% in summer.

Six Landsat scenes that fall in different zones of the UTM coordinate system were used. It is necessary to convert the scenes to a unique coordinate system prior to creating a mosaic covering the study area and clipping the needed section. To delineate sand using a multispectral classification approach, a set of training and validation sites is needed. Higher resolution imagery from SPOT, Rapid Eye, and IKONOS is used in identifying and selecting these sites.

#### *3.3.2 GIS project implementation*

The approach used in this study is summarized in **Figure 6**. The scenes are mosaicked, referenced to a common frame, and clipped for each of the study periods. Only the reflective bands available on TM and ETM+ sensor and their counterpart on OLI are stored for use in processing. The datasets collected for use in the project are summarized in **Table 5**.

The core component of the processing consists in performing a supervised classification of the multi-temporal Landsat data. As a precursor, a classification scheme that includes important land cover classes present in the study area is developed based on spectral clustering of input datasets and familiarity with the study area. The final scheme includes the following classes: water, vegetation, sand, wet soil, intertidal, and bedrock. Different configurations of the processing flow showed that the overall accuracy of the classification increased if the vegetation class was extracted first using the soil adjusted vegetation index (SAVI). One hundred and forty two training sites selected randomly across the remaining classes are then used in the supervised classification process.

The resultant classified maps were reclassified into a binary sand/no sand map, vectorized, and exported to GIS. **Figure 7** shows the resulting land cover and sand/no sand maps obtained for the 3 dates. Accuracy assessment of the land cover maps was performed using a set of 94 assessment sites selected with the help of higher resolution imagery. The results indicated that the overall accuracy of the classification was 87% for the 1992 map, 89% for the 2002 map, and 91% for the 2013 map. However, sand class alone was mapped with a higher accuracy for all 3 years. **Table 6** summarizes the size of each one of the classes for the 3 time

**95**

**Table 5.**

**Figure 6.**

*Sand mapping methodology flowchart.*

Landsat 8 OLI August

Landsat 7 ETM+ July

Landsat 4 TM June

**Data type Date Spatial** 

2013

2002

1992

farms, and dredging.

Abu Dhabi Emirate boundary shapefile

*Datasets used in the study.*

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

periods. It highlights the changes in the size of the sand class that can be attributed to different factors including sand encroachment, urban growth, establishment of

**resolution**

Rapid Eye 2013 5 m RGB Accuracy assessment IKONOS 2003 1 m Panchromatic Accuracy assessment SPOT panchromatic 1986 10 m Panchromatic Accuracy assessment

**Spectral characteristics**

30 m Reflective bands Classification

30 m Reflective bands Classification

30 m Reflective bands Classification

2013 — Vector Delimit study area

**Purpose**

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

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

#### **Figure 6.**

*Geographic Information Systems and Science*

*3.3.1 Requirements for mapping sand dune fields*

ranges between 80 and 90% in summer.

*3.3.2 GIS project implementation*

the project are summarized in **Table 5**.

representative period be acquired at anniversary dates.

IKONOS is used in identifying and selecting these sites.

are then used in the supervised classification process.

imagery used in the study.

Availability of similar spatial and spectral characteristics of the Landsat TM and ETM+ sensors during the study period ensured the provision of consistent reliable

Optical sensors have proven very useful in detecting sand over large areas. In particular, the use of discrete bands of multispectral sensors enables reliably to distinguish between sand and other land covers. Since the ultimate goal is to study changes in the sand cover extent, it is important that imagery used to map each

In this study, we develop an approach to detect and map sand fields using Landsat imagery collected in three different years: 1992, 2002, and 2013. This approach is used to create land cover and sand/non-sand maps over the study area for the three different dates. We assess their accuracy using higher resolution imagery and store them in vector format for use in change detection studies. The study area encompasses the whole Emirate of Abu Dhabi extending over 67,000 km<sup>2</sup>

is characterized by a harsh climate where temperatures reach 48°C and humidity

Six Landsat scenes that fall in different zones of the UTM coordinate system were used. It is necessary to convert the scenes to a unique coordinate system prior to creating a mosaic covering the study area and clipping the needed section. To delineate sand using a multispectral classification approach, a set of training and validation sites is needed. Higher resolution imagery from SPOT, Rapid Eye, and

The approach used in this study is summarized in **Figure 6**. The scenes are mosaicked, referenced to a common frame, and clipped for each of the study periods. Only the reflective bands available on TM and ETM+ sensor and their counterpart on OLI are stored for use in processing. The datasets collected for use in

The core component of the processing consists in performing a supervised classification of the multi-temporal Landsat data. As a precursor, a classification scheme that includes important land cover classes present in the study area is developed based on spectral clustering of input datasets and familiarity with the study area. The final scheme includes the following classes: water, vegetation, sand, wet soil, intertidal, and bedrock. Different configurations of the processing flow showed that the overall accuracy of the classification increased if the vegetation class was extracted first using the soil adjusted vegetation index (SAVI). One hundred and forty two training sites selected randomly across the remaining classes

The resultant classified maps were reclassified into a binary sand/no sand map, vectorized, and exported to GIS. **Figure 7** shows the resulting land cover and sand/no sand maps obtained for the 3 dates. Accuracy assessment of the land cover maps was performed using a set of 94 assessment sites selected with the help of higher resolution imagery. The results indicated that the overall accuracy of the classification was 87% for the 1992 map, 89% for the 2002 map, and 91% for the 2013 map. However, sand class alone was mapped with a higher accuracy for all 3 years. **Table 6** summarizes the size of each one of the classes for the 3 time

. It

**94**

*Sand mapping methodology flowchart.*


#### **Table 5.**

*Datasets used in the study.*

periods. It highlights the changes in the size of the sand class that can be attributed to different factors including sand encroachment, urban growth, establishment of farms, and dredging.

#### **Figure 7.**

*1992, 2002, and 2013 land cover and sand maps.*


**Table 6.**

*Size of the sand/non-sand classes for 1992, 2002, and 2013.*

#### **3.4 GIS-based wind farm site selection model offshore Abu Dhabi Emirate, UAE**

The development of turbines that convert wind energy into electrical energy put wind in a good position as a source of alternative renewable energy. This study aims to assess the feasibility of establishing wind farms offshore the Emirate of Abu Dhabi, UAE and to identify favorable sites for such farms using a geographic information system (GIS).

### *3.4.1 Requirements for wind farm site location*

Selecting the appropriate location for a wind farm is a key to its efficiency and success. Considering environmental, legal, and economic conditions, certain

**97**

**Table 8.**

**Table 7.**

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

locations were found completely inadequate and should be excluded from the selection process, whereas others varied in their degree of suitability. **Table 7** lists the set of conditions that inhibit the siting of a wind farm and lead to the exclusion of areas with these conditions from the selection process. **Table 8** lists the set of criteria that

Based on the set of criteria discussed in Section 3.4.1, needed datasets were collected, georeferenced to a common spatial frame, and ingested in the project's geodatabase. A GIS model is then built to create an exclusion mask from the input dataset (see **Figure 8**). Areas identified by the model are completely excluded from

Areas that are not excluded by the first stage of the model are candidates for wind farms. Their suitability is evaluated using the criteria listed in **Table 8** using a weighted sum overlay approach whose inputs are derived from the wind speed and

The suitability of a location (S) is then calculated from the reclassified inputs

S = wwind × ranked wind speed + wdepth × ranked water depth (2)

where wwind is the weight given to the wind speed criterion and wdepth is that

Given the higher importance of wind in the suitability of a location for wind farming, we assigned a value of 2 to wwind and 1 to wdepth. The suitability map resulting from running this model is presented in **Figure 9**. It indicates that only a small fraction of offshore Abu Dhabi Emirate is suitable for wind energy. A substantial

4.5–5.4 m/s: moderately suitable

>5.4 m/s: suitable

10–20 m: suitable >20 m: unsuitable

**Parameter Unsuitability condition**

Water/land mask Land

*Criteria used to exclude areas from the selection process.*

*Criteria used to rank non-excluded areas.*

**Parameter Suitability condition** Wind speed <4.5 m/s: unsuitable

Water depth 0–10 m: moderately suitable

Submarine cables Within 250 m Oil and gas wells Within 250 m Oil and gas pipelines Within 250 m Bird conservation sites Inside Environmentally protected areas Inside Maritime navigation corridor Inside

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

using the weighted sum defined in Eq. (2).

given to the water depth criterion.

*3.4.2 GIS project implementation*

the selection process.

water depth layers.

affect the suitability of a location for a wind farm site.

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

locations were found completely inadequate and should be excluded from the selection process, whereas others varied in their degree of suitability. **Table 7** lists the set of conditions that inhibit the siting of a wind farm and lead to the exclusion of areas with these conditions from the selection process. **Table 8** lists the set of criteria that affect the suitability of a location for a wind farm site.

#### *3.4.2 GIS project implementation*

*Geographic Information Systems and Science*

**3.4 GIS-based wind farm site selection model offshore Abu Dhabi Emirate, UAE**

Vegetation 147 518 440 Wet soil 5168 4341 2651 Intertidal 3069 2788 3175 Exposed bedrock 1874 1864 3821 Water 32,172 32,873 32,678 Non-sand land 10,258 9511 10,087 Sand 52,186 52,732 52,848

**) 2002 (km2**

**) 2013 (km2**

**)**

The development of turbines that convert wind energy into electrical energy put wind in a good position as a source of alternative renewable energy. This study aims to assess the feasibility of establishing wind farms offshore the Emirate of Abu Dhabi, UAE and to identify favorable sites for such farms using a geographic

Selecting the appropriate location for a wind farm is a key to its efficiency and success. Considering environmental, legal, and economic conditions, certain

**96**

**Figure 7.**

**Table 6.**

*1992, 2002, and 2013 land cover and sand maps.*

**Class 1992 (km2**

information system (GIS).

*3.4.1 Requirements for wind farm site location*

*Size of the sand/non-sand classes for 1992, 2002, and 2013.*

Based on the set of criteria discussed in Section 3.4.1, needed datasets were collected, georeferenced to a common spatial frame, and ingested in the project's geodatabase. A GIS model is then built to create an exclusion mask from the input dataset (see **Figure 8**). Areas identified by the model are completely excluded from the selection process.

Areas that are not excluded by the first stage of the model are candidates for wind farms. Their suitability is evaluated using the criteria listed in **Table 8** using a weighted sum overlay approach whose inputs are derived from the wind speed and water depth layers.

The suitability of a location (S) is then calculated from the reclassified inputs using the weighted sum defined in Eq. (2).

S = wwind × ranked wind speed + wdepth × ranked water depth (2)

where wwind is the weight given to the wind speed criterion and wdepth is that given to the water depth criterion.

Given the higher importance of wind in the suitability of a location for wind farming, we assigned a value of 2 to wwind and 1 to wdepth. The suitability map resulting from running this model is presented in **Figure 9**. It indicates that only a small fraction of offshore Abu Dhabi Emirate is suitable for wind energy. A substantial


**Table 7.**

*Criteria used to exclude areas from the selection process.*


#### **Table 8.**

*Criteria used to rank non-excluded areas.*

part of that area is considered moderately suitable. However, some suitable areas are close to mainland and to inhabited islands, such as Delma Island, and can be considered for wind farms to feed these areas.
