**2.3 Acquiring the data**

*Geographic Information Systems and Science*

racy of a variety of outputs [8, 9].

sensing data and GIS [10].

using GIS [11].

UAE [13].

**2. Research approach**

file into a geodatabase.

**2.1 Stating the problem**

**2.2 Defining the study area**

report writing.

the calibration of remote sensing inputs, data integration within a GIS can enhance the extraction of information from satellite imagery and has led to a synergistic approach in spatial data handling and modeling [5–7], hence improving the accu-

In this chapter, we will expose the power and benefit of integrating remote sensing and geographic information systems to model our environment through various case studies applied to the arid/hyper arid environment of the United Arab Emirates. Four case studies are introduced and discussed in Section 3 namely:

• Characterization of Al Ain city urban growth using multi-temporal remote

• Assessing landfill locations for waste management for the city of Abu Dhabi

• Mapping sand dune fields in Abu Dhabi Emirate over the period 1992–2013 [12].

• GIS-based wind farm site selection model offshore Abu Dhabi Emirate,

Remote sensing and GIS are incorporated into environmental modeling for addressing environmental issues and problems. The core of this approach is to use the power embedded in these geospatial techniques to develop and implement a GIS project. Remote sensing here is treated as the science, technology, and techniques used to acquire the wanted data concerning the study area, processing those data, extracting relevant information about the studied area, and exporting the resulting

A typical GIS project includes (1) stating the problem; (2) defining the study area; (3) acquiring, preparing, and automating the data; (4) processing the data; (5) building the geodatabase; (6) analyzing; and (7) visualizing, mapping, and

The first step of implementing a GIS project is to state the problem and identify

This step delineates a narrowed boundary of an area of interest. The information

GIS possesses many and convenient ways for demarkation of a project's boundary. ERDAS IMAGINE® and ArcGIS®, worldwide used GIS software packages, allow

from *Step* 2.1 tells us about the proper location where the problem occurred and

addresses the possible questions and answers under interest.

the objective of the analysis. The following questions need be addressed: What is the problem to solve? How is it solved now? Are there alternate ways to solve it using a GIS? What are the final products of the project—reports, working maps, and presentation-quality maps? Will the data be used for other purposes? What are the requirements for these? This step is important because the answers to these questions determine the scope of the project as well as how you implement the

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analysis.

Before locating and acquiring the needed data, a list of criteria should have been set to address the identified objectives of the problem to be solved in the study area.

Consider the following two real-world examples: Example 1: Landfill Locations for Waste Management of the City of Abu Dhabi

Using GIS (**Table 4**) and, Example 2: GIS-based wind farm site selection model offshore Abu Dhabi Emirate, UAE (**Tables 7** and **8**)*.*

Furthermore, the methodology needs to be analyzed to establish what kind of data is needed. The most important question that needs to be answered is Why do I need these data? If the data are truly needed, then this question is easily answered. If not, then the data are most likely not necessary to solve the problem.

To be able to work with data in GIS, you need to understand the nature and procedural steps of working with GIS data such as dataset formats, dataset attributes, dataset completeness, coordinate systems, and dataset sources (see **Tables 1** and **5**).


### **Table 1.**

*List of primary and secondary data used in the research.*

#### **2.4 Processing and preparing the data**

Remote sensing data need to be prepared before being used for information extraction. This operation is made up of two main sub-processes: *pre-processing* and *processing*.

*Pre-processing:* involves data restoration which means data correction. This involves radiometric, atmospheric, and geometric correction and map projection.

*Processing:* involves data enhancement, data classification, data validation, and data export to GIS format.

In a GIS project, data processed and exported from remote sensing will serve as one input into the GIS database. GIS has a database management system component to support the proper management of both spatial and attribute data. It also enables convenient linking and relating of various data records by their locations on a common coordinate system. Some common tasks should be executed during the data processing and preparing step; these are as follows:

*Re-defining and re-projecting data:* The purpose is to define or/and to convert a particular layer of data from one coordinate system to another. Working with GIS involves more than one GIS layer; therefore, acquired datasets may contain different projections. Different data projections lead to distortion of data and inaccuracy in the analysis.

*Conversion between raster and vector data models:* File formats can also be varied in the forms of raster (*for example*, data derived from the remote sensing process) or vector (shapefile or feature class). Feature classes and shapefiles usually come embedded with attribute data, which allow the user to easily select and manipulate the information of interest. Therefore, converting a raster file to vector enables the user to intersect other available vector data.

*Reclassification:* To perform certain analysis in most cases, data need to be reclassified beforehand. Reclassification is a local operation that performs raster data analysis on a point-by-point or cell-by-cell basis. Reclassification, also commonly referred to as recoding, will reduce the number of classes you are using in the analysis, thus facilitating the analysis process and resulting in more accurate results. There are different reclassification methods such as binary masking, classification reduction, classification ranking, and changing measurement scales.

*Data querying:* Data querying in GIS involves both query by attribute and query by location. Both use certain conditions that apply to either the spatial or the nonspatial component of the analyzed data. The purpose is to extract desired features based on their location, attributes, or both for analysis. This can be done through conditional statement imposed in location or/and attribute data table to select only specific information of interest.

*Data export:* To make a temporary layer permanent in a current geodatabase, data resulted from steps such as that of the above need to be exported and saved in a working geodatabase or a current working space for further or future work.

#### **2.5 Building the geodatabase**

Creating the database for a GIS project will involve assembling the existing data, reviewing it, and then preparing the data for analysis. Some of the data will be usable as such; other layers will need additional processing. Sometimes you need to extract data from a possibly larger original source file. Reduction of the size of datasets and their consolidation accelerate the ensuing data processing and management. Typically, data acquired may exist in various forms and shapes, e.g. different coordinate systems and file formats. It is necessary to prepare and consolidate all datasets into a commonly operable form. GIS has a database management system

**85**

**Figure 1.**

tions on a common coordinate system.

*(7) visualizing, mapping, and report writing.*

**2.6 Analyzing the data**

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

component to support the proper management of both spatial and attribute data. It also enables convenient linking and relating of various data records by their loca-

*Diagram of a typical GIS project: (1) stating the problem; (2) defining the study area; (3) acquiring, preparing, and automating the data; (4) processing the data; (5) building the geodatabase; (6) analyzing; and* 

GIS analysis covers a wide variety of operations that you can do with a geographic information system. These range from simple display of features to

*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 1.**

*Geographic Information Systems and Science*

**2.4 Processing and preparing the data**

processing and preparing step; these are as follows:

user to intersect other available vector data.

specific information of interest.

**2.5 Building the geodatabase**

*processing*.

in the analysis.

data export to GIS format.

Remote sensing data need to be prepared before being used for information extraction. This operation is made up of two main sub-processes: *pre-processing* and

*Pre-processing:* involves data restoration which means data correction. This involves radiometric, atmospheric, and geometric correction and map projection. *Processing:* involves data enhancement, data classification, data validation, and

In a GIS project, data processed and exported from remote sensing will serve as one input into the GIS database. GIS has a database management system component to support the proper management of both spatial and attribute data. It also enables convenient linking and relating of various data records by their locations on a common coordinate system. Some common tasks should be executed during the data

*Re-defining and re-projecting data:* The purpose is to define or/and to convert a particular layer of data from one coordinate system to another. Working with GIS involves more than one GIS layer; therefore, acquired datasets may contain different projections. Different data projections lead to distortion of data and inaccuracy

*Conversion between raster and vector data models:* File formats can also be varied in the forms of raster (*for example*, data derived from the remote sensing process) or vector (shapefile or feature class). Feature classes and shapefiles usually come embedded with attribute data, which allow the user to easily select and manipulate the information of interest. Therefore, converting a raster file to vector enables the

*Reclassification:* To perform certain analysis in most cases, data need to be reclassified beforehand. Reclassification is a local operation that performs raster data analysis on a point-by-point or cell-by-cell basis. Reclassification, also commonly referred to as recoding, will reduce the number of classes you are using in the analysis, thus facilitating the analysis process and resulting in more accurate results. There are different reclassification methods such as binary masking, classification

*Data querying:* Data querying in GIS involves both query by attribute and query by location. Both use certain conditions that apply to either the spatial or the nonspatial component of the analyzed data. The purpose is to extract desired features based on their location, attributes, or both for analysis. This can be done through conditional statement imposed in location or/and attribute data table to select only

*Data export:* To make a temporary layer permanent in a current geodatabase, data resulted from steps such as that of the above need to be exported and saved in a

Creating the database for a GIS project will involve assembling the existing data, reviewing it, and then preparing the data for analysis. Some of the data will be usable as such; other layers will need additional processing. Sometimes you need to extract data from a possibly larger original source file. Reduction of the size of datasets and their consolidation accelerate the ensuing data processing and management. Typically, data acquired may exist in various forms and shapes, e.g. different coordinate systems and file formats. It is necessary to prepare and consolidate all datasets into a commonly operable form. GIS has a database management system

working geodatabase or a current working space for further or future work.

reduction, classification ranking, and changing measurement scales.

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*Diagram of a typical GIS project: (1) stating the problem; (2) defining the study area; (3) acquiring, preparing, and automating the data; (4) processing the data; (5) building the geodatabase; (6) analyzing; and (7) visualizing, mapping, and report writing.*

component to support the proper management of both spatial and attribute data. It also enables convenient linking and relating of various data records by their locations on a common coordinate system.

#### **2.6 Analyzing the data**

GIS analysis covers a wide variety of operations that you can do with a geographic information system. These range from simple display of features to complex, multistep analytical models. Perhaps the simplest form of GIS analysis is presenting the geographic distribution of data. This is conceptually the same as sticking pins in a wall map, a simple but powerful method of detecting patterns. A second type of GIS analysis is querying, or selecting from, the database. Queries let you identify and focus on a specific set of features. There are two types of GIS queries, *attribute* queries (find features based on their attributes) and *location* queries (find features based on where they are). A third type of GIS analysis is finding what is near a feature (buffering); a powerful function of GIS analysis is that the output of one procedure can be used in another. Here, the buffered zone can be used in an attribute query. A fourth type of GIS analysis is overlaying different layers of features. You can create new information when you overlay one set of features with another. There are several types of overlay operations, but all involve joining two existing sets of features into a single new set of features. Finally, all these techniques and many others are combined into a more complex GIS analysis, thus creating detailed models of the world to solve complicated problems. It is possible to repeat an analysis using slightly different parameters several times and compare the results. This can allow you to refine your analysis techniques.

## **2.7 Visualizing and printing the results**

The last step in a GIS project is to present and communicate the results of your analysis. Your final product should effectively communicate your findings to your audience. The results of a GIS analysis can best be shown on a map. Nevertheless, they can also be disseminated through charts, reports, or videos and animated maps. You can print charts and reports separately, embed them in documents created by other applications, or place them on your map.

All the above steps are summarized in **Figure 1**.
