The population data of the study area since 1952 was taken from surveys (for many years) and census data (for the years 1952, 1961, 1979, 1994 and 2004) provided by the Department of Statistics of Jordan. The estimation of population in Amman before 1952 was collected from other scientific research.

Landsat 5-TM for the years 1984, 1999 and Landsat 8-OLI for the year 2014 images were acquired for this study, as they provide an appropriate and cost-effective source of information for a wide range of applications, including land change mapping. Images (data sets) were obtained from the United States Geological Survey (USGS) (http://glovis.usgs.gov/), having a ground resolution element of 30 × 30 m, the images were then subset to the border of the study area. Figure 2 shows the flowchart of this study.

The images of Landsat TM and Landsat OLI include different bands with different wavelengths as shown in Table 1.

Radiometric calibration and corrections is important component to the change detection [18] , as it can eliminate or reduce image differences introduced as a result of changing atmospheric conditions. For this, the three images were acquired in the same season. The scenes were selected to be radiometric correction, geometrically corrected by using ground control points (GCP’s) for road crosses with Global Position System (GPS), with an accuracy of ±3 meter calibrated. The transformation model was applied to correct the Landsat 8-OLI image. An image to image geometric correction was followed for correcting Landsat 5-TM images using the corrected image of Landsat 8-OLI.

The raw data of Landsat 8-OLI image was provided in Digital Number (DN) that would represent reflected radiance for each pixel at the top of atmosphere. Therefore, it was important to carryout radiometric correction

Source: Adapted From (http://glovis.usgs.gov).

for the images to convert the DN to reflectance values using RS software.

This was done using absolute radiometric correction by measuring reference spectral reflectance of reference object in the image. For this purpose ground measurement were collected by the Multispectral Scanner (MSR16) with similar wavelengths. The reference objects were a quarry floor and a motorway paved with asphalt. The DNs for the same location of the target objects were extracted and correlated with spectral reflectance measured by MSR16. A histogram stretch was also applied to ensure that reflectance values would be in the rage of 0% - 100%. Image enhancement techniques like histogram equalization are performed on each image to improve the quality of the image. The data of ground control points were adapted for each single classifier produced by its spectral signature for producing a series of classification maps. Digital image processing was manipulated by the Remote Sensing (RS) software used.

Landsat images were classified using hybrid classification technique. In the hybrid classification technique, images for the years 1984, 1999 and 2014 are classified by combining the results from the unsupervised classification and the supervised classification to obtain the highest possible accuracy. Accurate classifications are imperative to insure precise change detection results. A supervised classification method was carried out using training areas. Finally, the signature from the unsupervised classification was merged with the signature of supervised classification. This process enables us to assign each pixel of an image to a particular land cover group or class. Maximum Likelihood Algorithm was employed to detect the land cover types using RS software, then the final map was designed by a GIS software as shown in Figure 3 in the discussion section.

To determine the changes in land use/cover at different years, a hybrid classification system was used. Four land use/land cover (LULC) classes were included for the scheme of this research. These land use/cover classes or categories are: Built-up area, Agriculture & Forest land, Rangeland and Barren areas. The description of these classes is shown in Table 2.

A raster data model is used in GIS to represent continuous data over space and which allow to produce a specific layer that can be used by TerrSet land change modeler. Markov in TerrSetclark labs IDRISI software involves two techniques; the first one is to analyze expected LULC based on early and later maps of LULC that has enabled us to obtain transition probability matrix records, that is probability of each land cover category to change to the another category.

The Ca-Markov model was applied to predicting the change for each class in the year 2030 by using two land use/cover in 1999 and 2014 derived from satellite images, and to apply this model which based on the number of random process, X(t), if the Markov process for any moment of the time, , thus, the

random process will satisfy the equation:

where: t_n is the present time so that t_n+1 represents some points in the future and t₁, t₂, ・・・, t_n−1 represent various points in the past. Based on the present data, future is independent of the past. In other words, future of random process depends neither on where it is now nor where it was.

If the Markov chain is expressed by X[k], and the states is {x₁, x₂, x₃, ・・・}, then the probability of transition from the state i to the state j in one time instant is;

The first Municipal Council of Amman was established in 1907 during the Ottomans Empire, at which time the settlement consisted of fewer more than 300 families. Photographs of the early modern urban area around 1924 show a small settlement in the locality of the present-day downtown area, with open land extending to the north and east of the Roman Amphitheater [17] [19] . In 1918, the population of Amman was less than 5000 inhabitants, with an expansion estimated at 0.321 km² [20] . In 1921, Prince Abdullah (the First King of Jordan later on) declared Amman the capital of the new state of Trans Jordan and from then onwards, the city became the administrative and economic hub of the country. Indeed, it has been observed that the development of the city “was carried out in parallel with the construction of the state” [17] . During this the years 1921-1953, the city experienced gradual spatial expansion, where its population was estimated at 10,500 in 1930 and 45,000 in the early 1940s, with the city extending over 2.5 km² by 1947 [21] . In 1948, urban expansion increased to 4.123 km², due to the first wave of migrants of Palestinians, as a result of the foundation of the state of Israel, who moved to Jordan in general and to Amman in particular. The refugees were mainly accommodated in five hastily prepared camps. Two of these were sited in Amman, namely Al Hussein camp in the north of the city and Al Wehdat camp in the south [20] [21] . As a result of such migration and the strong internal migration from rural areas and other cities to Amman, the population growth rate of the city has always been higher than that of Jordan as a whole, standing at 9.6% in the 1950s whereas the population rate at the national level has never exceeded 4% [21] . The spatial expansion during the 1950s took the form of “climbing the hills and mountains” that surrounded the urban core as noted above. Planning at this stage didn’t attempt to guide or regulate urban growth and development.

Population size of Amman has dramatically increased to reach 250 thousand in 1952 soon after the Arab- Israeli conflict in 1948 [22] . The second major wave of refugees arrived in Amman after 1967 war between Israel and the three Arab states of Egypt, Syria and Jordan. Khadhim and Rajjal (1988) noted that this conflict sent Amman into “another spiral of uncontrolled growth” [23] , where the city’s population increased from 350 thousand before the conflict to around half a million shortly after it. The population of the city was estimated at 600 thousand in 1974 and increased to 777.8 thousands in 1979, with the city extending over an area of 101 km² as a result of the second large scale Palestinian migration combined with additional refugees from Lebanon after the Civil War in 1975 [20] .

Rapid urban growth in the city of Amman has greatly increased the expansion of the built up area during the period of 1984 to 1999. In 1994, the population of Amman has reached 1,307,017, representing an increase of 54.6% since last census in 1979 according to Population and Housing Census conducted in 1994. The urban expansion was estimated to be 150.764 km² in 1996 [20] . This expansion was due to the Gulf war in 1991, where about 400 thousand of refugees from Jordanian and Palestinian origins came back from Kuwait and other Gulf countries and settled in Amman. Therefore, Jordan in general, and Amman in Particular, has suffered from the problems of unemployment, poverty and foreign debt, which can be directly related to the third wave of refugees in 1991. With the situation worsened by the economic crisis in 1989 that led to a decline in the value of the Jordanian Dinar that also pushed more people from rural area and small cities to move to Amman.

The urban expansion continued to reach 162.924 km² in 2002 [20] , and the population growth of Amman has also increased to some 1,726,713 inhabitants amounted to 33.8% of Jordan’s population according to the Population and Housing Census which took place in 2004 after the creation of the geographically wider Greater Amman Municipality (GMA) area. However, due to the invasion of Iraq in 2003, the population has continued to grow at a phenomenal rate and the population of Amman was estimated at 2.17 million in 2007 which equals almost 40% of the national population. In March 2007, the Ministry of the Interior estimated that there was as many as 1.4 million Iraqi nationals were living in Amman [21] . The number of Iraqis has decreased to around 500 thousand, but there are no reliable statistics concerning their number. In addition, thousands of construction workers from Egypt and Syria used to live in the city at that time, but again there are no reliable statistics concerning their number especially that some of them used to work seasonally then go back to their countries.

The population of Amman was estimated at 2,528.5 thousands in 2013 by the Jordanian Department of Statistics [16] . Urban expansion had increased by 2013 due to three factors; first, the government’s decision in 2007 to merge five additional areas into the GMA, which resulted in an expansion of the area of Amman. The second factor was the enforced Iraqi migration in 2003 due to the Third Gulf War, with most refugees settling in Amman. The final factor is the current migration of Syrian refugees, which began in 2011 and still continuing.

Accuracy assessment is a vital element for testing the accuracy of the resultant classes (of the classification process. Several methods are used for testing the accuracy such as overall accuracy and the Kappa coefficient [24] . The confusion (error) matrix is used to represent the accuracy assessment [25] . A random sampling method was carried out in order to obtain the confusion matrix. In the confusion matrix, the columns represent the reference data and the rows indicate the classes generated from the classification process. To improve the interpretation of the confusion matrix, the Kappa coefficient is normally used to address the difference between the actual agreement and the change agreement. The Kappa coefficient was computed as [24] :

where: r is the number of rows in the confusion matrix; X_ii is the number of observation in row i and column I; X_i+ is the total number of observation in row I; X_+i is the total number of observation in column I; and N is the total number of observation included in matrix.

Tables 3-5 show the confusion (error) matrices resulting from the classified digital data. For TM-1984 land use/cover map, a total of 185 pixels were selected and were then checked with a reference to 1:50,000 topographic maps. The results show an overall accuracy of 86%. In terms of the producer’s accuracy and user’s accuracy,

Note: Number of pixels correctly classified = 160; overall classification accuracy = 86%; class 1 = Agriculture Land & Forest Land; class 2 = Built-up Land; class 3 = Rangeland; class 4 = Barren Land.

Note: Number of pixels correctly classified = 211; overall classification accuracy = 80%; class 1 = Agriculture Land & Forest Land; class 2 = Built-up Land; class 3 = Rangeland; class 4 = Barren Land.

Note: Number of pixels correctly classified = 205; overall classification accuracy = 85%; class 1 = Agriculture Land & Forest Land; class 2 = Built-up Land; class 3 = Rangeland; class 4 = Barren Land.

all classes were above 80% accept for Rangeland class with producer’s accuracy of 75%. Kappa index of agreement was 82% (Table 3). This value indicates that the classification process avoided 82% of the error.

For TM-1999 land use/cover map, a total of 274 pixels were selected and were then checked with a reference to 1:50,000 topographic maps. The results show an overall accuracy of 80%. In terms of the producer’s accuracy, all classes were above 65% and above 75% in terms of user’s accuracy. Kappa index of agreement was 72% (Table 4). This value indicates that the classification process avoided 72% of the error.

For Landsat 8-OLI-2014 land use/cover map, a total of 241 pixels were selected and were then checked with a reference to 1:50,000 topographic maps. The results show an overall accuracy of 85%. In terms of the producer’s accuracy, all classes were above 74% and above 78% in terms of user’s accuracy. Kappa index of agreement was 80% (Table 5). This value indicates that the classification process avoided 80% of the error.

Finally, overall accuracy of more than 80% for the three maps (1984, 1999 and 2014), indicate that this is

good evidence that the image processing approach adopted in this study has been effective in producing compatible land use/cover data over time.

This classification of LULC is performed based on the classification scheme of USGS Land cover system. The USGS system divided the land cover classes into two levels (shown in Table 6). GIS and remote sensing have been used to integrate the LULC classes focusing on urban/ built up area classes within the study area for the three time series to and generate a thematic map to examine dynamics of urban expansion of Amman (Figure 3). The results showed that the built up area has increased from 114.7 km² in 1984 to 191.1 km² in 1999 which indicates that the number of population has increased during this period as discussed in section 3.1. During the period 1984-1999, it was clear that the encroachment of urban/built up areas occurred to the directions of northwest and southeast of Amman [21] , and continued toward Zarqa city. In comparison, the Agriculture & Forest land has decreased from 393.6 km² to 386 km² during the same period. Another decrease was in Rangeland class which decreased by 145.2 km² compared to Barren land class gaining extra 76.4 km² during the same period due to the dry winter season of 1999. On the other hand, the rate of the encroachment of built up area during the period 1999-2014 expanded much more extensively in the same direction of the former period besides the expansion toward Al-Salt city. The built up area of Amman has increased by another 92 km² during the same period. The wet winter season in 2014 has influenced the agriculture and rangelands areas to increase (Table 6). Furthermore, a rapid urban sprawl occurred around Amman, since the sprawl normally takes place in radial direction around the city center or in linear direction along the highways [26] . Consequently, Potter et al., (2009) noted that the existence of transportation routes within the study area encouraged the urban sprawl to occur, where transport in the cities is as socially polarized as the structure of the cities themselves [21] .

The increase of Amman’s built-up area without a comprehensive planning had a negative impact on local environment; especially, on water resources which are already scarce [27] . On the other hand, there are many “informal” houses, which were built without planning permits in urban areas in Jordan and comprise more than half of the urban building stock, especially in poor areas [27] . The informal houses are considered a serious problem to the environment due to improper planning system. Moreover, Amman city remains the main economic center of Jordan, which offers plenty of services, jobs and business opportunities. Therefore, the flows of a big number of immigrants from the neighboring countries, after 1991 and 2003 and 2011 into Amman city [16] , caused an urgent need to urban planning studies, to determine the magnitude of encroachment resulted from the urban expansion centered mostly at the central part of Jordan. Thus, the study used Markov model to predict the 2030 LULC and predict encroachment of urban expansion and its direction for better planning for the area.