areas. A digital terrain model was used with the DSM created from the Ikonos stereo imagery to compute ... 5 presents scheme of population estimation from very high resolution satellite imagery linked with dwelling ..... Police Romana. 29545.
Population Estimation of North Sinai Governorate from Satellite Images for Sustainable Development Purpose Dr. Ayman Rashad Elshehaby, Dr. Lamyaa Gamal El-deen Taha and Dr. Ahmed Ibrahim Ramzi )National Authority of Remote Sensing and Space Science (NARSS
الملخص: شبّ رضيشة سيُبء يُطقت يًٓت ببنُسبت نًصش ػهى انًستٕييٍ انٕطُي ٔاالستشاتيزي ٔنكٍ ال يٕرذ يؼهٕيبث تفصيهيت يتٕفشة حٕل ػذد انسكبٌ ٔتٕصيؼٓىٔ ،يؼتبش رنك احذ انًشبكم انتي تٕارّ انبحٕث نتًُيت سيُبء .نزا اليضال ْزا انًٕضٕع يزبل بحج. يًخم سكبٌ سيُبء حٕاني ٪0..يٍ سكبٌ يصشٔ ،انًسبحت اإلرًبنيت ألسض سيُبء ْي .0000 :كيهٕيتش يشبغ. انٓذف انشئيسي يٍ ْزا انبحج ْٕ دساست استخذاو يشئيبث انصٕس انصُبػيّ نهحصش انحذيج نؼذد انسكبٌ نًسبػذة صبَغ انقشاس إلػذاد خطظ انتًُيت .انًشحهت األٔنى يٍ انًششٔع انبحخي 1021 - 1022نهٓيئّ انقٕييّ ْٕ إَتبد خشائظ يصٕسِ ٔيؼذنّ (انًشرغ ٔ WGS84اإلسقبط ٔ)UTMرنك ببستخذاو انًحطت انتصٕيشيت انشقًيت .انبحج يقتشس استخذاو أسحٕفٕتٕ ٔانخشائظ انشقًيت انًُتزت يٍ صٕس األقًبس انصُبػيت راث انقذسة انتفشيقيت انؼبنيت انًتُٕػت انفشديت ٔانًزسًت نهتُبؤ بؼذد انسكبٌ اػت ًبدا ػهى طشيقت رذيذة يبتكشة ٔيتطٕسة نتكٌٕ يُبسبت نخصبئص انًببَي انسكُيت خبصت في يحبفظت شًبل سيُبء ػًٕيب أ في يُبطق دساست يًبحهت .أيضب ،تى استخذاو أسبنيب يختهفت تقٕو أسبسب ػهى انتُبؤ بؼذد انسكبٌ نسُت انٓذف يٍ بيبَبث انتؼذاد انًتبحت نكم حبنت ػهى حذة الستخذايٓب كأسبط نهًقبسَت .يٍ َتبئذ انذساست ٔرذ أٌ َسبت انخطأ في تقذيش ػذد انسكبٌ نًذيُت انؼشيش ببستخذاو طشيقت انًسبحت انسكُيت ْي ٔ %7.18+رنك ببستخذاو ًَٕرد االَحذاس انخطي ٔ أٌ َسبت انخطأ ٔ %8.28+رنك ببستخذاو ًَٕرد حسببي يٍ انذسرت انخبَيت .أٌ َسبت انخطأ في تقذيش ػذد انسكبٌ نهًُبطق انحضشيت ببستخذاو طشيقت انًسبحت انسكُيت ْي نًشكض بئش ػبذ ْي %18.8-ببستخذاو ًَبرد االَحذاس انخطي أيضبَٔ ،سبت انخطأ في تقذيش ػذد انسكبٌ اػتًبدا ػهى طشيقت ػذ انٕحذاث انسكُيت يٍ صٕس األقًبس انصُبػيت راث انقذسة انتفشيقيت انؼبنيت رذا نًذيُتي انحسُت َٔخم ْي ػهى انتشتيب .%3...+ ٔ %2..8+يٍ َتبئذ انذساست ايكٍ إَتبد خشيطت انكخبفت انسكبَيت نًحبفظت شًبل سيُبء ػبيي .1021 ٔ 100.
Abstract: Sinai Peninsula is considered a very important region for Egypt at both the national and strategic levels. One of the important problems facing the researches of Sinai development there is not available detailed information about population and their distribution, so this is still a research topic. Sinai population represented approximately 0.6% from Egypt population and the total area of Sinai is 60000 square kilometer. In Egypt, Census years were 1947, 1960, 1966, 1976, 1986, 1996 and 2006. This means that every 10 years there was a new up-to-date census information and population. In between forecasting can be used to estimate population based on the available data and the used mathematical model. Census process is very hard and timely and costly
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consuming. Rapid growth of population in developing countries especially in Egypt leads to search alternative methods to estimate population. The main objectives of this research is using remote sensing images with different resolution used to estimate population up to date to help the decision maker to prepare the developing planning. The results of first phase of research project 2011 - 2012 is producing ortho-image (WGS84 ellipsoid and UTM projection) from digital photogrammetric workstation. The research proposed using ortho-images and digital maps produced from single and stereoscopic satellite images high resolution satellite images with different resolution and from single very high resolution satellite images
for estimating
population using novel and developed method to be suitable for the characteristic of residential buildings especially in Norther Sinai Governorate and generally for similar study areas. Also, an evaluation accuracy of population estimation using different novel methods has been done based mainly on predication of population for the target year to be used as reference from the available census data for each case. The results of study indicated that percentage of error in population estimation based on urban area for Markas Arish 1 has been found 8.27% using linear regression model and 7.17% using second order regression model. Percentage of error in population estimation based on urban area for Qism Markas Ber Abd has been found -18.86% using linear regression modelsِ Also, percentage of error in population estimation based on the number of housing units from very high resolution satellite images for Hasna city and Nekhel city have been found +13.7% and +9.33%.. One of the outcomes is production of population density map for 2006 and 2012..
Key Words Orthoimages images - Population estimation – Building Extraction- population density map.
Introduction One of the important problems facing the researches of Sinai development is there is no available information about population and their distribution.. The main source of population data are national censuses and related vital statistics, commonly collected by field survey, interviews, and / or mail response. A census produces a record of individuals at a particular instant in time. Population is considered one of important national projects. The main purpose of the census is to provide essential data and information for the socioeconomic planning and policy formulation to raise the standards of living (CAPMAS 1996). Census Stages are four stages: (1) The first stage: is the preparatory stage which had begun about three years prior to census date; (2) The second stage: is concerned with field operation stage in which direct interview method was applied for
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census enumeration and crow leaders were recruited from among the employees of government particular Ministry of Education; .The third stage: is the stage of data processing in which about 1000 university graduates were recruited to under take office coding and checking the work in each step; The fourth stage: devoted to verify and finalize the tabular results, as well as dissemination of data, the preliminary census results were issued in a relatively short period of time in order to meet the urgent local and national needs for census data, Population has been estimated from satellite image or aerial photos with acceptable accuracy for residential areas in Cairo, Egypt (Adeniyi P., 1983; El-Nokrashy and et al. 1992). Population estimation approaches can be grouped into two main categories: (1) Areal interpolation approaches: this approach uses census population data as the input and applies interpolation techniques to obtain a refined population surface. Areal interpolation methods are primarily designed for the zone transformation problem that involves transforming data from one set of spatial units to another. This approach uses census population data as the input and applies interpolation techniques to obtain a refined population surface. Areal interpolation methods can be further grouped into two categories based on if: (Ancillary information exists of Ancillary information not exists). (2) Statistical Modeling approaches: statistical modeling approach is more interested in inferring the relationship between population and other variables for the purpose of estimating the total population for an area. Statistical modeling methods can be further grouped into four categories based on the relationship or the correlation between populations and: (Urban areas Land uses, Dwelling units and Image pixel characteristics). (2-1) Correlation with Urban Areas: this category of methods is a general approach based on a functional relationship between urban areas and population size. This category of methods is a general approach based on a functional relationship between urban areas and population size. (2-2) Correlation with Land Use: this approach is based on correlating population counts with different types of land use areas. (2-3) Correlation with Dwelling Units: the total population of an area can be estimated by multiplying the total number of dwelling units with the number of persons normally living in a dwelling unit (Hsu, 1971; Lo and Chan, 1980). With the advance of very high spatial resolution satellite images “remote sensing data” and building extraction techniques, population estimation by dwelling unit counts may become a viable approach. The total population of an area can be estimated by multiplying the total number of dwelling units with the number of persons normally living in a dwelling unit. It is also possible to categorize dwelling units and apply a different persons-per-dwelling unit ratio to each category. This ratio can be derived from sample surveys or calculated from census data with the assumption that a
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single household occupies one dwelling unit. The total number of dwelling units in an area may be estimated from remote sensing images. (2-4) pixel approach the population is directly estimated and linked to the information contained in one pixel ((Anne Puissant, 2010. Webster, 1996; Harvey, 2002). Population estimated based on the reflectance in five bands of the sensor Thematic Mapper (TM), LANDSAT5, to estimate the population in the census tracts of a city in Australia (Harvey; 2002; Lu et al.; 2006; Iisaka et al., 1982) have used the images of the sensor ETM+, LANDSAT7, and the spectral mixture model to build an image of the impervious surfaces of a city in the United States of America. Using only this explanatory variable, they have estimated the population in the census tracts of the city. The zonal approach based on of subdividing the city into homogeneous areas (Wu et al., 2005; 2006). Estimation of population in informal settlement communities can be done using high resolution satellite image (Galeon, 2008). Also, high-resolution satellite imagery is a valuable tool for mapping urban areas and extracting land cover information. Ibrahim Shaker et al. 2011 evaluates a methodology for using Ikonos stereo imagery to determine the height and position of buildings in dense residential areas. A digital terrain model was used with the DSM created from the Ikonos stereo imagery to compute building heights.
Study Area and Data Set The Sinai Peninsula or Sinai is a triangular peninsula in Egypt about 60,000 km2 in area. It is situated between the Mediterranean Sea to the north, and the Red Sea to the south, and it is the part of Egyptian territory located in Asia as opposed to Africa. The bulk of the peninsula is divided into two Governorates (with three more splitting the Suez Canal area). The main Governorates are North Sinai Governorate and South Sinai Governorate. North Sainai Governorate has been selected as study area. It covers a total area of 27574 km2. Figure 1 presents location map of North Sinai and South Sinai Governorates. Figure 2 shows ortho-images covering North Sinai Governorate. This Ortho-image has been produced based on Stereo Spot 4 in the first phase of this study. Figure 3 shows administration border of Qisms North Sinai Governorate.
Maps The following Topographic Maps were available:
A 1:50 000 topographic maps.
A 1:2 500 Digital topographic maps produced by the National Authority of Remote Sensing and Space Sciences (NARSS).
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Satellite Data
Spot 4 resolution 20m Multispectral and panchromatic 10m Dated 2011 see table ν.1.
Stereo images Spot 4 Dated 2007,2008,2009
Landsat Enhanced Thematic Mapper Plus (ETM+) imagery.
QuickBird images2004.
Demographic Data One of the main goals of this research is to link the population with their living places to ease use in comprehensive and sustainable development at all levels. Where, population is the main element in any development process. This study based mainly in census 1986, and census 1996 and census 2006. Source of demographic data is Central agency for public mobilizations and statistics (CAPMAS), Statistical year book, (1986, 1996 and 2006).
Field Trips Data Attribute: types and characteristics of different buildings in the level of Qisms, cities and villages; number of stories and types of buildings.
Methodology: First phase from the research is producing orthorectified images from stereo SPOT 4 images using digital photogrammetric workstation and generation of DEM & DSM for the study area. The second phase is population estimation based on remotely sensed data. The available data, maps, satellite images in hand from different departments of NARSS, Egypt covered the study area Norther Sinai Governorate: So we proposed to work with different methodologies for population estimation based on integrated digital image analysis and GIS approach according to the available data in hand. Population estimation will be achieved in level of city or town and in the level of Qism. Population has been estimated in several levels using several techniques to be suitable for the available information and the nature of the study area. Areal interpolation methods method which based on census data to estimate or predicate population has been used as a reference and for comparison purpose on all levels of population estimation. This research establish and developed novelty methodologies using remotely sensed data to estimate population based on statistical modeling methods linked with,
urban Areas and dwelling units from very high
resolution satellite images. On the next phase new proposed based on linking population
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estimation with land uses and pixel characteristic. So in this study, proposed method for population estimation which will be applied based on: 1- Areal interpolation methods – will be used with census data. 2- Statistical modeling approach
Urban areas – will be used with Maps and high resolution satellite images.
Dwelling units – will be used with very high resolution satellite images.
Establish a novel methodology based on remotely sensed data for population estimation from mono and stereoscopic satellite images with high and very high resolution suitable for the Sinai cities .and the available data set. Figure 4 presents scheme of population estimation based on ortho-Images produced from panchromatic stereo Spot-4 images linked with urban areas. Figure 5 presents scheme of population estimation from very high resolution satellite imagery linked with dwelling units. Evaluation accuracy of population estimation using areal interpolation methods based on available census data.
Urbanization in Sinai The Urban Structure in Sinai consists of 14 cities and 95 rural local units and 217 villages, predominantly rural. The pattern types of urbanization in the Sinai There are three types of urbanization in Norther Sinai and they are: (1) Stable Urban - representing by agricultural communities and spread around the wells and water tanks. Examples of this type of urbanization are El -Masora, Zahera and El-shlalat. ;(2) Unstable Urban - representing by pastures and fishermen communities. It considered
housing seasons and they live in tents such as Lahqan
well and Lahma well and palm collection areas and areas of fishermen. and (3) Mobile Urban representing another pasture communities and lives in a group of tents that are easily disassembled and moved from one place to another source behind the availability of grazing.
Housing in Norther Sinai Governorate Housing in Norther Sinai Governorate is divided into: (1) Urban Housing - Concentrated in major cities, where apartment buildings, which reaches a height of eight roles, especially in the city of El Arish.; (2) Rural Housing - most prevalent in the Sinai and the general characteristics of the rural housing are: (Height is often one story. - Scattered housing and non-contiguous - One single family Live in one dwelling unit. - Inhabit from one tribe live in the same place and (3 )
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Industrial Housing - Exist in the form of Communities which live in residential buildings its not more than four residential units.
1- Population Estimation from Censuses Only: One of the main goals of the research is population estimation for 2011. Images are not available for 2006 (last census year) and 2011 (target year). Administrative borders of cities and villages in Norther Sinai is not precisely defined, such as most of the Governorates of Egypt, So, there are difficulty to estimate population from satellite images for most cities and villages and difficult to track the growth form. The first reason is Information for population of cities and villages are available only for the last three censuses result of the Israeli occupation of Sinai. The second reasons there are changing on administrative borders of some Marks and in this case data not is available for only two censuses or one census. This led to the weakness of the regression model to predicate population. In this research the relationship of the trend forecasting between a population and time can be represented using linear equation or second degree equation. This is due to the limited number of census data in the level of kism, city and village. This method will be applied to estimate population on the following Governorate, Qisms or Marakaz and cities.
a- Population of Qisms North Sinai Population of Qism Norther Sinai has been estimated based on areal interpolation method. Population of Qism Norther Sinai 2011 has been forecasted based on the last three census of Norther Sinai Governorate for accurate forecasting due using first order polynomial formula. Table 1 presents population of Qism Norther Sinai census 2006 and predicated 2011. Predicated population 2011 based on the census data in the level of of Qisms or Marakas Norther Sinai.
b- Population of Qism Arish 1 and Qism Ber El Abd Arish or El-Arīsh city is the capital of El Arish Markez and It is the largest city of Norther Sinai Governorate lying on the Mediterranean coast of the Sinai peninsula, 344 kilometers Norther east of Cairo. El Arish is a big Wadi called Wadi El Arish, which receives flash flood water from much of Norther and central Sinai. Arish characterized by its clear blue water, wide spread fruitful palm wood on its coast, and its soft white sand.
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Ber El-Abd city is the capital of Ber El-Abd Markez of Norther Sinai
Governorate. It lies 30
kilometer far on the eastern side of Belozum ( Boloza) on the Qantara -Arish Coastal road. Bardowel Lake is related to Ber El Abd Markez. Ber El Abd city is divided into 23 villages. Table 2 presents census data and predicted population of Qism Arish 1 and Qism Ber Abd 2011. In this case predicted population of Markaz Arish 1 and Markaz Ber Abd 2011 has been performed in two levels in the level of Qisms or Marakaz Norther Sinai Governorate and in the level of each Markaz
c- Population of Hasna City and Nekhel City Hasna city is the capital of Hasna Markez of Norther Sinai Governorate, and is situated on the middel of Sinai Peninsula. Hasna city is divided into 20 villages. Table 3 presents census data and predicted population of Hasna City 2004, 2008 and 2011 using second order regression function. Nekhel city is the capital of Nekhel Municipality (Markez) of Norther Sinai Governorate. It is located in the heart of Sinai Peninsula along the southerern border of Norther Sinai Governorate with Southern Sinai Governorate. It is located at the skirts of El-Tih Mountains and foothills at an elevation of 420.6 m. Coordinates of the city is 29°54'N; 33°45'E. Nekhel city is divided into 10 villages. Table 4 indicates census data and predicted population of Nekhel city 2004, 2008 and 2011 using linear regression function. In this case predicted population of Hasna City and Nekhel city2004, 2008 and 2011 has been performed in the level of city..
2- Population Estimation from Satellite Images In this research total study area of Northern Sinai Governorate is larger than 26 square kilometers, It included many cities, villages and courtyards and the population of the Governorate living in different types of buildings from high-rise buildings with multi-storey buildings to a simple courtyards. In addition to living in tents and huts which it is so difficult to identified its places from satellite images or from field survey because it is change for searching for food for cattle. In this study all the remote sensing techniques is used for the enumeration of the population from census data and from satellite images taken into consideration the nature of the housing and population in the northern Sinai Two novelty methods of population estimation from satellite have been established and developed.
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a- Population Estimation Using Correlation With Urban Population estimation using correlation with urban areas method is a general approach based on a functional relationship between urban areas and population size. This method has been applied on: Qism or Markaz Arish 1 and Qism or Markas Ber Abd. Also, Population estimation using correlation with urban areas has been applied on: Nekhel city and Hasna city. Markaz 1 is the largest city in North Sinai Governorate and the most densely populated city and urban area growth in both directions horizontal and vertical. As mentioned before Qism Ber Abd consists of 23 villages. Based on temporal resolution images, two or three or more than three of satellite images covering the same area at different times, one can be calculated the residential area for each time. After that, the formula of the predicated model has been determined as first order or second order polynomial formula to link the area with time. From this model one can predicate the residential area for any time or date. After that one can estimate population t any time by linking data with census. The residential areas have been calculated from several satellite images from different sources at different periods in order to monitor the growth of the city and linked area with population. Used satellite images:
Land Sat ETM + images 2001.
Orthoimages produced fro stereo pairs of Spot 4 2008.
Figure 6 shows Qism Arish 1 (Spot 4 images multispectral 20m) & Ortho-images 2008 (fused 10m) Figure 7 shows Qism Ber El-Abd (Spot 4 images multispectral 20m) & Ortho-images 2008 (fused 10m). Table 5 shows urban area (km2) for Qism Arish 1 and Qism Ber Abd. Figure 8 shows growth of urban area for Qism Arish 1 from Satellite images. Figure 9 shows growth of urban area for Qism Ber Abd from Satellite images. Table 6 shows population estimation based on urban area (km2) and % of error for Qism Arish 1 and Qism Ber Abd. Table 7 indicates census data and predicted population of and % of error in the level of Qism for Qism Arish 1 and Qism Ber Abd 2011 Figure 10 shows Qism Arish 1 (ETM+ 2001 and Orthoimages from Spot 4 stero pairs 2008 and urban growth. Figure 11 shows Qism Ber El Abd (ETM+ 2001 and Orthoimages from Spot 4 stereo pairs 2008 and urban growth .
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As mentioned before Markaz Arish 1 and Markaz Ber Abd 2011 has been performed in two levels in the level of Qisms or Marakaz Norther Sinai Governorate and in the level of each Markaz. In the level of Qisms or Marakaz North Sinai Governorate, one can concluded that percentage of error in population estimation based on urban area for Qism Arish 1 are + 8.27% using linear regression model and 7.17% second order regression model for predicated area. Percentage of error in population estimation based on urban area for Qism Ber Abd are -18.86% using linear regression model for predicated area. In the level of each Markaz, one can concluded that percentage of error in population estimation based on urban area for Qism Arish 1 are + 10.48% and + 5.43% using second order regression model linear regression model for predicated population. Percentage of error in population estimation based on urban area for Qism Arish 1 are + 5.41% and + 9.77% using linear regression model for predicated population Percentage of error in population estimation based on urban area for Qism Ber Abd are –15.99% and + 29.287% using using second order model and linear regression model .respectively. So, accurate prediction of population estimation should use the suitable regression model for the available data for each level (Qism, city and village).
a- Population Estimation Using Correlation with Dwellings Population estimation using correlation with dwelling units has been applied on Hasna city and Nekhel city. Both of Hasna city and Nekhel city covered with very high resolution satellite QuickBird images 0.60m resolution dated 2004. Urban area contain different kinds of basic patterns (e.g., individual houses, gardens, roads) are formed by different materials (e.g., red or grey roofs, different asphalts or different kinds of vegetation), while complex ones (e.g., urban districts, urban blocks). Produced DTM and DSM from digital images of the satellite SPOT 4 with RMS ±12 m, which means that it can not be used in determined building heights or in the extraction of 3D buildings. Also, High accurate DBM and DTM Models not available So, in this research, buildings has been extracted in 2D. Heights of buildings or number of stories has been determined from field counting and from using pictures captures from camera.
Hasna City and Nekhel City Based on very high resolution satellite images QuickBird image date 2004
residential buildings
have been extracted visually. After that residential building has been classified according to its
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area has been done. Three classes has been proposed for Hasna and Nekhel cities. Figure 12 shows Hasna city and Nekhel city (ETM+ 2001 and Orthoimages from Spot 4 stereo pairs 2008). Figure 13 shows types of building in Nekhel city. Figure 14 shows types of building in Hasna City.. Figure 15 shows enlarged part of Hasna city (QuicKBird 2004).. Figure 16 shows enlarged part of residential buildings in Hasna city.
Table 8 shows residential building
classification of Hasna city. Table 9 shows census data and predicted population of Hasna City 2004, 2008 and 2011. Figure 17 shows enlarged part of Nekhel city (QuickBird 2004). Figure 18 shows enlarged part of residential buildings of Nekhel city. Table 10 shows residential building classification of Nekhel city. Table 11 shows census data and predicted population of Nekhel city 2004, 2008 and 2011. Based on the obtained results, one can concluded that percentage of error in population estimation based on the number of housing units from satellite images from very high resolution satellite images for Hasna city and Nekhel city are +13.7% and +9.33%
Produced Vector Maps 1- Population in the level of Kism Map Figure 19 shows density map 2006. and Figure 20. shows density map 2011
2- City and Villages Map It was difficult for our surveying team to get coordinates of all villages in North Sinai Governorate, due to many reasons. The coordinates is very important to link place with Geographic Information Systenm (GIS). Coordinates of most villages have been measured in the finger of city or valleys which should be measured near the main post office of the city or village. The geographic coordinates of each city or vallige located in north Sinai Governorate has been measured. Field surveying group has been used handheld GPS. They faced many restrictions to get the geographic coordinates of some villages due to available fund, the character of inhabit, the complex of topography, security, and other reasons. ARC GIS 9.3 software has been used to produced village victor map. Each village identified as point with the following attributes: name, kism, urban rut an, ---- Shiakh___C, انًدذيٍ ة,Qism, Urban_Rura, X, Y, Population Male census 2006, Population female census 2006 and Total Population female census 2006. Table 13. presents attributes of cities and villages. Figure 21 shows villages of north Sinai Governorate and example for the attributes table. Figure 22 shows villages of North
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Sinai Governorate overlying mosaic ortho image 10 m and administration border. Table 12 indicates Attributes of cities and villages.
Sources of errors The process of calculating the population from census data and from remotely sensed data include many sources of errors, including: - Errors resulting from the census process it self. - Errors resulting from the movement of used satellite and its sensor. - Errors resulting from the resolution of the used images. - Errors resulting from the used models to predict the population. - Errors resulting from the used methodology in calculating population. - Errors resulting from the visual interpretation of the residential areas. - The resulting errors from the accuracy of geometric correction of images. - The resulting errors from date of different images. A comprehensive study of these errors should be done to quantify and assess their impact at the expense of the population and will be hold this in the next phase of the research, which will be applied in Southern Sinai Governorate.
Conclusions
Developed method of population estimation based on the integration of satellite imagery coupled with GIS and census data can be used to provide information on intra-urban population which is essential in many applications.
Population estimation based on the integration of satellite imagery and census data can be used to provide information on intra-census population, which is essential in many applications.
From the obtained result of the research, one can found that there is variation and a significant difference between the rates of population growth at all levels in Northern Sinai Governorate.
Population estimation models or dynamic changes of population models can be examined and applied to validate urban growth models if the time series image data or multitemporal remote sensing images become available.
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This study demonstrates that orthoimages produced from Spot 4 images could be used in north Sinai to provide reasonable and accurate population estimation by combining various remote sensing data.
In the level of Qisms or Marakaz North Sinai Governorate, one can concluded that percentage of error in population estimation based on urban area for Qism Arish 1 are 8.27% and 7.17%using linear and second orders regression models respectively for predicated area. Percentage of error in population estimation based on urban area for Qism Ber Abd are 18.86% using linear regression model for predicated area.
In the level of each Markaz, one can concluded that percentage of error in population estimation based on urban area for Qism Arish 1 are + 10.48% and + 5.43% using second and linear regression models respectively for predicated population. Percentage of error in population estimation based on urban area for Qism Arish 1 are + 5.41% and + 9.77% using second and linear regression models for predicated population Percentage of error in population estimation based on urban area for Qism Ber Abd are –15.99% and + 29.287% using using second order model and linear regression model respectively.
Accurate prediction of population estimation should use the suitable regression model for the available data for each level (Qism, city and village).
The major advantage population estimation using remotely sensed data is providing a timely update of a population database and its spatial distribution, which is difficult to obtain by conventional census approaches.
More research is needed to improve the population estimation through development of suitable models and use of multi-source data, such as high spatial resolution imagery and lidar, which are capable to identify individual buildings and measure the heights of the buildings.
Population has been estimated with high accuracy in high-density area based on dwelling counts using very high spatial resolution remote sensing images.
The study indicates that the administrative border of villages and cities of Norther Sinai Governorate needs to redraw.
References 1. Adeniyi, 1983, An Aerial Photographic Method For Estimating Urban Population, Photogrammetric Engineering and Remote Sensing, Vol. 49, N°4, pp.545-560 2. Anne Puissant, 2010”Estimating Population Using Remote Sensing Imagery” Department of Geography ،Ville, Environment Laboratory, University of Strasbourg, France 3. Central Agency for Public Mobilization and Statistics (CAPMAS) - census 1986.
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4. Central Agency for Public Mobilization and Statistics (CAPMAS) - census 1996. 5. Central Agency for Public Mobilization and Statistics (CAPMAS) - census 2006. 6. Galeon F., 2008, Estimation Of Population In Informal Settlement Communities Using High Resolution Satellite Image, The International Archives Of The Photogrammetry: Remote Sensing And Spatial Information Sciences, 37 (B4), Beijing. 7. El-Nokrashy M. A., Ibrahim M. Y., Eilawa M. A., Ramzi A. I,, (1992) “Evaluation of Using Photogrammetry in Parking Studies”, CERM Magazine, El-Azher university, vol.(14) no(11), Pp 403-415. 8. Harvey J.T., 2002, Estimation Census District Population From Satellite Imagery: some approaches and limitations, International Journal of Remote Sensing, Vol.23, N°10, pp.2071-2095. 9. Hsu Shin-Yi, 1971, Population Estimation, Photogrammetric Engineering, pp. 449-454. 10. Ibrahim F. Shaker 1, Amr Abd-Elrahman, Ahmed K. Abdel-Gawad and Mohamed A. Sherief, 2011”Building Extraction from High Resolution Space Images in High Density Residential Areas in the Great Cairo Region” Journal of Remote Sensing 2011, 3, 781791; doi:10.3390/rs3040781 11. Iisaka J.; Hegedus, E. 1982 Population Estimation From Landsat Imagery. Remote Sensing of Environment, v. 12, p. 259- 272. 12. LO C.P., CHAN H.F., 1980, “Rural Population Estimation From Aerial Photographs”, Photogrammetric Engineering And Remote Sensing, Vol. 46, Pp. 337-345. 13. Report 2005 -"Human Development Report 2005" 14. Webster, C.J., 1996. Population And Dwelling Unit Estimates From Space, Third World Planning Review, 18(2):155-176. 15. Wu , Murray A., 2006, Population Estimation Using Landsat Enhanced Thematic Mapper Imagery, 16. Wu , Qiu X., Wang L., 2005, Population Estimation Methods in GIS and Remote Sensing: A Review,GIScience &Remote Sensing, Vol. 42, N°1, pp. 1548-1603.
Figure 1 Location map of North Sinai and South Sinai Governorates.
Figure 2 Ortho-images covering North Sinai Governorate.
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Figure 3 Administration border of Qisms North Sinai Governorate.
Figure 4 Scheme of population estimation based on ortho-Images produced from Panchromatic stereo Spot-4 images linked with urban areas.
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Figure 5 Scheme of population estimation from very high resolution satellite imagery linked with dwelling units.
Table 1 Population of Qism North Sinai census 2006 and predicated 2011.
Qism Arish 1 Arish 2 Arish 3 Arish 4 Ber El Abd Hasna Nekhel Sheikh Zuwayid Rafah Police Romana police Qusema
Census population 2006 41177 48060 32255 20006 33788 13835 11023 45696 58615 29545 9681
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Predicated population 2011 In the level of Governorate 49412 57672 38706 24007 40545 16602 13227 54835 70338 35454 11617
Table 2 Census data and predicted population of Qism Arish 1 and Qism Ber Abd 2011
Qism
Arish 1
Year
Pop. Census
1986
12841
1996
25554
Used function or model
Predicated Pop. 2011
Y= 14.55 X² -56666.8 X + 55165054
50079
Y=1416.96x -2801728.16
Ber Abd
47778
2006
41177
r=0.9984
1986
27211
Y= 40.425 X² -161047.75 X + 160423919.2
1996
26457
40485
Y=328.64x -626813.44 2006
34081
33788
r=0.8150
Table 3 Census data and predicted population of Hasna City 2004, 2008 and 2011
1986 1996
Pop. Census Y 1340 1799
2006
2707
Year X
Used function or model
Predicted Pop. 2004
Predicted Pop. 2008
Predicted Pop. 2011
Y = 2.245 X² - 8893.69 X + 8809488.32
2489
2942
3329
Table 4 Census data and predicted population of Nekhel city 2004, 2008 and 2011
Year X 1986 1996 2006
Pop. Censu s Y 2518 ---2497
Used function or model
Predicted Pop. 2004
Y = -1.05 X + 4603.3
2499
17
Predicted Pop. 2008
2495
Predicted Pop. 2011
2491
Figure 6 Qism Arish 1 (Spot 4 images multispectral 20m) & Ortho-images 2008 (fused 10m).
Figure 7 Qism Ber El-Abd (Spot 4 images multispectral 20m) & Ortho-images 2008 (fused 10m).
Table 5 Urban area (km2) for Qism Arish 1 and Qism Ber Abd
ETM+ 2001 Km2
Ortho images 2008 Km2
Qism Arish 1
8..3
12.4
11.66
12.22
Google Earth Pop 28-92011 17.356810
Qism Ber Abd
3,705
5,853
4.34
---
----
Qism
Figure 8. Growth of urban area for Arish 1 from Satellite images
Google Google Earth Earth 22/11/2005 1/12/2007 Km2 Km2
Figure 9. Growth of urban area for Qism Ber Abd from Satellite images
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Table 6 Population estimation based on urban area (km2) and % of error in the level of Governorate for Qism Arish 1 and Qism Ber Abd
Qism
Arish 1
% Error in the level of Govern orate
Year
Used function or model
Predicated Urban Area (km2)2011
2001
96.73
Y= -0.06091 X² + 244.87794 X 246092.20974
12.591
45327
8.27%
y=0.6959x 1384.7444
14.7105
52957
- 7.17%
y=0.2723x 541.4049
6.1904
48192
18.86%
2006
2008
Ber Abd
Predicated Pop. 2011
Urban Area (km2)
11.66 5
2001
12.40 1 3.705
2006
4.34
2008
5.853
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Table 7 Census data and predicted population and % of error in the level of Qism for Qism Arish 1 and Qism Ber Abd 2011
Qism
Arish 1
Year
Pop. Census
1986
12841
1996
Used function or model
Predicated Pop. 2011
Y= 14.55 X² -56666.8 X + 55165054
2d 45327 Line 52957 2d 50079
25554
y=1416.96x-2801728.16
Ber Abd
2006
41177
r=0.9984
1986
27211
Y= 40.425 X² -161047.75 X + 160423919.2
1996
26457 y=328.64x -626813.44
2006
33788
r=0.8150
ETM+ 2001
2d 45327 Line 52957 Line 47778 Line 48192 2d 40485 Line 48192 Line 34081
% Error in the level of Qism 2d + 10. 48 Line + 5.43
2d +5.41 Line + 9.77
2d - 15.99
Line 29.28
Orthoimages from Spot 4 Urban growth stero pairs 2oo8 Arish 1 Figure 10 Arish 1 (ETM+ 2001 and Orthoimages from Spot 4 stero pairs 2008 and urban growth
.
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ETM+ 2001
Orthoimages from Spot 4 Urban growth of Ber Abd City stero pairs 2oo8 Ber Abd Figure 11. Qism Ber El Abd (ETM+ 2001 and Orthoimages from Spot 4 stero pairs 2008 and urban growth.
ETM+ 2001
Orthoimages from Spot 4 stero pairs 2oo8
ETM+ 2001
Orthoimages from Spot 4 stero pairs 2oo8
Figure 12 .Hasna city and Nekhel city (ETM+ 2001 and Orthoimages from Spot 4 stero pairs 2008.
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Nekhel city Figure 13 Types of building in Nekhel city.
Nekhel city Figure 14 Types of building in Hasna City Figure 14 Types of building in Hasna City.
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Figure 15. Enlarged part of Hasna city (QuicKBird 2004).
Figure 16. Enlarged part of residential buildings in Hasna city.
Table 8. Residential building classification of Hasna city.
Total Total no. number No. of of Average Predicated of No. of % Class Area Apartments apartments number of pupation dwelling stories Error /Story or Household 2004 units Household 2004 Class 150 82 2 2 328 3 Total 708
Table 9 Census data and predicted population of Hasna City 2004, 2008 and 2011
Year X
Pop. Census Y
1986 1996
1340 1799
2006
2707
Used function or model
Y = 2.245 X² - 8893.69 X + 8809488.32
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Predict Predicte Predicte ed d Pop. d Pop. Pop. 2011 2008 2004 2489
2942
3329
Figure 17. Enlarged part of Nekhel city (QuicKBird 2004).
Figure 18. Enlarged part of residential buildings of Nekhel city
Table 10 Residential building classification of Nekhel city
Total Total no. number No. of No. Of Average Predicated of % Class Area Apartments Of apartments number of pupation dwelling Error /Story stories or Household 2004 units Household 2004 Class 150 189 2 1 378 3 Total 682
Table 11 Census data and predicted population of Nekhel city 2004, 2008 and 2011.
Year X 1986 1996 2006
Pop. Censu s Y 2518 ---2497
Used function or model
Predicted Pop. 2004
Y = -1.05 X + 4603.3
2499
24
Predicte d Pop. 2008
2495
Predicted Pop. 2011
2491
Figure 19. Density map 2006.
Figure 20. Density map 2011.
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Figure 21 Villages of North Sinai Governorate and example for the attributes table.
Governorate Norther Sinai Norther Sinai Norther Sinai
Figure 22 Villages of North Sinai Governorate overlying mosaic ortho image 10m and administration border.
Table 12. Attributes of cities and villages. Pop, Shiakh Urban Male انًذيُتQism / City / Rura census 2006 Ber-lhfn بيش Arish 1 Urban 3505 ٍنحف ElResa Arish 1 Urban 3505 انشيست AlSalam
انسالو
Arish 1 Urban
26
3505
Pop. Femal census 2006 3108
Pop. Total census 2006 6614
3108
6614
3108
6614
