land use_land cover change detection_al kharj

Land use / land cover change detection through remote sensing in Al Kharj Saudi Arabia of 9

Land Use / Land Cover Change Detection using Remote Sensing Technology Al Kharj, Saudi Arabia

To Provide Scientific Bases to Regional Strategic Planners for Sustainable Development Qaisar Nadeem GIS / RS Consultant UIS Department High Commission for the Development of Arriyadh Kingdom of Saudi Arabia Tel: +966 1 488 3331 ext. 1716 Cell: +966 50 860 6418 Email: [email protected]

Adel N. Alfassam Spatial Information Manager UIS Department High Commission for the Development of Arriyadh Kingdom of Saudi Arabia Tel: +966 1 488 3331 ext. 1706 Email: [email protected]

Abstract Key words: Change Detection, Radiometric normalization, Land Use, agriculture, Saudi Arabia The influx of population into urban territories due to migration from rural areas coupled with rapid growth in urban population disturbs the ecological balance. This process hampers the socio economic sustainable development of any region. The absence of historic land cover record in many parts of the globe makes it difficult to examine the development trends / changes in the land use of any area, consequently efficient exploitation of natural resources becomes difficult to achieve. Satellite remote sensing is an ideal way to determine changes in land use / land cover enabling organizations to maintain the integrity of the data that they manage. This study is a manifestation of the use of remote sensing techniques to monitor the changes in land use / land cover of Al Kharj muhafazah of Saudi Arabia with main emphases on agricultural areas. Landsat Multi Spectral Scanner (MSS) data for year 1973, Landsat TM data for year 1987 and ETM+ data for year 2000 has been used for physiognomic change detection. Satellite image for year 1987 has been radiometrically normalized to remove the atmospheric effects and to make it pixel to pixel comparable with the image for year 2000. Different change detection techniques including Red Green Difference Image, post classification comparison, and Image Differencing are employed to analyze the changes. An increase in cultivable land has been observed from 1973 to 1987 whereas a decrease is visible from 1987 to 2000. The major reasons for this decrease have been described as the reduction of agricultural subsidy along with scarcity of water. It has been experienced that such studies provide scientific data to the urban strategic planners for developing effective regional development plans, for reducing migration pressure on urbanized cites through the impartial distribution of resources among all communities.


Introduction The influx of population into urban territories due to migration from rural areas coupled with rapid growth in population disturbs the ecological balance. This process hampers the socio economic sustainable development of any region (Gupta and Srivastava 2003). Expansion of suburban territory with encroachment in prime land is a matter of concern for all and in particular for the authorities associated with the urban planning and development. A modern nation, as a modern business, must have adequate information on these complex interrelated aspects of its activities in order to make decisions. Land use is only one such aspect, but knowledge about land use and land cover has become increasingly important as the Nation plans to overcome the problems of haphazard, uncontrolled development, deteriorating environmental quality, loss of prime agricultural lands, destruction of important wetlands, and wildlife habitat. The absence of historic land cover record in many parts of the globe makes it difficult to examine the development trends / changes in the land use of any area, consequently efficient exploitation of natural resources becomes difficult to achieve. The role of remote sensing in monitoring and mapping historic land cover changes is widely recognized (Roberts, 1998). Satellite remote sensing is not only a quick and cost effective method for mapping the world but it is also reliable and unbiased method. The periodic availability of remotely sensed data makes it well suited to change detection applications. Such information can be used in the decision making process, or used to monitor changes over time as an aid to updating information databases (Hall Amy) and enables organizations to maintain the integrity of the data that they manage. From its oil receipts, Kingdom of Saudi Arabia was able to modernize its infrastructure during the 70’s and 80’s while developing at the same time a heavily subsidized system of social services including health, agriculture, education and to a lesser extent housing. The public sector was able to absorb a good percentage of the entrants into the labour market (UNDP Saudi Arabia). The Kingdom of Saudi Arabia has made great progress over the past three decades in realizing the long-held objective of achieving self-sufficiency in food production. Saudi Arabia's agricultural development is now considered one of the major accomplishments of modern agriculture in the Middle East. The agricultural sector employs a significant number of people and utilizes the latest techniques to produce a variety of goods, stocking shelves in stores in Saudi Arabia and exporting excess supplies to countries across the globe. Agriculture's share of the Kingdom's gross domestic product (GDP) climbed from just 1.3 percent in 1970 to more than 6.4 percent in 1993. This process of development in agricultural sector is still in progress but in some areas of the Kingdom it is showing declination due to several factors, mainly removal of subsidy and scarcity of fresh water. Consequently a prominent change in land use of these areas is visible. It goes worst when it is analyzed with urbanization process. It results into a heavy migration pressure on nearby modern cities, unbalancing the socio-ecological settings of the area. Al Kharj is one of these areas where these changes are quite prominent over the period of three decades. It gets more importance as this is the nearest agricultural area to the capital city Arriyadh, and contributes major share in food products for the capital. Therefore any decline in agriculture of this area not only effects food supply to the city but also puts migration pressure on the capital. Study Objectives The main objective of this study is to monitor the land use land cover changes in Al Kharj area and evaluate the different remote sensing change detection techniques. The overall aim is to document the historical agricultural changes in this area to prepare scientific base for regional development plans. The specific objectives of the study are given below

1. Review of Remote sensing techniques used for change detection process. 2. Monitor spatial changes in land cover particularly in agricultural areas. 3. Provide the scientific bases for planning a regional development strategy.


Study Area The Study area is a part of one of the nineteen muhafazahs (Governorate) of Arriyadh region and is almost 80 Km to the south of capital city of Saudi Arabia. It lies between 46° 58’ 20“to 48° 05’ 8” East and 23° 45’ 17“to 24° 26’ 31” North. The area is mainly covered by agricultural farms and most of the food products are supplied to Arriyadh city from this area. Al Kharj city is located in the middle of study area.

The desert climate prevails in Arriyadh region, featuring high temperature during summer season reaching as high as 48°C and in winter 6-18° C. Annual rainfall varies from one place to another. In the Region's southern part annual rainfall average varies between 10 to 100 mm. Humidity average is about 33.1%. Soil of this region is classified as dry and young. The soil dryness due to scarcity of rainfall and an increase of transpiration rates leads to a decrease in soil cleaning, thus the Region’s soil can be classified as incomplete in terms of composition, particularly in places covered with sands, meanwhile the true soil exists in narrow strips such as oasis, valley beds, meadows, and flood sediments. The Region's topography consists mainly of plateaus, hills, elevated plains, sand dunes and valleys (ADA, 2004). The economic patterns of the Arriyadh region are dominated by the city of Arriyadh, the main center of government and public services. Northern part and Al Kharj muhafazah of the region is major agricultural production area. About 20% of these areas are suitable for agriculture. Major crops being cultivated are cereal, grains, with pockets of vegetable and fruits. Livestock farming is also in practice in these areas of the region. Industrialization process has also been started in the region by government. A small industrial area is located in Al Kharj city, which provides further growth and development opportunities. Data All the satellite data used in this research work is a courtesy of Global Land Cover Facility (GLCF) it is a very good source of historic Landsat images. The image from TM and ETM+ sensor having 165/043 path and row is used in this study acquired on 27 November 1987, and 16 December 2000 respectively. MSS 177/043 image acquired on 20 June 1973 is used as a starting point. All of these three satellite images were available in single band Geo tiff file format. After download color composites were prepared excluding thermal band from TM and ETM image. A study area was defined and all images were clipped by the study area boundary. Methodology In order to create a meaningful difference image from change detection, pixel to pixel registration between images is of utmost importance, as even half a pixel in error between images will produce erroneous results. (Hall Amy) The two satellite images of Landsat TM and ETM were compared to check their co-registration. It is found that both images are registered accurately and only have a positional off set less than 10 meter in some areas. Removal of atmospheric effects Several factors independent of ground cover can significantly affect reflectance as measured at the satellite. These include solar elevation, atmospheric conditions and topography. Adjusting imagery for atmospheric attenuation reduces the variation between temporally separate images so they appear to have been acquired under the same solar and atmospheric conditions, allowing for more accurate detection of landscape change.


X variable 1 Line Fit Ploty = 0.3562x + 17.646

R2 = 0.8494

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Landsat TM Actual 1987 Landsat Tm Normalized 1987 Landsat ETM 2000

Therefore, it is widely recognized that a set of remotely sensed images must be radiometrically normalized before being used in a change detection study. (Karin E Callahan, 2003). Many researchers have described the several radiometric normalization algorithms. The method used in this study is based on identification of pseudo invariant features, or features that are assumed to have the same spectral reflectance through both of images. These features include water bodies, urban areas or desert areas. In this method, statistical adjustments are based on the assumption that the differences in gray-level distributions of invariant objects are assumed to be a linear function (Schott et al., 1988) and a linear regression is applied to the target image based on the brightness values of reference image The DN values was extracted for each band of both images and entered into Microsoft Excel spread sheet. Keeping the values from image for year 1987 on x axis, scatter plot was drawn From this scatter plot gain and off set (line intercepts) values were calculated for each band of image 1987. These values of gain and offset were applied to the each band of the image through this formula DN* = DN x Gain + off set Where DN* = out put DN DN = input value of the pixel The resultant image is radiometrically normalized to the reference image Comparison of brightness values To illustrate the results of radiometric normalization, brightness values of two images have been compared. It is clearly visible that, the actual images acquired in 1987 and in 2000 have a big difference in brightness value for the same feature (desert in this case); it is because of the difference in atmospheric conditions over the period of one decade. After the normalization process brightness values of both images come closer to each other and now these two images are fit for making pixel to pixel change analyses. Visual interpretation of Satellite Images Agricultural Change After normalizing the Landsat TM image with Landsat ETM image, visually the images are explored to identify the potential change areas Image on the top is of Landsat MSS, acquired in 1973, this image shows that at that time, most of the area was barren and agricultural development process was not started. Image on left hand side is acquired in 1987, which shows a large area was under agricultural use. On the right hand side image acquired in 2000 depicts that most of the area has been degraded. Therefore through Visual analyses of satellite images it is very clear that from year 1973 to 1987 agricultural development started and waste land was converted into productive land whereas from 1987 to year 2000, and the process has been reversed Urban Changes The image on right hand side acquired in 2000 illustrates that city of Al-Kharj has grown as compared to Image acquired in 1973 (on top) and then 1987 (on left)


Urban Areas Agricultural Areas Image 1973 image 1973 Image 1987 image 2000 image 1987 image 2000 Red Green Difference Image The production of a red/green difference image is a widely used technique, and is particularly useful for interactive viewing of change areas. This technique involves displaying simultaneously one dataset in green and one dataset in red. The resultant combined image will contain mainly shades of yellow (indicating the same response between dates), but areas which have changed will appear as green or red. This technique is most effective where the magnitude of the areas to be found is anticipated as being quite large, such as cleared fields or changes in crop growth (Hall Amy). For creating a meaningful result from this technique the selection of bands is important. For vegetation changes its necessary both images are taken in the same month of year, as some vegetations are season dependent. The bare land has more brightness value as compared to vegetation or agricultural fields, therefore if some area has been degraded from agricultural to waste land it will reflect more incident sunlight corresponding to high brightness value and vice versa. In the image below current satellite image (2000) is displayed in red band, therefore red areas represents the decrease in agricultural areas and green tone represents the increase in agricultural and urban areas.

Urban Areas Agricultural Areas Supervised Classification Supervised classification has been run on all three images to prepare the land cover maps to compare them statistically. Supervised classification is one of two methods used to transform multispectral image data into thematic information classes (unsupervised classification being the other). This procedure typically assumes that imagery of a specific geographic area is gathered in multiple regions of the electromagnetic spectrum, for example Landsat TM or SPOT XS multispectral satellite imagery. Training areas were selected for four broad classes. Dry land and sandy deserts were classified more accurately, some mixing of built up areas and boundaries of agricultural fields was observed in some areas. The training areas then refined by changing threshold values until they are classified more closer to real situation. Threshold vales for each class were set by viewing spectral signatures of the class. Normalized Difference Vegetation index (NDVI) is also a reliable method of identifying the agricultural areas, (vegetation) but as the images were collected on different months of years therefore it is not used for vegetation


mapping. It is just used to improve the accuracy of classification. After supervised classification, statistics were calculated and area estimation for different land cover classes has been done. The four classes identified for this area in this study are described below

• Land, Agricultural This is the main emphases class and it includes all the cultivable lands. It includes the fields that have crop and the fields that don’t have crop but are capable to be cultivated. In some areas vegetation was also visible in agricultural fields, as all images are collected at different time of year therefore vegetation change does not project the true picture. • Land, Bare (Barren) This type of land cover is defined by Anderson James in USGS professional paper 964 as “Barren Land is land of limited ability to support life and in which less than one-third of the area has vegetation or other cover. In general, it is an area of thin soil, sand, or rocks. Vegetation, if present, is more widely spaced and scrubby. “In this study bare land refers to the lands that were not under cultivation in 1973 and then they were turned into agricultural until 1987 and again due to some reasons they are not under cultivation in 2000. Mainly this class is consisting of the land that has fertile soils and can be put into agriculture. • Land, Built Up This class corresponds to the urbanized areas. It also includes the mataled roads and other human dwellings. • Land, Desert /Sand This class represents the areas which are permanently under sand and deserts. These areas are permanently barren and can not be converted into cultivable land.

Land cover maps from all three dates are given below

MSS 1973 ETM 2000

TM 1987 Agricultural Changes from 1987 – 2000


Area estimation derived from Landsat Image 1973

4.234%

80.238%

0.203%15.324%

Land, AgriculturalLand, Bare Land, BuiltupDesert / Sand


36%

59%

2%3%



27%

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Quantitative Analyses

Negative values in above table represent decrease and positive values represent increase

Area in Hectares Difference (Ha) No Class Name 1973 1987 2000 1973-1987 = = 1987- 2000

1 Land, Agricultural 9549.136 85953.188 64783.273 76404.052 -21169.915 2 Land, Bare 180946.2 137834.114 156932.142 - 43112.12 19098.028 3 Land, Builtup 458.759 4328.968 6701.144 3870.209 2372.176 4 Desert / Sand 34557.34 7591.776 7291.487 -26965.563 -300.289 5 Total Study Area 235708.046 235708.046 235708.046


Agricultural Area Comparison

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Accuracy Assessment After a final classification map has been produced, an accuracy assessment should be made to measure the maps reliability. Generally, a number of random points or areas are selected on the map and their class assignments are checked. Ideally each point’s real world location is visited to determine its composition. Problems arise when there is not enough time or resources and/or locations are inaccessible. Also, with large scale imagery, vegetation patches may change seasonally, so that accuracy checks must be accomplished in the same season as imagery acquisition (Nevada University)

Photo interpretation can also be used to check for accuracy. Photo interpretation was used to assess the accuracy of classification because it was not possible to visit the area in the seasons when satellite images were acquired .It is observed that only agriculture field boundaries mix with some built-up areas. A classification confusion matrix is developed and overall accuracy is calculated. The confusion matrices are used to indicate the classification by comparing the classified image with some other known source. The ideal reference data is sample points collected during the ground truthing survey but if ground truthing data is not available then training regions defined for classification can also be used as a reference. The overall average classification accuracy for all three images has been found 97 %. It means the training areas defined for four classes are 97 % accurate and there is 3 % confusion in classifying some cells.

Conclusions This study exhibits land cover changes and provides unbiased scientific historic information for land use / land cover of Al Kharj area from 1973 to 2000. The increase in agricultural base in 70s and 80s is a result of efforts made by government for gaining food security. The declining trend in agricultural base provides many thought provoking questions to the regional planners and agricultural dignitaries. This decrease is alarming for food situations, as this area is a major contributor for food products in Arriyadh region. The reasons of decrease could be natural and some anthropogenic. In natural reasons scarcity of water is at top. Human reasons may include urbanization, removal of agricultural subsidy, and migration to the modern cities in search of modern life. Among different change detection techniques, “post classification comparison” is easy and most effective technique for statistical analyses of change areas. Our recommendations for future planning and for further investigations are as

• Socio-economic and metrological data should be used to highlight detailed reasons for land use changes

• Current satellite images (year 2007) and some historic images for 90s should be acquired and processed to know the change trend

• High resolution satellite images should be used to improve the results

• Regional developers should give more attentions towards agriculture and encourage people by providing them basic utilities in their area, so that migration trend could be discouraged

• Satellite Remote Sensing combined with GIS should be employed efficiently for mapping and monitoring.


Acknowledgments We would like to thank all the colleagues who helped us in completing this study, particularly to Mr. Sultan Al Sayyar, Director, UIS department ADA, for his moral support and valuable comments and guidance. We also appreciate very much the contribution made by “Strategic Planning for Arriyadh Region” team (Michael C Shand, Omar Hamza and Dr. Hans-Jorg Barth). References

1. Arriyadh Development Authority (2003 )Project Document "Strategic Planning for Arriyadh Region" Arriyadh, Saudi Arabia

2. Callahan Karin E. (2003) "Validation of a Radiometric Normalization Procedure for Satellite-Derived

Imagery Within a Change Detection Framework" Utah state university, USA

3. Hall, Amy “ER Mapper Change Detection Application Tutorial " Earth Resource Mapping, Australia 4. J.B. Collins and C.E. Woodcock, (1996) "An Assessment of Several Linear Change Detection Techniques

for Mapping Forest Mortality Using Multitemporal Landsat TM Data." Remote Sensing of Environment, Vol. 56, No. 1, pp. 66-67.

5. Meyer, W. B., and Turner II, B. L. (1994), Changes in land use and land cover: A global perspective.

Cambridge University Press, Cambridge

6. Roberts, Dar A.; Getulio T. Batista, Jorge L. G. Pereira, Eric K. Walker, and Bruce W. Nelson;(1998) "Change identification using multitemporal spectral mixture analysis: Applications in Eastern Amazonia" ( pp: 137 - 161) in Remote Sensing Change Detection - Environmental Monitoring Methods and Applications by Ross S. Lunetta and Christopher D. Elvidge (Eds.), Ann Arbor, USA.

7. Schott, J.R., C. Salvaggio and W.J. Volchok,(1988) "Radiometric scene normalization using pseudo

invariant features", Remote Sensing of Environment, Vol. 26, No. 1, pp. 1-16

8. Srivastava, S. K., Gupta, R. D. b (2003) "Monitoring of changes in land use/ land cover using multi- sensor satellite data" proceeding Map India 2003 conference New Delhi India

9. United Nations Development Program Saudi Arabia (2004) "www. undp.org.sa "

10. University of Nevada , Remote Sensing tutorial “http://www.ag.unr.edu/serdp/tutorial/descriptive.htm”

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