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Monitoring Spatial and Temporal Changes of Agricultural Lands in the Nile Delta and their Implications on Soil Characteristics Using Remote Sensing

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Authors Hereher, Mohamed El-Desoky

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MONITORING SPATIAL AND TEMPORAL CHANGES OF AGRICULTURAL LANDS IN THE NILE DELTA AND THEIR IMPLICATIONS ON SOIL CHARACTERISTICS USING REMOTE SENSING

By Mohamed El-Desoky Hereher

A Dissertation Submitted to the Faculty of the DEPARTMENT OF SOIL, WATER AND ENVIRONMENTAL SCIENCE

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2006

THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation

prepared by Mohamed El-Desoky Hereher

entitled MONITORING SPATIAL AND TEMPORAL CHANGES OF AGRICULTURAL LANDS IN THE NILE DELTA AND THEIR IMPLICATIONS ON SOIL CHARACTERISTICS USING REMOTE SENSING

and recommend that it be accepted as fulfilling the dissertation requirement for the

Degree of Doctor of Philosophy.

th ______Date: September 6 , 2006 Dr. Edward Glenn

th ______Date: September 6 , 2006 Dr. Stuart Marsh

th ______Date: September 6 , 2006 Dr. Alfredo Huete

th ______Date: September 6 , 2006 Dr. David Hendricks

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

th ______Date: September 6 , 2006 Dissertation Director: Dr. Edward Glenn

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under the rules of the library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgement of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: ______Mohamed El-Desoky Hereher

ACKNOWLEDGEMENTS

I would like to express my appreciation to my committee members for helping and advising. My deepest gratitude goes to Dr. Ed Glenn, the major advisor, for giving me the confidence, support and chance to work under his guidance and advising. Special thanks go to Dr. Stuart Marsh for helping, educating and allowing me to use the facilities in the Remote Sensing Center, Office of Arid Land Studies. I would like to acknowledge my other two committee members, Dr. Alfredo Huete and Dr. David Hendricks who supported me greatly through the entire Ph.D. in the SWES Department. My sincere gratitude goes to all the SEWS Department staff, especially Judith Ellwanger and Veronica Hirsch for assistance. Both Grant Casady and Choy Huang in the Office of Arid Land Studies deserve thanks and appreciation for helping and giving a hand during the processing of the satellite images. I would like to thank every one in the SWES Department who provided me with any help and assistance during my study at the University of Arizona. My deep gratitude goes to my family: my beloved mother, my brothers and sisters for their support. Special thanks go to my sister Faten for the great help. I also would like to extend my gratitude to my darling wife Rasha for support and patience, and to my beloved cute daughter Noran. My deep thanks go to the Ford Foundation for funding my study and residence in the USA through the fellowship they granted me. I would like also to thank the Egyptian Government and Mansoura University for allowing me this chance to get my Ph.D. from the University of Arizona, USA.

DEDICATION

I dedicate this dissertation to the spirit of my late father who passed away before the completion of my Ph.D. He was a great father and devoted his life for his family. May

Allah the Almighty bless him and shower him with mercy.

TABLE OF CONTENTS

ABSTRACT…………………………………………………………………...... 9 INTRODUCTION……………………………….……………………………... 11 I-PROBLEM STATEMENT……………………………………………….. 13 PRESENT STUDY……………………………………………………………... 15 I- THE NILE DELTA ……………………………………………………..... 15 II-DISSERTATION OVERVIEW…………………………………………. 17 REFERENCES…………………………………………………………………. 19

APPENDIX A: INVENTORY OF AREAL EXPANSION OF AGRICULTURAL LANDS OF THE NILE DELTA USING LANDSAT SATELLITE IMAGES………………………………………………………… 21 I- INTRODUCTION……..………………………………………………….. 21 II-LITERATURE REVIEW………………………………………………... 24 Agricultural Land Estimations……………………………………………... 24 Remote Sensing in Agricultural Studies………………………………….... 25 III- MATERIALS AND METHODS…..…………………………………... 27 Satellite Images……………………………………………………………… 27 Atmospheric Correction and Radiometric Normalization…………………. 27 Geometric Rectification, Resampling and Mosaicking…………………..… 28 Normalized Difference Vegetation Index (NDVI)……………………….… 29 Estimation of the Area of Agricultural Lands……………………………... 29 Change Detection of New Agricultural Area…………………………….… 30 IV-RESULTS………………………………………………………………… 32 V- DISCUSSION..…………………………………………………………… 33 Areal Extent of Agricultural Lands……………………………………….... 33 Agricultural Expansion versus Urban Encroachment…………………….. 34 Change in Agricultural Land Extent and its Implications on Soil Characteristics………………………………………………………………. 35 Accuracy of Change Detection……………………………………………... 36 VI-CONCLUDING REMARK……...……………………………………… 37 VII-REFERENCES…………………………………………………………. 38

TABLE OF CONTENTS continued

APPENDIX B: MONITORING SPATIAL AND TEMPORAL AGRICULTURAL LAND DYNAMICS IN THE NILE DELTA, 1984 – 2003 USING NOAA-AVHRR IMAGES……………………………………… 53 I-INTRODUCTION…………………………………………………………. 53 II-MATERIALS AND METHODS………………………………………… 57 Data Set…………………………………………………………………….... 57 Image Pre-processing……………………………………………………….. 58 Linear Mixture Analysis (LMA)………………………………………….… 58 Spatial and Temporal NDVI Variability along the Nile Delta…………….. 59 Change Detection of Temporal NDVI Variability…………………………. 60 NDVI and Seasonal Agricultural Change in Some Selected Locations…... 61 III-RESULTS AND DISCUSSION..……………………………………….. 62 Linear Mixture Analysis……………………………………………………. 62 Spatial and Temporal NDVI Variability along the Nile Delta…………….. 63 Change Detection of NDVI Variability…………………………………….. 65 NDVI and Seasonal Agricultural Change in Some Selected Locations…... 67 IV-CONCLUSIONS………………………………………………………… 69 V-REFERENCES…………………………………………………………… 70 APPENDIX C: A CHANGE DETECTION STUDY OF THE GREATER CAIRO AREA AND ITS VICINITY USING LANDSAT REMOTE SENSING: A CASE STUDY OF AGRICULTURAL LAND DWINDLING. 88 I-INTRODUCTION………………………………………………………… 88 II-MATERIALS AND METHODS………………………………………… 92 Study Area………………………………………………………………….... 92 Satellite Data………………………………………………………………… 93 Atmospheric Correction and Radiometric Normalization…………………. 93 Geometric Rectification and Resampling…………………………………... 94 Image Classification and Masking…………………………………………. 94 Accuracy Assessment……………………………………………………….. 95 Change Detection………………………………………………………….... 96 a) Post-Classification Change Detection……………………………... 96 b) Image Differencing……………………………………………….... 96 c) Principal Component Analysis (PCA)……………………………... 97

TABLE OF CONTENTS continued

III-RESULTS AND DISCUSSION………………………………………… 98 Spectral Signature Curves and the Normalized Difference Vegetation Index (NDVI)...... …………………………………………………………... 98 Image Classification and Masking……………………………………….… 98 Accuracy Assessment…………………………………………………….…. 99 a) Sample Size for Accuracy Assessment………………………….…. 99 b) Accuracy Assessment of Land Cover Maps…………………….…. 99 c) Allowable Errors (E)…………………………………………….…. 100 Change Detection………………………………………………………....… 101 a) Post-Classification Change Detection……………………………... 101 b) NDVI Image Difference…………………………………………..... 103 c) Principal Component Analysis…………………………………….. 104 Comparison of Change Detection Approaches…………………………….. 105 Driving Forces for Land Use/Cover Change………………………...... 106 IV-SUMMARY AND CONCLUSIONS……………………..…………...... 107 V-REFERENCES……………………………………………………………. 109 132 APPENDIX D: SOIL SALINITY PROBLEMS IN THE NILE DELTA…... I- INTRODUCTION………………………………………………………… 132 II- THE EXTENT AND CAUSES OF SOIL SALINITY………………… 132 III- CONTROL MEASURES FOR SOIL SALINITY……………………. 134 IV-REFERENCES…………………………………………………………... 136

ABSTRACT

Egypt witnesses an increasing population growth concomitant with limited water

and agricultural land resources. The objectives of this study were to utilize remotely

sensed data for the inventory of agricultural lands in the Nile Delta, monitoring spatial

and temporal variations in agricultural lands and quantifying agricultural land losses due

to urbanization. Inventory of agricultural lands was designed using two approaches:

thresholding and linear mixture analysis. We utilized 12 images from the Landsat

satellite: 4 from Multi-Spectral Scanner (1972), 4 from Thematic Mapper (1984) and 4

from Thematic Mapper (2003) covering the entire Nile Delta. In addition, a set of 480

NDVI images were obtained from the Advanced Very High Resolution Radiometer

(AVHRR) sensor that cover the period 1984-2003. Landsat images were subjected to atmospheric, radiometric and geometric corrections as well as image mosaicking.

Normalized Difference Vegetation Index (NDVI) was applied and thresholding for agricultural land cover revealed that the areal extent of agricultural lands was 3.68, 4.32 and 4.95 million acres (one acre = 0.96 Egyptian Feddan) in 1972, 1984 and 2003, respectively. Linear mixture analysis of the AVHRR-NDVI with the TM-NDVI images

showed that agricultural lands approached 4.11 and 5.24 million acres in 1984 and 2003,

respectively. Using multitemporal Principal Component Analysis (PCA) for the TM and

AVHRR images proved that reclamation activities were mostly along the western

margins of the Nile Delta. Spatio-temporal analysis showed that middle delta has the

highest agricultural vigor compared with the margins. Agricultural land loss was

estimated in some cities within the delta as well as in Greater Cairo area. We studied the 10

land cover classification and change in Greater Cairo area based on 5 Landsat images acquired in 1972, 1984, 1990, 1998 and 2003. Agricultural lands lost 28.43% (32,236 acres) between 1972 and 2003 with an annual loss of 1040 acres. Agricultural lands on the peripheries of Cairo and its satellite towns were the most vulnerable areas. Soil salinization was another limiting factor for land reclamation. The main conclusion confirms that remote sensing is an accurate, efficient and less expensive tool for the inventory and monitoring agricultural land change in Egypt.

INTRODUCTION

Egypt lies in the arid belt of the north-eastern corner of Africa, between latitudes

22o to 32o N and longitudes 25o to 34o E with an area of about one million km2.

Geomorphologically, Egypt is divided into four units: the Nile Valley and Delta; the

Western Desert; the Eastern Desert; and the Sinai Peninsula. Egypt is exclusively reliant on the River Nile for its water needs. This river is considered the artery of life for the

Egyptians. It is the primary source for drinking, irrigation, and industry. It is also important for internal navigation. The agricultural land of Egypt is distributed along the narrow flood plain of the Nile and its delta. In the past, and for many centuries, the annual Nile flooding provided Egypt with sediment that formed one of the most fertile lands in the world. According to Ball (1952), the thickness of the sediment column in the flood plain varies from 6.7 m in the south of Egypt to about 11.2 m in the Nile Delta to the north. Before constructing the Aswan High Dam in 1964, an annual 84 billion m3 of

water and 133.5 million tons of sediments were transported by the Nile in Egypt. From

these sediments, 124 million tons/year discharged to the Mediterranean Sea, and 9.5 million tons/year settled in the flood plain (Stanley and Warne, 1993).

After the 1952 Revolution, a new agricultural system was adopted in Egypt known as the Agrarian Reform. According to Beaumont et al. (1988), this system was applied when 40% of agricultural areas were held by less than 1% of the owners and 72% of the owners held 13% of the total agricultural lands. The Agrarian Reform introduced many laws to reduce the area owned by individual owners to only about 200 acres in 12

1952, 100 acres in 1962 and then 50 acres in 1969. As a result of this system, the

majority of cultivated land fragmented and transferred from the landlords to the peasants.

Limitations to agricultural expansions include water shortage, bad soil conditions

and high reclamation costs. The Egyptian share of the Nile water has been the same since

1959 (55.5 billion m3), however, the erection of the High Dam at Aswan in 1964 helped

regulate the water supply for irrigation throughout the year. Irrigation consumes 44 billion m3/year of water (El-Gibali and Badawy 1987). Soil salinization either due to

natural or human-induced influence constituted the major obstacle to any reclamation

effort. Abdel Hamid et al., (1992), noted that 80% of the salt-affected soils are distributed

in the Nile Delta around the northern lakes. Reclamation costs range from LE 3000 to LE

8000 (USD 900.9 to USD 2402.4 in 1992) per acre contingent on the grade of the land,

remoteness, and infrastructure (Biswas, 1993).

In Egypt, there are three cropping seasons: winter (shitwi), summer (seifi), and

autumn (nili) (Beaumont et al. 1988). The winter season extends from Nov. to May and

the major crops cultivated are Egyptian clover, wheat, beans, and vegetables. The

summer season extends from May to October and the main crops grown are cotton, rice,

maize, and vegetables. The autumn season, refers to the period after the Nile flood month

(Aug.) and the main crops are maize and vegetables (Ward, 1993). The average field size

ranges from 1-4 acres for the majority of cultivated lands. Few fields are greater than 10

acres and all cultivable lands are vegetated (Tucker et al. 1984). The majority of

cultivated fields are irrigated by flooding through conveying Nile water from the river

course through tens of main canals and hundreds of irrigation ditches. 13

One of the most successful applications of remote sensing is in the agronomic field. The availability of at least three decades of digital data in multiple wavebands of the spectrum (visible, near infrared and thermal bands) and large ground coverage makes remote sensing superior to field-based studies. The premise in using digital data in monitoring agricultural lands is based upon the unique interaction of vegetation biomass with solar electromagnetic radiation which differs from other land cover signatures, e.g. water and bare deserts. Vegetation indices are quantitative surrogates of the vegetation vigor (Campbell, 1987). They are mathematical models enhancing the vegetation signal for a given pixel. Most vegetation indices were calculated from the reflection in two bands of the spectrum: the visible red and near infrared reflection (Aronoff, 2005). These indices could be utilized effectively in land cover change, monitoring vegetation cover density and crop identification (Bannari et al. 1995). The very famous Normalized

Difference Vegetation Index (NDVI) is the model used in this study.

I-PROBLEM STATEMENT

A country of explosive population growth, Egypt has the lowest arable land per capita of any African country (Biswas, 1993). Population increased from 33 million in

1970 to about 70 million in 2000. Agricultural land increase is not comparable to population growth and census data are meager and even confusing. Egypt is implementing many ambitious national plans to reclaim and cultivate numerous desert areas in order to uphold food production for the fast growing population of the country.

This step is crucial in minimizing the gap between crop production and crop import.

According to Dennis (2001), the Egyptian imports of wheat made it the third largest 14

importer after China and Russia. Wheat imports increased from 1.2 million ton in 1961 to

7.4 million ton in 1998. In addition, maize imports increased from 0.1 to 3.1 million ton in the same period. Areas of reclamation potential include the fringes of the Nile Delta, the northern sector of Sinai Peninsula and the southern part of the Western Desert.

Expansion along the fringes of the delta is governed by limited water resources, topography and soil conditions. At the same time populated centers are creeping upon neighboring agricultural lands. Estimates suggest that about 20,000 – 100,000 acres are being lost annually to urbanization and brick making (Daniels 1983, World Bank, 1990).

For these reasons, there should be accurate estimations of agricultural lands and rates of agricultural land augmentations and losses. This is of utmost importance for planners and decision makers for the sustainability of development in Egypt.

PRESENT STUDY

I-THE NILE DELTA

The Nile Delta is one of largest and most well-known deltas in the world.

Herodotus, a Greek Historian (5th century B.C.), was the first to describe the Nile Delta from its morphological features stating its resemblance to the Greek letter with its apex

near the present Cairo in the south and its base along the Mediterranean Sea to the north.

In past historical times, there were seven branches of the River Nile terminating into the

Mediterranean Sea (Montasir, 1937 & Abu Al-Izz, 1971). These branches were (from

west to east): Canobic (the farthest west); Bolbbitic (the present Rosetta Branch);

Sebennytic; Phatnitic (the present Damietta Branch); Mendesian; Tanitic; and Pelusiac

(the farthest east). Five of these branches silted up and disappeared leaving only two

branches; Rosetta at the west and Damietta at the east (Snih and Weissbrod, 1973). The

surface area of the Nile Delta represents only about 3 % of Egypt; however it stands for

the bread basket for all Egyptians. The delta extends for 170 km from south to north and

its width is 220 km. It is flat and slopes very gently, where the general slope between

Cairo in the delta apex and the Mediterranean Sea in the north is only 12 m in 170 km

(Abu Al-Izz, 1971). The Nile Delta is bounded by the Western Desert on the west and

Suez Canal and the Eastern Desert on the east. The Nile Delta is exclusively dependent

on the Nile water for irrigation, since rainfall is very scant except for the coastal region.

The climate of the Nile Delta is generally Mediterranean with hot summers and mild winters. Air temperatures average 18-19°C during winter and 30-31°C during summer. The coldest month is January and the hottest is August. The mean annual temperature generally increases as we move farther from the coast. Precipitation is generally scarce except along the Mediterranean littoral region, where annual rainfall reaches 200 mm. The inland areas receive very little precipitation; Cairo receives rainfall of only 22 mm/year (Beaumont, 1993), occurring between October and May. The mean potential evapotranspiration ranges from 570 to 1140 mm/year in the northern part of the delta and exceeds 1140 mm/year in the southern part (Beaumont et al. 1988). According to Stanley and Warne (1998), the Nile Delta belongs to the hyperarid climate region.

Wind blows mostly from the north and northwest during summer and mostly from south and southwest during winter.

Based on the FAO Soil Classification Map for Egypt (Beaumont et al. 1988), the major soils of the Nile Delta region and its fringes include Fluvisols (the alluvial plain soils), Solonchaks (salt-affected soils), Gravelly Ermolithosols (desert pavement), and

Dynamic Ergosols (shifting sand dunes). In terms of soil texture, the majority of the delta soils are of dark heavy texture (40-70% clays) of alluvial parent materials (El-Nahal et al., 1977). The major clay minerals of the Egyptian alluvial soils formed by the deposition from the River Nile flooding are montmorillonite, illite and kaolinite which were derived from the eruptive rocks in the Ethiopian highlands (Hamdi 1967).

II-DISSERTATION OVERVIEW

In Appendix A, the objective was to estimate the cultivated lands in the Nile Delta in three different dates; 1972, 1984 and 2003. Our hypothesis is based upon the fact that agricultural land cover has a higher NDVI value compared with urban, desert and water

NDVI values. Consequently, NDVI thresholding for green biomass could help estimate areal expansion of agricultural lands. As one single image covers only about one quarter of the delta, we had to create a mosaic image consisting of four images from each of the

MSS and TM sensors. The utilized images were acquired in the same season (summer), were geometrically rectified and were corrected from any atmospheric interference. The

NDVI images were used to estimate agricultural land cover in 1972, 1984 and 2003 and to assess the agricultural land loss on the peripheries of some main cities in the Nile

Delta, namely Mansoura, El-Mahala, Tanta and Zagazig. Moreover, we applied the multitemporal PCA in order to detect the direction of major land reclamations beyond the

Nile Delta between 1984 and 2003.

In Appendix B, we used NDVI data from 480 satellite images acquired by the

AVHRR sensor covering the Nile Delta between 1984 and 2003. These data were obtained on a bi-monthly basis and were originally atmospherically corrected and geometrically rectified at NASA labs. We hypothesized that vigorous vegetation reflects higher long-term NDVI values and soil salinity lowers the NDVI value of agricultural lands. We applied a linear mixture analysis to estimate the areal expansion of agricultural lands of the Nile Delta using AVHRR images from 1984 and 2003 with the aid of TM images acquired in the same months. In addition, we created a one-image file compiling 18

the 480 images and then we generated a mean, a minimum, a maximum and a standard deviation images for the 480- layer NDV image in order to pursue spatio-temporal changes of the NDVI between 1984 and 2003. We applied a change detection approach using NDVI image differencing and multitemporal PCA to locate new cultivated lands and to monitor NDVI change. Finally, we selected four pixels from the mean NDVI

AVHRR image representing different soil salinity and distributed in middle, west, north and east of the delta to monitor long-term variability of the NDVI in relation to different soil salinity levels.

In Appendix C our concern was to estimate the agricultural and urban land cover/use change of the Greater Cairo area as about 20% of the total population in Egypt live in Cairo, and urban growth is the major threat to agricultural lands. The hypothesis was that any sudden decrease in NDVI along an urban/agricultural interface denotes a conversion of agricultural to urban land cover. We used five Landsat images obtained from the MSS (1972) and TM sensors (1984, 1990, 1998 and 2003). Atmospheric correction and geometric rectification was applied. Unsupervised classification was adopted to yield five land cover maps and a change detection using an integrated approach of post-classification, NDVI image differencing and multitemporal PCA was used to detect the agricultural/urban change. Finally, in Appendix D we explored some literature focusing on the nature, extent and control measures of soil salinity in the Nile

Delta as a problem facing agricultural expansion.

REFERENCES

Abdel Hamid, M. A., Shreshta, D., and Valenzuela, C. 1992. Delineating, mapping and monitoring of soil salinity in the Northern Nile Delta (Egypt) using Landsat data and a geographic information system. Egypt. J. Soil Sci. 32, No.3, pp. 463-481.

Abu Al-Izz, M. S. 1971. Landforms of Egypt. The American University Press in Cairo.

Aronoff, S. 2005. Remote sensing for GIS managers. ESRI Press, Ca.

Ball, J. 1952. Contribution to the geography of Egypt. Gov. Press, Cairo, Egypt.

Bannari, A., Morin, D., Bonn, F., and Huete, A. 1995. A review of vegetation indices. Remote Sensing Review, 13: 95-120.

Beaumont, P. 1993. Climate and hydrology. In, Craig, G. M. (edt.): The agriculture of Egypt. Oxford University Press, pp. 17-38.

Beaumont, P., Blake, G, and Wagstaff, J. 1988. The Middle East. David Fulton Publisher, London. 623 pp.

Biswas, A. 1993. Land resources for sustainable agriculture development in Egypt. Ambio, 22 (8): 556-560.

Campbell, J. B. 1987. Introduction to remote sensing. The Guilford Press, New York, USA.

Daniels, C. 1983. Egypt in the 1980s: The challenge. Economist Intelligence Unit Special Report No 158, London, 127 pp.

Dennis, W. 2001. The role of virtual water in efforts to achieve food security and other national goals, with an example from Egypt. Agricultural Water management, 49: 131-151.

El-Gibali, A. A. and Badawy, A. Y. 1978. Estimation of irrigation needs of Egypt. Egypt. J. Soil Sci., 18 (2): 159-179.

El-Nahal, M. A., Abd El-Aal, R. M. Abd El-Wahed A. A. and Raafat, I. 1977. Soil studies on the Nile Delta. Egyptian J. Soil Sci. 17, 1, pp. 55-65. 20

Hamdi, H. 1967. The mineralogy of the fine fraction of the alluvial soils of Egypt. J. Soil Sci. UAR, 7 (1): 15-21.

Montasir, A. H. 1937. Ecology of Lake Manzala. Bull. Fac. Sci., Cairo University, No. 12, pp. 1-50.

Sneh, A. and Weissbrodm T. 1973. Nile Delta: the defunct Pelusiac branch identified. Science, 180 (4081): 59-61.

Stanley, D., and Warne, A. 1993. Nile Delta: Recent geological evolution and human impact. Science, 260 (5108): 628-634.

Tucker, C. J, Gatlin, J. A., Schneider, S. R. 1984. Monitoring vegetation in the Nile Delta with NOAA-7 AVHRR imagery. Photogrammetric Engineering & Remote Sensing, 50 (1): 53-61.

Ward, P. N. 1993. Systems of the agricultural production in the Nile Delta. In Craig, M.M. (Edt.): The agriculture of Egypt. Oxford University Press.

World Bank 1990. Egypt: environmental issue. Draft Discussion Paper. World Bank, Washington, D.C., 45 P.

APPENDIX A

INVENTORY OF AREAL EXPANSION OF AGRICULTURAL LANDS OF THE

NILE DELTA USING LANDSAT SATELLITE IMAGES

I- INTRODUCTION

Irrigated agriculture appeared in Egypt thousands of years ago. There are,

however, no sustainable census data or accurate statistics of cultivated lands. Inventory of

agricultural lands has been carried out mostly by ground surveys, government reports and

personal estimations. Many studies give different estimates with some contradiction. The

last formal agricultural census was carried out in 1961 (Biswas, 1993).

The construction of the Aswan High Dam in 1964 provided additional water to

reclaim about 1.35 million acres in the Nile Valley and Delta (Beaumont et al. 1988).

Between 1952 and 1975, there were many political conditions that hindered the

reclamation of new desert lands. In 1978, a Green Revolution was planned by the

government to reclaim 2.9 million acres by the year 2000 (Barth and Shata, 1987).

Reclamation projects were mainly in three regions: the margins of the Nile Delta,

northwestern Sinai, and the southern sector of the country. According to Beaumont et al.

(1988), between 1960 and 1980, there were six major reclamation areas around the Nile

Delta and Sinai. These areas include: 1- El-Tahrir Province in the western Nile Delta; 2-

Maryut region near Alexandria; 3- Nubariya region south of Alexandria; 4- northern Nile

Delta to the south of Lake Burullus; 5-south of Lake Manzala; and 6- northwestern Sinai region. New agricultural projects east of the Nile Delta and northern Sinai are irrigation 22

by Nile water (50%) mixed with drainage water to secure water needs (Weshelns, 2002).

In addition, Egypt started a national plan to reclaim 1.43 million hectares (3.3 million acres) in the southern part of the country known as the Southern Valley Development

Project (SVDP) (Elarabawy and Tosswell, 1998). Water needs will be delivered from

Lake Nasser behind the High Dam at Aswan.

Although Landsat Thematic Mapper (TM) data have been extensively used in local agricultural and vegetation stress studies, the normalized difference vegetation index (NDVI) from the TM images has seldom been used for regional agricultural mapping due to their narrow regional coverage (185 km) compared with the other coarser resolution Advanced Very High Resolution Radiometer (AVHRR) images (2048-km).

The main objective of this study is to estimate the areal changes of agricultural lands of the Nile Delta for three decades demonstrating that remote sensing is a reliable means of monitoring agricultural lands. We used NDVI from MSS and TM instead of

AVHRR to estimate the agricultural coverage in the Nile Delta for the following reasons:

1) the Landsat project started in the early 1970s with archived data at moderate spatial resolution that are not available from AVHRR; 2) the spatial resolution of these images

(57 m and 28.5 m for MSS and TM, respectively) is very suitable to distinguish thousands of isolated communities (two to three houses) scattered in the agricultural fields of the Nile Delta which would not be detected by the coarser resolution AVHRR images; and 3)- Landsat, especially TM images, have more spectral bands in the visible,

NIR and thermal regions of the spectrum than AVHRR images, thus providing more information. 23

The concept of change detection using satellite images is primarily based on

quantifying the amount of change in radiance values, over some threshold, occurring in

the same pixel of two images acquired in two different dates. These images should be

obtained by the same sensor and should have the same pixel ground spacing, spectral

characteristics and should be acquired in the same climatic conditions. Among the most

common change detection techniques is the multi-temporal Principal Component

Analysis (PCA), which is a linear transformation of correlated raw variables into

uncorrelated variables or principal components. The resultant principal components are

arranged according to their variance. The first component represents the large amount of

variance (Jensen 1995, Hirosawa et al. 1996). The multi-temporal principal component analysis is applied to two or more stacked images, yielding different components each of which provides different information. The areas of change could be identified in the second, third, or consequent components. However, the difficulty in delineating the change and interpreting its occurrence represents the challenge in the multitemporal PCA application (Jensen, 1995).

II-LITERATURE REVIEW

Agricultural Land Estimations

The survey carried out by the Napoleon-led French Expedition estimated the

agricultural lands in Egypt at the end of the eighteenth century as about 1.9 million

hectares (4.71 million acres) and 60% of this cultivated areas occurred in the Nile Delta

(Stanhill, 1981). El-Togy (1974) mentioned that about 3.7 million acres lie in the Nile

Delta and this figure represents 60% of the total area under cultivation in the country.

Sadek (1993) estimated the total expansion of the cultivated land in west of the Nile

Delta for 35 years (1953-1988) to total 620,055 acres compared with 409,150 acres to the

east of the Nile Delta for 33 years (1953-1986). Biswas (1993) reported that the Egyptian

Public Authority for Survey assessed the total cultivated area in 1989 to be 7.8 million

acres. Said (1993) reported, based on data from the Egyptian Central Agency for

Statistics, that the total cultivated area of Egypt was 5.93 and 6.35 million acres in 1975

and in 1986, respectively. He also noted that the Remote Sensing Center of the Egyptian

Academy of Science and Technology estimated the area to total 6.34 million acres in

1979. Abu-Zeid (1993) reported that the agricultural area of Egypt was 6.25, 6.33 and

7.47 million acres in 1970, 1980 and 1990, respectively. Fahim et al. (1999) stated that the cultivated areas were 6.34, 7.5 and 7.76 million in 1976, 1990 and 1995, respectively.

Shalaby, et al. (2004) used two NOAA-AVHRR and SPOT images to monitor Egypt’s agricultural area in 1992 and 2000. They assessed agricultural area to be 8.69 and 9.96

million acres in 1992 and 2000, respectively. These contradicting estimates demonstrate

the need for the current study using the most reliable method to date, remote sensing. 25

Remote Sensing in Agricultural Studies

Marsh et al. (1992) mapped vegetation dynamics in the African Sahel using

NDVI from NOAA-AVHRR and SPOT/XS images. Fung and Siu (2000) applied the

NDVI derived from SPOT/HRV as a means for monitoring environmental changes in

Hong Kong, China. Ramirez et al. (2004) studied the relationships between the

vegetation index values and precipitation-growth stage cycles during 1996-1997 in Mesa

Central, Mexico. Waweru et al. (2004) studied the desertification in Kenya’s dry land

using NDVI from NOAA-AVHRR and TM images. Weiss et al. (2004) used AVHRR

images to monitor NDVI variations in central New Mexico, USA. Principal component

analysis has been widely used in multi-temporal change detection studies. Richards

(1984) applied PCA to highlight regions of localized change resulting from bushfires

using Landsat MSS images in New South Wales. Fung and LeDrew (1987) applied a

multitemporal PCA for land-cover change detection. Hirosawa et al. (1996) utilized the

multitemporal PCA to characterize vegetation communities in the State of Arizona based

on AVHRR data.

In Egypt, Tucker et al. (1984) studied the NDVI variations of the summer crops

of 1981 in the lower Nile Delta using NOAA-AVHRR images. Salem et al. (1995) mapped the land cover classes in an agro-ecosystem area near Alexandria in 1990 using some vegetation indices and a supervised classification approach. They reported that the environmental vegetation index (EVI) and normalized difference vegetation index

(NDVI) provided best greenness levels. They added that urban sprawl could eradicate

agricultural lands within 70 years in that studied area. 26

Lenney and Woodcock (1996) studied the effect of spatial resolution on

monitoring the status of agricultural land west of the Nile Delta. They observed that the

overall accuracy is high with using coarse resolution data (120 m), however, fine

resolution data (30 m), such as those from the TM are more accurate in extracting

information about individual field productivity.

Lenney, et al., (1996) using NDVI values of 10 TM images (1984-1993),

estimated the reduction of agricultural productivity in the western delta region, due to

high soil salinity, alkalinity, and water-logging to be 3.74% compared with 0.4% lost to

urban expansion. They also mentioned that during the same period, the reclaimed areas in

the Western Desert of Egypt increased by 43%. Sultan et al. (1999) monitored the

urbanization of the Nile Delta using Landsat MSS and TM for the years 1972, 1984 and

1990. They discovered that the supervised classification is not successful. They, however, applied a manual classification with masking. They found that urban areas increased by

3.6, 4.7, and 5.7% of the Nile Delta for those periods.

Lawrence et al., (2002) estimated the urban areas of Egypt using satellite MSS and TM images and a night-time imaging system and they reported that the urban areas of the country occupied only 3.7% of the total area. They also mentioned that over 30% of soils suitable for agriculture are under urban land use. Dewidar (2004) monitored the land use/cover changes along the coastal area of the Nile Delta using two satellite TM images acquired in 1984 and 1997. He applied image supervised classification for both images and assessed the amount of change for that region. He concluded that urban areas had been doubled and the agricultural areas had increased due to reclamation. 27

III- MATERIALS AND METHODS

Satellite Images To study the entire areal extent of agricultural lands in the Nile Delta (Fig. 1), a

set of Landsat MSS and TM satellite images were obtained for three different dates:

1972, 1984 and 2003. To cover the entire Nile Delta requires four Landsat MSS and four

TM images, comprising a total of twelve satellite images for the three dates in question

(Table 1). The satellite images were obtained from the United States Geological Survey

(USGS) and the Global Land Cover Facility (GLCF) of the University of Maryland

(WWW document retrieved in June 2005, (http://glcf.umiacs.umd.edu/portal/geocover/)).

All twelve images were free of clouds, of high quality and acquired at the peak

growth season in the Nile Delta, i.e. late summer. This was to ensure that all cultivable

lands were cultivated, all the cultivated crops are in their climax phase, and all the images

were obtained in approximately the same season and solar illumination angles. The major

crops grown during the time of acquisition of satellite images are rice, cotton, maize and

horticulture crops, such as tomato, potato, and cucumber.

Atmospheric Correction and Radiometric Normalization Atmospheric correction is a process by which the degradation of the image quality caused by the influence of atmospheric interferences (dust, haze, smoke, etc) is cured,

while radiometric correction involves the process of normalizing for variations resulting

from sensor system degradation, the illumination source, or the sensor viewing angle

(Aronoff, 2005). Atmospheric correction aims to retrieve the surface reflectance of each object by removing the impact of atmospheric effects. Atmospheric and radiometric 28

corrections for TM images were carried out in one step using the COST Model which is

based on Chavez (1996) dark-object subtraction method. The inputs to this model were

sun-earth distances, sun elevation angle, minimum digital number values (DN) of each

image (bands 1-5 and 7). The output image was in reflectance values ranging from 0 to 1.

For MSS images, the dark object subtraction approach was applied to all bands (1-4). The output images are in DN units. All the image processing techniques were applied using

ERDAS Imagine 8.7 Software.

Geometric Rectification, Resampling and Mosaicking Geometric correction is a process of warping the image to fit a planimetric grid or map projection. This process is crucial in remote sensing and change detection. By geometric rectification a given pixel remains at the same geographic location in all the images utilized in the study. In this study, the four TM images of 1984, which were obtained from the Global Land Cover Facility (GLCF) were used as the master images from which all the other images, including the MSS image, were rectified through image- to-image registration to the Universal Transverse Mercator Projection (UTM / zone 36

WGS 84) using a first-order polynomial transform. For each image registration, at least

20 prominent well-distributed ground control points (GCP) were chosen and a nearest neighbor resampling method was applied. The root mean square error (RMSE) ranged from 0.3 - 0.5-pixel for each registered TM image and less than one pixel for each MSS image revealing a higher precision of rectification. All the MSS images were resampled

to have a pixel size equal to the TM pixels (28.5 m). 29

For each date, the four images were mosaicked to form a single image. In this

process a histogram matching and a stitching process were applied. Thus, three final

images representing the study area were obtained for 1972, 1984 and 2003. After that, a

subset scene covering the Nile Delta and its fringes was created for each date (Fig. 2). As

each image is processed independently, there was no need for further image

normalization.

Normalized Difference Vegetation Index (NDVI) Vegetation indices are surrogates to the abundance and activity of the green

vegetation (Fung and Siu, 2000). The NDVI was calculated as (NIR-Red)/(NIR+Red)

(Rouse et al. 1974), where, NIR is the reflectance or brightness in the near infrared portion and Red is the reluctance or brightness in the red portion of the spectrum.

Theoretically, NDVI takes values from -1.0 to +1.0. Early studies using Landsat MSS mentioned that NDVI correlates significantly with the amount of green leaf biomass

(Tucker 1979). Generally, positive values indicate green, healthy vegetation and negative and near zero values represent non-vegetated land-cover such as water, urban areas, and deserts. We applied the NDVI algorithm to each of the three mosaics. The NDVI image mean, maximum and minimum values were identified.

Estimation of the Area of Agricultural Lands In order to accurately discriminate agricultural from non-agricultural land-cover, we had to pick a threshold in each NDVI image. A careful visual inspection for many points, especially at the margins of urban centers and at the delta fringes, was performed to determine the agricultural threshold in each NDVI image. Once the threshold was 30

determined, all the pixels equal to and greater than the threshold were considered as agricultural land, whereas the remaining pixels were considered non-agricultural lands.

Finally, a binary image (consisting of two classes, 0 and 1) was created. Agricultural areas took the value of one; recoded in green color, and the non-agricultural areas took the value of zero; recoded in black color. The total agricultural lands of the entire Nile

Delta in 1972, 1984 and 2003 were then obtained. In order to evaluate agricultural land loss due to urban sprawl in some selected locations, we assessed urbanization, based on the diminishing of agricultural lands around four main cities surrounded completely by agricultural lands within the Nile Delta namely: Tanta, El-Mahala, Mansoura and

Zagazig. We obtained three equal subset images within the three binary NDVI images of

1972, 1984 and 2003 for each city and assessed urban and agricultural land area change in 1972, 1984 and 2003.

Change Detection of New Agricultural Area We applied change detection to the TM subscenes of 1984 and 2003 images, because they were acquired by the same sensor and have the same spectral, spatial and radiometric characteristics. The objective of applying change detection was to locate the major land reclamations on both sides of the Nile Delta. Also, we compared the estimates of agricultural lands obtained by the binary NDVI images with the areas obtained from the multitemporal PCA change.

Principal component analysis is a common data transformation technique for the compression of information content of a number of bands into just two or three principal components (Jensen 1995). It is used primarily to reduce redundancy in information 31

content in each image caused by the correlation between the spectral bands (Lillesand and Keifer 2000). In the literature, it is mentioned that in multi-temporal principal component analysis images, the PC1 represents the majority of information present in all images (no change) and PC2 and PC3 and the subsequent components represent the areas of land cover change (Aronoff, 2005). In this study, PCA was applied to a twelve-band image consisting of the stacked 1984 and 2003 TM images together. The first six components, obtained from the transformation, were examined carefully in order to identify the component highlighting the change between the two dates. The areas of temporal change, including urban and agricultural lands were identified and assessed.

IV-RESULTS

The mean, maximum and minimum NDVI values of each image, as well as the threshold value of agricultural lands are shown in Table (2). Figure (3) presents a scatter diagram of the mean NDVI versus the threshold of agricultural lands. The total agricultural lands of the Nile Delta in the three binary NDVI images are shown in Figures

(4&5). Figure 6 and Table (3) show the change of the urban/agricultural lands in the cities of Tanta, El-Mahala, Mansoura and Zagazig. In Figure (7-A), we present the first six principal components of the twelve-band (1984-2003) image. The third component

(PCA-3) shows the locations of major changes (newly reclaimed projects and new urban areas). We recoded these areas in black (Fig. 7-B). Figures 8&9 and Table 4 show the agricultural lands obtained from the binary NDVI image of 1984 and 2003 and the areas of change obtained from the multi-temporal PCA-3 for the east and west sides of the delta.

V- DISCUSSION

Areal Extent of Agricultural Lands It is worth mentioning that the mean NDVI value of the three images increased

from 1972 (-0.08) through 1984 (0.21) to 2003 (0.30). This was accompanied by an

increase in the threshold value of the agricultural lands in each image. The correlation

coefficient (R2) was very high (0.99). The increased mean NDVI values in these three images may be attributed to the increase in agricultural productivity of crops in the Nile

Delta which is accompanied by increased vegetation vigor and green biomass. According

to Beaumont et al. (1988), in Egypt, the crop yield for cotton increased from 0.89

ton/acre in 1970/1974 to 1.14 ton/acre in 1984 and for maize from 1.57 ton/acre in

1970/1974 to 1.98 ton/acre in 1984.

The spatial extent of the agricultural land was estimated at 3.68, 4.32 and 4.95

million acres in 1972, 1984 and 2003, respectively. Between 1972 and 1984, the amount

of newly reclaimed areas was 0.64 million acres, with an annual rate of 53,333 acre. This

is compared with new 0.63 million acres between 1984 and 2003 with an annual rate of

only 33,157 acre. The total new agricultural lands between 1972 and 2003 are estimated

at 1.27 million acres with a mean annual rate of 40,967 acres.

According to El-Togy (1974), the agricultural lands in the Nile Delta constitute

60% of the total cultivated lands. Moreover, Biswas (1993), based on government

estimates, reported that agricultural lands of the Nile Delta, in addition to Cairo and Giza

agricultural lands, approach 62% of the total agricultural lands in Egypt. Based on these

assessments, the total agricultural lands in Egypt are estimated by this study at 6.03, 7.08 34

and 8.11 million acres in 1972, 1984 and 2003, respectively (Table 5). These figures

agree with many government reports and reveal high precision estimations.

Agricultural Expansion versus Urban Encroachment Although agricultural lands in the Nile Delta increased by 1.27 million acres into

the northern, eastern and western desert fringes between 1972 and 2003, urban sprawl on

old fertile lands was growing forward. In fact, this figure (1.27 million acres) is a net value of what was added by reclamation and what was lost to urbanism. In order to get an overview of the agricultural land loss in the delta, we estimated the urbanization rate in four cities in the middle delta: Tanta, El-Mahala, Mansoura and Zagazig using the binary

NDVI images for 1972, 1984 and 2003 (Table 3). All these cities witnessed an increase

of their urban corridors upon neighboring agricultural lands. This resulted from the continuous population growth within these cities. The maximum loss of agricultural land between 1972 and 2003 was in Mansoura City (14% of the area) and the minimum was in

Tanta City (8%). El-Mahala and Zagazig Cities lost 13 and 10% of their neighboring agricultural lands in the same period, respectively. On the other hand, Urban expansion was maximal in Zagazig City (19%) and 17% in Mansoura City. El-Mahala City

increased by 13% and Tanta City witnessed the minimal increase (10%). If we take into

account the other tens of main cities, hundreds of satellite towns, and thousands of

villages distributed along the rural Nile Delta, we can get a sense of how much

agricultural loss to urbanization occurred during the study period. It seems easier for

people to build upon old agricultural lands than move to new cities in desert. The 35

agricultural loss around Greater Cairo area, the capital of Egypt hosting millions of

people, is studied in details in Appendix C.

Change in Agricultural Land Extent and its Implications on Soil Characteristics It is well known that information concerning landforms and soils can be inferred

from satellite image analysis (Carrasco 2000). The trend of agricultural expansion in the

Nile Delta was observed to be mostly in the western fringes of the delta. However, many

reclamation projects were established to the east of the Nile Delta. The least direction was observed to the northern delta. Between 1984 and 2003, a total of 266,797acres were

reclaimed to the west of the delta compared with 109,553 acres to the east. Soil condition

and water availability are the most controlling factors of agricultural expansion. Soils in

the northeastern part of the Delta are mostly of marine-alluvium (El-Nahal 1977) with

higher salinity problems that require higher costs for reclamation as well as more water

for salt leaching. Soils to the east are covered by shifting sand dunes. Their physical and

chemical properties are not superior from the agricultural point of view. On the other hand, soils in the western fringes of the delta are mostly of calcareous soils originating from the limestone rocks in the vast Western Desert which were leveled by aeolian action

(Beaumont, et al. 1988). Water is delivered to that area through many main canals, in addition to the ground water reserves. The soil quality may therefore be the reason for increased reclamation in the western side of the Nile Delta.

Accuracy of Change Detection In order to compare the accuracy of the change detection estimations obtained from the PCA with the estimations from the binary NDVI images, we have to be aware that the multitemporal PCA identifies all kinds of change, such as urbanization on the

desert or on agricultural lands, reclamation of desert areas or even leveling of land for

reclamation. On the other hand, the thresholded NDVI image identifies only the areal

coverage of agricultural lands. For this study, the areas reclaimed in the western fringes

of the delta between 1984 and 2003 were estimated by the binary NDVI image

differencing to be 266,797 acres, whereas over the same period the PCA-3 change area is

estimated at 296,104 acres. As the PCA determines all kind of change, the difference in

these two values may be a result of other non-agricultural or pre-reclamation activities,

such as leveling of land.

The situation is a little bit different in the eastern fringes of the Nile Delta. There,

in addition to reclamation projects, many new urban and industrial communities along the

Cairo-Ismailia Road were established, e.g. the 10th of Ramadan City (Fig. 9). The

agricultural areas added between 1984 and 2003 NDVI binary difference image total

109,553 acres. On the other hand, the area obtained from the change detection by the

1984-2003 PCA image totals 209,497 acres, which reveals a contribution of non-

agricultural land in this estimation. The interpretation for this difference is that newly

established urban communities were detected in the PCA image.

VI-CONCLUDING REMARK

This study concludes that remote sensing could provide an indispensable tool for regional coverage, time-series, and high accuracy estimation of agricultural lands, especially in arid regions. The spectral and spatial characteristics as well as the low cost of Thematic Mapper (TM) images make it suitable for multitemporal change detection of agro-urban interactions. Although, there are ambitious plans to turn new desert regions into cultivated land, the continual sprawl of urban areas is devouring large amounts of fertile agricultural lands.

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Figure (1): The Nile Delta of Egypt.

Table (1): The Satellite Data used in this study. Sensor Acquisition Path/Row Spatial Sun Elevation Source Date Resolution Angle TM 24 Aug. 2003 176/38 28.5 57 USGS TM 24 Aug. 2003 176/39 28.5 57 USGS TM 16 Sept. 2003 177/38 28.5 52 USGS TM 16 Sept. 2003 177/39 28.5 53 USGS TM 20 Sept. 1984 176/38 28.5 51 GLCF TM 20 Sept. 1984 176/39 28.5 51 GLCF TM 11 Sept. 1984 177/38 28.5 53 GLCF TM 11 Sept. 1984 177/39 28.5 53 GLCF MSS 5 Oct. 1972 189/39 57.0 46 USGS MSS 31 Aug. 1972 190/38 57.0 56 GLCF MSS 31 Aug. 1972 190/39 57.0 56 GLCF MSS 19 Sept. 1972 191/38 57.0 51 GLCF

Figure 2. The three mosaic images of the Nile Delta obtained from the Landsat MSS and TM images of 1972, 1984 and 2003. 44

Table (2): The Statistical data of the NDVI images of the Nile Delta.

Image Year Mean NDVI Max. NDVI Min. NDVI Threshold NDVI 1972 -0.08 0.64 -0.88 0.14 1984 0.21 0.86 -0.65 0.21 2003 0.30 0.90 -1.00 0.32

0.35 0.3 0.25 0.2 y = 0.4775x + 0.1809 R2 = 0.9952 0.15 0.1 0.05

Threshold value of ag. area 0 -0.1 0 0.1 0.2 0.3 Mean NDVI of the image

Figure 3: A scatter diagram showing the relationship between the mean NDVI value of the image and the threshold of agricultural land.

Figure 4: Binary NDVI images showing the distribution of the agricultural lands of the Nile Delta in 1972, 1984 and 2003.

3 Area (million acre)

2 1972 1984 2003 Year

Figure 5: The area of agricultural lands in the Nile Delta in 1972, 1984 and 2003 as measured from the satellite data.

El-Mahala Mansoura Tanta Zagazig

1972 1984 2003

Tanta

El-Mahala

Mansoura

Zagazig

Figure 6: Columns A, B, and C show the 1972, 1984 and 2003 images, and rows 1,2,3, and 4 show the cities of Tanta, El-Mahala, Mansoura, and Zagazig. 48

Table 3: The areas, in acres, of urban and agricultural lands in the cities of Tanta, El-Mahala, Mansoura, and Zagazig between 1972 and 2003. 1972 1984 2003 Ag. (72-03) Urban (72-03) City urban Ag. urban Ag. urban Ag. acre % acre % Tanta 10427 16069 11019 15177 11447 14757 -1311 -8 1020 10 El-Mahala 6566 7788 6976 7271 7434 6765 -1023 -13 867 13 Mansoura 10224 14607 11841 12691 11915 12617 -1990 -14 1690 17 Zagazig 9922 20317 10488 19453 11776 18235 -2082 -10 1855 19

A A

New agricultural lands

PCA-1 PCA-1 PCA-2 PCA-3

PCA-3

PCA-4 PCA-5 PCA-6

New agricultural lands

PCA-3

Figure 7: Change detection of the 1984-2003 mosaics: A) PCA showing the six components, and B) PCA-3 after thresholding shows areas of change.

NDVI-1984 NDVI-2003 PCA-3_1984-2003

Figure 8: Comparison of agricultural areas in the Western Delta obtained from the binary NDVI of 1984 and 2003 and the areas of change obtained by thresholding PCA-3 of (1984-2003).

New Urban areas

NDVI-1984 NDVI-2003 PCA-3_1984-2003

Figure 9: Comparison of agricultural areas in Eastern Delta obtained from binary NDVI of 2003 and 2003 and the areas of change obtained by thresholding PCA-3 of (1984-2003).

Table (4): The difference in area of reclaimed lands in west and east delta obtained from NDVI images of 1984 and 2003 and multi-temporal PCA image. NDVI-1984 NDVI-2003 NDVI-difference 1984-2003-PCA West Nile Delta 40,658 307,455 266,797 296,104 East Nile Delta 80,180 189,733 109,553 209,497

Table (5): Comparison of the total agricultural lands of Egypt by the present study and previous studies. Agricultural Land, million acres year Reference 6.03 1972 This Study 6.16 1974 El-Togy 1974 6.34 1976 Fahim et al. 1999 7.08 1984 This Study 7.80 1989 Biswas 1993 7.47 1990 Abu-Zeid 1993 8.11 2003 This Study 9.96 2000 Shalaby et al. 2004

APPENDIX B

MONITORING SPATIAL AND TEMPORAL AGRICULTURAL LAND

DYNAMICS IN THE NILE DELTA, 1984 – 2003 USING NOAA-AVHRR

IMAGES

I-INTRODUCTION

With the launch of the first generation of the NOAA-AVHRR satellites, potential remote sensing imaging of high temporal resolution for monitoring terrestrial vegetation

emerged (Tucker et al. 1984). The premise behind agricultural applications of satellite-

acquired data is that the chlorophyll of the green leaf strongly absorbs red radiation (630-

690 nm), whereas leaf cellular structure strongly reflects the near infrared radiation (760-

900 nm) (Tucker, 1979). Recent studies have shown that the NDVI obtained from the

NOAA-AVHRR satellite provides useful information on vegetation dynamics as well as

land cover (Justice et al. 1985, Prince 1991, Goward and Prince 1995, and Fuller 1998).

Tucker (1979) noted that NDVI correlates strongly with green leaf biomass. In addition,

NDVI correlates with leaf area index (Wiegand et al. 1979), crop vigor (Sellers 1985),

chlorophyll content (Chappelle et al. 1992), and vegetation cycles (Weiss et al. 2001).

The NDVI was developed by Rouse et al. (1974). It is computed from the visible

and the near infrared (NIR) reflectance. In AVHRR data, the red (0.55 to 0.68 μm) and

NIR (0.73 to 1.1 μm) reflectance values for a given pixel are set in the following

equation: NDVI = (Ch2- Ch1)/(Ch2 + Ch1), where Ch1 and Ch2 are the red and near

infrared reflectance of the AVHRR. Theoretically, NDVI values range from -1 to +1. 54

Many factors, other than green biomass, affect the NDVI of a given pixel.

Atmospheric aerosols and water vapor lead to biased NDVI values (Justice et al. 1991).

Sun illumination and sensor angles as well as sensor system modify the true pixel weight

(Holben, 1986). Soil background, influenced by the texture; color; wetness and organic matter, is a serious problem influencing the NDVI of the canopy (Huete 1985).

Generally, the NDVI decreases with increasing soil brightness (Huete 1985). The effect of soil background upon NDVI becomes negligible when the green cover approaches

70% or more of the pixel area (Nicholson and Farrar, 1994). New vegetation indices, e.g.

Soil Adjusted Vegetation Index (SAVI) (Huete 1988) and Enhanced Vegetation Index

(EVI) (Huete et al. 2002) were developed to avoid soil background interferences but they do not have a time series of global coverage (Hermann et al. 2005).

The linear mixture analysis (LMA) approach seeks to quantify the weight of different spectral signatures combined linearly from different pure surface component

(soil, water, vegetation, etc) “endmembers” present in one pixel (Metternicht and

Fermont 1998). This approach aims to obtain information about land cover at a sub-pixel level (Coca et al. 2004). On the other hand, unmixing involves assessing the relative proportions of each surface component present in the pixel (Metternicht and Fermont

1998). Reference endmembers could be estimated from laboratory or field measurements

(Tompkins et al. 1997). Moreover, Adams et al. (1993) mentioned that endmember

spectra could be derived directly from image data. Many land-cover classification studies

were conducted using the linear mixture model (e.g. Adams et al. 1995; Carrasco et al.

2000; Elmore et al. 2000 and Lu et al. 2003). 55

Time series analysis of the NDVI can provide information on seasonal and annual vegetation dynamics of an area assuming that land cover and soil characteristics in that area are in the same conditions (Schmidt and Karnieli 2000). Moreover, many NDVI models have been developed to describe vegetation health and vigor. For example, the

Coefficient of Variation (COV) (Weiss et al. 2001) describes the ratio of the standard deviation value of a NDVI data set to the mean value of this data set. Weiss et al. (2001) showed that higher COV values reflect increased vegetation dynamics over the time period of investigation. They concluded that the COV provided satisfactory results on desertified areas of some rangelands in Saudi Arabia. In addition, Cogan (2001) developed a new index describing vegetation conditions. This index, the Vegetation

Condition Index (VCI), describes the vigor and health conditions of vegetation. He observed that this index worked very well in monitoring a drought episode in Kazakhstan in 1996. Although he incorporated climatic and thermal band data in his vegetation health index, the VCI alone could be sufficiently informative of the vegetation health.

Temporal change detection of the earth’s resources reflects the interactions and interprets the relationships between the natural resources and human impacts (Lu et al.

2004). Diverse techniques for change detection of land resources have also been reviewed (Singh 1989). Among these techniques are image differencing (Ridd and Liu,

1998) as pixel-to-pixel subtraction and multitemporal principal component analysis PCA

(Sunar, 1998). The majority of change detection studies were carried out using data from

MSS (Chavez and MacKinnon, 1994), TM (Guild et al. 2004), and SPOT (Fung and Siu, 56

2000) sensors. AVHRR data from the very course spatial resolution sensor were, however, seldom used. This study makes an endeavor to implement this approach.

Ever since the 1980s, archived AVHRR-NDVI data have been used extensively in global as well as regional vegetation studies, e.g. Tucker et al.1983; Tucker et al. 1984;

Hielkema, et al. 1987; Running and Nemani, 1988; Gupta, 1991; Gupta, 1992; Gupta et al. 1993; Farrar et al. 1994; Nicholson and Farrar 1994; Achard, 1995; Cihlar et al. 1996;

Ramsey et al. 1995; Gitelson et al. 1998; Walker and Mallawaarachchi 1998; Ricotta et al. 1999; Schmidt and Karnieli 2000; Weiss et al. 2001; Boken et al. 2004; Dilley et al.

2004; Ramirez et al. 2004; Shalaby et al. 2004; Weiss et al. 2004; Anyamba and Tucker

2005; Herrmann et al. 2005; and Goetz et al. 2006.

We concluded in Appendix A that agricultural lands of the Nile Delta as estimated from MSS and TM data were 3.68, 4.32 and 4.95 million acres in 1972, 1984 and 2003, respectively. The expansion occurred mostly in the western fringes compared with the eastern region and was lowest to the north. We attribute that to soil characteristics, availability of irrigation water and geographic locations in relation to major non-fresh water bodies, such as the Mediterranean Sea, Suez Canal and northern lagoons. In this Appendix, our primary objectives are to estimate the agricultural land cover using linear mixture analysis between TM and AVHRR images; to monitor the spatio-temporal NDVI variability (1984-2003) throughout the entire Nile Delta using

AVHRR-NDVI data; and to study the effect of soil salinity on the NDVI seasonal variability of selected pixels (64 km2 each) distributed along the fringes of the Nile Delta

(west, east and north) as well as in the middle delta. 57

II-MATERIALS AND METHODS

Data Set

The NDVI data used in this study were acquired from the Global Land Cover

Facility at the University of Maryland (WWW document retrieved in Feb. 2006,

(http://glcf.umiacs.umd.edu/data/gimms/)). The NDVI data from the AVHRR sensor,

onboard the NOAA polar orbiting satellite, were processed by the Global Inventory

Modeling and Mapping Studies (GIMMS) group at NASA’s Goddard Space Flight

Center (see Tucker et al. 1994 & 2005). GIMMS NDVI data were obtained as two bi- weekly composites; one representing the first two weeks of the month and the other representing the second two weeks, totaling twenty four bi-weekly NDVI composites per year. The bi-weekly NDVI compositing was applied on the basis of maximum value compositing (MVC) in order to minimize cloud interferences, haze and dust contamination (Holben 1986; Anyamba and Tucker et al. 2005). These data have the

advantage of being single band images. GIMMS NDVI images were georeferenced and

corrected for sensor degradation, illumination and view angle variations, and atmospheric

interferences (Huang et al. 1998 and Pinzon et al. 2004). Consequently, these data sets have a higher level of precision and accuracy (Herrmann et al. 2005). We used the

African Continent file which was georeferenced to the Albers Conical Equal Area

Projection with a Spheroid and Datum of WGS 84. The spatial resolution of these data is

8 km (Fig. 1). NDVI images are originally rescaled between -10000 and +10000. A total of 480 images covering the period from Jan. 1984 to Dec. 2003 were utilized. This period covers the same time span of the previous investigation using TM images (Appendix A). 58

Image Pre-processing

Since the NDVI data sets we obtained from the Global Land Cover Facility of the

University of Maryland were fully atmospherically corrected, normalized and

georeferenced by the GIMMS group, very little preprocessing was required. We imported

all the raw NDVI images of the whole African Continent to the ERDAS Imagine 8.7

environment. We created one image file consisting of 480 layers in which each layer

represents the 15-day composite image during the whole study period (20 years x 24

image/year). In order to check the validity and consistency of the whole time series data

set, one pixel in the Sahara Desert was chosen to monitor the 20-year variations of the

NDVI values. We observed that the NDVI stayed the same, with only ± 6% change. Then

we created a subscene image covering the entire delta. All the image processing

procedures were conducted using ERDAS Imagine 8.7 and ArcMap 9.1 Software.

Linear Mixture Analysis (LMA)

We applied the LMA in order to obtain the fractional occurrence of agricultural

lands of the Nile Delta using AVHRR-NDVI images for 1984 and 2003. The inputs to

this analysis were the two binary TM NDVI images of the 1984 and 2003 obtained in

Appendix A and the two AVHRR-NDVI images of Sept. 1984 and Aug. 2003 (the same

acquisition times of the TM images). The first step was attaining the NDVI values for 10

AVHRR-NDVI pixels (64 km2 each) fairly distributed along the entire delta and representing different ranges of NDVI values, and the corresponding NDVI for equal

areas obtained from both the TM in 1984 and 2003. The second step was estimating the 59

agricultural land cover fraction in each of the 10 areas for each TM image. The third step was creating a regression relationship between the NDVI of the 10 AVHRR pixels versus the percentage of agricultural land cover obtained from each area of the TM image. The fourth and last step was to estimate the areal extent of agricultural lands in both 1984 and

2003 AVHRR-NDVI images. The results were compared with the estimation of agricultural lands by thresholding the TM-NDVI images (See Appendix A).

Spatial and Temporal NDVI Variability along the Nile Delta

We created a mean NDVI image from the raw 480-layer image so that each pixel in the mean NDVI image has a mean value of 480 readings. Then we produced a spatial

NDVI variability map for the entire Nile Delta. In the same manner, we estimated a standard deviation image for the 480-layer image. Then we divided the standard deviation of each pixel by its mean value to obtain an index image called the Coefficient of Variation (COV) image (Weiss et al. 2001). This index reflects agricultural dynamics,

so that higher values refer to viable and more dynamic agriculture. In order to estimate

the vegetation conditions throughout the study area, we created new minimum and

maximum images for the 480-layer image and then we applied the Vegetation Condition

Index (Cogan, 2001). In this index, the VCI = (NDVI – NDVImin) / (NDVImax –

NDVImin), where NDVI is the mean value of each pixel, NDVImin and NDVImax are the minimum and maximum NDVI for the 480 readings of each pixel, respectively. Finally, we estimated the mean NDVI value of the entire delta for each year individually. We have thus obtained a twenty mean NDVI images for the twenty years (1984-2003).

Change Detection of Temporal NDVI Variability

In order to pursue temporal change detection in agricultural lands in terms of

NDVI variability over time, we applied (a) an image difference approach and (b) a multitemporal principal component analysis.

In image differencing, we applied the algorithm of pixel-to-pixel subtraction

(Singh, 1989) between the very first (1984) and the very last (2003) mean NDVI images in order to locate areas with increasing agricultural activities in terms of NDVI augmentation. We applied a threshold of 10% of change. Then we stacked each two bi- weakly images to form a single mean monthly NDVI image for each month for both 1984 and 2003. Finally, we subtracted each mean NDVI image for each month in 1984 from the corresponding month in 2003. For example, we subtracted the mean NDVI image of

Jan. of 1984 from the mean NDVI image of Jan. 2003, the mean NDVI image of Feb.

1984 from the mean NDVI image of Feb. 2003, and so forth until the mean NDVI image of Dec. 1984 from the mean NDVI image of Dec. 2003.

In addition, we applied a multi-temporal Principal Component Analysis (PCA) for the 480-NDVI-layer image in order to track the long-term NDVI changes over the entire

Nile Delta. PCA has been widely used in multi-temporal change detection (Aronoff,

2005). As we mentioned in Appendix A, the first PCA always provides the information available in all the data set, whereas the second and consequent components highlight areas of spatial and temporal change. We examined the first six PCA images in order to determine the component which provides the temporal change in NDVI and consequently agricultural land. 61

NDVI and Seasonal Agricultural Change in Some Selected Locations

The objective of this part is to address the variation of NDVI in relation to soil salinity, to designate the peak growth season at selected locations in the Nile Delta and the magnitude of seasonal NDVI at each location. We selected four pixels (8x8 km) distributed in west, east, north and middle delta. We determined the mean NDVI for each pixel over the twenty years of study. We used a soil salinity survey published for the Nile

Delta (El-Nahal et al. 1977) and a Soil Classification for the Nile Delta (Beaumont et al.

1988) as ancillary data for this analysis. We created long-term NDVI temporal curves for each location.

III-RESULTS AND DISCUSSION

Linear Mixture Analysis

The primary objective of applying the LMA is to obtain the areal extent of

agricultural lands from very coarse spatial resolution AVHRR images. As the pixel (8-

km) value of the AVHRR-NDVI image is a combination of spectral signatures from all

land cover units present, higher agricultural land cover increases the NDVI value (Fig. 2).

From Figure (3), the NDVI of the selected AVHRR pixels from the 1984 and 2003 years correlates strongly with the mean NDVI values of the equivalent areas in both the TM images of 1984 and 2003. The correlation coefficient is greater for 2003 image (0.95)

than for 1984 one (0.60). In addition, the AVHRR-NDVI for both the 1984 and 2003

images correlates significantly with the percent of agricultural land cover obtained from

the corresponding 1984 and 2003 TM NDVI images (Fig. 4). The correlation coefficient

is 0.80 for the 1984 image compared with 0.83 for the 2003 image. From the regression

equations, the agricultural lands in the Nile Delta in 1984 are estimated from the AVHRR data at 4.11 x 106 acres compared with 4.3 x 106 acres from the TM images (Appendix

A). The AVHRR underestimated the agricultural lands by 4.4%. On the other hand, the

agricultural lands were estimated at 5.24 x 106 acres in 2003 from the AVHRR (Table 1)

versus 4.95 x 106 acres from the 2003 TM image. In this year, AVHRR overestimated the

agricultural lands by 6.3% over the TM image. From these estimates, the linear mixture

analysis could estimate agricultural land cover very precisely. The results are very

comparable to the estimations obtained in Appendix A using TM data.

Spatial and Temporal NDVI Variability along the Nile Delta

From the mean NDVI map (1984-2003) of the Nile Delta (Fig. 5), it is clear that there is a kind of spatial zoning of mean NDVI along the entire delta area, i.e. decreasing the mean NDVI values from the delta toward its margins. The highest mean NDVI value

(>0.5) was observed for the bulk of the agricultural lands. This region is surrounded by a lower NDVI zone (0.4-0.5) from all sides of the delta. Areas falling in this class occur mainly along the western fringes of the delta. Areas of the third NDVI class (0.3-0.4) are generally smaller in area than the second class. These regions are located on the western and eastern margins and are sited in newly cultivated lands. Going farther to the east and west of the delta, the mean NDVI values decrease gradually. The lowest mean NDVI values are 0.3-0.2 and 0.2-0.1 and they represent areas either under reclamation or of lower quality soils (salt-affected soils).

The Coefficient of Variation (COV) map of the Nile Delta (Fig. 5) depicts conspicuously that the majority of delta agricultural lands fall in the middle class (0.2–

0.3), whereas the delta margins have lower COV values (0.1–0.2). Very few pixels on the west and northeast of the delta have high COV values. From this distribution, it can be said that the delta margins are generally less vigorous than the middle delta in terms of

COV values. The peripheries of the Nile Delta, including the Mediterranean Sea and the

Western and Eastern Deserts have very low COV values (<0.1) because their mean annual NDVI did not change over time and consequently their standard deviation did not too, as they are far from reclamation activities and human interactions. 64

The Vegetation Condition Index (VCI) map of the Nile Delta (Fig. 5) gives a

good prospect about the distribution of vegetation conditions along the entire delta. The

bulk of the agricultural lands of the delta falls within the high VCI class (0.4 – 0.6). Very

few areas fall within the very high class (0.6 – 0.8). These areas are completely located inside the delta and are in locations distant from the main cities, such as Cairo, as they were exposed to lower urbanization upon agricultural lands. The delta margins exhibit lower VCI values (0.2 – 0.4), which supports our findings from the previous mean NDVI and COV maps that the margins have lower agricultural vigor.

Comparing the results of the mean NDVI, COV and VCI maps with the FAO Soil

Classification Map (Beaumont et al. 1988) of the Nile Delta, the spatial changes of the agricultural lands could be attributed to many factors, such as soil quality and availability and quality of irrigation water. The major soils of the delta belong to the fertile soils of the Fluvisols which were formed over the past 8000-9000 years of Nile Flooding (Ball

1952 and Sultan et al. 1999) and are close to the main irrigation canals. Agricultural areas of this region have the highest mean NDVI, COV and VCI. As the NDVI correlates directly to the green biomass (Tucker, 1979), this region is considered the most productive and healthiest land in the delta. The delta margins have different soil types

(Beaumont et al. 1988). The northern and eastern margins are classified as Solonchaks

(salt-affected soils). On the other hand, the western margins of the delta belong to

Fluvisols and other soil types, mostly of calcareous origin. This may explain the increased expansion of agricultural lands on the western margins of the delta compared with the eastern and northern margins. 65

The temporal change of the mean annual NDVI (Fig. 6) reflects some kind of variability from 1984 to 2003. The lowest mean NDVI value was in 1985 (0.21), whereas the highest values were for 1990 and 1996 (0.22). All the other years’ values fall within these two figures. From Figure (6), it is noticeable that the mean annual NDVI distribution obeys a bimodal behavior, where there are two major peaks in 1990 and

1996. The long-term NDVI reveals an overall stable trend. This may explain the tradeoff between the new cultivated lands along the delta margins versus agricultural losses by urbanization along the peripheries of urban centers.

Change Detection of NDVI Variability

In image differencing, we subtracted the mean NDVI image of 1984 from the mean NDVI image of 2003 with a threshold of 10% (Fig. 7). It is obvious that areas increased in their NDVI are mostly located within the middle and the western fringes of the delta. These results agree with what we obtained from the TM images analysis

(Appendix A) where the majority of reclamation projects were implemented along the western fringes. On the other hand, areas decreased in their mean NDVI are those in the northern part of the delta and some scattered areas in the middle delta which have witnessed high rates of urbanization upon agricultural areas. The reason beyond the higher rates of expansion in the western part of the delta could be mainly attributed to the soil quality of these areas (mostly of Fluvisols and calcareous origins) which is higher than that of the northern and eastern delta (mostly of salt-affected and sandy soils).

The monthly image difference (Fig. 8) indicates that winter months (Nov., Dec. and Jan.) generally increased by 10% in their mean NDVI between 1984 and 2003. On 66

the other hand, summer and late summer months (May, June, Sept. and Oct.) decreased in

their mean NDVI by 10%. Other months (Feb., March, July and August) showed both

areas of increased and decreased trends, alternatively. The reasons for these changes on

the monthly level may be attributed to the change in crop cycles, sowing times, irrigation

schedules and other local farming conditions.

The principal component analysis (PCA) of the multitemporal AVHRR-NDVI

(480-layers) provided some supportive information about the temporal trends of the

NDVI (Fig. 9). The first principal component (PCA-1) carries the majority of information

in all the data set; thus it is not used to provide information about temporal change. On the other hand, the second (PCA-2), fourth (PCA-4) and sixth (PCA-6) components

provided some information about the long-term NDVI changes. PCA-2 designates areas

of increased NDVI over time, which implies the majority of the agricultural lands in the

delta. These findings agree with what was mentioned in the literature that agricultural

productivity for major crops has increased in the Nile Delta (Beaumont et al. 1988) as

modern agricultural and fertilization practices were applied. PCA-4 and PCA-6 highlight

areas of new agricultural lands (high NDVI). Bright areas in these components refer

mostly to the western fringes of the delta. This assures that the western fringes of the

delta received more attention in terms of agricultural activities.

NDVI and Seasonal Agricultural Change in Some Selected Locations

The locations of the selected pixels are shown in Figure (10). The maximum

average NDVI value was observed in the middle delta pixel (0.54) (Fig. 11). Referring to

El-Nahal et al. (1977) for soil salinity and agricultural productivity, this area is located within the least soil salinity zone (<4 ds/m) and has the highest productivity (good class).

Agricultural lands in the pixel of the western delta have an average NDVI value of 0.52, which is very close to the middle delta pixel. The soil salinity of this region is the same as that of the middle delta area (<4 ds/m), however, the agricultural productivity in this

region belongs to the moderate class (El-Nahal et al. 1977). This may explain why this

region has a lower average NDVI value than the middle delta area. On the other hand, the

eastern and northern Nile Delta pixels have average NDVI values of 0.50 and 0.47,

respectively. These regions have higher soil salinity than the other two areas; ranging

from 4-8 ds/m and their soil productivity is of moderate class (El-Nahal et al. 1977). The

higher soil salinity may be the reason for their lower average NDVI than the other two

pixels of the middle and west delta.

Refereeing to the FAO Soil Classification of the Nile Delta (Beaumont et al.

1988), both the regions in the middle and western Nile Delta belongs to the Fluvisols,

whereas the soils of the selected eastern and northern areas belongs to the Solonchaks

(salt-affected soils). These latter regions are low laying areas, close to the sea and are

exposed to sea-water intrusion resulting in higher salinity levels. The variations of these

soil characteristics may be the driving force for the difference mean NDVI value and

consequently the overall vegetation vigor. 68

Investigating the annual NDVI curves for the selected pixels (Fig. 12) reveals that there are two major agricultural cycles (seasons) at each location in particular and in the entire Nile Delta in general. The peak growth periods fall between February and March

(winter) and during August (summer). It is worth mentioning that the mean NDVI value of the winter crops (clover, wheat, barely and vegetables) is greater than the mean NDVI for the summer crops (rice, cotton and maize). A minor cropping season called the nili is incorporated within the summer season. The magnitude of seasonal cycles is greatest for the western, middle, and eastern pixels and lowest for the northern one. The maximum winter NDVI is observed in the western delta site, and the lowest is for the northern site.

On the other hand, the maximum summer NDVI value is observed for the western, middle and eastern sites, whereas the lowest value is for the northern pixel area. This may suggest that the northern area witnesses less vigorous vegetation in terms of NDVI than the other selected sites.

IV-CONCLUSIONS

• Remote sensing data from the NOAA-AVHRR archives could be used to study

regional expansion of irrigated agricultural lands in arid regions. Compilation of

AVHRR with other TM images and ground data, such as soil salinity, and soil

classification, could be very useful in monitoring long-term agricultural land change.

• Combining the mean NDVI, Coefficient of Variance (COV) and the Vegetation

Condition Index (VCI) help provide a more lucid view about the spatial agricultural

behavior than NDVI alone.

• The areas in the Nile Delta on the old fertile soils formed from the Nile flooding were

the healthiest in terms of mean NDVI, vegetation dynamics and vigor.

• The soil quality of the delta margins is generally lower than that of the middle delta

region as the margins belong to salt-affected soils, gravely soils, calcareous soils,

lithic soils, or to sandy dune soils. This is supported by our findings of the lower

mean NDVI, COV and VCI of the delta margins.

• Soil salinity survey and classification confirm NDVI distribution of the Nile Delta.

• There should be future studies on the discrimination of salt-affected soils in terms of

saline, sodic and saline-sodic soils, especially in the margins of the Nile Delta

receiving reclamation attention, and their relation to crop cycles and yield.

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Sultan, M., Fisk, M., Stein, T., Gamal, M., Hady, Y., El-Araby, H., Madani, A., Mehanee, S, and Becker, R. 1999. Monitoring the urbanization of the Nile Delta, Egypt. Ambio, 28: 628-631.

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Tompkins, S., Mustard, J. F., Pieters, C. M. and Forsyth, D. W. 1997. Optimization of endmembers for spectral mixture analysis. Remote Sensing of Environment, 59: 472-489. 75

Tucker, C. J, Gatlin, J. A., Schneider, S. R. 1984. Monitoring vegetation in the Nile Delta with NOAA-7 AVHRR imagery. Photogrammetric Engineering & Remote Sensing, 50 (1): 53-61.

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Figure 1: The African Continent from AVHRR, 8-km pixel, NDVI data showing the study area.

Figure 2: The locations of the selected pixels in AVHRR and their corresponding agricultural land cover in the TM images. The values of NDVI are from the AVHRR image and the % Agricultural lands are from the TM images.

1984

0.7 y = 8E-05x + 0.0365 0.6 R2 = 0.6088 0.5 0.4

TM 0.3 0.2 0.1 0 0 1000 2000 3000 4000 5000 6000 7000 8000 AVHRR

2003

0.8 y = 8E-05x + 0.1432 R2 = 0.9541 0.6

0.4 TM

0.2

0 0 1000 2000 3000 4000 5000 6000 7000 8000 AVHRR

Figure 3: A regression relationship between the AVHRR-NDVI and TM-NDVI for the same area.

y = 0.016x - 6.3604 100 1984 R2 = 0.8049 80

% Ag. in TM in % Ag. 20

0 0 1000 2000 3000 4000 5000 6000 7000 8000 NDVI-AVHRR

y = 0.0116x + 9.7735 100 2003 R2 = 0.8315 80

% Ag. in TM in Ag. % 20

0 0 1000 2000 3000 4000 5000 6000 7000 8000 NDVI-AVHRR

Figure 4: A regression relationship between the AVHRR-NDVI and TM-Ag. % for the same area.

Table (1): Calculations of the areal coverage of agricultural lands (in acres) from the AVHRR-NDVI images. Year Mean Number Total Equation % Ag. AVHRR TM Ag. NDVI of pixels Area km2 Areas Ag. Area Area 1984 1996 1056 67,584 Y = 0.016X – 6.36 25.57 4.11 x 106 4.3 x 106 2003 1970 1056 67,584 Y = 0.016X + 9.77 32.62 5.24 x 106 4.9 x 106

Figure 5: Top: The spatial distribution of mean NDVI along the Nile Delta. NDVI is multiplied by 10000. Middle: COV (mean/stdev) values of the Nile Delta from AVHRR images. Bottom: VCI of the Nile Delta from AVHRR images.

2300 y = -0.7135x + 2204.2 R2 = 0.0222

2200

2100 Mean NDVIx10000

2000

84 85 6 87 88 89 90 91 92 3 4 5 6 7 98 9 0 01 02 03 19 19 198 19 19 19 19 19 19 199 199 199 199 199 19 199 200 20 20 20 Figure 6: The mean annual NDVI values of the Nile Delta 1984-2003.

Figure 7: Pixel-to pixel subtraction of the 1984 and 2003 AVHRR images. Green refers to increase above 10%, red refers to decrease below 10% and black refers to no change.

Figure 8: Pixel-to pixel subtraction of Jan. to Dec. of the 1984 and 2003 AVHRR images. Green refers to increase above 10%, red refers to decrease below 10% and black refers to no change.

PC_1 PC_2 PC_3

PC_4 PC_5 PC_6

Figure 9: The multitemporal principal component analysis of the 480-laye image of the Nile Delta. The circles refer to areas increased in their long-term NDVI.

Figure 10: The locations of the selected pixels.

8000

6000

4000

NDVIX10000 2000

0 Middle North West East

Figure 11: The average 20 year NDVI for the four pixels.

8000 Middle North East West

6000

4000 NDVI x 10000 2000

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Figure 12: The annual NDVI cycle of the four selected pixels.

APPENDIX C

A CHANGE DETECTION STUDY OF THE GREATER CAIRO AREA AND ITS

VICINITY USING LANDSAT REMOTE SENSING: A CASE STUDY OF

AGRICULTURAL LAND DWINDLING

I-INTRODUCTION

Land use and land cover of the earth’s surface are dynamically changing as a result of natural factors and/or anthropogenic activities. When there is a lack or contradiction of other data sources, remote sensing becomes a unique and powerful tool

in mapping and monitoring land use/cover change. Moreover, information obtained by

remote sensing can cover large spatial extents with lower costs than the traditional

ground survey analysis (Martin and Howarth 1989). Land use/cover classification and

surveys using remote sensing have been conducted successfully in several areas (e.g.

Lyon et al. 1998, Baban and Yusof 2001, Yuan et al. 2005). The magnitude of change can be addressed by analyzing a time-series satellite images. The basic principle behind using digital data in change detection is that any change in land cover results in a change in the radiance value that is separable from the change caused by other factors, e.g. illumination and sensor view angles, atmospheric conditions and soil moisture status

(Mass, 1999). Generally, Change detection by remote sensing is widely used for environmental monitoring and agricultural applications (Fischer, 1995).

To obtain accurate change detection results, the acquired digital data should be acquired by the same sensor, spatial, spectral, radiometric resolutions, same season and 89

acquisition time (Lillesand and Kiefer, 2000). The most utilized sources of digital data

for change detection are Landsat MSS, Landsat TM, SPOT and MODIS. To attain a better understanding of the process of land use/cover changes, the research should answer at least one of the following questions (Lambin 1997):

1- Where do land use/cover changes occur?

2- At what rate do land use/cover changes progress?

3- What are the natural and/or socio-economic (anthropogenic) variables driving the

land use/cover change?

Several change detection techniques using digital images have been reported in the literature. Actually, there is no one standard method that can work for all change detection studies; one approach could work better for a given area than another.

However, the major and most common schemes are: 1- the pre-classification approach, which includes composite analysis; image differencing; image ratio; principal component analysis and change vector analysis and 2- the post-classification comparison method

(Lunetta and Elvidge, 1998). Recent work has identified that image differencing is the most accurate approach in change detection studies (Petit et al, 2001). However, this method only delineates the change/no change areas and does not provide a detailed change detection matrix (Lu et al. 2004). Singh (1989) mentioned that image ratio and vegetation index differencing are important techniques in digital change detection. Lyon et al. (1998) used vegetation indices differencing as an approach for change detection of a vegetated land cover in Mexico. Principal component analysis is one of the most useful methods in multi-temporal digital change detection. Byrne et al. (1980) used PCA for 90

monitoring land cover change in the South Coast of New South Wales. Li and Yeh

(1998) applied principal component analysis on multi-temporal TM images for monitoring urbanization in the Pearl River Delta, China. On the other hand, a post- classification change detection approach involves a complete change detection matrix.

Among the studies using post-classification comparison are Petit et al. (2001) and

Muttitanon and Tripathi (2005). Mundia and Aniya (2005) applied a post-classification approach to map land use/land cover change regarding the urban expansion in Nairobi

City, Kenya as obtained from MSS, TM and ETM+ satellite images.

In Egypt, One of the most critical problems concerning land use/cover change is the encroachment of urban areas upon productive agricultural lands. Besides the Nile water, arable soils and agricultural lands are absolutely the major natural resources of the country. Population now exceeds 70 million and it is expected to reach 111 million by

2030 (Brown and Kane, 1994). Only less than 5% out of the one million square kilometers of Egypt’s area is inhabited by people (Sultan et al. 1999). This situation exerts high pressures upon agricultural lands. Daniels (1983) mentioned that about

20,000 acres of prime arable land may be lost annually to urban encroachment.

According to Sultan et al. (1999) some urban areas of the Nile Delta have increased by

58% since 1972 and Egypt could lose 12% of its total agricultural area due to urbanization by 2010. Lawrence et al. (2002), using Landsat and night-time images, estimated the urban area of Egypt to total 3.5%. They mentioned that over 30% of arable soils are under urban land-cover. Fahim et al. (1999) estimated the rate of annual urban growth of two cities in the Nile Delta; Tanta and El-Mahala El-Kobra. They showed that 91

the annual growth rate was 18.8% for the former and 32.8% for the latter for the period

1987 to 1995.

Greater Cairo is clearly suffering from urban encroachment upon fertile agricultural lands. Urban areas have doubled in the last three decades. According to

Sutton and Fahmi (2001), a master plan was issued and approved by the Egyptian

Government in 1983 for restructuring the Greater Cairo area into 16 homogenous

settlements sectors. This scheme aimed to protect agricultural lands around Cairo, to

sustain a growing population, and to upgrade the public infrastructure. Although new

cities, such as the 10th of Ramadan and 6th of October, were established to transfer people

from the central city to the neighboring desert, agglomeration and urban sprawl are still going forward. Moreover, the majority of the new settlements failed to attract people out of the capital. This situation made Cairo one of the cities with highest populated densities in Egypt. In spite of that, this research suffered from scanty data, particularly population statistics and urban changes mapping. However, this study provides a new and confident approach for monitoring urban and agricultural area changes from space instead of ground survey. The results will be of interest to the government and to the regional planners. The main objectives of the present study are: to map, based on remote sensing, the different land use/cover units in Greater Cairo area and to quantify the spatial and temporal changes that have occurred on land use/cover in the past three decades with a focus on agricultural and urban areas in light of the proposed restructuring plan of the

Greater Cairo area and its vicinity.

II-MATERIALS AND METHODS

Study Area Cairo, the capital of Egypt, is one of the biggest cities worldwide. The city

encompasses the majority of the country’s economy and cultural centers and about one

fifth of the Egyptian population lives in Cairo. The population growth of Cairo has

doubled several times. At the end of the past century Cairo was the home for about 17

million people and now there is a baby born every 29 seconds (Bhatia, 1994; Sutton and

Fahmi, 2001). Administratively, the Greater Cairo area includes three governorates:

Cairo in the east, Giza in the west, and Qalubia in the north.

The study area covers an area of about 1350 km2 representing the Greater Cairo

area between 31° 05/ 04// E to 31° 30/ 23// E and 29° 55/ 25// N to 30° 13/ 16// N (Fig. 1).

The climate is generally Mediterranean with hot summers and mild winters. Summer

temperatures approach 32° and winter temperatures fall to 20°. Cairo receives a rainfall

of only 22 mm/year (Beaumont, 1993). Precipitation generally occurs between October

and May. The mean potential evapotranspiration exceeds 1140 mm/year (Beaumont et al.

1988). Wind blows mostly from the north and northwest during summer and mostly from south and southwest during winter. The main land cover features of the study area are: 1-

agricultural lands on the fluvial Holocene deposits of the River Nile, 2- barren desert

areas of the Eocene and Oligocene plains covered by sediments of gravel and pebbles

(Abu Al-Izz 1971), 3- Water bodies (the Nile), and 4- the urban areas.

Satellite Data To generate a time series study for land use/cover change in the Greater Cairo region, we used a set of five Landsat images acquired between 1972 and 2003. All the images have high quality and are free of clouds (Table 1). The MSS image of 1972 and the TM image of 1984 were obtained from the Global Land Cover Facility, University of

Maryland (http://glcf.umiacs.umd.edu/portal/geocover/). They were orthorectified to the

Universal Transverse Mercator Projection (UTM / zone 36, WGS 84). The other three

TM images were purchased from the United States Geological Survey (USGS). To ensure a better discrimination of all the land cover classes, all the images were acquired in the same season to reduce sun elevation variations. In addition, these images were obtained in late summer, a season of peak growth of summer crops in the Nile Delta, to better magnify the spectral signature of agricultural land from other land cover classes and to ensure that there were a very limited number of fallow fields. Topographic maps

(1:50,000), and a geologic map of Cairo were used as ancillary data.

Atmospheric Correction and Radiometric Normalization All the image processing techniques were applied using ERDAS Imagine 8.7 software. We followed the same procedures as described in Appendix A. Atmospheric and radiometric corrections for TM images were done using the COST Model which is based on Chavez’ (1996) enhanced dark-object subtraction. The inputs to this model were sun-earth distances, sun elevation angle, and the minimum digital number values (DN) of each image (bands 1-5 and 7). The output image was in reflectance units ranging from 0 to 1. For the MSS image, the COST Model was not suitable, thus the dark object subtraction approach was applied to the image (bands 1-4). 94

Geometric Rectification and Resampling The TM Image of 1984 was used as the master image from which all the other

images, including the MSS image, were rectified through image-to-image registration to the Universal Transverse Mercator (UTM / zone 36 WGS 84) using a first order polynomial transform. For each image, at least 20 prominent well distributed ground control points (GCP) were chosen and a nearest neighbor resampling method was applied. The root mean square error (RMSE) was less than 0.5-pixel for each registered

TM image and <1.0 pixel for the MSS one. The MSS image was resampled to have a pixel size of 28.5 m. After geometric correction and resampling, a subset of each image covering the study area (1429x1169 pixels) was generated for consequent image processing (Fig. 2).

Image Classification and Masking For image classification, the thermal band (TM-band 6) was eliminated from the analysis. All the four MSS bands were used in the classification. Unsupervised classification, a mathematical separation of pixels into natural groupings based upon the

spectral behavior of these pixels (Jensen, 1995), was applied. This classification scheme

is simple and accurate. We started the unsupervised classification requesting fifty clusters

in each image. The ISODATA (Iterative Self-Organizing Data Analysis) algorithms in

ERDAS Imagine were used in the clustering process. The original output clusters were

then recoded into four land cover classes based on previous field experience and on the

spectral signature curves and NDVI values of each land-cover. These land cover classes

were agricultural lands, urban areas, bare areas, and water bodies represented by the Nile

(Table 2). Several iterations (24) were applied in the classification process to improve the 95

output classification units. Finally, each classified and recoded image was filtered

through a 3x3 majority filter to recode isolated pixels classified differently than the bulk

of the pixels in the window. We prepared a land-cover map for each of the five images.

After classification, two mask images showing urban and agricultural lands independently were prepared. The mask image is a binary image of two values; zero and one.

Accuracy Assessment We depended on reference pixels in each image as the basis of accuracy assessment. For each classified image, a total of at least 100 pixels were selected in the corresponding 6-band image, or the 4-band MSS image, based upon a stratified random approach (the number of reference points in each class correlates with the area covered by that class). A classification accuracy matrix was prepared for each classified image based on an unbiased visual inspection of the false color composite image. The results provide the user’s accuracy, producer’s accuracy, overall accuracy and the overall kappa coefficient.

Change Detection a) Post-Classification Change Detection

For quantitative change detection, a post-classification approach was applied. In this scheme, the change of each class at each date is addressed. This approach is advantageous as it could be applied to data of different sensors, it is easy to understand and implement and is often the most reliable approach (Weismiller et al. 1977 and Jensen

and Narumalani, 1992). It also provides a complete change matrix. The accuracy of the

change, however, is based on the classification accuracy (Jensen, 1995). The magnitude

and location of change (‘from-to’ change detection matrix, Jensen, 1995) were

determined for the entire study period (1972-2003). The major land cover changes were

color coded. These changes are: from agricultural to urban, from bare areas to urban,

from bare areas to agricultural areas, from water to agricultural areas and from water to

urban areas.

b) Image Differencing

This approach involves the subtraction of two images, on a pixel-to-pixel basis, of

the same sensor, spatial, spectral, and radiometric resolutions. The pixels of change are

distributed in the two tails of the histogram of the change image. Recent studies showed

that image differencing is the most accurate change detection technique (Singh, 1989).

Change in the visible portion (brightness) detects the conversion of rural-to-urban land;

near-infrared differences (greenness) describe the changes from vegetation to non-

vegetation; and the change in the shortwave-infrared (SWIR) reflects change in moisture

content (wetness) (Lunetta and Elvidge 1998). Since we have satellite images from two 97

different sensors (MSS and TM) and with different spatial and spectral resolutions, we applied image difference change detection for the earliest and latest TM images only, i.e. the TM of 1984 and 2003. The NDVI difference image was created using the 1984 and

2003 TM images. The NDVI is the normalized ratio of the red and near infrared reflectance. The equation used for NDVI is: ρ NIR - ρ Red (Weiss et al. 2004). ρ NIR + ρ Red Theoretically, values of NDVI fall between -1 to +1. NDVI values close to 1 refer to high biomass of green vegetation. For NDVI image difference, we used thresholds of change of 10%, 15%, 20%, and 30% as well as a standard deviation of 1, 1.5, 2 and 3. c) Principal Component Analysis (PCA)

The principal component analysis is a common data transformation technique for the compression of information content of a number of bands into just two or three principal components (Jensen 1995). PCA is a linear transformation which defines a new orthogonal coordinate system in which the data are decorrelated (Wiemker et al. 1997).

PCA highlights the areas of temporal change but does not provide information about the type of change. The challenge in PCA is how to grasp the change and interpret the meaning behind this change. In this research, PCA was applied to the six-band TM images of 1984 and 2004 separately and then for a twelve-band image consisting of both

1984 and 2004 images together. The eigenvalues of each image as well as the composite

(twelve-band) image were calculated and compared. The areas of temporal change were identified. The entire image processing scheme is shown in Figure (3).

III-RESULTS AND DISCUSSION

Spectral Signature Curves and the Normalized Difference Vegetation Index (NDVI) The spectral relationship and signature curves of the four different land use/cover

classes of the Greater Cairo area (agricultural lands, urban areas, bare desert areas and

water bodies) are shown in Figure (4). Bare areas have the highest reflectance in all TM

bands, whereas water bodies (the River Nile) represent the lowest reflectance values in all

bands. Green vegetation has its unique spectral signature, where red color is mostly

absorbed and the near infrared (NIR) has the peak reflectance. Urban areas are similar but

have lower reflection than bare areas.

The NDVI is directly related to the photosynthetically active radiation within a

pixel (Tucker et al. 1981). Due to their high NDVI values, agricultural lands could be

easily distinguished in areas of agricultural and non agricultural land cover. The mean

NDVI values for the four land cover classes in all images of the Greater Cairo area are shown in Figure. (5). Agricultural areas have the maximum NDVI values (up to 0.65), while water bodies have values as low as -0.2. Urban and bare areas have average NDVI values of 0.022 and -0.022, respectively. The NDVI value of water is not as low as expected because the Nile is shallow (up to 3-m depth) and the water is not very clear.

Image Classification and Masking We obtained five land cover maps covering the entire 31 years of study. Each map represents the distribution of agricultural, urban, bare areas and water (Figs. 6&7).

Masked images for urban and agricultural lands are shown in Figures (8&9).

Accuracy Assessment

a) Sample Size for Accuracy Assessment

Some analysts suggest that there should be a defined sample size needed to

compute the overall accuracy (Jensen, 1995). One of the equations used to determine the

number of points in the sample size is the Fitzpatriick-Lins (1981) equation, in which: Z2 (p) (q) N = E2 Where, p is the expected percent of accuracy, q = 100 – p, E is the allowable

error, and Z = 2 (standard normal deviate for the 95% two-tail confidence level, 1.96). In

this research, we expect an accuracy of at least 90% for all five land-cover maps with an allowable error of 6%, thus the number of points needed in each image sample size is:

(22*90*10)/6*6 = 100 points. So that one hundred points were chosen randomly (based

on stratified random approach) for accuracy estimation.

b) Accuracy Assessment of Land Cover Maps

The accuracy of each of the five land cover maps obtained from the unsupervised

classification was higher than the expected accuracy. The overall accuracy was the

highest for the land cover map of 1972 (94%) (Tables 3 to 7). The lowest map accuracy

was in 1984 (92%). Land-cover maps of 1990, 1998 and 2003 had overall accuracies of

93%, 93.45% and 93%, respectively. The overall kappa statistics of the five maps was

also the highest for the 1972 derived map, whereas the lowest was for the 1998 map

(0.85). The overall kappa statistics for the 1984, 1990, and 2003 land-cover maps were

0.87, 0.89 and 0.89, respectively. The producer’s accuracy denotes the percentage of

actual reference pixels that were properly identified. On the other hand, user’s accuracy 100

refers to the percentage of classified pixels that represent any category on ground. In all the land cover maps, water has the highest producer and user accuracies (100%), whereas urban areas have the lowest producer and user accuracies. The absolute lowest producer and user accuracy was for the urban class (81.25%) for the 1982 map. c) Allowable Errors (E)

After classification, the allowable errors for the classified images of 1972 (94%),

1984 (92%), 1990 (93%), 1998 (93.45%) and 2003 (93%) were estimated using the Z2 (p) (q) Fitzpatriick-Lins (1981) equation: E2 = ( ) 1/2 N E for land cover map of 1972 = [(2*2*94*6)/100] 1/2 = 4.74

E for land cover map of 1984 = [(2*2*92*8)/100] 1/2 = 5.42

E for land cover map of 1990 = [(2*2*93*7)/100] 1/2 = 5.10

E for land cover map of 1998 = [(2*2*93.45*6.55)/100] 1/2 = 4.94

E for land cover map of 2003 = [(2*2*93*7)/100] 1/2 = 5.1

Therefore, the overall accuracy for each land cover map at 95% confidence interval is as following:

For land cover map of 1972, the overall accuracy = 94 ± 4.74,

For land cover map of 1984, the overall accuracy = 92 ± 5.42,

For land cover map of 1990, the overall accuracy = 93 ± 5.10,

For land cover map of 1998, the overall accuracy = 93.45 ± 4.94, and

For land cover map of 2003, the overall accuracy = 93 ± 5.10.

101

Change Detection a) Post-Classification Change Detection

Land Cover Change of 1972-1984 In 1972, the surface water, urban, agricultural, and bare areas constituted 2.11,

16.11, 34, and 47.51% of the study area (Table 8). Twelve years later, all the land

use/cover classes’ areas, except urban centers, had decreased. Most losses were in the

agricultural land, where about 7% (8,036.25 acres) had converted to urbanization. This

translates into an annual loss of 669.68 acres over this period. The areas of agricultural change were on the peripheries of Cairo City and around the urban centers to the north.

Bare areas lost only 3.21% to urbanization. The majority of this loss was due to the

building of many settlements in the eastern Cairo desert and the construction of many roads. The total urban centers increased by 31.96% of its original status in 1972

(17,042.61 acres increase). Water losses (7.59%) were due the construction of many

bridges and river bank armoring. The majority of the bridges crossing the Nile were

constructed in late 1970s and early 1980s.

Land Cover Change of 1984-1990 Between 1984 and 1990, a great deal of infrastructure was constructed to provide

for the huge urban expansion of Greater Cairo, which increased by an additional

26,670.04 acres over the area of 1984 (70,364.90 acres). A ring road was established in

late 1980s and started at the east of Cairo. Although the total loss of agricultural areas

was less than during the previous period, the annual loss rate was greater than that period

(1,033 acres/year) (Table 9). Bare desert areas lost 13.44% (20,458.46 acres) to

urbanization. This was the greatest urbanization into bare desert. Water loss was minimal 102

(0.11%). The areas added to the urban corridors from bare desert amounted to about three

times as from the agricultural lands.

Land Cover Change of 1990-1998 Urban areas increased by 13,300.92 acres from 1990 to 1998. The majority of this

increase was cut from agricultural fields on the fringes of Cairo City. Agricultural loss

rate was estimated at 1,366.9 acres/year. The ring road was completed around the

northern periphery of the city and was mostly established on agricultural lands. On the

other hand, urban expansion into bare areas was only 1,975.40 acres. Surface water lost

404.03 acres (Table 10). The loss of water was mostly due to the classification of some water as agricultural areas because some water plants e.g. water hyacinth (Eichhornia crassipes) flourished on the margins of the Nile.

Land Cover Change of 1998-2003 From 1998 to 2003 urbanization and built-up areas increased by 12,290.90 acres.

This can be attributed to the construction and expansion of new settlement communities.

Agricultural loss rate to urbanization (1,413.36 acres/year) was greater than urbanization in bare desert areas (937.5 acres/year). The total loss of bare desert areas to urbanization

was 4,143.69 acres. The surface water area decreased by 2.4% as some constructions

including filling of the River Nile margins occurred (Table 11).

Land Cover Change of 1972-2003 This map shows the composite change that has occurred over the 31 years. Urban

areas doubled in that period. The total urban area was 53,322.29 acres (213.28 km2) in

1972 and 122,626.80 acres (490.51 km2) in 2003 with an increase of 230%. This figure 103

agrees with what Yin et al. (2005) determined for the total built up area of Greater Cairo using Landsat TM and TM+ assisted by CORONA and IKONOS images. They estimated

the total built-up area in 1999 to be 460.4 km2. On the other hand, this study shows that

agricultural lands shrunk to only 71.57% of their total area (113,382.2 acres in 1972 to

81,145.65 acres in 2003). The annual loss of agricultural areas was estimated in this study

at 1,040 acres. The city’s agricultural fringes were the areas most exposed to urban

encroachment. On the other hand, new communities established on the bare desert areas

were 31,632.90 acres. This means that for the 31 years of study, greater Cairo expanded

by 32,236.6 acres into contiguous agricultural areas and by 31,632.9 acres from the

neighbor bare desert area (Table 12). The locations of the land cover change between all classes are shown in Figure (10). b) NDVI Image Difference

The application of the NDVI image difference is widely used in change detection.

It could be used to show the trade-off between urban and agricultural areas of two dates.

Any sudden decrease of NDVI corresponds to change of agricultural to urban areas,

especially agricultural-urban interface (Masek et al. 2000). This change detection method

could be useful in addressing transformation of agricultural lands. In the NDVI change

images, areas coded in red and green were experiencing a decrease and increase in NDVI

values beyond the threshold, respectively, whereas areas of gray and black colors were

not changed. We applied 10, 15, 20 and 30%, and 1, 1.5, 2 and 3 standard deviations as

the basis of the amount of change. It is obvious from Figures (11&12) that the NDVI

values decreased between the images of 1984 and 2003 in the fringes of the city due to 104

the continual urban encroachment upon agricultural fields. Other satellite urban centers

are surrounded by red rings which indicate urban sprawls on agricultural lands. The green

color refers to the increase of NDVI above the threshold value and this is attributed to the

increased agricultural productivity or to the new green belts which were established in

Cairo. Moreover, as urban areas have higher NDVI values than bare areas, the areas of urban expansion into the bare desert appeared in green. These areas are located in the eastern margin of the city. A threshold of 10% and 1-standard deviation in the change images showed clearly the location of urban sprawl upon agricultural land and the new settlements in the neighboring desert. With increasing the threshold (30% and 3 standard

deviation), the new communities and settlements, such as New Cairo City, El-Obour

City, El-Salam City and the new roads, could be identified from the bare desert area in

the change image.

c) Principal Component Analysis

Like image differencing, PCA does not provide detailed change matrixes. The

principal component analysis was applied to the 1984 and 2003 images separately. The

eigenvalues of each image are shown in Tables (13&14). The percent of variation indicates the amount of information contained in each component. As seen in Tables

(13&14), most of the information content is provided by the first components in both

images, where 97% of the information is present in the first component of the 1984-PCA

image and 95% of the information is provided by the first component of the 2003-PCA

image. The information content decreases from the second through the consequent

components until the last one or two components which are just typically noise. 105

The eigenvalues of the stacked twelve-band (1984-2003) image are shown in

Table (15). The first six principal components are shown in Figure (13). In this analysis,

the first component shows the common features of the two images (features that did not change). This component represents 89.8% of the variance between the two images dates.

On the other hand, areas of change are discernible in the second component, where it represents 6.73% of the information. The change features are the darker or the lighter objects in the PCA2 image. Dark objects are those which changed from bare desert to either urban or agricultural lands. Lighter objects refer to the areas changed from agricultural to urban areas (See Fig. 13). PCA3 represents 1.65% of the information and shows the changes that occurred on the water bodies (the Nile and the main canals). The changes occurring on the water body may be attributed to the change of water quality, specially the turbidity and sediment load. PCA4 shows the change around the satellite urban centers between the two dates. The fifth component, PCA5, shows the new green belts (golf courses) constructed in that period.

Comparison of Change Detection Approaches

Comparing the change detection results obtained by the three different change approaches; post-classification, NDVI image difference and principal component analysis, we observed that post-classification approach was superior. It showed the magnitude of change for each land cover map. It had accuracy assessment matrixes dependent on the accuracy of the classified images, and the accuracies of each individual land-cover map is greater than 90%, which shows a high level of precision. However,

NDVI image differencing showed the locations of change accurately. The accuracy of 106

this approach is dependent on delineating the appropriate threshold of change. It is a difficult job to assign the threshold beyond which the change occurred. The principal component analysis was considered the most difficult to utilize. It showed the areas of change but less accurately than the band differencing. The challenge in PCA is to identify and interpret the component which indicates the change. All in all, these three approaches of change detection worked in an integrated fashion.

Driving Forces for Land Use/Cover Change

Socio-economic issues are the most important factors driving the land use/cover change. The annual population growth rate of Cairo (2.74%, Sutton and Fahmi 2001) is the most prominent factor. In addition, internal immigration from rural areas to Cairo exceeded its carrying capacity. Egyptian people find better living standards and services in Cairo. Moreover, Cairo receives 2-3 million people daily from all the country for work and for governmental facilities, as Cairo lies at the heart of Egypt. It is also the place of intersection of the major road network and railroads of the country. These factors, in addition to the rapid economic development, forced the government to construct many new settlements and infrastructures, which prolonged urbanism. In addition, millions of tourists visit Cairo all the year round to enjoy its climate and its hundreds of historical places, which entailed the construction of many roads and facilities. Topographic influences had driven urbanization in just two directions: the northeastern flat desert area and the northern/northwestern agricultural lands. Other directions are occupied by mountains and elevated plateaus. The Giza pyramids lie upon one of these; the western plateau. 107

IV-SUMMARY AND CONCLUSIONS

Remote sensing is an invaluable tool for monitoring spatial and temporal land cover change, particularly when other sources are scant. The Greater Cairo area was monitored by five Landsat images between 1972 and 2003. Based on a post-classification approach, there were four land-cover classes in each image: agricultural, urban, bare areas and water bodies. Accuracy assessment was 94%, 92%, 93%, 93.54 and 93% for the 1972, 1984, 1990, 1998 and 2003 land-cover maps, respectively. The overall accuracy of the five land change maps was 86.62. The study showed severe urban encroachment upon agricultural lands. The rate of agricultural loss was 670 acres/year in

1972-1984, 1033 acres/year in 1984-1990, 1367 acres/year in 1990-1998 and 1413 acres/year in 1998-2003. The overall annual loss rate between 1972 and 2003 was 1040 acres. This figure is a high alarm for Egypt. Urban areas increased from 213 km2 in 1972 to 490.5 km2 in 2003. Urban sprawl during that period was in two directions; to the north

and northwest where crop fields encompass the capital; and to the northeast where bare

areas existed. Bare desert areas decreased by 20.12% from 1972 to 2003. The lowest

decrease was in water bodies. The drivers of these changes are the demographic and the socioeconomic changes occurring in Cairo during this 31-year period. NDVI image

difference of the 1984-2003 images showed that the location of change areas are mostly

similar to those obtained by the ‘from-to’ change matrix. The principal component

analysis transformation of the twelve-band image (1984-2003) provided useful information about the locations of urban change and the new green belts constructed in the area. Post-classification was the most accurate approach used in this study. 108

Comparing image differencing and principal component analysis, the former could discriminate the change areas most easily. This study concludes that Egypt is losing great areas of fertile agricultural lands formed thousands of years ago. Although new communities were constructed in the neighboring desert, agricultural loss was greater than in-desert urbanization.

109

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Figure 1: Location map of Greater Cairo, Egypt.

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Table (1): Landsat images data of the study area. Sensor Acquisition Date Path/Row Spatial Resolution Sun Elevation Angle MSS 31 Aug. 1972 190/39 57 m 55 TM 20 Sept. 1984 176/39 28.5 m 51 TM 4 Aug. 1990 176/39 28.5 m 57 TM 11 Sept. 1998 176/39 28.5 m 54 TM 24 Aug. 2003 176/39 28.5 m 57

115

Figure 2: False color composites (RGB, 432) showing the Greater Cairo area.

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Table 2: Land-cover classification scheme. Land cover classes Description Agricultural areas Crop fields, gardens, golf courses and in-town stadiums and playgrounds. Urban areas Residential, industrial, commercial, transportation, airports, and mixed. Bare areas Deserts, the pyramids plateau, mountains, valleys, roads under construction, and bare river islands. Water bodies The River Nile, the Ismailia Canals, and water springs.

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Landsat Landsat Landsat Landsat Landsat MSS image TM image TM image TM image TM image

1972 1984 1990 1998 2003

Atmospheric Correction

Geometric Correction

Subscene

Image Classification Change Detection

Quantitative Qualitative (Magnitude and location) Location of change

Accuracy Assessment

Post-classification NDVI-image difference PCA (All changes) (Loss of agricultural areas) (Minor changes)

Land-Cover Maps for 1972, Land-Cover change 1984, 1990, 1998 and 2003 detection output

Figure 3: Flow chart diagram showing the whole image processing approach used in this study. 118

160 Ag. areas 120

80 Bare areas NIR

40 Urban Water 0 04080120160 Red

0.5 0.4 0.3 0.2

R0.1 e fle c ta n c e 0 B1 B2 B3 B4 B5 B7 Water Agricultural areas Urban areas Bare Areas

Figure 4: Top: Space scatter plot of the different land cover units in the red and near infrared digital numbers. Bottom: Spectral signatures of the four land-cover classes; agricultural, urban, bare areas and water (based on representative pixels at each class).

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Agricultural Urban Bare areas Water 0.8 0.6 0.4 0.2 NDVI 0 -0.2 -0.4

Figure 5: The mean NDVI values of the land-cover classes of the satellite images of Greater Cairo area.

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Figure 6: Land-cover maps of Greater Cairo based on the Landsat images.

121

200000 Water Agricultural areas Urban areas Bare areas

150000

100000 Area (acres)

50000

0 1972 1984 1990 1998 2003 Year

Figure 7: Summary of land-cover change maps between 1972-2003.

122

100% 132% 182%

206% 230% Figure 8: Change in urban areas in the study area between 1972 and 2003.

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Figure 9: Change in agricultural lands in the study area between 1972 and 2003.

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Table (3): Accuracy assessment of the 1972 land-cover. Reference data Water Agricultu Urban Bare Raw Users Kappa ral areas areas areas total Accuracy% Water 2 0 0 0 2 100.00 1.00 Agricultural areas 0 33 1 0 34 97.06 0.95 Urban areas 0 2 13 1 16 81.25 0.77 Bare areas 0 0 2 46 48 95.83 0.92 Column total 2 35 16 47 100

Classified image Classified Producers 100.00 94.29 81.25 97.87 Accuracy% Overall accuracy 94.00% Overall kappa 0.90

Table (4): Accuracy assessment of the 1984 land-cover. Reference data Water Agricultu Urban Bare Raw Users Kappa ral areas areas areas total Accuracy% Water 2 0 0 0 2 100.00 1.00 Agricultural areas 0 29 0 1 31 96.77 0.95 Urban areas 0 3 18 0 21 85.71% 0.81 Bare areas 0 0 4 42 46 91.30% 0.88 Column total 3 32 22 43 100

Classified image Classified Producers 100.00 90.91 81.82 97.67 Accuracy Overall accuracy 92.00% Overall kappa 0.87

Table (5): Accuracy assessment of the 1990 land-cover. Reference data Water Agricultu Urban Bare Raw Users Kappa ral areas areas areas total Accuracy% Water 2 0 0 0 2 100.00 1.00 Agricultural areas 0 28 2 0 30 93.33 0.90 Urban areas 0 3 25 1 29 86.21 0.80 Bare areas 0 0 1 38 39 97.44 0.95 Column total 2 31 28 39 100

Classified image Classified Producers 100.00 90.32 89.29 97.44 Accuracy% Overall accuracy 93.00% Overall kappa 0.89

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Table (6): Accuracy assessment of the 1998 land-cover. Reference data Water Ag. Urban Bare Raw Users Kappa areas areas areas total Accuracy% Water 2 0 0 0 2 100.00 1.00 Agricultural areas 0 25 4 0 29 85.71 0.82 Urban areas 0 0 27 3 30 87.10 0.80 Bare areas 0 0 2 37 39 94.87 0.91 Column total 2 25 33 40 100 Classified image Classified Producers Accuracy% 100.00 100 81.82 92.50 Overall accuracy 93.45% Overall kappa 0.85

Table (7): Accuracy assessment of the 2003 land-cover. Reference data Water Agricultu Urban Bare Raw Users Kappa ral areas areas areas total Accuracy% Water 2 0 0 0 2 100.00 1.00 Agricultural areas 0 24 0 0 24 100.00 1.00 Urban areas 0 2 32 3 37 86.49 0.79 Bare areas 0 0 2 35 37 94.59 0.91 Column total 2 26 34 38 100

Classified image Classified Producers 100.00 92.31 94.12 92.11 Accuracy% Overall accuracy 93.00 Overall kappa 0.89

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Table (8): Summary of change in land-cover between 1972 and 1984 in acre. 1972 1984 1984-1972 % Water 6989.76 6459.08 -530.68 -7.59 Agricultural areas 113382.25 105346.00 -8036.25 -7.08 Urban areas 53322.29 70364.90 17042.61 31.96 Bare deserts areas 157215.32 152160.00 -5055.32 -3.21

Table (9): Summary of change in land-cover between 1984 and 1990 in acre. 1984 1990 1990-1984 % Water 6459.08 6451.65 -7.43 -0.11 Agricultural areas 105346.00 99147.62 -6198.38 -5.88 Urban areas 70364.90 97034.94 26670.04 37.90 Bare deserts areas 152160.00 131701.54 -20458.46 -13.44

Table (10): Summary of change in land-cover between 1990 and 1998 in acre. 1990 1998 1998-1990 % Water 6451.65 6047.62 -404.03 -6.26 Agricultural areas 99147.62 88212.48 -10935.14 -11.03 Urban areas 97034.94 110335.85 13300.92 13.71 Bare deserts areas 131701.54 129726.14 -1975.40 -1.50

Table (11): Summary of change in land-cover between 1998 and 2003 in acre. 1998 2003 2003-1998 % Water 6047.62314 5902.30838 -145.3148 -2.40 Agricultural areas 88212.4845 81145.6509 -7066.834 -8.01 Urban areas 110335.854 122626.794 12290.9 11.14 Bare deserts areas 129726.143 125582.464 -4143.679 -3.19

Table (12): Summary of change in land-cover between 1972-2003 in acre. 1972 2003 2003-1972 % Water 6989.761 5902.308 -1087.45 -15.56 Agricultural areas 113382.2 81145.65 -32236.6 -28.43 Urban areas 53322.29 122626.8 69304.5 129.97 Bare deserts areas 157215.3 125582.5 -31632.9 -20.12

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Figure 10: Land use/cover change of Greater Cairo 1972-2003.

128

Figure 11: Change detection by NDVI image difference (1984-2003) using 10, 15, 20 and 30% change.

Figure 12: Pixel to pixel change detection of NDVI between 1984-2003 images using different standard deviations, A: 1, B: 1.5, C: 2 and D: 3.

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Table (13): Eigenvalue of the twelve components of the TM images (1984) of Greater Cairo. Component Eigenvalue Percent of Eigenvalue PC1 12363.45 97.19662 PC2 204.1306 1.604795 PC3 112.5154 0.884552 PC4 23.88234 0.187754 PC5 13.63561 0.107198 PC6 2.425361 0.019067

Table (14): Eigenvalue of the twelve components of the TM images (2003) of Greater Cairo. Component Eigenvalue Percent of Eigenvalue PC1 8313.844 95.43673 PC2 270.8517 3.109176 PC3 98.34641 1.128943 PC4 15.78655 0.181218 PC5 10.22575 0.117384 PC6 2.313174 0.026554

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Table (15): Eigenvalue of the twelve components of the TM images (1984-2003) of Greater Cairo. Component Eigenvalue Percent of Eigenvalue PC1 19715.9 89.83522 PC2 1478.171 6.735265 PC3 363.3273 1.655496 PC4 169.0496 0.770272 PC5 93.97262 0.428139 PC6 57.97501 0.264134 PC7 27.56636 0.125592 PC8 17.8409 0.081283 PC9 10.06747 0.045867 PC10 8.227974 0.037487 PC11 2.796926 0.012743 PC12 1.841467 0.00839

131

Urban encroachment

Urban expansion

Green belts

PC1 PC2

Expanded Urban centers

The Nile

PC3 PC4

Golf courses

PC5 PC6

Figure 13: Multitemporal Principal Components of the TM 1984-2003 image.

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APPENDIX D

SOIL SALINITY PROBLEMS IN THE NILE DELTA

I- INTRODUCTION

According to the FAO (1988), salt-affected soils are defined by the accumulation of salts in the root zone area of plants to the limit which interferes with their natural growth. They occur in all the continents and under almost all climatic conditions.

However, their distribution is more extensive and obvious in arid and semi-arid regions.

According to the US Salinity Laboratory Staff (1954), saline soils are those in which their saturated extracts have an electric conductivity, ECe, of more than 4 ds/m at 25°C and their exchangeable-sodium-percentage (ESP) is less than 15%. The excessive increase of salts in the soil adversely affects plant growth either by inhibiting plant water uptake or by damaging the cell structures in the root area (Greenway and Munns, 1980).

Soil salinization is the major motivation of land degradation, losing productive soils, and the most direct path to desertification, particularly in arid and semi-arid regions

(Dregne, 2002, Kassas, 1987, Le Houerou, 2002). Arid regions (26% of the world area) are those in which the ratio of precipitation to potential evapotranspiration (aridity index) ranges from 0.05 to 0.20 (Dregne et al. 1991).

II- THE EXTENT AND CAUSES OF SOIL SALINITY

The problem of soil salinity in Egypt is ancient. Waterbury, (1979) mentioned that

1.5 million acres of agricultural land were lost to saline soils between the end of the

Roman era and the beginning of Islamic era. Soil salinization was triggered by the 133

erection of the High Dam at Aswan, which prevented leaching of salts out of the soil

profile (FAO, 1986). Lenney et al., (1996) using Landsat imagery, estimated the reduction of agricultural productivity, due to high soil salinity, alkalinity, and water- logging in the Nile Delta area to be 3.74% between 1984 and 1993. Irrigation with little and/or poor quality water is the most prominent source of salinization (Chhabra, 1996).

By the year 2017, irrigation water will add 40.4 million tons of salts to the Egyptian soils each year (Gad and Abdel Samie, 2000). Aboukhaled et al., (1975) estimated the percentage of the salt-affected soils to the total cultivated lands to be 60% in northern delta, 25% in the middle delta, and 20% in southern delta. On the other hand, Abdel

Hamid et al., (1992), stated that 80% of the salt-affected soils are located in the northern part of Egypt (Nile Delta), around the northern lakes. Younes et al., (1993) attributed the increasing of salt levels in the Egyptian soils to the perennial irrigation, which was introduced after the construction of the Aswan High Dam, without complementary drainage. In this system, the groundwater becomes higher throughout the year, due to inadequate drainage, and the salts are not flushed out of the soils. Also, in some areas, over-irrigation combined with inadequate drainage resulted in increased soil salinity and higher water tables. According to Fathi and Soliman (1977), perennial irrigation has resulted in increasing the salinity of irrigation water.

Groundwater quality has a triggering impact on soil salinity, particularly along the coastal part of the Nile Delta. The salinity of groundwater aquifers was estimated at

5000 mg l-1 in some regions near the Mediterranean coast and was mainly below

1500 mg l-1 throughout the country (RIGW, 1992). 134

Kotb et al., (2000): attributed the problem of soil salinity in the Nile Delta to three reasons: 1- seawater intrusion, 2- irrigation with low quality water, and 3- inadequate field drainage. Hamdi et al., (1996) using Landsat TM images observed that the extremely and highly saline soils are located in the northern and northeastern delta in

Kafr El-Sheikh and Dakahliya Governorates.

III- CONTROL MEASURES FOR SOIL SALINITY

Control measures to diminish salt accumulations in the soil profile depend mainly on flushing salts with excess fresh water and implementing an enhanced drainage system through the surface and/or subsurface means (FAO 1988). Kotb et al., (2000) suggested that there should be a government role in establishing an effective drainage system in agricultural lands besides irrigation with high quality water. In some areas where water quality is not adequate, he encourages cultivating rice. According to El-Quosy (1994), rice is cultivated in about 50% of the Nile Delta. This crop can live in flooded and heavy soils, such as those in the delta. Rice is also an important economic cash crop in this area.

The natural vegetation can also play a role in the management of saline soils. In northern Egypt, Ghaly, (2002) studied the effect of natural grasses namely Ghab

(Phragmites australis) and Nisela (Panicum repens) compared to traditional leaching and gypsum applications on reducing the salinity and alkalinity of heavy clay texture saline soils. It was found that these grasses reduced the salinity of highly saline alkali soil and converted them to non-saline non-alkali soil within two years. Helalia, et al. (1992) compared cultivation of Amshot grass (Echinochloa stagninum) with ponding and gypsum addition on reducing the alkalinity and salinity of highly saline-sodic soil in 135

northern Egypt. They found that Amshot grass reduced both the alkalinity and salinity of the soil in this part more effectively. Also, they found that this grass gives a high yield that can be used as animal fodder.

136

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Ghaly, F. M. 2002. Role of natural vegetation in improving salt affected soil in northern Egypt. Soil & Tillage Research, 64: 173–178. 137

Greenway H. and Munns R. 1980. Mechanisms of salt tolerance in non-halophytes. Ann. Rev. Plant Physiol. 31:149-190.

Hamdi, H., El-Khatib, H., Hawela, F., Salem, A. W., Saleh, H., Kamal, H., Khalil, K. I., Fahim, M., and El-Akiaby, A. 1996. Monitoring salinity problems areas. Egypt. J. Soil Sci., 36 (1-4): 345-327.

Helalia, A., E1-Amir, S., Abou-Zeid, S. T., and Zaghloul, K.F. 1992. Bio-reclamation of saline-sodic soil by Amshot grass in northern Egypt. Soil & Tillage Research, 22: 109-115.

Kassas, M. 1987. Seven paths to desertification. Desertification Control Bulletin, 15: 24- 26.

Kotb, H. S., Tsugihiro W., Yoshihiko O. and Kenneth K. T. 2000. Soil salinization in the Nile Delta and related policy issues in Egypt. Agricultural Water Management, 43 (2 ): 239-261.

Le Houerou, H. N. 2002. Man-made deserts: Desertification processes and threats. Arid Land Research and Management, 16: 1-36.

Lenney, M. P., Woodcock, C. E. Collins, J. B., and Hamdi, H. 1996. The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of the Environment, 56:8-20.

Research Institute for Groundwater (RIGW). 1992. Groundwater resources and projection of groundwater development. Water Security Project, National Water Research Centre, Egypt, pp.37.

U. S. Salinity Lab. Staff 1954. Diagnosis and improvement of saline and alkaline soils. USDA, Handbook No. 60.

Waterbury, J. 1979. The Nile stops at Aswan. Part II: International hydropolitics. Northwest Africa series, V. XXII, No. 2, American Universities Field Staff, Hanover, N. H.

Younes, H. A., Gad, A. and Abdel Rahman, M. 1993. Utilization of different remote sensing techniques for the assessment of soil salinity and water table levels in the Serry Command Area, Egypt. Egypt. J. Soil Sci. 33, No.4, pp. 343-354.