MONITORING COASTAL ZONE LAND USE AND LAND COVER CHANGES OF ABU DHABI USING REMOTE SENSING

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1 Photonirvachak Journal of the Indian Society of Remote Sensing, Vol. 34, No. 1, 2006 MONITORING COASTAL ZONE LAND USE AND LAND COVER CHANGES OF ABU DHABI USING REMOTE SENSING M.M. YAGOUB AND GIRIDHAR REDDY KOLAN ** * Geography Program, College of Humanities and Social Sciences, United Arab Emirates University, P.O. Box 17771, Al-Ain, UAE ** Master Degree Program of RS & GIS, College of Humanities & Social Sciences United Arab Emirates University, Abu Dhabi, Corresponding author : myagoub@uaeu.ac.ae ABSTRACT Coastal zones are most vulnerable for landuse changes in this rapid industrialization and urbanization epoch. It is necessary to evaluate land use land cover (LULC) changes to develop efficient management strategies. The main objective of this paper is to evaluate and quantify Abu Dhabi coastal zone LULC changes from 1972 to 2000 using multi-temporal LANDSAT satellite data and digital change detection techniques. Supervised classification coupled with expert visual interpretation techniques were used to produce LULC classified images with an accuracy of 88%. Change detection process was achieved by applying post-classification comparison techniques in ENVI software. From this study it has been observed that the important coastal landuse types of Abu Dhabi coast.i.e. wetlands and woody Vegetation (Mangrove, represented by a single species, Avicennia marina) have been reduced drastically in their extent due to reclamation, dredging, tipping and other anthropogenic activities along the coastal zone. However, it has been observed that there is rapid increase in the man-made plantation and managed vegetation from 1990 to 2000 due to the Abu Dhabi government initiation. This study has given good insight into Abu Dhabi coastal zone changes during last 3 decades. Introduction Coastal zones are high biological productive regions and important components of the global bio system. These zones have wealth of mixed species and genetically diversified habitat and are major carbon sink and oxygen sources. Thus, these zones play a vital role in regulating climate and global ecosystem (Nemani and Running, 1995). Received 10 July, 2005; in final form 28 December, 2005

2 58 M.M. Yagoub and Giridhar Reddy Kolan More than world s half population lives within 60 km of the coast and would rise to almost three quarters by 2020 (Anon, 1992). Abu Dhabi Coastal zone is under increasing anthropogenic stresses due to growing population and urbanization. Thus, there is an urgent need to evaluate land use land cover (LULC) changes to develop efficient costal zone management strategies. The Abu Dhabi coast is extraordinarily shallow and gently shelving with numerous inshore and near shore islands. There are some small, entirely manmade islands also. Most of the coastal zone is affected by reclamation, dredging and tipping activities. Accurate assessment of coastal landuselandcover changes will help in framing better management strategies for future. Remote sensing satellite data provides a synoptic view of the coastal zones (Green et al., 1998; Robinson, 1994; Sathyendranath et al., 2004). The modern scientific technologies of remote sensing and digital image processing are extremely useful in periodic assessment of the coastal LULC changes and analyze them to formulate better management (Klemas, 1986; Specter and Gayle, 1990). There are many case studies that used satellite imagery and digital image processing techniques to map coastal zones, coastal landforms and shoreline conditions (Borges et al., 2004; Konecny, 2003; Voute, 1986). Study Area The study area is located between N, E and 24 57' N, 54 58' E comprises of Abu Dhabi coast, in the United Arab Emirates (Fig. 1). The Arabian Gulf coast of the UAE is about 450 km long. By contrast the Arabian Gulf littoral of the UAE is an exemplary development of active coastal sabkha, surrounded by dune and gravel desert. Mangrove, represented by a single species, Avicennia marina, covers extensive areas although Fig. 1. Location of study area

3 Monitoring Coastal Zone Land Use and Land Cover Changes not continuously. It is known to have been more extensive both historically and in the recent past (Simon, 1995). About 80% of Abu Dhabi emirate population is living near the coast. Abu Dhabi coast consisting of Oil and Gas wells, electricity generation plants, desalination plants, Industrial and Civil Ports, commerce and tourism centers. In the year 2001, the Environmental Research and Wildlife Development Agency (ERWDA) issued the first Abu Dhabi Coastline Oil Spill Protection Priorities Atlas that shows sensitive marine and coastal areas such as mangroves, coral reef, sea grasses, and natural resources (Blasco, 2001). In 2004 ERWDA and Japan Oil Development Co., Ltd (JODCO) carried out a collaborative study to investigate the potential use of satellite imagery (Terra-ASTER) in assessing Abu Dhabi shallow water vegetation types (Khaleej Times, 2004). In 2005, ERWDA held a symposium on integrated coastal zone management and the symposium concluded with a series of recommendations to use remote sensing technology for management and conservation of Abu Dhabi coastal zones. Materials Scene wise Geo-referenced LANDSAT satellite data for years 1972, 1990 and 2000 were obtained from the Global Land Cover Facility at the University of Maryland, USA ( index.shtml). The satellite data sensors characteristics are summarized in Table 1. Methodology The research involved two main steps, in the first step, classification of satellite data for LULC types. The second step concentrated on the change detection Analysis in the LULC types. Analysis of satellite data includes Registration, Classification and Change detection using post-classification comparison (Fig. 2). Satellite data analysis is done by using Environment for Visualizing Images (ENVI) and Spatial Analyst (Arc GIS) Software. Data preparation Acquired satellite data is re-registered using Image to Map registration technique. High resolution vector data (roads and coastline) were used as reference to achieve horizontal accuracy of m. Then each image was cropped to study area using Region of interest (ROI) cropping method. These three cropped images were re-projected to a common projection: Universal Transverse macerator (UTM) with WGS84 datum and Zone 40 North. Table 1: Acquired satellite data sensors characteristics Sensor Characteristics Path Date of Data Source and Row Acquisition Landsat Multi Spatial Resolution: 57 M Path:172 29th Nov Global Land Cover Facility, spectral Sensor Spectral Resolution: 8 bit Row :43 University of Maryland, USA (MSS) Number of Bands: 4 (WRS-1) Landsat Spatial Resolution: 28 M Path:160 28th Aug Global Land Cover Facility, Thematic Spectral Resolution: 8 bit Row-43 University of Maryland, USA Mapper (TM) Number of Bands: 7 (WRS-2) Landsat Enhanced Spatial Resolution: 28 M Path:160 23rd Aug Global Land Cover Facility, Thematic Mapper Spectral Resolution: 8 bit Row-43 University of Maryland, USA (ETM) Number of Bands: 7 (WRS-2)

4 60 M.M. Yagoub and Giridhar Reddy Kolan Classification Fig. 2. Methodology Classification of multi-spectral images is the process of sorting pixels into a finite number of individual classes, or categories of data, based on their data file values (Figs. 3 and 4). There are two methods of classification, (1) Unsupervised and (2) Supervised. Unsupervised classification is based on statistical patterns that are inherent in the image data itself where as supervised classification is based on the cognition and skills of the image specialist. In this study, an integrated approach (Supervised classification + Expert visual Interpretation) was applied to classify images into different classes of LULC. Fig. 3. Landsat satellite data of 2000

5 Monitoring Coastal Zone Land Use and Land Cover Changes Fig. 4. Classified image of 2000 LULC training sets were prepared representing each known LULC category that appears fairly homogeneous on the image. In total nine (9) LULC classes were recognized (Table 2). Using these signature files, supervised classification was done by Minimum Distance statistical processing in ENVI software. Accuracy assessment was done by generating confusion matrix by comparing the classified image and ground truth training sets (Table 3: confusion matrix table of 2000). In the classified images some of the LULC classes (Builtup and Built-up under development) were intermixed and also the industrial areas class not came accurately. To increase accuracy we delineated some portions of these classes by visual interpretation method and then same were conglomerated into the classified images using Spatial Analyst (Arc GIS) software. This integrated approach provided better accuracy (> 88 %) of image classification. Change detection by post-classification comparison Post-classification comparison is the most commonly used quantitative method of change detection (Jensen, 1996; Mas, 1999; Lillesand and Kiefer, 2000) with fairly good results. Postclassification comparison is sometimes referred to

6 62 M.M. Yagoub and Giridhar Reddy Kolan as delta classification (Coppin et al., 2004). It involves independently produced spectral classification results from different data sets, followed by a pixel-by-pixel or segment-by-segment comparison to detect changes in the classes. All the classified images were resampled to 30 m pixel size. Three change maps for , , and were prepared by postclassification comparison method using ENVI and Arc GIS software. Three change matrix tables (Tables 4-6) showing changes from one class to other class were also prepared. These changes were quantified in-terms of area in km 2. Results and Discussion The main objective of this study is to evaluate and quantify landuse landcover changes that have been taken place in Abu Dhabi costal zone from 1972 to 2000 using Remote sensing technologies (Fig. 5). Table 2 shows the LULC changes and areas of each LULC type in km 2. From this table we can observe that in 30 years woody vegetation has reduced from 122 to 27 km 2 and similarly wetlands are reduced from 432 to 168 km 2 this is due to rapid urbanization and industrialization along the coast line. Water class (deepwater) is increased from 1972 to 1990, this is due to deep trenching and dredging activities along the coastal line and same class has been decreased from 1990 to 2000, this is due to man made islands development and reclamation. At the same time the decrease in shallow water can be attributed to the same urban and industrial developmental activities along the coast line. The decrease in wetlands with shrubs / grass class from 1972 to 2000 is due to rapid urbanization and industrialization along the coast line. These Table 2: Change in land use cover classes along Abu Dhabi coast 1972 to 2000 (Areas are given in km 2 ) Abu Dhabi Year-1972 Year-1990 Rate of Year-2000 Rate of Rate of Coastal Landuse- change in % change in % change in % Landcover Classes ( ) ( ) ( ) Waters Shallow waters (Turbidity & sedimentation) Wetlands with Shrubs / Grass Barren Lands Woody Vegetation (Mangroves & Others) Managed Lands (Plantation & Improvements) Built-up Areas Built-up Under Development Industrial

7 Monitoring Coastal Zone Land Use and Land Cover Changes anthropogenic activates had limited entering of high tides and backwaters on to the main land, which are main source of wetlands along Arabian Gulf. Similarly, decrease in woody vegetation (Mangroves and Others) class is also due to the above anthropogenic activities. Barren lands have increased from 1972 to 1990, because most of the wetlands are converted to barren lands due to non-availability of tidal water / backwater and moisture. But from 1990 to 2000 this class decreased due to new settlement and infrastructure developments (urban, suburban Fig. 5. Change detection map of from 1972 to 2000

8 64 M.M. Yagoub and Giridhar Reddy Kolan developments and industrialization). Managed lands (plantation and improvements) class has been increased in its areal extent due to regular social forestry and plantation activities. We can observe a rapid increase in rate of change from 1990 to 2000, which indicates that, plantation and social forestry activates have been taken up more intensively during this period (Ouis, 2002). The last three classes viz. built-up areas, built-up under development and industrial classes have been increased in their areal extent from 1972 to We can observe a rapid increase in the rate of change Ground Truth (Per cent) Table 3: Confusion Matrix of classified 2000 satellite data. Overall Accuracy = (79276/89816) %, Kappa Coefficient = Class Water Shallow- Wetlands Builtup- Industrial Baren- Woody- Managed- Builtup- Total water Areas lands vegeta- lands under tion Unclassified Water Shallow_water Wetlands Builtup_Areas Industrial Baren_lands Woody_vegetation Managed_lands Builtup_under Total Class Commission Omission Commission Omission (Per cent) (Per cent) (Pixels) (Pixels) Water / /9444 Shallow_water / /9899 Wetlands / /4179 Builtup_Areas / /10891 Industrial / /34601 Baren_lands / /7748 Woody_vegetation / /1260 Managed_lands / /2081 Builtup_under / /9713

9 Monitoring Coastal Zone Land Use and Land Cover Changes Table 4: Change Matrix from To Change in areas from one class to other class Areas are given in km 2 LULC From Water Shallow Wetlands Barren Woody Managed Builtup Builtup Industrial LULC waters with Lands Vegetation Lands Areas Under (Turbidity Shrubs / (Mangroves (Plantation Developand Grass and and ment Sedi- Others) Improvementation) ment) Water Shallow waters (Turbidity and Sedimentation) Wetlands with Shrubs / Grass Barren Lands Woody Vegetation (Mangroves and Others) Managed Lands 0.64 (Plantation and Improvement) Builtup Areas 7.51 Builtup Under Development Industrial of built-up areas from 1990 to 2000, which is a period of rapid urbanization. Here it has to be mentioned that the whole development in Abu Dhabi is fuel by the oil revenues, determination of the UAE Government, and above all the guidance of His Highness Sheikh Zayed Bin Sultan Al Nahayan. With the new regulations (June 2005) that free real estate market, it is expected that more residential, commercial, and industrial activities will take place in the future (Gulf News, 2005). Conclusion This study has given good insight into Abu Dhabi coastal zone changes during the last three decades. From this study it has been observed that important costal landuse types like wetlands and woody vegetation (mangroves and others) are drastically reduced. Proper landuse management strategies need to develop to protect these important costal zone landuse types before extinction. Conservation of coastal zones is a priority for all

10 66 M.M. Yagoub and Giridhar Reddy Kolan Table 5: Change Matrix from To Change in areas from one class to other class Areas are given in km 2 LULC From Water Shallow Wetlands Barren Woody Managed Builtup Builtup Industrial LULC waters with Lands Vegetation Lands Areas Under (Turbidity Shrubs / (Mangroves (Plantation Developand Grass and and ment Sedi- Others) Improvementation) ment) Water Shallow waters (Turbidity and Sedimentation) Wetlands with Shrubs / Grass Barren Lands Woody Vegetation (Mangroves and Others) Managed Lands (Plantation and Improvement) Builtup Areas Builtup Under Development Industrial nations, therefore coastal zone spatial and nonspatial database has to be prepared and integrated to facilitate better management and decision making polices. The data-gathering capabilities of space-borne remote sensors have generated great enthusiasm over the prospect of establishing remote sensing based systems for the continuous monitoring of costal zones. Digital image processing techniques, change detection procedures coupled with spatial analysis provide valuable tools for scientists, resource managers and policy makers for efficient mapping and monitoring the coastal zone. Acknowledgements The authors would like to thank the Global Land Cover Facility at the University of Maryland for providing the satellite data ( glcf.umiacs.umd.edu/index.shtml)

11 Monitoring Coastal Zone Land Use and Land Cover Changes Table 6: Change Matrix from To Change in areas from one class to other class Areas are given in km 2 LULC From Water Shallow Wetlands Barren Woody Managed Builtup Builtup Industrial LULC waters with Lands Vegetation Lands Areas Under (Turbidity Shrubs / (Mangroves (Plantation Developand Grass and and ment Sedi- Others) Improvementation) ment) Water Shallow waters (Turbidity and Sedimentation) Wetlands with Shrubs / Grass Barren Lands Woody Vegetation (Mangroves and Others) Managed Lands (Plantation and Improvement) Builtup Areas 7.46 Builtup Under Development Industrial References Anon. W. (1992). Agenda 21, United Nations Conference on Environment and Development, Rio de Janeiro, Brazil, June 3-14, pp Blasco, F.T. (2001). Mapping coastal ecosystem: Why and how. Lecture Notes on Remote Sensing (Abu Dhabi: Emirates Heritage Club). Borges, R., Hernandez-Guerra, A. and Nykjaer, L. (2004). Analysis of sea surface temperature time series of the south-eastern North Atlantic. International J. of Remote Sensing, 25(5): Coppin, P., Jonckheere, I., Nackaerts, K. and Muys, B. (2004). Digital change detection methods in ecosystem monitoring: A review. International J. of Remote Sensing, 25(9): Gulf News (2005). The Free of the Real Estate Market in Abu Dhabi. [Date: 10/09/2005]. Green, E.P., Clark, C.D., Mumby, P.J., Edwards, A.J. and Ellis, A.C. (1998). Remote Sensing techniques for mangrove mapping. International J. of Remote Sensing, 19(5): Jensen. J.R. (1996). Introductory Digital Image Processing: A remote sensing perspective. Upper Saddle River, NJ: Prentice Hall, Inc.

12 68 M.M. Yagoub and Giridhar Reddy Kolan Khaleej Times (2004). Research on Marine and Coastal Areas [Date: 09/06/2004]. Klemas, V. (1986). Remote sensing of coastal resources in developing countries. Proceedings of the conference of Remote Sensing and its impact on Developing Countries, Rome, June 1986: Pontifical Academy of Sciences, pp Konecny, G. (2003). Geoinformation: Remote Sensing, Photogrammetry, and Geographic Information Systems. London: Taylor and Francis. Lillesand, T.M. and Kiefer, R.W. (2000). Remote Sensing and Image Interpretation. 4 th ed., New York: John Wiley & Sons. Mas, J.F. (1999). Monitoring land cover changes: A comparison of change detection techniques. International J. of Remote Sensing, 20: Nemani, R.R. and Running, S.W. (1995). Satellite monitoring of global land cover changes and their impact on climate change. Climate Change, 31: Ouis, Pernilla (2002). Greening the emirates: The modern construction of nature in the United Arab Emirates. Cultural Geographies, 9(3): Robinson, I.S. (1994). Satellite Oceanography: An introduction for oceanographers and remote sensing scientists. Chichester: John Wiley & Sons. Sathyendranath, S., Platt, T., Horne, E., Borstad, G., Stuart, V., Payzant, L., Maass, H., Kepkay, P., Li, W., Spry, J. and Gower, J. (2004). A multispectral remote sensing study of coastal waters off Vancouver Island. International J. of Remote Sensing, 25(5): Simon, Aspinall (1995). A Directory of wetlands in the Middle East: United Arab Emirates. Gland, Switzerland: IUCN-The World Conservation Union Publisher. Specter, C. and Gayle, D. (1990). Managing technology transfer for coastal zone development: Caribbean experts identify major issues. International J. of Remote Sensing, 11(10): Voute, C. (1986). The future generation of resources satellites. ITC J., 4:

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