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Abu Dhabi Island: Analysis of Development and Vegetation Change Using Remote Sensing (1972-2000)
 

Abu Dhabi Island: Analysis of Development and Vegetation Change Using Remote Sensing (1972-2000)

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Presented at the 2nd International Conference on Earth Observation for Global Changes, Chengdu, China. ...

Presented at the 2nd International Conference on Earth Observation for Global Changes, Chengdu, China.

Abdulhakim Abdi & Anand Nandipati

http://www.geospatialtechnologist.com/

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    Abu Dhabi Island: Analysis of Development and Vegetation Change Using Remote Sensing (1972-2000) Abu Dhabi Island: Analysis of Development and Vegetation Change Using Remote Sensing (1972-2000) Presentation Transcript

    • Abu Dhabi Island: Analysis of Development and Vegetation Change Using Remote Sensing (1972-2000) EOGC International Conference May 28, 2009 Chengdu, China Abdulhakim Abdi & Anand Nandipati Erasmus Mundus Masters Programme in Geospatial Technologies ISEGI - IFGI - UJI, 2008 - 2010
    • 2 Contents • Introduction • Data • Methodology • Analysis & Visualization • Conclusion ` 2
    • Objective Use of remote sensing methods and GIS tools to study the change in the landscape of Abu Dhabi Island and surrounding areas brought on by development. 3
    • Introduction The United Arab Emirates’ GDP in 1972 was $1.6 billion and swelled to $ 103 in 2004 (UAE-NMC, 2008). The country had undergone tremendous change over since it got independence in 1971. Several programs have been implemented to “beautify” the desert landscape and included heavy afforestation and agricultural projects. 4
    • Study Area United Arab Emirates Source: Google Maps 5
    • Study Area 1960 6
    • Study Area 1970 7
    • Study Area Present 8
    • Data Landsat 7 WRS-2, Path 160, Row 043 2000-08-23 2000 Landsat ETM+ 1972 Landsat MSS Landsat 1 WRS-1, Path 172, Row 043 1972-11-29 Source: Global Land Cover Facility, UMD, Maryland, USA 9
    • Data Landsat ETM+ Imagery from November 1, 2008 with Scan Line Corrector-OFF 10
    • Methodology Image Classification Definition of Feature Post- Accuracy Mapping Identification Classification Classification Assessment Approach and Selection 11
    • Image Classification Definition of Feature Post- Accuracy Mapping Identification Classification Classification Assessment Approach and Selection 12
    • Mapping Approach Minimum Mapping Unit (MMU) : Pixel Pixel Satellite Sensor Spectral Range Bands Used Resolution L1 MSS multi-spectral 0.5 - 1.1 µm 1, 2, 3, 4 60 meter ETM+ multi- 0.450 – 1.175 L7 1, 2, 3, 4, 5 30 meter spectral µm Satellites and sensors used for the study We selected set of contiguous pixels for our classification Softwares used: ENVI, IDRISI Andes & ArcGIS 13
    • Image Classification Definition of Feature Post- Accuracy Mapping Identification and Classification Classification Assessment Approach Selection 14
    • NDVI Map showing NDVI based on 2000 Landsat 7 ETM+ 15
    • Sample Selection Land Vegetation Shallow Water Deep Water 16
    • Image Classification Definition of Feature Post- Accuracy Mapping Identification Classification Classification Assessment Approach and Selection 17
    • “when sufficient training samples are available and the feature of land covers in a dataset is normally distributed, Maximum likelyhood classification (MLC) may yield an accurate classification result” (Lu,D and Weng, Q, 2007) 18
    • Maximum Likelihood Land Vegetation Shallow Water Deep Water 19
    • Image Classification Definition of Feature Post- Accuracy Mapping Identification Classification Classification Assessment Approach and Selection 20
    • Sieve Majority Analysis 21
    • MLC Sieve Majority Analysis Output 22
    • Image Classification Definition of Feature Post- Accuracy Mapping Identification Classification Assessment Classification Approach and Selection 23
    • The Overall Accuracy of the 1972 image was 99.04% with a Kappa Coefficient of 0.98. The 2000 image produced an Overall Accuracy of 99.47% and a Kappa Coefficient of 0.99. 24
    • Analysis & Visualization 25
    • 26
    • 27
    • Land Cover 1972 2000 28
    • What's the change ? 1972 to 2000 29
    • Vegetation increased by 3700% 17% of existing land was created from shallow and deep water 30
    • 31
    • 32
    • 1972 Land Cover 2000 Land Cover Area (sq km) Percentage based on 2000 Land Cover Area land Land 405 83% Shallow water Land 61 13% Deep water Land 17 4% Land cover changes from 1972 to 2000 Percentage based on 1972 Land Cover 2000 Land Cover Area (sq km) 2000 Land Cover Area Vegetation Vegetation 3 2% Land Vegetation 76 59% Shallow water Vegetation 48 38% Deep water Vegetation 1 1% 33
    • Land Cover 1972 Graphs 29.9% 43.5% Land Cover Area (Sq Km) Land Land 500 Vegetation Shallow water Vegetation 3 26.3% 0.3% Deep water Shallow water 302 Deep water 344 Land Cover 2000 30.2% 42.0% Land Cover Area (Sq Km) Land 483 Vegetation 128 Land 16.7% Vegetation Shallow water 192 11.1% Shallow water Deep water Deep water 347 34
    • Conclusion • The results clearly indicate the permanent alteration of landscape in Abu Dhabi island and surrounding areas. • It is the combination of new technologies and techniques, such as remote sensing methods and GIS tools provides the greatest value to study land cover changes. 35
    • This work was supported by the European Commission, Erasmus Mundus Programme, M.Sc. in Geospatial Technologies, project no.2007-0064 This work could not have been completed without the assistance of Vanessa Joy Anacta and Ashwin Dhakal. We would also like to thank Dr. Mario Caetano, Instituto Geográfico Português and Instituto Superior de Estatística e Gestão de Informação, Universidade Nova de Lisboa for their invaluable support and advice in carrying out this study. Also special thanks to Institute für Geoinformatik, Westfälische Wilhelms-Universität Münster. 36
    • Compare with reclamation in Dubai Graphs n tables in same page Kappa 謝謝 Thank you for your attention 37