Presented at the 2nd International Conference on Earth Observation for Global Changes, Chengdu, China. Abdulhakim Abdi & Anand Nandipati http://www.geospatialtechnologist.com/

1. 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. 2
Contents
• Introduction
• Data
• Methodology
• Analysis & Visualization
• Conclusion `
2

3. 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

4. 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

5. Study Area
United Arab Emirates
Source: Google Maps
5

6. Study Area
1960
6

7. Study Area
1970
7

8. Study Area
Present
8

9. 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

10. Data
Landsat ETM+ Imagery from November 1, 2008 with Scan Line Corrector-OFF
10

11. Methodology
Image
Classification
Definition of Feature
Post- Accuracy
Mapping Identification Classification
Classification Assessment
Approach and Selection
11

12. Image
Classification
Definition of Feature
Post- Accuracy
Mapping Identification Classification
Classification Assessment
Approach and Selection
12

13. 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

14. Image
Classification
Definition of Feature
Post- Accuracy
Mapping Identification and Classification
Classification Assessment
Approach Selection
14

15. NDVI
Map showing NDVI based on 2000 Landsat 7 ETM+
15

16. Sample Selection
Land
Vegetation
Shallow
Water
Deep
Water
16

17. Image
Classification
Definition of Feature
Post- Accuracy
Mapping Identification Classification Classification Assessment
Approach and Selection
17

18. “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

19. Maximum Likelihood
Land
Vegetation
Shallow
Water
Deep
Water
19

20. Image
Classification
Definition of Feature Post- Accuracy
Mapping Identification Classification
Classification Assessment
Approach and Selection
20

21. Sieve
Majority
Analysis
21

22. MLC Sieve Majority Analysis Output
22

23. Image
Classification
Definition of Feature
Post-
Accuracy
Mapping Identification Classification Assessment
Classification
Approach and Selection
23

24. 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

25. Analysis
&
Visualization
25

26. 26

27. 27

28. Land Cover
1972 2000
28

29. What's the change ?
1972 to 2000
29

30. Vegetation increased by 3700%
17% of existing land was created from
shallow and deep water
30

31. 31

32. 32

33. 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%
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34. 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
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35. 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

36. 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

37. Compare with reclamation in Dubai
Graphs n tables in same page
Kappa
謝謝
Thank you for your attention
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