1. Co-authors: Maryam Altaf & Intikhab Ulfat
A Comparative Study of Land use and Land cover Analysis of Karachi using MODIS and Landsat Datasets
Jibran Khan, Dawood
Co-authors: Maryam Altaf & Intikhab Ulfat

2. Procedure Introduction and Objectives Site and Data Acquisition
Image Processing and Methodology
Results and Discussion
Limitations and Future Work
Conclusion

3. Introduction and Objectives
OUR AIM IS:
“TO DEVELOP A NEW TECHNIQUE TO STUDY LAND USE AND LAND COVER (LULC) CHANGE OF KARACHI – A FIRST OF ITS KIND COMPARATIVE APPROACH USING MODIS AND LANDSAT DATA”
Land cover composition has a direct influence on urban ecological system
Karachi has been through intense urbanization during last two decades and many studies have already been performed using Landsat data
Applicability of MODIS data has been tested for the LULC analysis through this comparative study

4. Introduction and Objectives
Considering the variety and complexity of urban ecological studies, our approach is restrictive only to NDVI, Slope and Aspect
Factors such as:
- Soil and moisture content
- Anthropogenic factors
are not considered as significant during this study

5. Site and Data Acquisition
The subject of our study is the city of Karachi
Multi-temporal single date daily surface reflectance data at 500m resolution acquired from USGS has been used in this study (year 2011)
Landsat 7 Imagery of Karachi has also been used (year 2011)
Town boundaries maps, DEM of Karachi is also used
Study Area showing map of Karachi (Source: S.J.H. Kazmi, Geography Department, UoK)

6. Image Processing & Methodology
Decision tree classification of Karachi (using MODIS Surface reflectance, DEM derived slope & aspect of Karachi and band 2 of MODIS)
Unsupervised classification of both MODIS and Landsat Imageries (5 number of classes)
Comparison of both classified MODIS and Landsat data

7. Image Processing & Methodology
Visual assessment of both classified data
Comparison of both datasets yielded gross errors due to difference in spatial resolution
Correlation analysis of both MODIS and Landsat data
Error matrix generation for the unseen training sites

8. Results: Decision Tree Classification
Decision Tree of Karachi
High Impervious
Vegetation
Low Impervious
Water
Open area

9. Results: Unsupervised K-Means Classification
Unsupervised Classification of Landsat 7 (left) and MODIS data (right) of Karachi

10. Results: Comparative Analysis
Image showing the comparison of graphs of pixels data of classified Landsat (left) and MODIS (right) data (Source: ArcGIS v10.2)

11. Results: Correlation Analysis
Image showing the statistical analysis of both MODIS and Landsat data (Correlation coefficient of 0.96 is found)
Image showing the correlation of pixels data using R software

12. Overall Accuracy 0.596491228 (Approx. 60%) Ground Truth (Percent)
Results: Error Matrix
Overall Accuracy (Approx. 60%)
Kappa Coefficient =
Ground Truth (Pixels)
Class
Water
Vegetation
High Impervios
Low Impervious
Open Area
Unclassified
Class 1 64
126
Class 2
131 19
Class 3
212 30
Class 4
152
292 22
Class 5 4
222
151
Total
257
387
544
173
Ground Truth (Percent)
Class
Water
Vegetation
High Impervios
Low Impervious
Open Area
Unclassified
Class 1
100
49.03
Class 2
50.97
4.91
Class 3
54.78
5.51
Class 4
39.28
53.68
12.72
Class 5
1.03
40.81
87.28
Total

13. Results: Error Matrix Class Producer Accuracy User Accuracy (Percent)
(Percent)
(Pixels)
Class 1
100
33.68
64/64
64/190
Class 2
50.97
87.33
131/257
131/150
Class 3
54.78
87.6
212/387
212/242
Class 4
53.68
62.66
292/544
292/466
Class 5
87.28
40.05
151/173
151/377

14. Conclusion and Discussion
A new map of land cover of Karachi has been developed which is generally consistent with Landsat-derived land cover map
Comparison of MODIS and Landsat data showed a high level of correspondence
Correlation coefficient of 0.96 is found showing a strong degree of association between both datasets
For unseen training sites an error matrix has been generated with overall accuracy of 60%
However with some limitations, the results demonstrated the applicability of MODIS data and decision tree classifier approach

15. Limitations and future prospects
The unavailability of Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted (NBAR) data at 500m resolution presented a limitation here
The availability of BRDF corrected and improved 500 m MODIS data could help rectify classification inaccuracy
The careful selection of cloud free surface reflectance data is also important
Performing field observations could result in reducing classification errors

16. Questions/Suggestions?

17. Thank you!