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

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

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

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

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)

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

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

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

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

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

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

Overall Accuracy 0.596491228 (Approx. 60%) Ground Truth (Percent) Results: Error Matrix Overall Accuracy 0.596491228 (Approx. 60%) Kappa Coefficient = 0.4773 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

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

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

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

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