A COMPARATIVE STUDY OF LAND USE AND LAND COVER ANALYSIS OF KARACHI USING MODIS AND

LANDSAT DATASETS

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

JIBRAN KHAN, DAWOOD CO-AUTHORS: MARYAM ALTAF & INTIKHAB ULFAT

• Introduction and Objectives

• Site and Data Acquisition

• Image Processing and Methodology

• Results and Discussion

• Limitations and Future Work

• Conclusion

PROCEDURE

INTRODUCTION AND OBJECTIVES

• 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

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”

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

Water

VegetationHigh ImperviousLow Impervious

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 correlation of pixels data using R software

Image showing the statistical analysis of both MODIS and Landsat data (Correlation coefficient of 0.96 is found)

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 0 0 0 0 0Class 1 64 126 0 0 0Class 2 0 131 19 0 0Class 3 0 0 212 30 0Class 4 0 0 152 292 22Class 5 0 0 4 222 151Total 64 257 387 544 173

Ground Truth (Percent)Class Water Vegetation High Impervios Low Impervious Open Area

Unclassified 0 0 0 0 0

Class 1 100 49.03 0 0 0

Class 2 0 50.97 4.91 0 0

Class 3 0 0 54.78 5.51 0

Class 4 0 0 39.28 53.68 12.72

Class 5 0 0 1.03 40.81 87.28

Total 100 100 100 100 100

RESULTS: ERROR MATRIX

Class Producer Accuracy User Accuracy Producer Accuracy User Accuracy

  (Percent) (Percent) (Pixels) (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

Questions/Suggestions?

THANK YOU!