1. Change Detection Analysis of Eco-Sensitive Area using Remotely Sensed Data By Abhijat Arun Abhyankar

2. Outline of the talk Remote Sensing Introduction and Objective of the study Study Area Data Methodology Results and Discussion Future Work

3. Remote sensing Remote Sensing is the science and art of making measurements of an object or environment without coming into physical contact with target

4. Introduction and Objectives The International Geosphere-Biosphere Programme (IGBP) and the Human Dimensions of Global Environmental Change Programme (HDP) have acknowledged the importance of land use change studies in developing our understanding of global environmental change Satellite images have inherent advantages 1)Spatial 2)Temporal revisit 3)Images of inaccessible areas 4)Time less than survey method This work depicts changes in land-use/ land-cover for the area covering ten kilometre radius around the limestone mining site of Lafarge Surma Cement Company, located in Shella, (situated about 96 km away to the south of Shillong, the Capital of Meghalaya).

5. Study Area and Data IRS P6 LISS III: January 7, 2008 and March 9, 2010, field visit The study are considered is an area of 10 km radius (aerial distance) from the 25 o 11’18’’ N latitude & 91 o 37’28’’ E Longitudes. This is mine site of Lafarge Umiam Mining Pvt. Ltd. The area is entirely rural and sparsely populated Community Development (CD) blocks of Shella Bholaganj and Mawsynram both under the district jurisdiction of East Khasi Hills. The village Nongtrai is about 2.5 km away from the mine area while Shella Bazar and Pyrkan are within the radius of 2 km from the mining zone. The nearest township is at Cherrapunji, known to be the ‘rainiest’ place of the world.

6. IRS P6 satellite Launch dateOctober 17, 2003 Launch siteSHAR, Sriharikota Launch vehicle PSLV-C5 PayloadsLISS-4, LISS-3, AWiFS-A, AWiFS-B OrbitPolar Sun Synchronous Orbit height817 km Orbit inclination 98.7 o Orbit period101.35 min Number of Orbits Per day 14 Local time of equator crossing 10:30 am Repetivity (LISS-3) 24 days Revisit5 days Lift-Off mass1360 kg Attitude and orbit control 3-axis body stabilised using Reaction Wheels, Magnetic Torquers and Hydrazine Thrusters Power Solar Array generating 1250 W, Two 24 Ah Ni-Cd batteries SensorResolutionColour LISS-IV Mono5.8 m black and white LISS-III23 mmultispectral AWiFS60 mmultispectral SensorLISS-III Resolution23 m Swath127 km (bands 2, 3, 4) 134 km (band 5 -MIR) Repetitive25 days Spectral Bands0.52 - 059 microns (B2) 0.62 - 0.68 microns (B3) 0.77 - 0.86 microns (B4) 1.55 - 1.7 microns (B5)

7. Methodology Field visit to study area Identification of different landcover classes and recording these using GPS Procurement of cloud free satellite data-geocoded Identification and extraction of sample landcover classes on the satellite imagery (six landcover classes were identified namely, dense forest, sparse forest, barren land, crops, water and dry channel) Dense forest/Medium forest: canopy cover greater than 40% Sparse forest: canopy between 10 to 40% of canopy cover Scrub land: less than 10% of canopy cover (Forest Conservation Act, 1980) Using supervised classification with Maximum likelihood estimator, preparation of landcover map-temporally Change detection analysis

8. False color composite (FCC) of sample landcovers

9. Scatter plots of sample landcovers Barren Land Crop Dense Forest Dry Channel Sparse Forestwater

10. Supervised Classification with Maximum Likelihood Estimator This method assumes the training dataset selected for each landcover distribution-normally distributed. Using these parameters, probability of unknown pixel falling in various classes is calculated. We have identified six landcover classes. Hence for each of the pixel-we obtain have six probability value. The higher probability value-class is assigned to unknown pixel Mathematically,

11. False Color Composite of January 7, 2008 using IRS P6 LISS III False Color Composite of March 9, 2010 using IRS P6 LISS III

12. Landcover map of January 7, 2008 using IRS P6 LISSIII image Classes7-Jan-08 Crop land20.3 Dense forest127.7 Sparse forest/ Scrub land90.4 Water1.3 Dry channel0.7 Barren land74.4 Total Area314.8

13. Landcover map of March 9, 2010 using IRS P6 LISS III image Classes9-Mar-10 Crop land30.2 Dense forest137.0 Sparse forest/Scrub land63.8 Water6.7 Dry channel1.5 Barren land75.6 Total Area314.8

14. Results and Discussion Dense forest area-increased Barren land area-no change Sparse forest/Scrub land-reduced Crop-increased Water-increased Dry Channel-increased

15. Future work Accuracy Assessment of landcover map Discriminant analysis for landcover classification ANN for landcover classification Comparison of Landcover results with 2006

16. THANK YOU and QUESTIONS