1. Resource Appraisal with Remote Sensing techniques A perspective from Land-use/Land-cover by Basudeb Bhatta Computer Aided design Centre Computer Science and Engg. Dept. Jadavpur University kolkata

2. Introduction There can be almost endless applications of remote sensing for the monitoring and management of land-use and land-cover. Each application itself has specific demands, for sensor (optical, thermal, microwave), resolutions, attribute data, and procedures. With the availability of very high spatial resolution satellites and advanced geospatial analytical techniques in the recent years, the applications have been multiplied.

3. LAND USE Or LAND COVER

4. Study of Land-use/Land-cover Important for: Change monitoring (to balance conservation, conflicting uses, and development pressure) Resource management (sustainable management and protection of land-use/land-cover resources) Planning activities (for future development)

5. Some Application Areas Natural resource management Wildlife habitat protection Baseline mapping for GIS input Urban expansion/encroachment Routing and logistics planning for seismic/exploration/resource extraction activities Damage delineation (tornadoes, flooding, volcanic, seismic, and fire activities) Legal boundaries for tax and property evaluation Target detection (identification of earth surface features)

6. Land Cover Identification and Mapping Data requirements Multispectral optical image (preferably post monsoon) Radar image Thermal image

7. Land Cover Identification and Mapping Multispectral day-time optical image

8. Land Cover Identification and Mapping Multispectral night-time optical image Night-time image shows urban areas

9. Land Cover Identification and Mapping Thermal Image DayBefore dawn

10. Land Cover Identification and Mapping Day-time thermal image of Kolkata

11. Land Cover Identification and Mapping Thermal Image Band 1, Day Band 1, Night CC, Day CC, Night Thermal Infrared Multi-spectral Scanner Courtesy: NASA

12. Land Cover Identification and Mapping Radar Image Radar image for flood monitoring

13. Land Cover Identification and Mapping Radar Image R : C-band HV G : L-band HV B : L-band VV Bright blue-green: forest Reddish-brown: grassland Dark blue: rough lava flow Black: smooth lava flow Courtesy: Microimages Inc. Kilauea volcano, Hawaii, USA

14. Land-use/Land-cover Change

16. March 14, 2010 March 13, 2003

17. Land-use/Land-cover Change2007 2009

18. June 17, 1975July 10, 1992August 1, 2000

19. Land-use/Land-cover Change Courtesy: NASA March 14, 2011 August 8, 2008 Ishinomaki, Japan

20. Identification of Changes Visual (manual) Identification Automatic Identification Semi-automatic (man-machine interactive) Identification

21. Visual Comparison

22. Multi-temporal Colour Composite 19871992

23. Multi-temporal Colour Composite

24. Automatic Change Detection 2 -7 2546 3 6 8 589 795 968 Image 1 7531 1 6 8 930 993 627 Image 2 -5015 2-459 0-202 0341 Output

25. Automatic Change Detection 1987 1997 Continuous Image

26. Automatic Change Detection

27. Semi-automatic Change Detection Classification of multi-temporal image stack

28. Semi-automatic Change Detection Classifying multi-temporal images individually Converting the classified images in to vector Aggregation of vector polygons Vector overlay

29. Vector Overlay Land-cover 1990Land-cover 2000 20001990

30. Land-use/Land-cover Change What to identify? Momentarily change or seasonal change or annual change? Seasonal change and annual change are mixed within the same image. Cycle of seasonal change can be rather complex. Spatial resolution is a challenge for long temporal gap.

31. Momentarily Change

32. Momentarily Change Imaging

33. Scene Specific Momentarily Change

35. Examples – Urban growth/sprawl

36. Examples – Illegal Construction

37. Examples – Crop type Landsat-TM and SAR data merged to identify crop type

38. Examples – Crop Damage Courtesy: CCRS

39. Examples – Burn Mapping Courtesy: NASA

40. Examples – Monitoring Afforestation

41. Examples – Crop Phenology