1. ASTER image – one of the fastest changing places in the U.S. Where??

2. USGS Image
Colors represent nature of change (e.g., more or less greenness, etc.)

3. 2011 Minimum
2011 Sea ice extent
NASA Video

4. Change Detection in Remote Sensing
Using multi-temporal satellite imagery to measure change

5. Learning Objectives What is remotely sensed change detection?
Why is image pre-processing so important for change detection?
What are some common change detection algorithms, and what are their advantages and disadvantages?
How do you interpret change detection images?

6. What is change detection?
The use of remotely sensed data to map the spatial extent and magnitude of changes on the land surface through time
Vegetation
Urban growth
Industrial development (e.g., gas wells, mines, etc.)
Coastal zone changes
Climate change effects (e.g., sea ice)

7. LAND COVER CHANGES
104 46W
042 08N
1976 – 2002

8. July 2002
April 2002

9. 2002
1984

10. 2000-Sep-10
1999-July-06

11. 108 43W
041 45N
LAND COVER CHANGES
1984 – 2002

12. Basic Steps for Satellite-based Change Detection
Acquire multi-date imagery
Preprocess imagery (geometric, radiometric)
Apply change detection algorithm
Characterize change (transition matrices, visual interpretation, etc.)
Report results

13. Image Acquisition
Imagery must be available at least for start and end of change period (need to know satellite launch dates)
May require intermediate images for trends
Imagery should be of similar spatial, spectral, and radiometric resolution (not always possible)
Imagery usually should be from same time of year for each year

14. Preprocessing for Change Detection
Images must be carefully registered!
Map projection is important
Painstaking registration of each date to a “master” date
Atmospheric correction usually important
Book on image registration techniques.

15. Change Detection Algorithms
Many strategies for manually or digitally mapping change from satellite (or aerial) imagery
Will discuss 5 commonly used methods, but there are variations and much room for creativity

16. Change Detection Techniques
Image algebra (differencing, ratios)
Post classification comparison
Change vector analysis
Visual interpretation of image stacks
Automated analysis of multi-date stacks (e.g., classification, PCA, etc.)

17. Image Algebra Compare 2 or more dates using simple algebra
Subtract single bands or enhancements from two dates
Must select a threshold difference to separate real change from “noise”
Atmospheric differences between dates can cause problems
Interpret range of results (from large negative to large positive) in context of band or enhancement.
No change = 0, but this may be in middle (medium grey) of the possible range of outcomes.

18. Image Algebra (cont.)
Calculate the ratio of single bands or enhancements (e.g., NDVI) from each of 2 dates
No change = 1, change result can vary from very small (<<1) to very large (>>1).
Again, interpret in context of bands or enhancements

19. Land Cover Change in Pinedale Area from 1989 to 2006 using NIR Band only (2002 – 1989 NIR)

20. Same change (2006-1989) but with NDVI instead of NIR.

21. Post Classification Comparison
Classify (map) each image from different dates
Use comparable classification schemes and strategies
Use training areas that are stable (don’t change much between dates)
Classify both images
Compare the classifications
Generate “transition matrices” that are analogous to “error matrices” to analyze the types of changes that occur.

22. Pan-tropical area transition matrix for the period 1980-1990

23. Change vector analysis
Characterize direction and magnitude of vector depicting temporal movement of each pixel in feature space
Band 4
Date 1
Date 2
Band 3

24. Change vector analysis
USGS images

25. Image Display
Display a single band (or index) from each of 3 dates as red, green, and blue.
How will “no change” appear??
How will changed areas appear??
Not quantitative – but good for visual exploration of change
Requires 3 dates (can do with 2 but doesn’t work as well)

26. Change Stack for Pinedale anticline
1989 = Red
2002 = Green
2006 = Blue

27. Image “stack” of 1970s, 1980s and 2002 NDVI images colored R,G,B respectively. Why the green cast over the whole image?

28. Multi-date Compositing
Create a single image by stacking all the bands from two or more dates in one file
Use Supervised or Unsupervised classification, or other techniques (e.g. PCA) to create maps or “change bands”
Some classes or bands may correspond to changed pixels
Inefficient and hard to interpret

29. 3rd principal component of stack of 1989, 2002, and 2006 NDVI

30. On-screen Digitizing
Put two image up on screen side by side and manually digitize areas that have changed
Only practical for small areas
Labor intensive
But…may work well for some projects

31. Change Detection Issues Things to keep in mind for all of the algorithms
Atmospheric effects – may need to correct for atmospheric differences (and sun angle differences, etc.)!
Spatial error – must accurately register all of the image dates
Must consider other causes of change besides the ones you are interested in (e.g. wet vs. dry years, etc.)

32. Summary – Change Detection
Change detection is a very useful application of remote sensing—used a lot (= good job skill)
Various methods have strengths and weaknesses…choice depends on application
Important to control for changes that don’t relate to the change you are studying (e.g. atmospheric effects)