1. Mapping land cover change and terrestrial dynamics over northern Canada using multi-temporal Landsat imagery Christopher Butson† Robert Fraser‡ †Prologic Consulting, 75 Albert Street, Suite 206, Ottawa, Ontario, Canada. K1P 5E7 ‡Natural Resources Canada, Canada Centre for Remote Sensing, 588 Booth St. Ottawa, Ontario, Canada. K1A 0Y7

2. Presentation Outline • Introduction • Research Objectives • Data & Materials • Methodology • Cross-Correlation Analysis • Change Vector Analysis • Theil-Sen Regression Analysis • Results • Conclusions • Future Work

3. Introduction Northern areas are characterized by: • Low air and soil temperatures • Permafrost • Short growing season and limited productivity • Climate data indicate large relative warming at high latitudes. Intergovernmental Panel on Climate Change (IPCC) projects an increase in global mean surface temperature of 1º to 3.5º C by 2100 and an increase in sea level by 15-95cm.

4. Goal: Develop automated methods for detecting past and future land cover changes in the north and use this information to report on carbon fluxes for UNFCCC and track indicators of climate change in Canada. Where: Four pilot sites have been setup along the forest-tundra boundary (tree line) in northern Canada. Yukon-NWT, Manitoba, Ontario, Quebec. How: Use various change methods to monitor; I) Natural disturbances (tundra fires, vegetation) and II) Human induced changes (mining and settlements).

5. Research Objectives The main objective of this research is to develop an automated change detection technique for use with Landsat imagery to quantify past and present land cover changes in northern areas. More specifically, we aim to: • Test three change detection approaches for quantifying land cover changes in Landsat imagery. • Quantify total changed area, and land cover changes throughout the specified time periods using circa 2000 imagery as the base-year over four pilot areas located in the forest-tundra transition zone of northern Canada.

6. Data & Materials Map of Canada highlighting the locations of the four pilot sites.

7. Landsat Scene Selection: Study sites #1-4, represent the multi-temporal sites under investigation. Sites #5-7 represent the overlap image pairs that the change methods were tested on.

8. Methodology

9. Cross-Correlation Analysis (CCA) uses a land cover map to delineate spectral cluster statistics between the baseline image year (Time 1) and each scene in the temporal sequence (Time 2). Calculating the Z-statistic deviations from the cluster mean identifies change pixels within each land cover cluster. Cross-Correlation Analysis (CCA)

10. Change Vector Analysis (CVA) uses two spectral channels to map both the: 1) magnitude of change and, 2) the direction of change between the two (spectral) input images for each date. Change Vector Analysis (CVA)

11. Much like typical image regression change, we use Theil-Sen as it is more robust to sample outliers than ordinary least-squares regression. Medians are outlier resistant measures of central tendency and the method uses the median of all pairwise slopes to calculate the slope of the regression line. The median value of the sample offsets represents the intercept. Theil-Sen Regression Analysis (TSA)

12. TSA con’t… • Generate mask to sample pixels in each land cover • Samples are used to build a regression equation • for each cover type using the baseline circa 2000 ETM+ scene as the regressor and each scene in the temporal sequence as the response. • A change mask was created by mapping pixels characterizing large residuals away from the regression line

13. The overlap scene acquired earlier in the season was used as the baseline image while the latter scene was considered the time 2 map By analyzing only the overlap portion between the two orbital paths, we assumed that the land surface (and thus land cover) does not change between acquisition dates Results – Objective #1

14. Comparison of techniques for burned vegetation (high probability): Results – Objective #1 con’t… CVA CCA RGB=4,5,3 TSA

15. Results – Objective #1 con’t… Comparison of techniques for regenerating vegetation (medium probability): RGB=4,5,3 CVA CCA TSA

16. Study site#1: Changes 1992-2000, Inuvik, NWT Results – Objective #2 a) 1992, RGB=1,2,3 b) 2000, RGB c) Prob-Change

17. Results - Objective #2 con’t… Study site #2: Changes 1985-2001, Churchill, MN a) 2001, RGB b) 1985, Prob-Change c) 1991, Prob-Change

18. Results - Objective #2 con’t…

19. CVA –Does not rely on the quality/accuracy of a baseline land cover map to identify changes. Relatively large commission errors but less noise in some cases. CCA – Relies on the quality/accuracy of a land cover map to identify changes. May under estimate land cover changes. TSA- Relies on the quality/accuracy of a land cover map to correctly classify changes. Although the commission errors were much lower in the overlap analysis, the change maps were still noisy. Computationally intensive. Conclusions

20. Validate change conditions using historic and current ancillary data Develop interactive thresholding Spatial aggregation of change pixels Analyze seasonal change detection limitations Apply change methods to northern mosaic of Canada Assess land cover/land use changes for UNFCCC reporting in the north Future work

21. Baseline Classification Circa 2000 Landsat ETM+ 90m landcover of northern Canada – Version I (preliminary) Olthof, I., Butson, C., Fernandes, R., Fraser, R., Latifovic, R. and Orazietti, J. (2004). Landsat ETM+ mosaic of northern Canada. Canadian Journal of Remote Sensing, submitted 06/04.

22. Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/) for the use of the Landsat MSS imagery Canadian Space Agency (CSA)- Government Related Initiatives Program (GRIP) funding Acknowledgements