1. A METHODOLOGY TO SELECT PHENOLOGICALLY SUITABLE LANDSAT SCENES FOR FOREST CHANGE DETECTION IGARSS 2011, Jul, 27, 2011 Do-Hyung Kim, Raghuram Narashiman, Joseph O. Sexton, Chengquan Huang, John R. Townshend Global Land Cover Facility, University of Maryland - College Park

2. CONTENT 1. Background 2. DATA 3. METHOD 4. RESULT 5. DISCUSSION 6. REFERENCE 2

3. Background Influence of phenology on forest change detection – example of path 116/ row 32 (Korea) Profile based techniques by time series data can resolve the issue of influence of phenology on change detection performance (Coppin et. al., 2004) 3 Sep 2 1999 Oct 7 2006 Aug 28 2006 Change detection Original Scene Replacement Scene Change detection Aug 28 2006 False forest change by seasonality

4. Background Global Land Survey – Global, orthorectified, typically cloud-free Landsat imagery centered on the years 1975, 1990, 2000 and 2005 with a preference for leaf-on conditions(Gutman, 2008). LARGE AREA SCENE SELECTION INTERFACE (LASSI) – Global Land Survey 2005 is a dataset which is selected using such an automated method, LARGE AREA SCENE SELECTION INTERFACE (LASSI) (Franks, 2002). An automated scene selection method which specialized for forest cover change detection is needed – Seasonality is not the only one parameter for LASSI. – GLS is not only for forest cover change analysis. 4

5. DATA MODIS data – MOD13C1 : 5km NDVI dataset for the years 2000-2009 (Huete et. al., 2002) Land cover data – MOD12C1 : 5km Land Cover dataset (Friedl el. al., 2002) consists of the IGBP classification system from which the % forest, % evergreen, % deciduous and % crop layers were extracted. Landsat METADATA – Metadata of globally available Landsat scenes dating back from the 1970s to present(http://landsat.usgs.gov/consumer.php ) 5 SpatialTemporal DataResolution Extent MODIS NDVI (MOD13C1) 5 km16 d2000-2009 MODIS Land Cover (MOD12C1) 5 km1 yr2001

6. METHOD DATA process 6 S = pixels > 40% deciduous & number of samples > 15 from MOD13C1 When I = composite (1<= i <=23) and j = year (2000 <= j <= 2009) Median value of the above samples for each i th composite at jth year NDVI ij = Median(S) 10 year norm, NORM at each i composite NORM i = Median (NDVI ij )

7. METHOD Filtering 7 NDVI ij value which is greater or smaller than NORM i +- σ(NDVI ij ) is replaced by NORM i NDVI Composites

8. METHOD Peak growing season selection SOP and EOP 8

9. METHOD Scene selection – web based app 9 User input Perform Search Metadata Search results Search Conditions – Date/Month/Year, Quality, Cloud, Path/Row UNZIP Shown as Table Landsat 7 ETM+ (SLC-on) Landsat 7 ETM+ (SLC-off) Landsat 1-5 TM Landsat 4-5 MSS Landsat 1-3 MSS Data Base update tool Data Base SOP, EOP SOP, EOP for each WRS2 tiles

10. 10 Deciduous forest Path/Rows 1546 7836 Number of Path/row Deciduous GLS

11. RESULT SOP, EOP Temporal consistency Trend compared to latitude and biome GLS replacement scene 11

12. 12 SOP of 10 year norm

13. 13 EOP of 10 year norm

14. 14 SOP variation from 1999 to 2007 Variation (date)

15. 15 EOP variation from 1999 to 2007 Variation (date)

16. Start of Peak by latitude and by Biomes Temperate Broad Leaf Tropical Dry Broad Leaf

17. End of Peak by latitude

18. GLS 2000 scenes need to be replaced 1546 7836 Number of Scenes 424

19. 1546 7836 Numbers of Scenes 435 GLS 2005 scenes need to be replaced

20. Replacement scene selection Browse through available scene list Pick the best image based on visual observation Criteria: Minimal cloud cover and within phenology bounds

21. P17 R28: Canada Peak Season Range: 5/25/2002 – 9/30/2002 GLS2000 date: 5/15/2002 Replacement scene date: 8/24/2001 GLS Replacement scene

22. P22R49 (Guatemala) Peak Season Range: 6/10/1999 – 11/17/1999 GLS date is just out of date range. Replacement scene has clouds. This is an example of replacement scene not being a better choice. GLS image: 12/4/1999Replacement image: 8/6/1999

23. Replacement Scenes 23 GLS 2000 284 Replacement / 424 scenes need to be replaced GLS 2005 252 Replacement /435 scenes need to be replaced

24. DISCUSSION 1. Snow effect 2. Scale issue 3. Selection of path/row with seasonality 4. Threshold selection 5. Validation against ground measurement 24

25. Acknowledgement This work has been carried out as part of the Global Forest Cover Change project, funded by the NASA MEaSUREs program (NNH06ZDA001N-MEASURES) 25

26. Reference A. Huete, et al., “Overview of the radiometric and biophysical performance of the MODIS vegetation indices,” Remote Sensing of Environment, vol. 83, no.1-2, pp. 195-213, Nov., 2002. Friedl, M.A., et al., “The MODIS land cover product: multi-attribute mapping of global vegetation and land cover properties from time series MODIS data,” Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), vol. 4, pp. 3199-3201, 2002 Coppin et. al., "Digital change detection methods in ecosystem monitoring: a review, “ IN T. J. REMOTE SENSING, 10 MAY, 2004, VOL. 25, NO. 9, 1565–1596 U.S. Geological Survey (2010, Dec. 30), Landsat Bulk Metadata Service. Available: http://landsat.usgs.gov/consumer.php Gutman, G., Byrnes, R., Masek, J., Covington, S., Justice, C., Franks, S., and R. Headley, Towards monitoring land cover and land-use changes at a global scale: The Global Land Survey 2005, Photogrammetric Engineering and Remote Sensing, 74, 6-10, 2008. Franks, S., Masek, J.G., Headley, R.M.K., Gasch, J., and Arvidson, T., Large Area Scene Selection Interface (LASSI). Methodology for selecting Landsat imagery for the Global Land Survey 2005, in press Photogrammetric Engineering and Remote Sensing. 26