Evaluation of Landsat-7 SLC-off image products for forest change detection

Canadian Journal of Remote Sensing

Volumn 34, Issue 2, 2008, Pages 93-99

  Wulder, Michael A ^a   Ortlepp, Stephanie M ^a   White, Joanne C ^a   Maxwell, Susan ^b  

^a PACIFIC FORESTRY CENTRE   (Canada)

^b EARTH RESOURCES OBSERVATION AND SCIENCE EROS CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COPYING; PIXELS; SATELLITE IMAGERY; SIGNAL DETECTION; SIZE DISTRIBUTION;

CHANGE-DETECTION; CONTINUED USE; DATA SOURCING; FOREST CHANGE; FOREST INVENTORIES; FORWARD MOTION; IMAGE PRODUCTS; LANDSAT-7; LANDSAT-7 ETM+; OFF MODE; REFERENCE DATA; REFERENCE IMAGES; SCAN LINE; SEGMENT-BASED; SPATIAL CONFIGURATIONS; SPATIAL DISTRIBUTION OF; SPECTRAL QUALITY; TIME PERIODS; TM IMAGES;

GALLIUM ALLOYS;

ALLOY; FORESTRY; GALLIUM; IMAGE ANALYSIS; REPROGRAPHY; SATELLITES; SIGNALS;

EID: 49749122178     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m08-020     Document Type: Article

References (40)

1. Allen, T.R., and Kupfer, J.A. 2001. Spectral response and spatial pattern of Fraser fir mortality and regeneration, Great Smoky Mountains, USA. Plant Ecology, Vol. 156, pp. 59-74.
2. Bedard, F., Reichert, F., Dobbins, R., and Trepanier, I. 2008. Evaluation of segment-based gap-filled Landsat ETM+ SLC-off satellite data for land cover classification in southern Saskatchewan, Canada. International Journal of Remote Sensing, Vol. 29, No. 7. pp. 2041-2054.
3. Canadian Forest Service. 2007. The state of Canada’s forests 2007. Canadian Forest Service, Natural Resources Canada, Ottawa, Ont. Available from http://canadaforests.nrcan.gc.ca [cited 28 February 2008].
4. Cohen, W., and Goward, S. 2004. Landsat’s role in ecological applications of remote sensing. BioScience, Vol. 54, No. 6, pp. 535-545.
5. Desclee, B., Bogaert, P., and Defourny, P. 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment, Vol. 102, pp. 1-11.
6. Epting, J., Verbyla, D., and Sorbel, B. 2005. Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment, Vol. 96, pp. 328-339.
7. Falkenstrom, H., and Ekstrand, S. 2002. Evaluation of IRS-1C LISS-3 satellite data for defoliation assessment on Norway spruce and Scots pine. Remote Sensing of Environment, Vol. 82, pp. 208-223.
8. Foody, G.M. 2003. Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development. International Journal of Remote Sensing, Vol. 24, pp. 4035-4046
9. Franklin, S.E., and Wulder, M.A. 2002. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, Vol. 26, No. 2, pp. 173-205.
10. Geomatics Canada. 1996. Standards and specifications of the National Topographic Database Edition 3.18. Catalogue No. M52-70/1996, Minister of Supply and Services Canada, Sherbrooke, Que.
11. Han, T., Wulder, M.A., White, J.C., Coops, N.C., Alvarez, M.F., and Butson, C. 2007. An efficient protocol to process Landsat images for change detection with tasseled cap transformation. Geoscience and Remote Sensing Letters, Vol. 4, No. 1, pp. 147-151.
12. Hansen, A.J., Neilson, R.P., Dale, V.H., Flather, C.H., Iverson, L.R., Currie, D.J., Shafer, S., Cook, R., and Bartlein, PJ. 2001. Global change in forests: responses of species, communities, and biomes. Bioscience, Vol. 51, pp. 765-779.
13. Hansen, M.C., Roy, D.P., Lindquist, E., Adusei, B., Justice, C.O., and Altstatt, A. 2008. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, Vol. 112, No. 5, pp. 2495-2513.
14. Healey, S.P., Yang, Z., Cohen, W.B., and Pierce, D.J. 2006a. Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sensing of Environment, Vol. 101, pp. 115-126.
15. Healey, S.P., Cohen, W.B., Yang, Z., and Kennedy, R. 2006b. Remotely sensed data in the mapping of forest harvest patterns. In Understanding forest disturbance and spatial pattern. Edited by M.A. Wulder and S.E. Franklin. CRC Press, Boca Raton, Fla. 246 p.
16. Hickey, G.M., Innes, J.L., Kozak, R.A., Bull, G.Q., and Vertinsky, I. 2005. Monitoring and information reporting for sustainable forest management: An international multiple case study analysis. Forest Ecology and Management, Vol. 209, pp. 237-259.
17. Irons, J., and Masek, J.G. 2006. Requirements for a Landsat data continuity mission. Photogrammetric Engineering & Remote Sensing, Vol. 72, pp. 1102-1108.
18. Jin, S., and Sader, S.A. 2005. Comparison of time-series tasselled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment, Vol. 94, pp. 364-372.
19. Malingreau, J.P. 1993. Satellite monitoring of the world’s forests: A review. Unasylva, Vol. 44, pp. 31-38.
20. Markham, B.L., Storey, J.C., Williams, D.L., and Irons, J.R. 2004. Landsat sensor performance: history and current status. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, pp. 2691-2694.
21. Masek, J. 2007. White paper on use of gap-filled products for the Mid-Decadal Global Land Survey (GLS). Available from http://GLS.umd.edu/ documents.html [cited 29 January 2008].
22. Maxwell, S.K., Schmidt, G.L., and Storey, J.C. 2007. A multi-scale approach to filling gaps in Landsat ETM+ SLC-off images. International Journal of Remote Sensing, Vol. 28, pp. 5339-5356.
23. McMichael, C.E., Hope, A.S., Roberts, D.A., and Anaya, M.R. 2004. Post-fire recovery of leaf area index in California chaparral: a remote sensing-chronosequence approach. International Journal of Remote Sensing, Vol. 25, pp. 4743-4760.
24. Muchoney, D.M., and Haack, B.N. 1994. Change detection for monitoring forest defoliation. Photogrammetric Engineering & Remote Sensing, Vol. 60, pp. 1243-1251.
25. Nemmour, H., and Chibani, Y. 2006. Fuzzy neural network architecture for change detection in remotely sensed imagery. International Journal of Remote Sensing, Vol. 27, pp. 705-717.
26. Prenzel, B.G., and Treitz, P. 2006. Spectral and spatial filtering for enhanced thematic change analysis of remotely sensed data. International Journal of Remote Sensing, Vol. 27, pp. 835-854.
27. Price, K.P., and Jakubauskas, M.E. 1998. Spectral retrogression and insect damage in lodgepole pine successional forests. International Journal of Remote Sensing, Vol. 19, pp. 1627-1632.
28. Rogan, J., Franklin, J., and Roberts, D. 2002. A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sensing of Environment, Vol. 80, pp. 143-156.
29. Scaramuzza, P., Micijevic, E., and Chander, G. 2004. SLC gap-filled products: Phase one methodology. Available from http://landsat.usgs.gov/data_products/slc_off_data_products/documents/SLC_Gap_Fill_Methodology.pdf [cited 22 February 2008].
30. Skakun, R.S., Wulder, A.M., and Franklin, S.E. 2003. Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sensing of Environment, Vol. 86, pp. 433-443.
31. Treitz, P., and Rogan, J. 2004. Remote sensing for mapping and monitoring land-cover and land-use change: An introduction. Progress in Planning, Vol. 61, pp. 269-279.
32. USGS. 2004. Phase 2 gap-fill algorithm: SLC-off gap-filled products gap-fill algorithm methodology. Available from www.ga.gov.au/image_cache/ GA4861.pdf [cited 22 February 2008].
33. Wilson, E.H., and Sader, S.A. 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, Vol. 80, pp. 385-396.
34. Woodcock, C.E., Macomber, S.A., Pax-Lenney, M., and Cohen, W.B. 2001. Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors. Remote Sensing of Environment, Vol. 78, pp. 194-203
35. Wulder, M., and Franklin, S.E. 2001. Polygon decomposition with remotely sensed data: Rationale, methods, and applications. Geomatica, Vol. 55, No. 1, pp. 11-21.
36. Wulder, M.A., Kurz, W.A., and Gillis, M. 2004. National level forest monitoring and modeling in Canada. Progress in Planning, Vol. 61, pp. 365-381.
37. Wulder, M.A., Dymond, C.C., White, J.C., Leckie, D.G., and Carroll, A.L. 2006a. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. Forest Ecology and Management, Vol. 221, pp. 27-41.
38. Wulder, M.A., White, J.C., Bentz, B., Alvarez, M.F., and Coops, N.C. 2006b. Estimating the probability of mountain pine beetle red-attack damage. Remote Sensing of Environment, Vol. 101, pp. 150-166
39. Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., and Woodcock, C.E. 2008. Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, Vol. 112, No. 3, pp. 955-969.
40. Zhang, C., Li, W., and Travis, D. 2007. Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach. International Journal of Remote Sensing, Vol. 28, pp. 5103-5122.