The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring
Open accessAbstract
Free and open access to the Landsat archive has enabled the implementation of national and global terrestrial monitoring projects. Herein, we summarize a project characterizing the change history of Canada’s forested ecosystems with a time series of data representing 1984–2012. Using the Composite2Change approach, we applied spectral trend analysis to annual best-available-pixel (BAP) surface reflectance image composites produced from Landsat TM and ETM+ imagery. A total of 73,544 images were used to produce 29 annual image composites, generating ∼400 TB of interim data products and resulting in ∼25 TB of annual gap-free reflectance composites and change products. On average, 10% of pixels in the annual BAP composites were missing data, with 86% of pixels having data gaps in two consecutive years or fewer. Change detection overall accuracy was 89%. Change attribution overall accuracy was 92%, with higher accuracy for stand-replacing wildfire and harvest. Changes were assigned to the correct year with an accuracy of 89%. Outcomes of this project provide baseline information and nationally consistent data source to quantify and characterize changes in forested ecosystems. The methods applied and lessons learned build confidence in the products generated and empower others to develop or refine similar satellite-based monitoring projects.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
High-Resolution Global Maps of 21st-Century Forest Cover Change
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013) · Science · doi:10.1126/science.1244693 · Cited by 11,704
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., et al. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.026 · Cited by 4,815
Good practices for estimating area and assessing accuracy of land change
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.02.015 · Cited by 3,078
Self-citationObject-based cloud and cloud shadow detection in Landsat imagery
Zhu, Z., & Woodcock, C. E. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.10.028 · Cited by 2,208
Kennedy, R. E., Yang, Z., & Cohen, W. B. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.07.008 · Cited by 1,842
A Landsat Surface Reflectance Dataset for North America, 1990–2000
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., Gao, F., Kutler, J., & Lim, T.-K. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.857030 · Cited by 1,627
Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., & Myneni, R. B. (2001) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2000jd000115 · Cited by 1,544
Optical remotely sensed time series data for land cover classification: A review
Gómez, C., White, J. C., & Wulder, M. A. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.03.008 · Cited by 1,217
Self-citationOpening the archive: How free data has enabled the science and monitoring promise of Landsat
Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., & Woodcock, C. E. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.01.010 · Cited by 1,206
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Fifty years of Landsat science and impacts
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113195 · Cited by 771
Self-citationCoops, N. C., Tompalski, P., Goodbody, T. R. H., Queinnec, M., Luther, J. E., Bolton, D. K., White, J. C., Wulder, M. A., van Lier, O. R., & Hermosilla, T. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112477 · Cited by 406
Self-citationZhang, H. K., & Roy, D. P. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.05.024 · Cited by 346
Satellite Remote Sensing Contributions to Wildland Fire Science and Management
Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Oliva, P. (2020) · Current Forestry Reports · doi:10.1007/s40725-020-00116-5 · Cited by 259
Metadata
- Authors
- Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W., & Campbell, L. B.
- Venue
- International Journal of Digital Earth
- Publisher
- Informa UK Limited
- Published
- 2016-06-13
- Volume
- 9
- Issue
- 11
- Pages
- 1035-1054
- ISSN
- 1753-8947 (ISSN-L)
- 1753-8955
- OpenAlex
- W2439576902
- Cited by
- 293
- References
- 55
- FWCI
- 19.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
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