438
Views
5
CrossRef citations to date
0
Altmetric
Research Article

Spatial Random Forest (S-RF): A random forest approach for spatially interpolating missing land-cover data with multiple classes

ORCID Icon, & ORCID Icon
Pages 3756-3776 | Received 31 Jul 2020, Accepted 18 Nov 2020, Published online: 14 Feb 2021

References

  • Achard, F., H. D. Eva, H.-J. Stibig, P. Mayaux, J. Gallego, T. Richards, J.-P. Malingreau, et al. 2002. “Determination of Deforestation Rates of the World’s Humid Tropical Forests.” Science 297 (5583): 999–1002. August. 00368075. doi:10.1126/science.1070656.
  • Asner, G. P. 2001. “Cloud Cover in Landsat Observations of the Brazilian Amazon.” International Journal of Remote Sensing 22 (18): 3855–3862. 1366–5901. doi10.1080/01431160010006926.
  • Asner, G. P., D. E. Knapp, E. N. Broadbent, P. J. C. Oliveira, M. Keller, and J. N. Silva. 2005. “Selective Logging in the Brazilian Amazon.” Science 310: 480–482. doi:10.1126/science.1118051.
  • Belgiu, M., and D. Lucian. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114 (April): 24–31. 0924–2716. doi10.1016/J.ISPRSJPRS.2016.01.011.
  • Elith, J., J. R. Leathwick, and T. Hastie. 2008. “A Working Guide to Boosted Regression Trees.” Journal of Animal Ecology 77 (4): 802–813. July. 0021–8790. doi:10.1111/j.1365-2656.2008.01390.x.
  • ESA. 2019. Climate change initiative: Land cover map 2015. https://maps.elie.ucl.ac.be/CCI/viewer/
  • European Space Agency. 2015. “Land Cover CCI PRODUCT USER GUIDE VERSION 2.0.” Technical report.
  • Foody, G. M. 2002. Status of land cover classification accuracy assessment. doi:10.1016/S0034–4257(01)00295–4.
  • Ganganwar, V. 2012. “An Overview of Classification Algorithms for Imbalanced Datasets.” Technical report 4. www.ijetae.com
  • Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. “High-resolution Global Maps of 21st-century Forest Cover Change.” Science (New York, N.Y 342 (6160): 850–853. November. 1095–9203. doi:10.1126/science.1244693.
  • Hansen, M. C., R. S. Defries, J. R. G. Townshend, and R. Sohlberg. 2000. “Global Land Cover Classification at 1 Km Spatial Resolution Using a Classification Tree Approach.” International Journal of Remote Sensing 21 (6–7): 1331–1364. January. 0143–1161. doi:10.1080/014311600210209.
  • Hastie, T., R. Tibshirani, and J. Friedman. 2008. The Elements of Statistical Learning. 2nd ed. New York: Springer. isbn: 978–0–387–84857–0.
  • Helmstedt, K. J., and M. D. Potts. 2018. “Valuable Habitat and Low Deforestation Can Reduce Biodiversity Gains from Development Rights Markets.” Edited by Silvia Carvalho. Journal of Applied Ecology 55, no. 4: 1692–1700. July. 00218901. doi:10.1111/1365-2664.13108.
  • Hijmans, R. J. 2017. “Package ’Raster’: Geographic Data Analysis and Modeling.” https://cran.r-project.org/web/packages/raster/raster.pdf
  • Holloway, J., K. J. Helmstedt, K. Mengersen, and M. Schmidt. 2019. “A Decision Tree Approach for Spatially Interpolating Missing Land Cover Data and Classifying Satellite Images.” Remote Sensing 11 (15): 1796. July. doi:10.3390/rs11151796.
  • Kim, D.-H., J. O. Sexton, P. Noojipady, C. Huang, A. Anupam, S. Channan, M. Feng, and J. R. Townshend. 2014. “Global, Landsat-based Forest-cover Change from 1990 to 2000.” Remote Sensing of Environment 155 (Supplement C): 178–193. 0034–4257. doi:10.1016/j.rse.2014.08.017.
  • Krawczyk, B. 2016. “Learning from Imbalanced Data: Open Challenges and Future Directions.” Progress in Artificial Intelligence 5 (4): 221–232. November. doi:10.1007/s13748-016-0094–0.
  • Kuhn, M. 2018. “Package ’Caret’.” Technical report. https://cran.r-web/packages/caret/caret.pdf
  • Lloyd, C. D., and P. M. Atkinson. 2002. “Deriving DSMs from LiDAR Data with Kriging.” International Journal of Remote Sensing 23 (12): 2519–2524. June. 01431161. doi:10.1080/01431160110097998.
  • Lu, G. Y., and D. W. Wong. 2008. “An Adaptive Inverse-distance Weighting Spatial Interpolation Technique.” Computers and Geosciences 34 (no. 9): 1044–1055. September. 00983004. doi:10.1016/j.cageo.2007.07.010.
  • Mariethoz, G.2014. Multiple-point Geostatistics: Stochastic Modeling with Training Images. New York: John Wiley & Sons Inc.
  • Olofsson, P., G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, and M. A. Wulder. 2014. “Good Practices for Estimating Area and Assessing Accuracy of Land Change.” Remote Sensing of Environment 148 (May): 42–57. 0034–4257. doi10.1016/J.RSE.2014.02.015.
  • Ozelkan, E., S. Bagis, E. C. Ozelkan, B. B. Ustundag, M. Yucel, and C. Ormeci. 2015. “Spatial Random Forest (S-RF): A random forest approach for spatially interpolating missing land cover data with multiple classes.” International Journal of Remote Sensing 36 (4): 1000–1025. February. 0143–1161. doi:10.1080/01431161.2015.1007248.
  • Pringle, M. J., M. Schmidt, and J. S. Muir. 2009. “Geostatistical Interpolation of SLC-off Landsat ETM+ Images.” ISPRS Journal of Photogrammetry and Remote Sensing 64: 654–664. doi:10.1016/j.isprsjprs.2009.06.001.
  • Ryan, H. T., and N. V. Chawla. 2013. “Imbalanced Datasets: From Sampling to Classifiers.” In Imbalanced Learning, 43–59. Hoboken, NJ: John Wiley & Sons. June. doi:10.1002/9781118646106.ch3.
  • Singh, P., and N. Komodakis. 2018. “Cloud-Gan: Cloud Removal for Sentinel- 2 Imagery Using a Cyclic Consistent Generative Adversarial Networks.” In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, 1772–1775. Valencia, Spain: IEEE, July. isbn: 978-1-5386-7150-4. doi:10.1109/IGARSS.2018.8519033.
  • Team, R Core. 2017. R: A Language and Environment for Statistical Computing. Vienna, Austria. https://www.r-project.org
  • Yin, G., G. Mariethoz, and M. Matthew. 2016. “Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method.” Remote Sensing 9 (1): 12. December. 2072–4292. doi:10.3390/rs9010012.
  • Yin, G., G. Mariethoz, Y. Sun, and M. F. McCabe. 2017. “A Comparison of Gap-filling Approaches for Landsat-7 Satellite Data.” International Journal of Remote Sensing 38 (23): 6653–6679. December. 0143–1161. doi:10.1080/01431161.2017.1363432.
  • Zhang, C., L. Weidong, and D. J. Travis. 2009. “Restoration of Clouded Pixels in Multispectral Remotely Sensed Imagery with Cokriging.” International Journal of Remote Sensing 30 (9): 2173–2195. May. 0143–1161. doi:10.1080/01431160802549294.
  • Zhang, C., W. Li, and D. Travis. 2007. “Gaps-fill of SLC-off Landsat ETM+ Satellite Image Using a Geostatistical Approach.” International Journal of Remote Sensing 28 (22): 5103–5122. November. 0143–1161. doi:10.1080/01431160701250416.
  • Zhu, X., D. Liu, and J. Chen. 2012. “A New Geostatistical Approach for Filling Gaps in Landsat ETM+ SLC-off Images.” Remote Sensing of Environment 124 (September): 49–60. 00344257. doi10.1016/j.rse.2012.04.019.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.