Land-cover mapping in the Nujiang Grand Canyon: integrating spectral, textural, and topographic data in a random forest classifier

References

• Atkinson, P. M., and P. Lewis. 2000. “Geostatistical Classification for Remote Sensing: An Introduction.” Computers & Geosciences 26: 361–371. doi:10.1016/s0098-3004(99)00117-x.
   Web of Science ®Google Scholar
• Benediktsson, J., J. Chanussot, and M. Fauvel. 2007. “Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments.” In Multiple Classifier Systems, edited by M. Haindl, J. Kittler, and F. Roli, 501–512. Heidelberg: Springer.
   Google Scholar
• Berberoglu, S., P. J. Curran, C. D. Lloyd, and P. M. Atkinson. 2007. “Texture Classification of Mediterranean Land Cover.” International Journal of Applied Earth Observation and Geoinformation 9: 322–334. doi:10.1016/j.jag.2006.11.004.
   Web of Science ®Google Scholar
• Bishop, J. B. 2006. “An Evaluation of the Effect of Terrain Normalization on Classification Accuracy of Landsat ETM+ Imagery.” MS diss., University of New Hampshire.
   Google Scholar
• Björn, W., J. Benediktsson, and J. Sveinsson. 2012. “Random Forest Classification of Remote Sensing Data.” In Signal and Image Processing for Remote Sensing, edited by C. H. Chen, 365–374. 2nd ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Blesius, L., and F. Weirich. 2005. “The Use of the Minnaert Correction for Land-Cover Classification in Mountainous Terrain.” International Journal of Remote Sensing 26: 3831–3851. doi:10.1080/01431160500104194.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45: 5–32. doi:10.1023/a:1010933404324.
   Web of Science ®Google Scholar
• Carpenter, G. A., S. Gopal, S. Macomber, S. Martens, and C. E. Woodcock. 1999. “A Neural Network Method for Mixture Estimation for Vegetation Mapping.” Remote Sensing of Environment 70: 138–152.
   Web of Science ®Google Scholar
• Carr, J. R., and F. P. de Miranda. 1998. “The Semivariogram in Comparison to the Co-Occurrence Matrix for Classification of Image Texture.” IEEE Transactions on Geoscience and Remote Sensing 36: 1945–1952. doi:10.1109/36.729366.
   Web of Science ®Google Scholar
• Chan, J. C.-W., and D. Paelinckx. 2008. “Evaluation of Random Forest and Adaboost Tree-Based Ensemble Classification and Spectral Band Selection for Ecotope Mapping Using Airborne Hyperspectral Imagery.” Remote Sensing of Environment 112: 2999–3011. doi:10.1016/j.rse.2008.02.011.
   Web of Science ®Google Scholar
• Chica-Olmo, M., and F. Abarca-Hernández. 2000. “Computing Geostatistical Image Texture for Remotely Sensed Data Classification.” Computers & Geosciences 26: 373–383. doi:10.1016/s0098-3004(99)00118-1.
   Web of Science ®Google Scholar
• Chica-Olmo, M., and F. Abarca-Hernández. 2006. “Variogram Derived Image Texture for Classifying Remotely Sensed Images.” In Remote Sensing Image Analysis: Including the Spatial Domain, edited by S. M. De Jong and F. D. Van der Meer, 93–111. Heidelberg: Springer.
   Google Scholar
• Coburn, C. A., and A. C. B. Roberts. 2004. “A Multiscale Texture Analysis Procedure for Improved Forest Stand Classification.” International Journal of Remote Sensing 25: 4287–4308. doi:10.1080/0143116042000192367.
   Web of Science ®Google Scholar
• Congalton, R. G. 2008. Assessing the Accuracy of Remotely Sensed Data-Principles and Practices. 2nd ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Curran, P. J. 1988. “The Semivariogram in Remote Sensing: An Introduction.” Remote Sensing of Environment 24: 493–507. doi:10.1016/0034-4257(88)90021-1.
   Web of Science ®Google Scholar
• Curran, P. J., and P. M. Atkinson. 1998. “Geostatistics and Remote Sensing.” Progress in Physical Geography 22: 61–78. doi:10.1177/030913339802200103.
   Web of Science ®Google Scholar
• Duro, D. C., S. E. Franklin, and M. G. Dube. 2012. “Multi-Scale Object-Based Image Analysis and Feature Selection of Multi-Sensor Earth Observation Imagery Using Random Forests.” International Journal of Remote Sensing 33: 4502–4526. doi:10.1080/01431161.2011.649864.
   Web of Science ®Google Scholar
• Eiumnoh, A., and R. P. Shrestha. 2000. “Application of DEM Data to Landsat Image Classification – Evaluation in a Tropical Wet-Dry Landscape of Thailand.” Photogrammetric Engineering & Remote Sensing 66: 297–304.
   Web of Science ®Google Scholar
• Ferro, C. J. S., and T. A. Warner. 2002. “Scale and Texture in Digital Image Classification.” Photogrammetric Engineering & Remote Sensing 68: 51–63.
   Web of Science ®Google Scholar
• Gao, Y., and W. Zhang. 2009. “A Simple Empirical Topographic Correction Method for ETM+ Imagery.” International Journal of Remote Sensing 30: 2259–2275. doi:10.1080/01431160802549336.
   Web of Science ®Google Scholar
• Ghimire, B., J. Rogan, V. Galiano, P. Panday, and N. Neeti. 2012. “An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA.” Geoscience & Remote Sensing 49: 623–643. doi:10.2747/1548-1603.49.5.623.
   Web of Science ®Google Scholar
• Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27: 294–300. doi:10.1016/j.patrec.2005.08.011.
   Web of Science ®Google Scholar
• Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson, and IEEE. 2004. “Random Forest Classification of Multisource Remote Sensing and Geographic Data. ” In IGARSS 2004: IEEE International Geoscience and Remote Sensing Symposium Proceedings, Vols. 1–7: Science for Society: Exploring and Managing a Changing Planet, Anchorage, AK, September 20–24, 1049–1052.
   Google Scholar
• Gong, P., D. J. Marceau, and P. J. Howarth. 1992. “A Comparison of Spatial Feature Extraction Algorithms for Land-Use Classification with SPOT HRV Data.” Remote Sensing of Environment 40: 137–151. doi:10.1016/0034-4257(92)90011-8.
   Web of Science ®Google Scholar
• Hale, S. R., and B. N. Rock. 2003. “Impact of Topographic Normalization on Land-Cover Classification Accuracy.” Photogrammetric Engineering and Remote Sensing 69 (7): 785–791.
   Web of Science ®Google Scholar
• Ham, J., Y. Chen, M. M. Crawford, and J. Ghosh. 2005. “Investigation of the Random Forest Framework for Classification of Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 43: 492–501.
   Web of Science ®Google Scholar
• Haralick, R. M., K. Shanmugam, and I. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man and Cybernetics SMC-3 (6): 610–621.
   Web of Science ®Google Scholar
• Holder, L. B., I. Russell, Z. Markov, A. G. Pipe, and B. Carse. 2005. “Current and Future Trends in Feature Selection and Extraction for Classification Problems.” International Journal of Pattern Recognition and Artificial Intelligence 19: 133–142. doi:10.1142/S0218001405004010.
   Web of Science ®Google Scholar
• Jakomulska, A., and K. C. Clarke. 2001. “Variogram-Derived Measures of Textural Image Classification Application to Large-Scale Vegetation Mapping.” In GeoENV III – Geostatistics for Environmental Applications, edited by P. Moneztiez, D. Allard, and R. Froidevaux, 345–355. Amsterdam: Kluwer Academic Publishers.
   Google Scholar
• Janssen, L. L. F., M. N. Jaarsma, and E. T. M. van der Linden. 1990. “Integrating Topographic Data with Remote Sensing for Land-Cover Classification.” Photogrammetric Engineering and Remote Sensing 56: 1503–1506.
   Web of Science ®Google Scholar
• Kobayashi, S., and K. Sanga-Ngoie. 2009. “A Comparative Study of Radiometric Correction Methods for Optical Remote Sensing Imagery: The IRC vs. Other Image-Based C-Correction Methods.” International Journal of Remote Sensing 30 (2): 285–314. doi:10.1080/01431160802356781.
   Web of Science ®Google Scholar
• Lawrence, R. L., S. D. Wood, and R. L. Sheley. 2006. “Mapping Invasive Plants Using Hyperspectral Imagery and Breiman Cutler Classifications (randomForest).” Remote Sensing of Environment 100: 356–362. doi:10.1016/j.rse.2005.10.014.
   Web of Science ®Google Scholar
• Li, P., T. Cheng, and J. Guo. 2009. “Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification.” Photogrammetric Engineering and Remote Sensing 75: 147–157.
   Web of Science ®Google Scholar
• Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques for Improving Classification Performance.” International Journal of Remote Sensing 28: 823–870. doi:10.1080/01431160600746456.
   Web of Science ®Google Scholar
• Marceau, D. J., P. J. Howarth, J. M. Dubois, and D. J. Gratton. 1990. “Evaluation of the Grey-Level Co-Occurrence Matrix Method for Land-Cover Classification Using SPOT Imagery.” IEEE Transactions on Geoscience and Remote Sensing 28: 513–519.
   Web of Science ®Google Scholar
• Meyer, P., K. I. Itten, T. Kellenberger, S. Sandmeier, and R. Sandmeier. 1993. “Radiometric Corrections of Topographically Induced Effects on Landsat TM Data in an Alpine Environment.” ISPRS Journal of Photogrammetry and Remote Sensing 48: 17–28. doi:10.1016/0924-2716(93)90028-1.
   Web of Science ®Google Scholar
• Nichol, J., K. H. Law, and M. S. Wong. 2006. “Empirical Correction of Low Sun Angle Images in Steeply Sloping Terrain: A Slope‐Matching Technique.” International Journal of Remote Sensing 27: 629–635. doi:10.1080/02781070500293414.
   Web of Science ®Google Scholar
• Pal, M. 2003. “Random Forests for Land Cover Classification.” Paper presented at the IGARSS 2003: IEEE International Geoscience and Remote Sensing Symposium, Vols. I–VII, Proceedings: Learning from Earth’s Shapes and Sizes, Toulouse, July 21–25.
   Google Scholar
• Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26: 217–222. doi:10.1080/01431160412331269698.
   Web of Science ®Google Scholar
• Pal, M., and G. M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data by SVM.” IEEE Transactions on Geoscience and Remote Sensing 48 (5): 2297–2307.
   Web of Science ®Google Scholar
• Reeder, D. H. 2002. “Topographic Correction of Satellite Images: Theory and Application.” PhD diss., Dartmouth College.
   Google Scholar
• Riano, D., E. Chuvieco, J. Salas, and I. Aguado. 2003. “Assessment of Different Topographic Corrections in Landsat-TM Data for Mapping Vegetation Types.” IEEE Transactions on Geoscience and Remote Sensing 41: 1056–1061. doi:10.1109/tgrs.2003.811693.
   Web of Science ®Google Scholar
• Rodríguez-Galiano, V. F., F. Abarca-Hernández, B. Ghimire, M. Chica-Olmo, P. M. Atkinson, and C. Jeganathan. 2011. “Incorporating Spatial Variability Measures in Land-Cover Classification Using Random Forest.” Procedia Environmental Sciences 3: 44–49. doi:10.1016/j.proenv.2011.02.009.
   Google Scholar
• Rodriguez-Galiano, V. F., M. Chica-Olmo, F. Abarca-Hernandez, P. M. Atkinson, and C. Jeganathan. 2012. “Random Forest Classification of Mediterranean Land Cover Using Multi-Seasonal Imagery and Multi-Seasonal Texture.” Remote Sensing of Environment 121: 93–107. doi:10.1016/j.rse.2011.12.003.
   Web of Science ®Google Scholar
• Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
   Web of Science ®Google Scholar
• Scott, D. W. , ed. 2008. “The Curse of Dimensionality and Dimension Reduction.” In Multivariate Density Estimation: Theory, Practice, and Visualization, 195–217. New York: John Wiley & Sons.
   Google Scholar
• Sesnie, S. E., B. Finegan, P. E. Gessler, S. Thessler, Z. R. Bendana, and A. M. S. Smith. 2010. “The Multispectral Separability of Costa Rican Rainforest Types with Support Vector Machines and Random Forest Decision Trees.” International Journal of Remote Sensing 31: 2885–2909. doi:10.1080/01431160903140803.
   Web of Science ®Google Scholar
• Shrestha, D. P., and J. A. Zinck. 2001. “Land Use Classification in Mountainous Areas: Integration of Image Processing, Digital Elevation Data and Field Knowledge (Application to Nepal).” International Journal of Applied Earth Observation and Geoinformation 3: 78–85. doi:10.1016/s0303-2434(01)85024-8.
   Google Scholar
• Soenen, S. A., D. R. Peddle, and C. A. Coburn. 2005. “SCS + C: A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain.” IEEE Transactions on Geoscience and Remote Sensing 43: 2148–2159. doi:10.1109/tgrs.2005.852480.
   Web of Science ®Google Scholar
• Stumpf, A., and N. Kerle. 2011. “Object-Oriented Mapping of Landslides Using Random Forests.” Remote Sensing of Environment 115 (10): 2564–2577. doi:10.1016/j.rse.2011.05.013.
   Web of Science ®Google Scholar
• Teillet, P. M., B. Guindon, and D. G. Goodenough. 1982. “On the Slope-Aspect Correction of Multispectral Scanner Data.” Canadian Journal of Remote Sensing 8: 84–106. doi:Issn 0008-2821.
   Google Scholar
• Thomson, A. G., and C. Jones. 1990. “Effects of Topography on Radiance from Upland Vegetation in North Wales.” International Journal of Remote Sensing 11: 829–840. doi:10.1080/01431169008955058.
   Web of Science ®Google Scholar
• Woodcock, C. E., A. H. Strahler, and D. L. B. Jupp. 1988. “The Use of Variograms in Remote Sensing: I. Scene Models and Simulated Images.” Remote Sensing of Environment 25: 323–348. doi:10.1016/0034-4257(88)90108-3.
   Web of Science ®Google Scholar
• Wulder, M. A., S. E. Franklin, J. C. White, M. M. Cranny, and J. A. Dechka. 2004. “Inclusion of Topographic Variables in an Unsupervised Classification of Satellite Imagery.” Canadian Journal of Remote Sensing 30: 137–149. doi:10.5589/m03-063.
   Web of Science ®Google Scholar