Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation

International Journal of Remote Sensing

Volumn 36, Issue 4, 2015, Pages 954-978

  Maxwell, A E ^a   Warner, T A ^b   Strager, M P ^b   Conley, J F ^b   Sharp, A L ^a  

^a Alderson Broaddus College   (United States)

^b WEST VIRGINIA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; FORESTRY; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE ENHANCEMENT; NEAREST NEIGHBOR SEARCH; OPTICAL RADAR; SATELLITE IMAGERY; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; CLASSIFICATION AND REGRESSION TREE; GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS; GREY LEVEL CO-OCCURRENCE MATRIXES; HIGH-DIMENSIONAL FEATURE SPACE; K NEAREST NEIGHBOURS (K-NN); LIGHT DETECTION AND RANGING;

LEARNING ALGORITHMS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LIDAR; MINING; REMOTE SENSING; SATELLITE IMAGERY;

ALGORITHMS; CLASSIFICATION; DATA PROCESSING; IMAGE ANALYSIS; REGRESSION ANALYSIS;

EID: 84923357893     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2014.1001086     Document Type: Article

References (116)

1. Agresti, A. 2007. An Introduction to Categorical Data Analysis, 2nd ed., 400. Hoboken, NJ: Wiley-Interscience.
2. Agüera, F., F. J. Aguilar, and M. A. Aguilar. 2008. “Using Texture Analysis to Improve Per-Pixel Classification of Very High Resolution Images for Mapping Plastic Greenhouses.” ISPRS Journal of Photogrammetry and Remote Sensing 63 (6): 635–646. doi:10.1016/j.isprsjprs.2008.03.003.
3. Alfaro-Cortes, E., M. Gamez-Martinez, and N. Garcia-Rubio. 2012. “Adabag: Applies multiclass AdaBoost.M1, AdaBoost-SAMME and bagging, R Package Version 3.1.” Accessed June1, 2013. http://CRAN.R-project.org/package=adabag
4. Anderson, A. T., and J. Schubert. 1976. “ERTS-1 Data Applied to Strip Mining.” Photogrammetric Engineering & Remote Sensing 42: 211–219.
5. Anderson, A. T., D. Schultz, N. Buchman, and H. M. Nock. 1997. “Landsat Imagery for Surface-Mine Inventory.” Photogrammetric Engineering & Remote Sensing 43 (8): 1027–1036.
6. Asner, G. P., M. Keller, R. Pereira Jr., and J. C. Zweede. 2002. “Remote Sensing of Selective Logging in Amazonia: Assessing Limitation Based on Detailed Field Observations, Landsat ETM+, and Textural Analysis.” Remote Sensing of Environment 80 (3): 483–496. doi:10.1016/S0034-4257(01)00326-1.
7. Baatz, M., and A. Schäpe. 2000. “Multiresolution Segmentation – an Optmization Approach for High Quality Multi-Scale Image Segmentation.” In Angewandte Geographische Informationsverarveitung XII, edited by J. Strobl, T. Blaschke, and G. Griesebener, 12–23. Berlin: Herbert Wichmann Verlag.
8. Baker, B. A., T. A. Warner, J. F. Conley, and B. E. McNeil. 2013. “Does Spatial Resolution Matter? A Multi-Scale Comparison of Object-Based and Pixel-Based Methods for Detecting Change Associated with Gas Well Drilling Operations.” International Journal of Remote Sensing 34 (5): 1633–1651. doi:10.1080/01431161.2012.724540.
9. Bauer, E., and R. Kohavi. 1999. “An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants.” Machine Learning 36 (1/2): 105–139. doi:10.1023/A:1007515423169.
10. Bernhardt, E. S., and M. A. Palmer. 2011. “The Environmental Costs of Mountaintop Mining Valley Fill Operations for Aquatic Ecosystems of the Central Appalachians.” Annals of the New York Academy of Sciences 1223 (1): 39–57. doi:10.1111/j.1749-6632.2011.05986.x.
11. Blanchard, S. D., M. K. Jakubowski, and M. Kelly. 2011. “Object-Based Image Analysis of Downed Logs in Disturbed Forested Landscapes Using Lidar.” Remote Sensing 3: 2420–2439. doi:10.3390/rs3112420.
12. Blaschke, T. 2003. “Object-Based Contextual Image Classification Built on Image Segmentation.” 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Greenbelt, MD, October 27–28, 13–119.
13. Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65: 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
14. Blaschke, T., G. J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Q. Feitosa, F. van der Meer, H. van der Werff, F. van Coillie, and D. Tiede. 2014. “Geographic Object-Based Image Analysis – Towards a New Paradigm.” ISPRS Journal of Photogrammetry and Remote Sensing 87 (100): 180–191. doi:10.1016/j.isprsjprs.2013.09.014.
15. Blaschke, T., and J. Strobl. 2001. “What’s Wrong with Pixels? Some Recent Developments Interfacing Remote Sensing and GIS.” GIS – Zeitschrift Für Geoinformationssysteme 14 (6): 12–17.
16. Bork, E. W., and J. G. Su. 2007. “Integrating Lidar Data and Multispectral Imagery for Enhanced Classification of Rangeland Vegetation: A Meta Analysis.” Remote Sensing of Environment 111 (1): 11–24. doi:10.1016/j.rse.2007.03.011.
17. Bradley, J. V. 1968. Distribution-Free Statistical Tests, 388. Englewood Cliffs, NJ: Prentice Hall.
18. Brantberg, T. 2007. “Classifying Individual Tree Species under Leaf-Off and Leaf-On Conditions Using Airborne Lidar.” ISPRS Journal of Photogrammetry and Remote Sensing 61 (5): 325–340. doi:10.1016/j.isprsjprs.2006.10.006.
19. Breiman, L. 1996. “Bagging Predictors.” Machine Learning 24 (2): 123–140. doi:10.1007/BF00058655.
20. Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
21. Brennan, R., and T. L. Webster. 2006. “Object-Oriented Land Cover Classification of Lidar-Derived Surfaces.” Canadian Journal of Remote Sensing 32 (2): 162–172. doi:10.5589/m06-015.
22. Burges, C. J. C. 1998. “A Tutorial on Support Vector Machines for Pattern Recognition.” Data Mining and Knowledge Discovery 2 (2): 121–167. doi:10.1023/A:1009715923555.
23. Chan, J. C. W., N. Laporte, and R. S. Defries. 2003. “Textural Classification of Logged Forests in Tropical Africa Using Machine-Learning Algorithms.” International Journal of Remote Sensing 24 (6): 1401–1407. doi:10.1080/0143116021000050538.
24. Chen, Y., W. Su, J. Li, and Z. Sun. 2009. “Hierarchical Object Oriented Classification Using Very High Resolution Imagery and Lidar Data over Urban Areas.” Advances in Space Research 43 (7): 1101–1110. doi:10.1016/j.asr.2008.11.008.
25. Chica-Olmo, M., and F. Abarca-Hernández. 2000. “Computing Geostatistical Image Texture for Remotely Sensed Data Classification.” Computers & Geosciences 26 (4): 373–383. doi:10.1016/S0098-3004(99)00118-1.
26. Chubey, M. S., S. E. Franklin, and M. A. Wulder. 2006. “Object-Based Analysis of IKONOS-2 Imagery for Extraction of Forest Inventory Parameters.” Photogrammetric Engineering & Remote Sensing 72 (4): 383–394. doi:10.14358/PERS.72.4.383.
27. Chust, G., I. Galparsoro, Á. Borja, J. Franco, and A. Uriarte. 2008. “Coastal and Estuarine Habitat Mapping, Using Lidar Height and Intensity and Multi-Spectral Imagery.” Estuarine, Coastal and Shelf Science 78 (4): 633–643. doi:10.1016/j.ecss.2008.02.003.
28. Congalton, R. G., and K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed., 183. London: Taylor and Francis.
29. Costa, G. A. O. P., R. Q. Feitosa, T. B. Cazes, and B. Feijó. 2008. Genetic Adaption of Segmentation Parameters. In Object-based Image Analysis, edited by T. Blaschke, S. Lang, and G. J. Hay, 679–695. Berlin: Springer-Verlag.
30. Cowen, D. C., J. R. Jensen, C. J. Hendrix, M. E. Hodgson, and S. R. Schill. 2000. “A GIS-Assisted Rail Construction Econometric Model that Incorporates Lidar Data.” Photogrammetric Engineering and Remote Sensing 66 (11): 1323–1328.
31. Cutler, D. R., T. C. Edwards Jr., K. H. Beard, A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler. 2007. “Random Forests for Classification in Ecology.” Ecology 88 (11): 2783–2792. doi:10.1890/07-0539.1.
32. DeFries, R. S., and J. C. W. Chan. 2000. “Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data.” Remote Sensing of Environment 74 (3): 503–515. doi:10.1016/S0034-4257(00)00142-5.
33. Dietterich, T. G. 1998. “Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms.” Neural Computation 10 (7): 1895–1923. doi:10.1162/089976698300017197.
34. Dingle Robertson, L., and D. J. King. 2011. “Comparison of Pixel- and Object-Based Classification in Land Cover Change Mapping.” International Journal of Remote Sensing 32: 1505–1529. doi:10.1080/01431160903571791.
35. Drăgut, L., and T. Blaschke. 2006. “Automated Classification of Landform Elements Using Object-Based Image Analysis.” Geomorphology 81: 330–344. doi:10.1016/j.geomorph.2006.04.013.
36. Drummond, M. A., and T. R. Loveland. 2010. “Land-Use Pressure and a Transition to Forest-Cover Loss in the Eastern United States.” Bioscience 60 (4): 286–298. doi:10.1525/bio.2010.60.4.7.
37. Duro, D. C., S. E. Franklin, and M. G. Dubé. 2012a. “A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery.” Remote Sensing of Environment 118: 259–272. doi:10.1016/j.rse.2011.11.020.
38. Duro, D. C., S. E. Franklin, and M. G. Dubé. 2012b. “Multi-Scale Object-Based Image Analysis and Feature Selection of Multi-Sensor Earth Observation Imagery Using Random Forests.” International Journal of Remote Sensing 33 (14): 4502–4526. doi:10.1080/01431161.2011.649864.
39. Ferro, C. H. S., and T. A. Warner. 2002. “Scale and Texture in Digital Image Classification.” Photogrammetric Engineering & Remote Sensing 68 (1): 51–63.
40. Flood, M. 2001. “Laser Altimetry: From Science to Commercial Lidar Mapping.” Photogrammetric Engineering & Remote Sensing 67 (11): 1209–1217.
41. Foody, G. M. 2004. “Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy.” Photogrammetric Engineering & Remote Sensing 70 (5): 627–633. doi:10.14358/PERS.70.5.627.
42. Franklin, S. E., R. J. Hall, L. M. Moskal, A. J. Maudie, and M. B. Lavigne. 2000. “Incorporating Texture into Classification of Forest Species Composition from Airborne Multispectral Images.” International Journal of Remote Sensing 21 (1): 61–79. doi:10.1080/014311600210993.
43. Freund, Y., and R. E. Schapire. 1996. “Experiments with the New Boosting Algorithm.” Machine Learning: Proceeding of the Thirteenth International Conference, San Francisco, CA, July 3–6, 148–156.
44. Friedl, M. A., C. E. Brodley, and A. H. Strahler. 1999. “Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales.” IEEE Transactions on Geoscience and Remote Sensing 37 (2): 969–977. doi:10.1109/36.752215.
45. Friedl, M. A., D. K. McIver, J. C. F. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, S. Gopal, A. Schneider, A. Cooper, A. Baccini, F. Gao, and C. Schaaf. 2002. “Global Land Cover Mapping from MODIS: Algorithms and Early Results.” Remote Sensing of Environment 83: 287–302. doi:10.1016/S0034-4257(02)00078-0.
46. Ghimire, B., J. Rogan, and J. Miller. 2010. “Contextual Land-Cover Classification: Incorporating Spatial Dependence in Land-Cover Classification Models Using Random Forests and the Getis Statistic.” Remote Sensing Letters 1 (1): 45–54. doi:10.1080/01431160903252327.
47. Ghimire, B., J. Rogan, V. Rodríguez-Galiano, P. Panday, and N. Neeti. 2012. “An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA.” GIScience & Remote Sensing 49 (5): 623–643. doi:10.2747/1548-1603.49.5.623.
48. Guo, L., N. Chehata, C. Mallet, and S. Boukir. 2011. “Relevance of Airborne Lidar and Multispectral Image Data for Urban Scene Classification Using Random Forests.” ISPRS Journal of Photogrammetry and Remote Sensing 66: 56–66. doi:10.1016/j.isprsjprs.2010.08.007.
49. Guo, Q., M. Kelly, P. Gong, and D. Liu. 2007. “An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery.” GIScience & Remote Sensing 44 (1): 24–47. doi:10.2747/1548-1603.44.1.24.
50. Hansen, M. C., and B. Reed. 2000. “A Comparison of the IGBP Discover and University of Maryland 1 Km Global Land-Cover Products.” International Journal of Remote Sensing 21: 1365–1373. doi:10.1080/014311600210218.
51. Haralick, R. M., K. Shanmugam, and I. H. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man and Cybernetics 3 (6): 610–621. doi:10.1109/TSMC.1973.4309314.
52. Haralick, R. M., and K. S. Shanmugam. 1974. “Combined Spectral and Spatial Processing of ERTS Imagery Data.” Remote Sensing of Environment 3 (1): 3–13. doi:10.1016/0034-4257(74)90033-9.
53. Hardin, P. J., and C. N. Thomson. 1992. “Fast Nearest Neighbor Classification Methods for Multispectral Imagery.” TheProfessional Geographer 44 (2): 191–202. doi:10.1111/j.0033-0124.1992.00191.x.
54. Irons, J. R., and R. L. Kennard. 1986. “The Utility of Thematic Mapper Sensor Characteristics for Surface Mine Monitoring.” Photogrammetric Engineering & Remote Sensing 52: 389–396.
55. Joachims, T. 1998. “Text Categorization with Support Vector Machines: Learning with Many Relevant Features.” Proceedings of European Conference on Machine Learning, Chemnitz, April 21–23, 137–142.
56. Johansen, K., N. C. Coops, S. E. Gergel, and Y. Stange. 2007. “Application of High Spatial Resolution Satellite Imagery for Riparian and Forest Ecosystem Classification.” Remote Sensing of Environment 110: 29–44. doi:10.1016/j.rse.2007.02.014.
57. Kaasalainen, S., E. Ahoka, E. Hyyppa, and J. Suomalainen. 2005. “Study of Surface Brightness from Backscattering Laser Intensity: Calibration of Laser Data.” IEEE Geoscience and Remote Sensing Letters 2 (3): 255–259. doi:10.1109/LGRS.2005.850534.
58. Kazar, S. A., and T. A. Warner. 2013. “Assessment of Carbon Storage and Biomass on Minelands Reclaimed to Grassland Environments Using Landsat Spectral Indices.” Journal of Applied Remote Sensing 7 (1): 073583. doi:10.1117/1.JRS.7.073583.
59. Keene, T., and J. Skousen. 2010. “Mine Spoil Reclamation with Switchgrass for Biofuel Production.” 2010 National Meeting of the American Society for Mining and Reclamation, Pittsburgh, PA, June 5–11, 1–16.
60. Ke, Y., L. J. Quackenbush, and J. Im. 2010. “Synergistic Use of Quickbird Multispectral Imagery and Lidar Data for Object-Based Forest Species Classification.” Remote Sensing of Environment 114: 1141–1154. doi:10.1016/j.rse.2010.01.002.
61. Kim, M., M. Madden, and T. A. Warner. 2009. “Forest Type Mapping Using Object-Specific Texture Measures from Multispectral IKONOS Imagery: Segmentation Quality and Image Classification Issues.” Photogrammetric Engineering & Remote Sensing 75 (7): 819–829. doi:10.14358/PERS.75.7.819.
62. Kim, M., T. A. Warner, M. Madden, and D. Atkinson. 2011. “Multi-Scale Texture Segmentation and Classification of Salt Marsh Using Digital Aerial Imagery with Very High Spatial Resolution.” International Journal of Remote Sensing 32: 2825–2850. doi:10.1080/01431161003745608.
63. Laliberte, A., E. L. Fredrickson, and A. Rango. 2007. “Combining Decision Trees with Hierarchical Object-Oriented Image Analysis for Mapping Arid Rangelands.” Photogrammetric Engineering & Remote Sensing 73: 197–207. doi:10.14358/PERS.73.2.197.
64. Lawrence, R., A. Bunn, S. Powell, and M. Zambon. 2004. “Classification of Remotely Sensed Imagery Using Stochastic Gradient Boosting as a Refinement of Classification Tree Analysis.” Remote Sensing of Environment 90 (3): 331–336. doi:10.1016/j.rse.2004.01.007.
65. Liaw, A., and M. Wiener. 2002. “Classification and Regression by Randomforest.” R News 2 (3): 18–22.
66. Lillesand, T. M., R. W. Kiefer, and J. W. Chipman. 2008. Remote Sensing and Image Interpretation, 6th ed., 756. Hoboken, NJ: Wiley & Sons.
67. Lin, Y., and J. P. Mills. 2010. “Factors Influencing Pulse Width of Small Footprint, Full Waveform Airborne Laser Scanning Data.” Photogrammetric Engineering & Remote Sensing 76 (1): 49–59. doi:10.14358/PERS.76.1.49.
68. Liu, D., and F. Xia. 2010. “Assessing Object-Based Classification: Advantages and Limitations.” Remote Sensing Letters 1: 187–194. doi:10.1080/01431161003743173.
69. Mallinis, G., N. Koutsias, M. Tsakiri-Strati, and M. Karteris. 2008. “Object-Based Classification Using Quickbird Imagery for Delineating Forest Vegetation Polygons in a Mediterranean Test Site.” ISPRS Journal of Photogrammetry and Remote Sensing 63: 237–250. doi:10.1016/j.isprsjprs.2007.08.007.
70. Master, L. L., S. R. Flack, and B. A. Stein. 1998. Rivers of Life: Critical Watersheds for Protecting Freshwater Biodiversity, 77. Arlington, TX: The Nature Conservancy.
71. Mathieu, R., J. Aryal, and A. K. Chong. 2007. “Object-Based Classification of IKONOS Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas.” Sensors 7: 2860–2880. doi:10.3390/s7112860.
72. Maxwell, A. E., M. P. Strager, T. A. Warner, N. P. Zegre, and C. B. Yuill. 2014. “Comparison of NAIP Orthophotography and Rapideye Satellite Imagery for Mapping of Mining and Mine Reclamation.” GIScience & Remote Sensing 51 (3): 301–320. doi:10.1080/15481603.2014.912874.
73. Maxwell, A. E., T. A. Warner, M. P. Strager, and M. Pal. 2014. “Combining Rapideye Satellite Imagery and Lidar for Mapping of Mining and Mine Reclamation.” Photogrammetric Engineering & Remote Sensing 80 (2): 179–189. doi:10.14358/PERS.80.2.179-189.
74. McIver, D. K., and M. A. Friedl. 2002. “Using Prior Probabilities in Decision-Tree Classification of Remotely Sensed Data.” Remote Sensing of Environment 81 (2–3): 253–261. doi:10.1016/S0034-4257(02)00003-2.
75. Meinel, G., and M. Neubert. 2004. “A Comparison of Segmentation Programs for High Resolution Remote Sensing Data.” International Archives of the ISPRS 35: 1097–1105.
76. Meneguzzo, D. M., G. C. Liknes, and M. D. Nelson. 2013. “Mapping Trees outside Forests Using High-Resolution Aerial Imagery: A Comparison of Pixel- and Object-Based Classification Approaches.” Environmental Monitoring and Assessment 185: 6261–6275. doi:10.1007/s10661-012-3022-1.
77. Merriam, E. R., J. T. Petty, M. P. Strager, A. E. Maxwell, and P. F. Ziemkiewicz. 2013. “Scenario Analysis Predicts Context-Dependent Stream Response to Landuse Change in a Heavily Mined Central Appalachian Watershed.” Freshwater Science 32 (4): 1246–1259. doi:10.1899/13-003.1.
78. Meyer, D., E. Dimitriadou, K. Hornik, A. Weingessel, and F. Leisch. 2012. “e1071: Misc functions of the department of statistics (e1071).” R Package Version 1.6-1. Accessed June1. http://CRAN.R-project.org/package=e1071
79. Muchoney, D., J. Borak, H. Chi, M. Friedl, S. Gopal, J. Hodges, N. Morrow, and A. Strahler. 2000. “Application of the MODIS Global Supervised Classification Model to Vegetation and Land Cover Mapping of Central America.” International Journal of Remote Sensing 21 (6–7): 1115–1138. doi:10.1080/014311600210100.
80. Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng. 2011. “Per-Pixel Vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery.” Remote Sensing of Environment 115: 1145–1161. doi:10.1016/j.rse.2010.12.017.
81. O’Neil-Dunne, J. P. M., S. W. MacFaden, A. R. Royar, and K. C. Pelletier. 2013. “An Object-Based System for Lidar Data Fusion and Feature Extraction.” Geocarto International 28 (3): 227–242. doi:10.1080/10106049.2012.689015.
82. Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
83. Pal, M., and P. M. Mather. 2005. “Support Vector Machines for Classification in Remote Sensing.” International Journal of Remote Sensing 26 (5): 1007–1011. doi:10.1080/01431160512331314083.
84. Palmer, M. A., E. S. Bernhardt, W. H. Schlesinger, K. N. Eshleman, E. Fourfoula-Georgiou, M. S. Hendryx, A. D. Lemly, G. E. Likens, O. L. Loucks, M. E. Power, P. S. White, and P. R. Wilcock. 2010. “Mountaintop Mining Consequences.” Science 327: 148–149. doi:10.1016/j.isprsjprs.2011.11.002.
85. Parks, N. F., G. W. Petersen, and G. M. Baumer. 1987. “High-Resolution Remote Sensing of Spatially and Spectrally Complex Coal Surface Mines of Central Pennsylvania: A Comparison between SPOT, MSS, and Landsat-5 Thematic Mapper.” Photogrammetric Engineering & Remote Sensing 53 (4): 415–420.
86. Platt, R. V., and L. Rapoza. 2008. “An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification.” The Professional Geographer 60 (1): 87–100. doi:10.1080/00330120701724152.
87. Pontius, R. G.Jr., and M. Millones. 2011. “Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment.” International Journal of Remote Sensing 32 (15): 4407–4429. doi:10.1080/01431161.2011.552923.
88. Prakash, A., and R. P. Gupta. 1998. “Land-Use Mapping and Change Detection in a Coal Mining Area – a Case Study in the Jharia Coalfield, India.” International Journal of Remote Sensing 19 (3): 391–410. doi:10.1080/014311698216053.
89. Pu, R., S. Landry, and Q. Yu. 2011. “Object-Based Urban Detailed Land Cover Classification with High Spatial Resolution IKONOS Imagery.” International Journal of Remote Sensing 32: 3285–3308. doi:10.1080/01431161003745657.
90. R Core Development Team. 2012. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Accessed June1, 2013. http://www.R-project.org
91. Rathore, C. S., and R. Wright. 1993. “Monitoring Environmental Impacts of Surface Coal-Mining.” International Journal of Remote Sensing 14 (6): 1021–1042. doi:10.1080/01431169308904394.
92. Rodríguez-Galiano, V. F., M. Chica-Olma, F. Abarca-Hernández, 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.
93. Rodríguez-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.
94. Saylor, K. L. 2008. “Land Cover Trend Project: Central Appalachians.” U.S. Department of the Interior, U.S. Geological Survey. Washington, DC. Accessed June1, 2013. http://landcovertrends.usgs.gov/east/eco69Report.html
95. Schliep, K., and K. Hechenbichler. 2014. “Package ‘kknn’.” Accessed July7. http://140.247.115.176/mirrors/cran.r-project.org/web/packages/kknn/kknn.pdf
96. Sen, S., C. E. Zipper, R. H. Wynne, and P. F. Donovan. 2012. “Identifying Revegetated Mines as Disturbance/Recovery Trajectories Using an Interannual Landsat Chronosequence.” Photogrammetric Engineering & Remote Sensing 78 (3): 223–235. doi:10.14358/PERS.78.3.223.
97. Simmons, J. A., W. S. Currie, K. N. Eshleman, K. Kuers, S. Monteleone, T. L. Negley, B. R. Pohlad, and C. L. Thomas. 2008. “Forest to Reclaimed Mine Land Use Change Leads to Altered Ecosystem Structure and Function.” Ecological Applications 18 (1): 104–118. doi:10.1890/07-1117.1.
98. Smith, A. 2010. “Image Segmentation Scale Parameter Optimization and Land Cover Classification Using the Random Forest Algorithm.” Journal of Spatial Science 55: 69–79. doi:10.1080/14498596.2010.487851.
99. Stal, C., F. Tack, P. De Maeyer, A. De Wulf, and R. Goossens. 2013. “Airborne Photogrammetry and Lidar for DSM Extraction and 3D Change Detection over an Urban Area- a Comparative Study.” International Journal of Remote Sensing 34 (4): 1087–1110. doi:10.1080/01431161.2012.717183.
100. Steele, B. M. 2000. “Combining Multiple Classifiers An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping.” Remote Sensing of Environment 74 (3): 545–556. doi:10.1016/S0034-4257(00)00145-0.
101. Steele, B. M., J. C. Winne, and R. L. Redmond. 1998. “Estimation and Mapping of Misclassification Probabilities for Thematic Land-Cover Maps.” Remote Sensing of Environment 66 (2): 192–202. doi:10.1016/S0034-4257(98)00061-3.
102. Stehman, S. V., and R. L. Czaplewski. 1998. “Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles.” Remote Sensing of Environment 64: 331–344. doi:10.1016/S0034-4257(98)00010-8.
103. Stein, B. A., L. S. Kutner, and J. S. Adams. 2000. Precious Heritage: The Status of Biodiversity in the United States, 399. New York: Oxford University Press.
104. Townsend, P. A., D. P. Helmers, C. C. Kingdon, B. E. McNeil, K. M. De Beurs, and K. N. Eshleman. 2009. “Changes in the Extent of Surface Mining and Reclamation in the Central Appalachians Detected Using a 1976-2006 Landsat Time Series.” Remote Sensing of Environment 113: 62–72. doi:10.1016/j.rse.2008.08.012.
105. Trimble. 2011. Ecognition Developer 8.64.1 User Guide. Munich: Trimble.
106. Tso, B., and P. Mather. 2003. Classification Methods for Remotely Sensed Data, 352. New York: CRC Press.
107. Tyc, G., J. Tulip, D. Schulten, M. Krischke, and M. Oxfort. 2005. “The Rapideye Mission Design.” Acta Astronautica 56 (1–2): 213–219. doi:10.1016/j.actaastro.2004.09.029.
108. Vapnik, V. N. 1995. The Nature of Statistical Learning Theory, 188. New York: Springer-Verlag.
109. Walter, V. 2004. “Object-Based Classification of Remote Sensing Data for Change Detection.” ISPRS Journal of Photogrammetry and Remote Sensing 58 (3–4): 225–238. doi:10.1016/j.isprsjprs.2003.09.007.
110. Warner, T. 2011. “Kernel-Based Texture in Remote Sensing Image Classification.” Geography Compass 5 (10): 781–798. doi:10.1111/j.1749-8198.2011.00451.x.
111. Warner, T. A., and F. Nerry. 2009. “Does Single Broadband or Multispectral Thermal Data Add Information for Classification of Visible, Near- and Shortwave Infrared Imagery of Urban Areas?” International Journal of Remote Sensing 30 (9): 2155–2171. doi:10.1080/01431160802549286.
112. Wickham, J. D., K. H. Riitters, T. G. Wade, M. Coan, and C. Homer. 2007. “The Effect of Appalachian Mountaintop Mining on Interior Forest.” Landscape Ecology 22: 179–187. doi:10.1007/s10980-006-9040-z.
113. Yu., O., P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer. 2006. “Object-Based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery.” Photogrammetric Engineering & Remote Sensing 72: 799–811. doi:10.14358/PERS.72.7.799.
114. Yuill, C. 2003. “Landscape Use Assessment: Mountaintop Mining and the Mountaintop Mining Region of West Virginia.” Draft Programmatic Environmental Impact Statement on Mountaintop Mining/Valley Fills in Appalachia III: F–12.
115. Zégre, N. P., A. J. Miller, A. Maxwell, and S. J. Lamont. 2014. “Multiscale Analysis of Hydrology in a Mountaintop Min-Impacted Watershed.” Journal of the American Water Resources Association. doi:10.1111/jawr.12184.
116. Zhou, W. 2013. “An Object-Based Approach for Urban Land Cover Classification: Integrating Lidar Height and Intensity Data.” IEEEGeoscience and Remote Sensing Letters 10 (4): 928–931. doi:10.1109/LGRS.2013.2251453.