Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

Remote Sensing

Volumn 5, Issue 7, 2013, Pages 3212-3238

  Corcoran, Jennifer M ^a   Knight, Joseph F ^a   Gallant, Alisa L ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b U S GEOLOGICAL SURVEY   (United States)

Author keywords

Classification; Data integration; Decision tree; Random forest; Wetland

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION CONFIDENCE; CLASSIFICATION METHODS; LAND COVER CLASSIFICATION; RANDOM FOREST CLASSIFICATION; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; REMOTE SENSING PLATFORMS;

DATA INTEGRATION; DECISION TREES; INPUT OUTPUT PROGRAMS; MAPPING; REMOTE SENSING; SATELLITES; WETLANDS;

CLASSIFICATION (OF INFORMATION);

EID: 84883234670     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs5073212     Document Type: Article

References (97)

1. Vymazal, J. Constructed wetlands for wastewater treatment. Ecol. Eng. 2005, 25, 475-477.
2. Mitsch, W.J.; Gosselink, J.G. The value of wetlands: Importance of scale and landscape setting. Ecol. Econ. 2000, 35, 25-33.
3. Lane, C.R.; D'Amico, E. Calculating the ecosystem service of water storage in isolated wetlands using LiDAR in North Central Florida, USA. Wetlands 2010, 30, 967-977.
4. Deschamps, A.; Greenlee, D.; Pultz, T.J.; Saper, R. Geospatial Data Integration for Applications in Flood Prediction and Management in the Red River Basin. In Proceedings of IEEE International Geosciences and Remote Sensing Symposium and the 24th Canadian Symposium on Remote Sensing, Toronto, Canada, 24-28 June, 2002; pp. 3338-3340.
5. Van der Kamp, G.; Hayashi, M. The groundwater recharge function of small wetlands in the semi-arid northern prairies. Wetlands 1998, 8, 39-56.
6. Acharya, G.; Barbier, E.B. Valuing groundwater recharge through agricultural production in the Hadejia-Nguru Wetlands in Northern Nigeria. Agr. Econ. 2000, 22, 247-259.
7. Hamilton, S.K.; Kellndorfer, J.; Lehner, B.; Tobler, M. Remote sensing of floodplain geomorphology as a surrogate for biodiversity in a tropical river system (Madre De Dios, Peru). Geomorphology 2007, 89, 23-38.
8. Richardson, D.M.; Holmes, P.M.; Esler, K.J.; Galatowitsch, S.M.; Stromberg, J.C.; Kirkman, S.P.; Pyšek, P.; Hobbs, R.J. Riparian vegetation: Degradation, alien plant invasions, and restoration prospects. Divers. Distrib. 2007, 13, 126-139.
9. Mayer, P.M.; Galatowitsch, S.M. Diatom communities as ecological indicators of recovery in Restored Prairie Wetlands. Wetlands 1999, 19, 765-774.
10. Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.; Tignor, M.; Miller, H. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2007.
11. Hearne, R. Evolving water management institutions in the Red River Basin. Environ. Manage. 2007, 40, 842.
12. Environmental Laboratory. Corps of Engineers Wetlands Delineation Manual; Wetland Research Program; US Army Corps of Engineers: Vicksburg, MS, USA, 1987; Y-87-1.
13. Wissinger, S.A. Ecology of Wetland Invertebrates. In Invertebrates in Freshwater Wetlands of North America: Ecology and Management; Batzer, D.P., Rader, R.B., Wissinger, S.A., Eds.; WIley: New York, NY, USA, 1999; pp. 1043-1086.
14. Van der Sande, C.J.; de Jong, S.M.; de Roo, A.P.J. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int. J. Appl. Earth Obs. Geoinf. 2003, 4, 217-229.
15. Ramsey, E.; Rangoonwala, A.; Middleton, B.; Lu, Z. Satellite optical and radar data used to track wetland forest impact and short-term recovery from Hurricane Katrina. Wetlands 2009, 29, 66-79.
16. Nel, J.; Roux, D.; Abell, R.; Ashton, P.; Cowling, R.; Higgins, J.; Thieme, M.; Viers, J. Progress and challenges in freshwater conservation planning. Aquat. Conserv: Mar. Freshw. Ecosyst. 2009, 19, 474-485.
17. Mayer, A.L.; Lopez, R.D. Use of remote sensing to support forest and wetlands policies in the USA. Remote Sens. 2011, 3, 1211-1233.
18. Dahl, T.E. Wetlands Losses in the United States 1780's to 1980's; US Department of the Interior, Fish and Wildlife Service, Office of Biological Services: Washington, DC, USA, 1990; p. 13.
19. Minnesota Department of Natural Resources. Wetlands Status and Trends. Available online: http://www.dnr.state.mn.us/eco/wetlands/wstm_prog.html (accessed on 20 August 2010).
20. Haas, E.M.; Bartholomé, E.; Lambin, E.F.; Vanacker, V. Remotely sensed surface water extent as an indicator of short-term changes in ecohydrological processes in Sub-Saharan Western Africa. Remote Sens. Environ. 2011, 115, 3436-3445.
21. Goetz, S. Remote sensing of riparian buffers: Past progress and future prospects. J. Am. Water Resour. Assoc. 2006, 42, 133-143.
22. Dahl, T.E.; Watmough, M.D. Current approaches to wetland status and trends monitoring in Prairie Canada and the Continental United States of America. Can. J. Remote Sens. 2007, 33, S17-S27.
23. Moore, I.; Grayson, R.; Landson, A. Digital Terrain Modeling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process. 1991, 5, 330.
24. Sader, S.A.; Ahl, D.; Liou, W. Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sens. Environ. 1995, 53, 133-144.
25. Li, R.; Zhu, A.; Song, X.; Li, B.; Pei, T.; Qin, C. Effects of spatial aggregation of soil spatial information on watershed hydrological modeling. Hydrol. Process. 2012, 26, 1390-1404.
26. Evans, T.L.; Costa, M.; Telmer, K.; Silva, T.S.F. Using ALOS/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2010, 3, 560-575.
27. Li, G.; Lu, D.; Moran, E.; Dutra, L.; Batistella, M. A Comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. ISPRS J. Photogramm. 2012, 70, 26-38.
28. Pereira, F.R.S.; Kampel, M.; Cunha-Lignon, M. Mapping of mangrove forests on the Southern Coast of São Paulo, Brazil, using Synthetic Aperture Radar data from ALOS/PALSAR. Remote Sens. Lett. 2012, 3, 567-576.
29. Bwangoy, J.B.; Hansen, M.C.; Roy, D.P.; Grandi, G.D.; Justice, C.O. Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices. Remote Sens. Environ. 2010, 114, 73-86.
30. Castañeda, C.; Ducrot, D. Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery. J. Environ. Manage. 2009, 90, 2270-2277.
31. Li, J.; Chen, W. Clustering Synthetic Aperture Radar (SAR) imagery using an automatic approach. Can. J. Remote Sens. 2007, 33, 303-311.
32. Bolstad, P.V.; Lillesand, T.M. Rule-based classification models: Flexible integration of satellite imagery and thematic spatial data. Photogramm. Eng. Remote Sensing 1992, 58, 965-971.
33. Friedl, M.A.; Brodley, C.E. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 1997, 61, 399-409.
34. Li, J.; Chen, W. A rule-based method for mapping Canada's wetlands using optical, radar and DEM data. Int. J. Remote Sens. 2005, 26, 5051-5069.
35. Pal, M.; Mather, P. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 2003, 86, 554-565.
36. Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS J. Photogramm. 2012, 67, 93-104.
37. Naidoo, L.; Cho, M.A.; Mathieu, R.; Asner, G. Classification of savanna tree species, in the Greater Kruger National Park Region, by integrating hyperspectral and lidar data in a Random Forest data mining environment. ISPRS J. Photogramm. 2012, 69, 167-179.
38. Guo, L.; Chehata, N.; Mallet, C.; Boukir, S. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS J. Photogramm. 2011, 66, 56-66.
39. Minnesota Department of Natural Resources. Natural History-Minnesota's Geology. Available online: http://www.dnr.state.mn.us/snas/naturalhistory.html (accessed on 31 March 2011).
40. United States Department of Agriculture. National Agricultural Statistics Service (NASS) Cropland Data Layer. Available online: http://www.nass.usda.gov/research/Cropland/SARS1a.htm (accessed on 31 March 2011).
41. Minnesota Department of Administration (AdminMN). Office of Geographic and Demographic Analysis State Demographic Center, 2010 Census: Minnesota City Profiles. Available online: http://www.demography.state.mn.us/CityProfiles2010/index.html (accessed on 12 September 2012).
42. National Oceanic and Atmospheric Administration. National Climatic Data Center (NCDC). Normals, Means, and Extremes for Duluth, MN. Available online: http://www.ncdc.noaa.gov/cdo-web/datasets/NORMAL_MLY/stations/GHCND:USW0 0014913/detail (accessed on 12 August 2012).
43. Minnesota Department of Natural Resources. State Climatology Office, MN Climatology Working Group Historical Climate Data. Available online: http://climate.umn.edu/doc/historical.htm (accessed on 9 April 2012).
44. Cowardin, L.; Carter, V.; Golet, F.; LaRoe, E. Classification of Wetlands and Deepwater Habitats of the United States; US Department of the Interior, Fish and Wildlife Service: Washington, DC, USA, 1979; pp. 1-79.
45. United States Fish and Wildlife Service. National Wetlands Inventory (NWI). Available online: http://www.fws.gov/wetlands/(accessed on 11 October 2011).
46. Foody, G.M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 2002, 80, 185-201.
47. Yuan, F.; Sawaya, K.E.; Loeffelholz, B.C.; Bauer, M.E. Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sens. Environ. 2005, 98, 317-328.
48. Lunetta, R.; Congalton, R.; Fenstermaker, L.; Jensen, J.; McGwire, K.; Tinny, L. Remote Sensing and Geographic Information System data integration: Error sources and research issues. Photogramm. Eng. Remote Sensing 1991, 57, 677-687.
49. Janssen, L.L.F.; van der Wel, F.J.M. Accuracy assessment of satellite derived land-cover data: A review. Photogramm. Eng. Remote Sensing 1994, 60, 419-426.
50. Liaw, A.; Wiener, M. Classification and Regression by randomForest. R News 2002, 18-22.
51. Harken, J.; Sugumaran, R. Classification of Iowa wetlands using an airborne hyperspectral image: A comparison of the Spectral Angle Mapper classifier and an object-oriented approach. Can. J. Remote Sens. 2005, 31, 167-174.
52. Hogg, A.R.; Todd, K.W. Automated discrimination of upland and wetland using terrain derivatives. Can. J. Remote Sens. 2007, 33, S68-S83.
53. Breiman, L. Random Forests. Machine Learn. 2001, 45, 5-32.
54. US Geological Survey. Accuracy of the National Elevation Dataset (NED). Available online: http://ned.usgs.gov/Ned/accuracy.asp (accessed on 10 March 2011).
55. Tarboton, D.G.; Bras, R.L.; Rodriguez-Iturbe, I. On the extraction of channel networks from digital elevation data. Hydrol. Process. 1991, 5, 81-100.
56. Natural Resources Conservation Service, US Department of Agriculture. Soil Survey Geographic (SSURGO) Database for St. Louis and Carlton County, MN. Available online: http://soildatamart.nrcs.usda.gov (accessed on 1 September 2009).
57. Zhu, Z.; Woodcock, C.E.; Rogan, J.; Kellndorfer, J. Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data. Remote Sens. Environ. 2012, 117, 72-82.
58. Baker, C.; Lawrence, R.; Montagne, C.; Patten, D. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision tree based models. Wetlands 2006, 26, 465.
59. Shih, S.F.; Jordan, J.D. Landsat mid-infrared data and GIS in regional surface soil-moisture assessment. J. Am. Water Resour. Assoc. 1992, 28, 713-719.
60. Song, C.; Woodcock, C.E.; Seto, K.C.; Lenney, M.P.; Macomber, S.A. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sens. Environ. 2001, 75, 230-244.
61. Ozesmi, S.L.; Bauer, M.E. Satellite remote sensing of wetlands. Wetlands Ecol. Manage. 2002, 10, 381-402.
62. Crist, E.P.; Cicone, R.C. A physically-based transformation of Thematic Mapper data-The TM tasseled cap. IEEE Trans. Geosci. Remote Sens. 1984, 22, 256.
63. Kauth, R.; Thomas, G. The Tasselled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as seen by Landsat. In Proceedings of Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA, 29 June-1 July 1976; pp. 4B 41-51.
64. Cohen, W.B.; Spies, T.A. Estimating structural attributes of Douglas-fir/western Hemlock Forest stands from Landsat and SPOT imagery. Remote Sens. Environ. 1992, 41, 1-17.
65. Dymond, C.C.; Mladenoff, D.J.; Radeloff, V.C. Phenological differences in tasseled cap indices improve deciduous forest classification. Remote Sens. Environ. 2002, 80, 460-472.
66. Jin, S.; Sader, S.A. Comparison of time series tasseled cap wetness and the Normalized Difference Moisture Index in detecting forest disturbances. Remote Sens. Environ. 2005, 94, 364-372.
67. Kaya, S. Personal Communication, Environment Canada, Canada Center for Remote Sensing, Ottawa, ON, Canada, 30 June 2010.
68. Bouchemakh, L.; Smara, Y.; Boutarfa, S.; Hamadache, Z. A Comparative Study of Speckle Filtering in Polarimetric Radar SAR Images. In Proceedings of 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria, 7-11 April 2008; pp. 1-6.
69. Parmuchi, M.G.; Karszenbaum, H.; Kandus, P. Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based classifier. Can. J. Remote Sens. 2002, 28, 175-186.
70. van Zyl, J. Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Trans. Geosci. Remote Sens. 1989, 27, 36-45.
71. Henderson, F.M.; Lewis, A.J. Radar detection of wetland ecosystems: A review. Int. J. Remote Sens. 2008, 29, 5809-5835.
72. Baghdadi, N.; Bernier, M.; Gauthier, R.; Neeson, I. Evaluation of C-band SAR data for wetlands mapping. Int. J. Remote Sens. 2001, 22, 71-88.
73. Slatton, K.C.; Crawford, M.M.; Chang, L.D. Modeling temporal variations in multipolarized radar scattering from intertidal coastal wetlands. ISPRS J. Photogramm. 2008, 63, 559-577.
74. Wang, Y.; Davis, F.W. Decomposition of polarimetric Synthetic Aperture Radar backscatter from upland and flooded forests. Int. J. Remote Sens. 1997, 18, 1319-1332.
75. Freeman, A.; Durden, S. A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 1998, 36, 963-973.
76. Cloude, S.; Pottier, E. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 1997, 35, 68-78.
77. Corcoran, J.; Knight, J.; Brisco, B.; Kaya, S.; Cull, A.; Murnaghan, K. The integration of optical, topographic, and radar data for wetland mapping in Northern Minnesota. Can. J. Remote Sens. 2011, 37, 564.
78. Sartori, L.R.; Imai, N.N.; Mura, J.C.; Novo, E.M.L.M.; Silva, T.S.F. Mapping Macrophyte Species in the Amazon Floodplain wetlands using fully polarimetric ALOS/PALSAR data. IEEE Trans. Geosci. Remote Sens. 2011, 49, 4717-4728.
79. Brisco, B.; Kapfer, M.; Hirose, T.; Tedford, B.; Liu, J. Evaluation of C-band polarization diversity and polarimetry for wetland mapping. Can. J. Remote Sens. 2011, 37, 82-92.
80. Congalton, R., Green, K., Eds. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed.; CRC Press: Boca Raton, FL, USA; 2008; p. 200.
81. Wright, C.; Gallant, A. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sens. Environ. 2007, 107, 582-605.
82. Jensen, J.R.; Christensen, E.J.; Sharitz, R. Nontidal wetland mapping in South Carolina using airborne multispectral scanner data. Remote Sens. Environ. 1984, 16, 1-12.
83. Nobrega, R.A.; O'Hara, C.G.; Quintanilha, J.A. An Object-Based Approach to Detect Road Features for Informal Settlements near Sao Paulo, Brazil. In Object Based Image Analysis; Blaschke, T., Lang, S. and Hay, G., Eds.; Springer: Heidelberg/Berlin, Germany, 2008; pp. 589-607.
84. Doxani, G.; Siachalou, S.; Tsakiri-Strati, M. An object-oriented approach to urban land cover change detection. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, XXXVII, 1655-1660.
85. Sesnie, S.E.; Gessler, P.E.; Finegan, B.; Thessler, S. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sens. Environ. 2008, 112, 2145-2159.
86. Steininger, M.K. Tropical secondary forest regrowth in the Amazon: Age, area and change estimation with Thematic Mapper data. Int. J. Remote Sens. 1996, 17, 9-27.
87. Tuomisto, H.; Poulsen, A.D.; Ruokolainen, K.; Moran, R.C.; Quintana, C.; Celi, J.; Cañas, G. Linking floristic patterns with soil heterogeneity and satellite imagery in Ecuadorian Amazonia. Ecol. Appl. 2003, 13, 352-371.
88. Whitcomb, J.; Moghaddam, M.; McDonald, K.; Podest, E.; Chapman, B. Decadal Change in Northern Wetlands Based on Differential Analysis of JERS and PALSAR Data. In Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 12-17 July 2009; pp. III-951-III-954.
89. Carrão, H.; Gonçalves, P.; Caetano, M. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sens. Environ. 2008, 112, 986-997.
90. Jackson, Q.; Landgrebe, D.A. An adaptive classifier design for high-dimensional data analysis with a limited training data set. IEEE Trans. Geosci. Remote Sens. 2001, 39, 2664-2679.
91. Ho, T.K.; Basu, M. Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 289-300.
92. Townsend, P.A. Mapping seasonal flooding in forested wetlands using multi-temporal RADARSAT SAR. Photogramm. Eng. Remote Sensing 2001, 67, 857-864.
93. Yang, L.; Huang, C.; Homer, C.; Wylie, B.; Coan, M. An approach for mapping large-area impervious surfaces: Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Can. J. Remote Sens. 2003, 29, 230-240.
94. Baker, C.; Lawrence, R.; Montagne, C.; Patten, D. Change detection of wetland ecosystems using Landsat imagery and change vector analysis. Wetlands 2007, 27, 610-619.
95. Augusteijn, M.F.; Warrender, C.E. Wetland classification using optical and radar data and neural network classification. Int. J. Remote Sens. 1998, 19, 1545-1560.
96. Hess, L.L.; Melack, J.M.; Filoso, S.; Wang, Y. Delineation of inundated area and vegetation along the Amazon Floodplain with the SIR-C Synthetic Aperture Radar. IEEE Trans Geosci. Remote Sens. 1995, 33, 896-904.
97. Hess, L.L.; Melack, J.M.; Novo, E.M.L.M.; Barbosa, C.C.F.; Gastil, M. Dual-season mapping of wetland inundation and vegetation for the Central Amazon Basin. Remote Sens. Environ. 2003, 87, 404-428.