A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research

Remote Sensing of Environment

Volumn 177, Issue , 2016, Pages 89-100

  Khatami, Reza ^a   Mountrakis, Giorgos ^a   Stehman, Stephen V ^b  

^a 402 Baker Laboratory   (United States)

^b STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Classification accuracy; Environmental monitoring; Indices; Land cover mapping; Machine learning; Multi time angle imagery; Remote sensing; Support vector machines; Texture

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA MINING; DECISION TREES; FEATURE EXTRACTION; IMAGE ANALYSIS; INPUT OUTPUT PROGRAMS; LEARNING SYSTEMS; MAPPING; MAXIMUM LIKELIHOOD; PIXELS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SUPPORT VECTOR MACHINES; TEXTURES;

CLASSIFICATION ACCURACY; ENVIRONMENTAL MONITORING; INDICES; LAND COVER MAPPING; MULTI-TIME/ANGLE IMAGERY;

IMAGE CLASSIFICATION;

ACCURACY ASSESSMENT; ALGORITHM; DATA PROCESSING; ENVIRONMENTAL MONITORING; IMAGE ANALYSIS; IMAGE RESOLUTION; IMAGERY; LAND CLASSIFICATION; LAND COVER; MACHINE LEARNING; META-ANALYSIS; REMOTE SENSING; SUPPORT VECTOR MACHINE;

EID: 84958236853     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.02.028     Document Type: Article

References (99)

1. Alcantara C., Kuemmerle T., Prishchepov A.V., Radeloff V.C. Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sensing of Environment 2012, 124:334-347.
2. Anderson M.C., Allen R.G., Morse A., Kustas W.P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment 2012, 122:50-65.
3. Asner G.P., Broadbent E.N., Oliveira P.J.C., Keller M., Knapp D.E., Silva J.M.M. Condition and fate of logged forests in the Brazilian Amazon. Proceedings of the National Academy of Sciences of the United States of America 2006, 103(34):12947-12950.
4. Asner G.P., Levick S.R., Kennedy-Bowdoin T., Knapp D.E., Emerson R., Jacobson J., et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proceedings of the National Academy of Sciences of the United States of America 2009, 106(12):4947-4952.
5. Birdsey R., Angeles-Perez G., Kurz W.A., Lister A., Olguin M., Pan Y., et al. Approaches to monitoring changes in carbon stocks for REDD+. Carbon Management 2013, 4:519-537.
6. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
7. Brodley C.E., Friedl M.A. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment 1997, 61(3):399-409.
8. Brown De Colstoun E.C., Story M.H., Thompson C., Commisso K., Smith T.G., Irons J.R. National park vegetation mapping using multitemporal landsat 7 data and a decision tree classifier. Remote Sensing of Environment 2003, 85(3):316-327.
9. Carlson T.N., Ripley D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 1997, 62(3):241-252.
10. Carpenter G.A., Gopal S., Macober S., Martens S., Woodcock C.E., Franklin J. A neural network method for efficient vegetation mapping. Remote Sensing of Environment 1999, 70(3):326-338.
11. Carrão H., Gonçalves P., Caetano M. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sensing of Environment 2008, 112(3):986-997.
12. Chan J.C.W., Paelinckx D. 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 2008, 112(6):2999-3011.
13. Chang Y.L., Liang L.S., Han C.C., Fang J.P., Liang W.Y., Chen K.S. Multisource data fusion for landslide classification using generalized positive boolean functions. IEEE Transactions on Geoscience and Remote Sensing 2007, 45(6):1697-1708.
14. Chavez P.S. Image-based atmospheric corrections - Revisited and improved. Photogrammetric Engineering and Remote Sensing 1996, 62(9):1025-1036.
15. Chen D., Stow D.A., Gong P. Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case. International Journal of Remote Sensing 2004, 25(11):2177-2192.
16. Cihlar J. Land cover mapping of large areas from satellites: Status and research priorities. International Journal of Remote Sensing 2000, 21(6-7):1093-1114.
17. Clark R.N., Roush T.L. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research 1984, 89(B7):6329-6340.
18. Clark M.L., Roberts D.A., Clark D.B. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment 2005, 96(3-4):375-398.
19. Coburn C.A., Roberts A.C.B. A multiscale texture analysis procedure for improved forest stand classification. International Journal of Remote Sensing 2004, 25(20):4287-4308.
20. Colby J.D., Keating P.L. Land cover classification using landsat TM imagery in the tropical highlands: The influence of anisotropic reflectance. International Journal of Remote Sensing 1998, 19(8):1479-1500.
21. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20(3):273-297.
22. Cover T.M., Hart P.E. Nearest Neighbor Pattern classification. IEEE Transactions on Information Theory 1967, IT-13(1):21-27.
23. Dash J., Mathur A., Foody G.M., Curran P.J., Chipman J.W., Lillesand T.M. Land cover classification using multi-temporal MERIS vegetation indices. International Journal of Remote Sensing 2007, 28(6):1137-1159.
24. DeFries R.S., Houghton R.A., Hansen M.C., Field C.B., Skole D., Townshend J. Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s. Proceedings of the National Academy of Sciences of the United States of America 2002, 99(22):14256-14261.
25. Del Frate F., Schiavon G., Solimini D., Borgeaud M., Hoekman D.H., Vissers M.A.M. Crop classification using multiconfiguration C-band SAR data. IEEE Transactions on Geoscience and Remote Sensing 2003, 41(7 PART I):1611-1619.
26. Duca R., Del Frate F. Hyperspectral and multiangle CHRIS-PROBA images for the generation of land cover maps. IEEE Transactions on Geoscience and Remote Sensing 2008, 46(10):2857-2866.
27. Dymond C.C., Mladenoff D.J., Radeloff V.C. Phenological differences in tasseled cap indices improve deciduous forest classification. Remote Sensing of Environment 2002, 80(3):460-472.
28. Evans J., van Donkelaar A., Martin R.V., Burnett R., Rainham D.G., Birkett N.J., et al. Estimates of global mortality attributable to particulate air pollution using satellite imagery. Environmental Research 2013, 120:33-42.
29. Fauvel M., Benediktsson J.A., Chanussot J., Sveinsson J.R. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing 2008, 46(11):3804-3814.
30. Fialko Y., Sandwell D., Simons M., Rosen P. Three-dimensional deformation caused by the Bam, Iran, earthquake and the origin of shallow slip deficit. Nature 2005, 435(7040):295-299.
31. Franklin S.E., Wulder M.A. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography 2002, 26(2):173-205.
32. Franklin S.E., Peddle D.R., Dechka J.A., Stenhouse G.B. Evidential reasoning with landsat TM, DEM and GIS data for landcover classification in support of grizzly bar habitat mapping. International Journal of Remote Sensing 2002, 23(21):4633-4652.
33. Fukushima H., Higurashi A., Mitomi Y., Nakajima T., Noguchi T., Tanaka T., et al. Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance. Journal of Oceanography 1998, 54(5):417-430.
34. Geerling G.W., Labrador-Garcia M., Clevers J.G.P.W., Ragas A.M.J., Smits A.J.M. Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data. International Journal of Remote Sensing 2007, 28(19):4263-4284.
35. Gilbert M., Xiao X., Pfeiffer D.U., Epprecht M., Boles S., Czarnecki C., et al. Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia. Proceedings of the National Academy of Sciences of the United States of America 2008, 105(12):4769-4774.
36. Giustarini L., Hostache R., Matgen P., Schumann G.J.P., Bates P.D., Mason D.C. A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Transactions on Geoscience and Remote Sensing 2013, 51:2417-2430.
37. Gong P., Wang J., Yu L., Zhao Y., Zhao Y., Liang L., et al. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing 2013, 34:2607-2654.
38. Grekousis G., Mountrakis G., Kavouras M. An overview of 21 global and 43 regional land-cover mapping products. International Journal of Remote Sensing 2015, 36:5309-5335.
39. Guerschman J.P., Paruelo J.M., Di Bella C., Giallorenzi M.C., Pacin F. Land cover classification in the Argentine pampas using multi-temporal landsat TM data. International Journal of Remote Sensing 2003, 24(17):3381-3402.
40. Hansen M.C., Potapov P.V., Moore R., Hancher M., Turubanova S.A., Tyukavina A., et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342:850-853.
41. Hansen M.C., Stehman S.V., Potapov P.V., Loveland T.R., Townshend J.R.G., DeFries R.S., et al. Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data. Proceedings of the National Academy of Sciences of the United States of America 2008, 105(27):9439-9444.
42. Heikkinen V., Korpela I., Tokola T., Honkavaara E., Parkkinen J. An SVM classification of tree species radiometric signatures based on the Leica ADS40 sensor. IEEE Transactions on Geoscience and Remote Sensing 2011, 49(11 PART 2):4539-4551.
43. Held A., Ticehurst C., Lymburner L., Williams N. High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing. International Journal of Remote Sensing 2003, 24(13):2739-2759.
44. Hong B., Limburg K.E., Hall M.H., Mountrakis G., Groffman P.M., Hyde K., et al. An integrated monitoring/modeling framework for assessing human-nature interactions in urbanizing watersheds: Wappinger and Onondaga Creek watersheds, New York, USA. Environmental Modelling & Software 2012, 32:1-15.
45. Huang H., Gong P., Clinton N., Hui F. Reduction of atmospheric and topographic effect on landsat TM data for forest classification. International Journal of Remote Sensing 2008, 29(19):5623-5642.
46. Hussain M., Chen D., Cheng A., Wei H., Stanley D. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing 2013, 80:91-106.
47. Ji C.Y. Land-use classification of remotely sensed data using Kohonen self-organizing feature map neural networks. Photogrammetric Engineering and Remote Sensing 2000, 66(12):1451-1460.
48. Keegan K.M., Albert M.R., McConnell J.R., Baker I. Climate change and forest fires synergistically drive widespread melt events of the Greenland ice sheet. Proceedings of the National Academy of Sciences of the United States of America 2014, 111(22):7964-7967.
49. Khatami R., Mountrakis G. Implications of classification of methodological decisions in flooding analysis from hurricane Katrina. Remote Sensing 2012, 4(12):3877-3891.
50. Knyazikhin Y., Schull M.A., Stenberg P., Mõttus M., Rautiainen M., Yang Y., et al. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences of the United States of America 2013, 110(3). (811-812+E185-E192).
51. Kuplich T.M., Freitas C.C., Soares J.V. The study of ERS-1 SAR and landsat TM synergism for land use classification. International Journal of Remote Sensing 2000, 21(10):2101-2111.
52. Lambin E.F., Meyfroidt P. Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences of the United States of America 2011, 108(9):3465-3472.
53. Lillesand T.M., Kiefer R.W., Chipman J.W. Remote sensing and image interpretation 2004, Wiley, Hoboken NJ. 5th ed.
54. Liu H., Weng Q. Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile virus outbreak in Los Angeles in 2007. Remote Sensing of Environment 2012, 117:57-71.
55. Lobitz B., Beck L., Huq A., Wood B., Fuchs G., Faruque A.S.G., et al. Climate and infectious disease: use of remote sensing for detection of vibrio cholerae by indirect measurement. Proceedings of the National Academy of Sciences of the United States of America 2000, 97(4):1438-1443.
56. Lu D., Weng Q. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 2007, 28(5):823-870.
57. McMenamin S.K., Hadly E.A., Wright C.K. Climatic change and wetland desiccation cause amphibian decline in Yellowstone National Park. Proceedings of the National Academy of Sciences of the United States of America 2008, 105(44):16988-16993.
58. Mendenhall C.D., Sekercioglu C.H., Brenes F.O., Ehrlich P.R., Daily G.C. Predictive model for sustaining biodiversity in tropical countryside. Proceedings of the National Academy of Sciences of the United States of America 2011, 108(39):16313-16316.
59. Moher D., Liberati A., Tetzlaff J., Altman D.G., Altman D., Antes G., et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine 2009, 6(7).
60. Mountrakis G., Im J., Ogole C. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 2011, 66:247-259.
61. Mountrakis G., Watts R., Luo L., Wang J. Developing collaborative classifiers using an expert-based model. Photogrammetric Engineering and Remote Sensing 2009, 75(7):831-843.
62. Mundt J.T., Glenn N.F., Weber K.T., Prather T.S., Lass L.W., Pettingill J. Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques. Remote Sensing of Environment 2005, 96(3-4):509-517.
63. Myneni R.B., Dong J., Tucker C.J., Kaufmann R.K., Kauppi P.E., Liski J., et al. A large carbon sink in the woody biomass of northern forests. Proceedings of the National Academy of Sciences of the United States of America 2001, 98(26):14784-14789.
64. Myneni R.B., Yang W., Nemani R.R., Huete A.R., Dickinson R.E., Knyazikhin Y., et al. Large seasonal swings in leaf area of Amazon rainforests. Proceedings of the National Academy of Sciences of the United States of America 2007, 104(12):4820-4823.
65. Nagendra H., Gadgil M. Biodiversity assessment at multiple scales: Linking remotely sensed data with field information. Proceedings of the National Academy of Sciences of the United States of America 1999, 96(16):9154-9158.
66. Ogilvie A., Belaud G., Delenne C., Bailly J., Bader J., Oleksiak A., et al. Decadal monitoring of the Niger Inner Delta flood dynamics using MODIS optical data. Journal of Hydrology 2015, 523:368-383.
67. Peterson D.L., Price K.P., Martinko E.A. Discriminating between cool season and warm season grassland cover types in northeastern Kansas. International Journal of Remote Sensing 2002, 23(23):5015-5030.
68. Potapov P.V., Turubanova S.A., Tyukavina A., Krylov A.M., McCarty J.L., Radeloff V.C., et al. Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment 2015, 159:28-43.
69. Ranson K.J., Sun G., Kharuk V.I., Kovacs K. Characterization of forests in Western Sayani Mountains, Siberia from SIR-C SAR data. Remote Sensing of Environment 2001, 75(2):188-200.
70. Ricchetti E. Multispectral satellite image and ancillary data integration for geological classification. Photogrammetric Engineering and Remote Sensing 2000, 66(4):429-435.
71. Richter R. Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. International Journal of Remote Sensing 1997, 18(5):1099-1111.
72. Rindfuss R.R., Walsh S.J., Turner B.L., Fox J., Mishra V. Developing a science of land change: Challenges and methodological issues. Proceedings of the National Academy of Sciences of the United States of America 2004, 101(39):13976-13981.
73. Rio J.N.R., Lozano-García D.F. Spatial filtering of radar data (RADARSAT) for wetlands (brackish marshes) classification. Remote Sensing of Environment 2000, 73(2):143-151.
74. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating errors. Nature 1986, 323(6088):533-536.
75. Schmidt K.S., Skidmore A.K., Kloosterman E.H., Van Oosten H., Kumar L., Janssen J.A.M. Mapping coastal vegetation using an expert system and hyperspectral imagery. Photogrammetric Engineering and Remote Sensing 2004, 70(6):703-715.
76. Schwalm C.R., Williams C.A., Schaefer K., Baldocchi D., Black T.A., Goldstein A.H., et al. Reduction in carbon uptake during turn of the century drought in western North America. Nature Geoscience 2012, 5:551-556.
77. Sedano F., Gong P., Ferrão M. Land cover assessment with MODIS imagery in southern African Miombo ecosystems. Remote Sensing of Environment 2005, 98(4):429-441.
78. 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 Sensing of Environment 2008, 112(5):2145-2159.
79. Shackelford A.K., Davis C.H. A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing 2003, 41(9 PART I):1920-1932.
80. Skidmore A.K., Pettorelli N., Coops N.C., Geller G.N., Hansen M., Lucas R., et al. Environmental science: Agree on biodiversity metrics to track from space. Nature 2015, 523:403-405.
81. Sluiter R., Pebesma E.J. Comparing techniques for vegetation classification using multi- and hyperspectral images and ancillary environmental data. International Journal of Remote Sensing 2010, 31(23):6143-6161.
82. Smits P.C., Dellepiane S.G., Schowengerdt R.A. Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach. International Journal of Remote Sensing 1999, 20(8):1461-1486.
83. 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 Sensing of Environment 2001, 75(2):230-244.
84. Stehman S.V. Design, analysis, and inference for studies comparing thematic accuracy of classified remotely sensed data: A special case of map comparison. Journal of Geographical Systems 2006, 8(2):209-226.
85. Strahler A.H. The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment 1980, 10(2):135-163.
86. Su L., Chopping M.J., Rango A., Martonchik J.V., Peters D.P.C. Differentiation of semi-arid vegetation types based on multi-angular observations from MISR and MODIS. International Journal of Remote Sensing 2007, 28(6):1419-1424.
87. Su L., Chopping M.J., Rango A., Martonchik J.V., Peters D.P.C. Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery. Remote Sensing of Environment 2007, 107(1-2):299-311.
88. Syed T.H., Famiglietti J.S., Chambers D.P., Willis J.K., Hilburn K. Satellite-based global-ocean mass balance estimates of interannual variability and emerging trends in continental freshwater discharge. Proceedings of the National Academy of Sciences of the United States of America 2010, 107(42):17916-17921.
89. Taylor S., Kumar L., Reid N. Mapping Lantana camara: Accuracy comparison of various fusion techniques. Photogrammetric Engineering and Remote Sensing 2010, 76(6):691-700.
90. Thenkabail P.S., Enclona E.A., Ashton M.S., Van Der Meer B. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment 2004, 91(3-4):354-376.
91. Tottrup C. Improving tropical forest mapping using multi-date landsat TM data and pre-classification image smoothing. International Journal of Remote Sensing 2004, 25(4):717-730.
92. Townshend J.R., Masek J.G., Huang C., Vermote E.F., Gao F., Channan S., et al. Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges. International Journal of Digital Earth 2012, 5:373-397.
93. Tsai F., Philpot W.D. A derivative-aided hyperspectral image analysis system for land-cover classification. IEEE Transactions on Geoscience and Remote Sensing 2002, 40(2):416-425.
94. Watts J.D., Powell S.L., Lawrence R.L., Hilker T. Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery. Remote Sensing of Environment 2011, 115(1):66-75.
95. Weng Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment 2012, 117:34-49.
96. Wilkinson G.G. Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Transactions on Geoscience and Remote Sensing 2005, 43(3):433-440.
97. Yang C., Everitt J.H., Johnson H.B. Applying image transformation and classification techniques to airborne hyperspectral imagery for mapping Ashe juniper infestations. International Journal of Remote Sensing 2009, 30(11):2741-2758.
98. Youngentob K.N., Roberts D.A., Held A.A., Dennison P.E., Jia X., Lindenmayer D.B. Mapping two eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data. Remote Sensing of Environment 2011, 115(5):1115-1128.
99. Yu L., Liang L., Wang J., Zhao Y., Cheng Q., Hu L., et al. Meta-discoveries from a synthesis of satellite-based land-cover mapping research. International Journal of Remote Sensing 2014, 35(13):4573-4588.