Accuracy assessment of seven global land cover datasets over China

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 125, Issue , 2017, Pages 156-173

  Yang, Yongke ^a,b   Xiao, Pengfeng ^a,b   Feng, Xuezhi ^a,b   Li, Haixing ^a,b  

^a NANJING UNIVERSITY   (China)

^b NANJING NORMAL UNIVERSITY   (China)

Author keywords

Accuracy validation; Global land cover dataset; Spatial agreement; Spatial variation in accuracy

Indexed keywords

MOLECULAR PHYSICS; PHOTOGRAMMETRY;

ACCURACY ASSESSMENT; ACCURACY VALIDATION; CLASSIFICATION SCHEME; HOMOGENEOUS REGIONS; LAND COVER; LAND COVER DATASETS; NOCV1; SCIENTIFIC COMMUNITY; SPATIAL VARIATIONS;

PRODUCED WATER;

ACCURACY ASSESSMENT; DATA SET; IGBP; LAND COVER; NUMERICAL MODEL; SATELLITE DATA; SPATIAL ANALYSIS; SPATIAL VARIATION; TWENTY FIRST CENTURY;

CHINA;

EID: 85012280058     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2017.01.016     Document Type: Article

References (90)

1. Ahl, D.E., Gower, S.T., Burrows, S.N., Shabanov, N.V., Myneni, R.B., Knyazikhin, Y., Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ. 104 (2006), 88–95.
2. An, Y.M., Zhao, W.W., Zhang, Y.H., Accuracy assessments of the GLOBCOVER dataset using global statistical inventories and FLUXNET site data. Acta Ecologica Sinica 32 (2012), 314–320.
3. Achard, F., Mollicone, D., Stibig, H.J., Aksenov, D., Laestadius, L., Li, Z., Potapov, P., Yaroshenko, A., Areas of rapid forest cover change in boreal Eurasia. For. Ecol. Manage. 237 (2006), 322–334.
4. Arino, O., Bicheron, P., Achard, F., Latham, F., Witt, R., Weber, J.L., GLOBCOVER–the most detailed portrait of Earth. Eur. Space Agency Bullet. 136 (2008), 25–31.
5. Atkinson, P.M., Lewis, P., Geostatistical classification for remote sensing: an introduction. Comput. Geosci. 26 (2000), 361–371.
6. Baghdadi, N., Bernier, M., Gauthier, R., Neeson, I., Evaluation of C-band SAR data for wetlands mapping. Int. J. Remote Sens. 22 (2001), 71–88.
7. Balzter, H., Rowland, C.S., Saich, P., Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry. Remote Sens. Environ. 108 (2007), 224–239.
8. Bartalev, S.A., Belward, A.S., Erchov, D.V., Isaev, A.S., A new SPOT4-Vegetation derived land-cover map of Northern Eurasia. Int. J. Remote Sens. 24 (2003), 1977–1982.
9. Bartholomé, E., Belward, A., GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26 (2005), 1959–1977.
10. Bicheron, P., Amberg, V., Bourg, L., Petit, D., Huc, M., Miras, B., Brockmann, C., Hagolle, O., Delwart, S., Ranera, F., Leroy, M., Arino, O., Geolocation assessment of MERIS GlobCover orthorectified products. IEEE Trans. Geosci. Remote Sens. 49 (2011), 2972–2982.
11. Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc, M., Bontemps, S., Leroy, M., Achard, F., and Herold, M., 2008. Globcover-products description and validation report. European Space Agency.
12. Bontemps, S., Defourny, P., Bogaert, E. V., Arino, O., Kalogirou, V., Perez, J.R., 2010. GLOBCOVER 2009 Products Description and Validation Report. European Space Agency.
13. Buddenbaum, H., Schlerf, M., Hill, J., Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. Int. J. Remote Sens. 26 (2005), 5453–5465.
14. Campbell, J., Spatial correlation effects upon accuracy of supervised classification of land cover. Photogram. Eng. Remote Sens. 47 (1981), 355–364.
15. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Global land cover mapping at 30m resolution: a POK-based operational approach. ISPRS J. Photogram. Remote Sens. 103 (2015), 7–27.
16. Clark, M.L., Aide, T.M., Grau, H.R., Riner, G., A scalable approach to mapping annual land cover at 250 m using MODIS time series data: a case study in the Dry Chaco ecoregion of South America. Remote Sens. Environ. 114 (2010), 2816–2832.
17. Clark, M.L., Roberts, D.A., Clark, D.B., Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens. Environ. 96 (2005), 375–398.
18. Cleveland, W.S., Robust locally weighted regression and smoothing scatterplots. J. Am Stat. Assoc. 74 (1979), 829–836.
19. Colditz, R.R., López Saldaña, G., Maeda, P., Espinoza, J.A., Tovar, C.M., Hernández, A.V., Benítez, C.Z., Cruz López, I., Ressl, R., Generation and analysis of the 2005 land cover map for Mexico using 250m MODIS data. Remote Sens. Environ. 123 (2012), 541–552.
20. Comber, A., Fisher, P., Brunsdon, C., Khmag, A., Spatial analysis of remote sensing image classification accuracy. Remote Sens. Environ. 127 (2012), 237–246.
21. Congalton, R., Gu, J., Yadav, K., Thenkabail, P., Ozdogan, M., Global land cover mapping: a review and uncertainty analysis. Rem. Sens. 6 (2014), 12070–12093.
22. Congalton, R.G., Using spatial autocorrection analysis to explore the errors in map generated from remotely sensed data. Photogram. Eng. Remote Sens., 54, 1988 587-292.
23. Dalponte, M., Orka, H.O., Gobakken, T., Gianelle, D., Næsset, E., Tree species classification in boreal forests with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 51 (2013), 2632–2645.
24. Danko, D.M., The digital chart of the world project. Photogram. Eng. Rem. Sens. 58 (1992), 1125–1128.
25. Defourny, P., Kirches, G., Brockmann, C., Boettcher, M., Peters, M., Bontemps, S., Lamarche, C., Schlerf, M., Santoro. M., 2016. Land Cover CCI: Product User Guide Version 2. Accessed March 5, 2016. < http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-PUG-v2.5.pdf>.
26. DeFries, R., Hansen, M., Townshend, J., Global discrimination of land cover types from metrics derived from AVHRR Pathfinder data. Remote Sens. Environ. 54 (1995), 209–222.
27. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Global consequences of land use. Science 309 (2005), 570–574.
28. Foody, G.M., Status of land cover classification accuracy assessment. Remote Sens. Environ. 80 (2002), 185–201.
29. Foody, G.M., Local characterization of thematic classification accuracy through spatially constrained confusion matrices. Int. J. Remote Sens. 26 (2005), 1217–1228.
30. Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., Huang, X., MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114 (2010), 168–182.
31. Friedl, M.A., McIver, D.K., Hodges, J.C., Zhang, X., Muchoney, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83 (2002), 287–302.
32. Fritz, S., See, L., Identifying and quantifying uncertainty and spatial disagreement in the comparison of global land cover for different applications. Glob. Change Biol. 14 (2008), 1057–1075.
33. Fritz, S., See, L., Rembold, F., Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa. Int. J. Remote Sens. 31 (2010), 2237–2256.
34. Gao, H., Jia, G.S., Spatial and quantitative comparison of satellite-derived land cover products over China. Atmos. Ocean. Sci. Lett. 5 (2012), 426–434.
35. Giri, C., Zhu, Z., Reed, B., A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sens. Environ. 94 (2005), 123–132.
36. Gong, P., Assessment of global land cover map accuracies using Fluxnet location data. Prog. Nat. Sci. 19 (2009), 754–759 (in Chinese).
37. Gong, P., Wang, J., Yu, L., Zhao, Y.C., Zhao, Y.Y., Liang, L., Niu, Z.G., Huang, X.M., Fu, H.H., Liu, S., Li, C.C., Li, X.Y., Fu, W., Liu, C., Xu, Y., Wang, X.Y., Cheng, Q., Hu, L.Y., Yao, W.B., Zhang, H., Zhu, P., Zhao, Z.Y., Zhang, H.Y., Zheng, Y.M., Ji, L.Y., Zhang, Y.E., Chen, H., Yan, A., Guo, J.H., Yu, L., Wang, L., Liu, X.J., Shi, T.T., Zhu, M.H., Chen, Y.L., Yang, G.W., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z.L., Chen, J., Chen, J., Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34 (2013), 2607–2654.
38. Gong, P., Yu, L., Li, C., Wang, J., Liang, L., Li, X., Ji, L., Bai, Y., Cheng, Y., Zhu, Z., A new research paradigm for global land cover mapping. Ann. GIS 22 (2016), 87–102.
39. Hansen, M.C., Defries, R.S., Townshend, J.R.G., Sohlberg, R., Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens. 21 (2000), 1331–1364.
40. Hansen, M.C., Reed, B., A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products. Int. J. Remote Sens. 21 (2000), 1365–1373.
41. Henderson, F.M., Lewis, A.J., Radar detection of wetland ecosystems: a review. Int. J. Remote Sens. 29 (2008), 5809–5835.
42. Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A., Schmullius, C., Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ. 112 (2008), 2538–2556.
43. Hou, X.Y., 1:1000000 Vegetation Map of China. 2001, Science Press, Beijing.
44. Hyde, O., Dubayah, R., Walker, W., Blair, J.B., Hofton, M., Hunsaker, C., Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/INSAR, ETM+, Quickbird) synergy. Remote Sens. Environ. 102 (2006), 63–73.
45. Jung, M., Henkel, K., Herold, M., Churkina, G., Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sens. Environ. 101 (2006), 534–553.
46. Kasischke, E.S., Bourgeau-Chavez, L.L., Monitoring South Florida wetlands using ERS-1 SAR imagery. Photogram. Eng. Rem. Sens. 63 (1997), 281–291.
47. Kuenzer, C., Leinenkugel, P., Vollmuth, M., Dech, S., Comparing global land-cover products–implications for geoscience applications: an investigation for the trans-boundary Mekong Basin. Int. J. Remote Sens. 35 (2014), 2752–2779.
48. Kyriakidis, P.C., Dungan, J.L., A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions. Environ. Ecol. Stat. 8 (2001), 311–330.
49. Kyriakidis, P.C., Shortridge, A.M., Goodchild, M.F., Geostatistics for conflation and accuracy assessment of digital elevation models. Int. J. Geograp. Inform. Sci. 13 (1999), 677–707.
50. Latifovic, R., Olthof, I., Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sens. Environ. 90:2 (2004), 153–165.
51. Laurin, G.V., Liesenberg, V., Chen, Q., Guerriero, L., Del Frate, F., Bartolini, A., Coomes, D., Wilebore, B., Lindsell, J., Valentini, R., Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa. Int. J. Appl. Earth Obs. Geoinf. 21 (2013), 7–16.
52. Lee, J., Grunes, M., Ainsworth, T., Hajnsek, I., Mette, T., Papathanassiou, K., Forest classification based on L-band polarimetric and interferometric SAR data. ESA Special Publ. 586 (2005), 1–6.
53. Li, Y.C., Gong, P., Chen, J., Liu, C.X., He, C.Y., Landscape pattern and its dynamical change in north China during 1989–1999. J. Soil Water Conserv. 19 (2005), 143–146 (in Chinese).
54. Liu, Z., He, C., Zhang, Q., Huang, Q., Yang, Y., Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landsc. Urban Plan. 106:1 (2012), 62–72.
55. Liu, J.Y., Zhang, Z.X., Zhuang, D.F., Wang, Y.M., Zhou, W.C., Zhang, S.W., Li, R.D., Jiang, N., Wu, S.X., A study on the spatial-temporal dynamic changes of land-use and driving forces analyses of China in the 1990s. Geograp. Res. 22 (2003), 1–12 (in Chinese).
56. Liu, J.Y., Kuang, W.H., Zhang, Z.X., Xu, X.L., Qin, Y.W., Ning, J., Zhou, W.C., Zhang, S.W., Li, R.D., Yan, C.Z., Wu, S.X., Shi, X.Z., Jiang, N., Yu, D.S., Pan, X.Z., Chi, W.F., Spatiaotemporal characteristics, patterns and causes of land-use changes in China since the late 1980s. J. Geog. Sci. 24:2 (2014), 195–210.
57. Loveland, T.R., Belward, A.S., The IGBP-DIS global 1 km land cover data set, DISCover: first results. Int. J. Remote Sens. 18 (1997), 3289–3295.
58. Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L., Merchant, J.W., Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 21 (2000), 1303–1330.
59. Martinez, J., Letoan, T., Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens. Environ. 108 (2007), 209–223.
60. Maselli, F., Conese, C., Petkov, L., Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. ISPRS J. Photogram. Rem. Sens. 49 (1994), 13–20.
61. Mayaux, P., Eva, H., Gallego, J., Strahler, A.H., Herold, M., Agrawal, S., Naumov, S., De Miranda, E.E., Di Bella, C.M., Ordoyne, C., Validation of the global land cover 2000 map. IEEE Trans. Geosci. Remote Sens. 44 (2006), 1728–1739.
62. McCallum, I., Obersteiner, M., Nilsson, S., Shvidenko, A., A spatial comparison of four satellite derived 1km global land cover datasets. Int. J. Appl. Earth Obs. Geoinf. 8 (2006), 246–255.
63. Meer, F.V.D., Classification of remotely-sensed imagery using an indicator kriging approach: application to the problem of calcite-dolomite mineral mapping. Int. J. Remote Sens. 17 (1996), 1233–1249.
64. Niu, Z.G., Shan, Y.X., Zhang, H.Y., Accuracy assessment of wetland categories from the GlobCover2009 data over China. Wetland Sci. 10 (2012), 389–395 (in Chinese).
65. Neumann, M., Ferro-Famil, L., Reigber, A., Estimation of forest structure, ground, and canopy layer characteristics from multibaseline polarimetric interferometric SAR data. IEEE Trans. Geosci. Remote Sens. 48 (2010), 1086–1104.
66. Pérez-Hoyos, A., García-Haro, F.J., San-Miguel-Ayanz, J., Conventional and fuzzy comparisons of large scale land cover products: application to CORINE, GLC2000, MODIS and GlobCover in Europe. ISPRS J. Photogram. Rem. Sens. 74 (2012), 185–201.
67. Pérez-Hoyos, A., García-Haro, F.J., San-Miguel-Ayanz, J., A methodology to generate a synergetic land-cover map by fusion of different land-cover products. Int. J. Appl. Earth Obs. Geoinf. 19 (2012), 72–87.
68. Ran, Y.H., Li, X., Lu, L., Evaluation of four remote sensing based land cover products over China. Int. J. Remote Sens. 31 (2010), 391–401.
69. Reed, B.C., Schwartz, M.D., Xiao, X., Remote sensing phenology: status and the way forward. Noormets, A., (eds.) Phenology of Ecosystem Processes, 2009, Springer, New York, 231–246.
70. Running, S.W., Ecosystem disturbance, carbon, and climate. Science 321 (2008), 652–653.
71. Scepan, J., Menz, G., Hansen, M.C., The DISCover validation image interpretation process. Photogram. Eng. Rem. Sens. 65 (1999), 1075–1081.
72. Sedano, F., Gong, P., Ferrao, M., Land cover assessment with MODIS imagery in southern African Miombo ecosystems. Remote Sens. Environ. 98 (2005), 429–441.
73. Schneider, A., Friedl, M.A., Potere, D., A new map of global urban extent from MODIS satellite data. Environ. Res. Lett. 4:4 (2009), 2–12.
74. Schneider, A., Friedl, M.A., Potere, D., Mapping global urban areas using MODIS 500-m data: new methods and datasets based on ‘urban ecoregions’. Remote Sens. Environ. 114:8 (2010), 1733–1746.
75. Schull, M.A., Knyazikhin, Y., Xu, L., Samanta, A., Carmona, P.L., Lepine, L., Jenkins, J., Ganguly, S., Myneni, R., Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data. J. Quant. Spectrosc. Radiat. Transfer 112 (2011), 736–750.
76. Steele, B.M., Winne, J.C., Redmond, R.L., Estimation and mapping of misclassification probabilities for thematic land cover maps. Remote Sens. Environ. 66 (1998), 192–202.
77. Stibig, H.J., Achard, F., Fritz, S., A new forest cover map of continental Southeast Asia derived from SPOT- VEGETATION satellite imagery. Appl. Veg. Sci. 7:2 (2004), 153–162.
78. Strahler, A.H., Boschetti, L., Foody, G.M., Friedl, M.A., Hansen, M.C., Herold, M., Mayaux, P., Morisette, J.T., Stehman, S.V., Woodcock, C.E., Global Land Cover Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps. 2006, European Communities, Luxembourg.
79. Tateishi, R., Hoan, N.T., Kobayashi, T., Alsaaideh, B., Tana, G., Phong, D.X., Production of global land cover data-GLCNMO2008. J. Geograp. Geol. 6:3 (2014), 99–123.
80. Tchuenté, A.T.K., Roujean, J.L., De Jong, S.M., Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. Int. J. Appl. Earth Obs. Geoinf. 13:2 (2011), 207–219.
81. Townsend, P.A., Mapping seasonal flooding in forested wetlands using multi-temporal Radarsat SAR. Photogram. Eng. Rem. Sens. 67 (2001), 857–864.
82. Tsendbazar, N.E., Bruin, S., Herold, M., Assessing global land cover reference datasets for different user communities. ISPRS J. Photogram. Rem. Sens. 10:3 (2015), 93–114.
83. van Oort, P.A.J., Bregt, A.K., de Bruin, S., de Wit, A.J.W., Stein, A., Spatial variability in classification accuracy of agricultural crops in the Dutch national land-cover database. Int. J. Geograp. Inform. Sci. 18 (2004), 611–626.
84. Wu, W.B., Shibasaki, R., Yang, P., Ongaro, L., Zhou, Q., Tang, H., Validation and comparison of 1 km global land cover products in China. Int. J. Remote Sens. 29 (2008), 3769–3785.
85. Xiao, P.F., Wang, X.H., Feng, F.Z., Zhang, X.L., Yang, Y.K., Detecting China's urban expansion over the past three decades using nighttime light data. IEEE J. Select. Topics Appl. Earth Observ. Rem. Sens. 7:10 (2014), 4095–4105.
86. Xiao, X., Hagen, S., Zhang, Q., Keller, M., Moore, B., Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images. Remote Sens. Environ. 103 (2006), 465–473.
87. Xu, W.T., Wu, B.F., Yan, C.Z., Huang, H.P., China land cover 2000 using SPOT VGT S10 data. J. Rem. Sens. 9 (2005), 204–304 (in Chinese).
88. Yu, L., Wang, J., Gong, P., Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach. Int. J. Remote Sens. 34 (2013), 5851–5867.
89. Yu, X., Zhuang, D., Monitoring forest phenophases of Northeast China based on MODIS NDVI data. Resour. Sci. 28 (2006), 111–117.
90. Zhang, Z.X., Wang, X., Zhao, X.L., Liu, B., Yi, L., Zuo, L.J., Wen, Q.K., Liu, F., Xu, J.Y., Hu, S.G., A 2010 update of national land use/cover database of China at 1:100000 scale using medium spatial resolution satellite images. Remote Sens. Environ. 149 (2014), 142–154.