Abstract
As more satellite-derived land cover products used in the study of global change, especially climate modeling, assessing their quality has become vitally important. In this study, we developed a distance metric based on the parameters used in weather research and forecasting (WRF) to characterize the degree of disagreement among land cover products and to identify the tolerance for misclassification within the International Geosphere Biosphere Programme (IGBP) classification scheme. We determined the spatial degree of disagreement and then created maps of misclassification of Moderate Resolution Imaging Spectoradiometer (MODIS) products, and we calculated overall and class-specific accuracy and fuzzy agreement in a WRF model. Our results show a high level of agreement and high tolerance of misclassification in the WRF model between large-scale homogeneous landscapes, while a low level of agreement and tolerance of misclassification appeared in heterogeneous landscapes. The degree of disagreement varied significantly among seven regions of China. The class-specific accuracy and fuzzy agreement in MODIS Collection 4 and 5 products varied significantly. High accuracy and fuzzy agreement occurred in the following classes: water, grassland, cropland, and barren or sparsely vegetated. Misclassification mainly occurred among specific classes with similar plant functional types and low discriminative spectro-temporal signals. Some classes need to be improved further; the quality of MODIS land cover products across China still does not meet the common requirements of climate modeling. Our findings may have important implications for improving land surface parameterization for simulating climate and for better understanding the influence of the land cover change on climate.
Similar content being viewed by others
References
Ahlqvist, O., 2005: Using uncertain conceptual spaces to translate between land cover categories. International Journal of Geographical Information Science, 19, 831–857.
Ahlqvist, O., 2008: Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: A study of 1992 and 2001 U.S. National land cover database changes. Remote Sens. Environ., 112, 1226–1241.
Bartholomé, E., and A. S. Belward, 2005: GLC2000: A new approach to global land cover map** from earth observation data. Inter. J. Remote Sens., 26, 1959–1977.
Benítez, P., I. McCallum, M. Obersteiner, and Y. Yamagata, 2004: Global supply for carbon sequestration: Identifying least-cost afforestation sites under country risk considerations. Interim Report, IR-04-022, International Institute for Applied Systems Analysis, Laxenburg, Austria, 22pp.
Bicheron, P., and Coauthors, cited 2008: Globcover: Products description and validation report. [Available online at http://ionia1.esrin.esa.int/docs/ GLOBCOVER Products Description Validation Report I2.1.pdf]
Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 Modeling System. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585.
DeFries, R. S., and Coauthors, 1995: Map** the land surface for global atmosphere-biosphere models: Towards continuous distributions of vegetation’s functional properties. J. Geophys. Res., 100(D10), 20867–20882.
Dirmeyer, P. A., D. Niyogi, N. de Noblet-Ducoudré, R. E. Dickinson, and P. K. Snyder, 2010: Impacts of land use change on climate. Int. J. Climatol., 30, 1905–1907.
Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grummann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi: 10.1029/2002JD003296.
Feddema, J. J., K.W. Oleson, G. B. Bonan, L.O. Mearns, L. E. Buja, G. A. Meehl, and W. M. Washington, 2005: The importance of land-cover change in simulating future climates. Science, 310, 1674–1678.
Findell, K. L., E. Shevliakova, P. C. D. Milly, and R. J. Stouffer, 2007: Modeled impact of anthropogenic land cover change on climate. J. Climate, 20, 3621–3634.
Foody, G. M., 2002: Status of land cover classification accuracy assessment. Remote Sens. Environ., 80, 185–201.
Friedl, M. A., and Coauthors, 2002: Global land cover map** from modis: Algorithms and early results. Remote Sens. Environ., 83, 287–302.
Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang, 2010: Modis collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168–182.
Fritz, S., and L. See, 2005: Comparison of land cover maps using fuzzy agreement. International Journal of Geographical Information Science, 19, 787–807.
Fritz, S., and L. See, 2008: Identifying and quantifying uncertainty and spatial disagreement in the comparison of global land cover for different applications. Global Change Biology, 14, 1057–1075.
Fu, C., 2003: Potential impacts of human-induced land cover change on east asia monsoon. Global and Planetary Change, 37, 219–229.
Ge, J., J. Qi, B. M. Lofgren, N. Moore, N. Torbick, and J. M. Olson, 2007: Impacts of land use/cover classification accuracy on regional climate simulations. J. Geophys. Res., 112, D05107, doi: 10.1029/2006JD007404.
Giri, C., Z. Zhu, and B. Reed, 2005: A comparative analysis of the global land cover 2000 and modis land cover data sets. Remote Sens. Environ., 94, 123–132.
Govindasamy, B., P. B. Duffy, and K. Caldeira, 2001: Land use changes and northern hemisphere cooling. Geophys. Res. Lett., 28(2), 291–294, doi: 10.1029/2000GL006121.
Hansen, M. C., and B. Reed, 2000: A comparison of the IGBP-DISCover and university of maryland 1km global land cover products. Int. J. Remote Sens., 21, 1365–1373.
Hansen, M. C., R. S. Defries, J. R. G. Townshend, and R. Sohlberg, 2000: Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21, 1331–1364.
Herold, M., P. Mayaux, C. E. Woodcock, A. Baccini, and C. Schmullius, 2008: Some challenges in global land cover map**: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ., 112, 2538–2556.
Hou, X. Y., 2001: Vegetation Atlas of China (1:1000000). Science Press, Bei**g, 280pp. (in Chinese)
IPCC, 2001: Climate Change 2001: The Scientific Basis: Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change, J. T. Houghton et al., Eds., Cambridge University Press, Cambridge and New York, 881pp.
Iwao, K., K. Nishida, T. Kinoshita, and Y. Yamagata, 2006: Validating land cover maps with degree confluence project information. Geophys. Res. Lett., 33, L23404, doi: 10.1029/2006GL027768.
Jain, A. K., and X. Yang, 2005: Modeling the effects of two different land cover change data sets on the carbon stocks of plants and soils in concert with CO2 and climate change. Global Biogeochemical Cycles, 19, GB2015, doi: 10.1029/2004GB002349.
Kaptué, T. A. T., J.-L., Roujean, and S. M. de Jong, 2011: Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. International Journal of Applied Earth Observation and Geoinformation, 13, 207–219.
Krankina, O., D. Pflugmacher, D. J. Hayes, A. D. McGuire, M. Hansen, and T. Haeme, 2011: Vegetation cover in the eurasian arctic: Distribution, monitoring, and role in carbon cycling. Eurasian Arctic Land Cover and Land Use in a Changing Climate, G. Gutman, and A. Reinssell, Eds., Springer-Verlag, Netherlands, 79–108.
Latifovic, R., and I. Olthof, 2004: Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sens. Environ., 90, 153–165.
Lawrence, P. J., and T. N. Chase, 2007: Representing a new modis consistent land surface in the community land model (CLM 3.0). J. Geophys. Res., 112, G01023, doi: 10.1029/2006JG000168.
Liu, J., 1996: Macro-Scale Survey and Dynamic Study of Natural Resources and Environment of Chinese by Remote Sensing. Chinese Science and Technology Press, Bei**g, 353pp. (in Chinese)
Liu, J., M. Liu, X. Deng, D. Zhuang, Z. Zhang, and D. Luo, 2002: The land use and land cover change database and its relative studies in China. Journal of Geographical Sciences, 12, 275–282.
Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohlen, Z. Zhu, L. Yang, and J. W. Merchant, 2000: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens., 21, 1303–1330.
McCallum, I., M. Obersteiner, S. Nilsson, and A. Shvidenko, 2006: A spatial comparison of four satellite derived 1 km global land cover datasets. International Journal of Applied Earth Observation and Geoinformation, 8, 246–255.
Milly, P. C. D., and A. B. Shmakin, 2002: Global modeling of land water and energy balances. Part II: Land-characteristic contributions to spatial variability. Journal of Hydrometeorology, 3, 301–310.
Pflugmacher, D., and Coauthors, 2011: Comparison and assessment of coarse resolution land cover maps for northern eurasia. Remote Sens. Environ., 115, 3539–3553.
Pielke, R. A., 2005: Land use and climate change. Science, 310, 1625–1626.
Pitman, A. J., and Coauthors, 2009: Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study. Geophys. Res. Lett., 36, L14814, doi: 10.1029/2009GL039076.
Potter, C., P. Gross, S. Klooster, M. Fladeland, and V. Genovese, 2008: Storage of carbon in U.S. Forests predicted from satellite data, ecosystem modeling, and inventory summaries. Climatic Change, 90, 269–282.
Poulter, B., P. Ciais, E. Hodson, H. Lischke, F. Maignan, S. Plummer, and N. E. Zimmermann, 2011: Plant functional type map** for earth system models. Geoscientific Model Development, 4, 993–1010.
Ran, Y. H., X. Li, and L. Lu, 2009: China land cover classification at 1 km spatial resolution based on a multisource data fusion approach. Advances in Earth Science, 24, 192–203. (in Chinese)
Ran, Y. H., X. Li, and L. Lu, 2010: Evaluation of four remote sensing based land cover products over china. Int. J. Remote Sens., 31, 391–401.
Ran, Y. H., X. Li, L. Lu, and Z. Y. Li, 2012: Large-scale land cover map** with the integration of multisource information based on the Dempster-Shafer theory. International Journal of Geographical Information Science, 26(1), 169–191.
Sertel, E., A. Robock, and C. Ormeci, 2010: Impacts of land cover data quality on regional climate simulations. Int. J. Climatol., 30, 1942–1953.
Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note, NCAR/TN-475+STR, 113pp.
Strahler, A., D. Muchoney, J. Borak, M. Friedl, S. Gopal, E. Lambin, and A. Moody, 1999: Modis land cover product: Algorithm theoretical basis document, version 5.0. Boston University, 66pp.
Strahler, A., and Coauthors, 2006: Global land cover validation: Recommendations for evaluation and accuracy assessment of global land cover maps. EUR 22156 ENDG, European Commission-DG Joint Research Centre, Institute for Environment and Sustainability, Luxembourg, 48pp.
Takata, K., K. Saito, and T. Yasunari, 2009: Changes in the Asian monsoon climate during 1700–1850 induced by preindustrial cultivation. Proc. the National Academy of Sciences, 106(24), doi: 10.1073/pnas.0807346106.
Townshend, J. R. G., C. Justice, W. Li, C. Gurney, and J. McManus, 1991: Global land cover classification by remote sensing: Present capabilities and future possibilities. Remote Sens. Environ., 35, 243–255.
Wu, L., and X. Li, 2004: China Glacier Information System. Ocean Press, Bei**g, China, 135pp. (in Chinese)
Zhang, S., 2002: An introduction of wetland-science database in China. Scientia Geographica Sinica, 22, 188–189. (in Chinese)
Zhang, X. S., S. Z. Sun, S. P. Yong, Z. D. Zhou, and R. Q. Wang, 2007: Vegetation Map of the People’s Republic of China (1:1000000). Geological Publishing House, Bei**g, 1228pp. (in Chinese)
Zhao, M., and A. J. Pitman, 2002: The regional scale impact of land cover change simulated with a climate model. Int. J. Climatol., 22, 271–290.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gao, H., Jia, G. Assessing disagreement and tolerance of misclassification of satellite-derived land cover products used in WRF model applications. Adv. Atmos. Sci. 30, 125–141 (2013). https://doi.org/10.1007/s00376-012-2037-4
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-012-2037-4