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Assessing disagreement and tolerance of misclassification of satellite-derived land cover products used in WRF model applications

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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.

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References

  • Ahlqvist, O., 2005: Using uncertain conceptual spaces to translate between land cover categories. International Journal of Geographical Information Science, 19, 831–857.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Foody, G. M., 2002: Status of land cover classification accuracy assessment. Remote Sens. Environ., 80, 185–201.

    Article  Google Scholar 

  • Friedl, M. A., and Coauthors, 2002: Global land cover map** from modis: Algorithms and early results. Remote Sens. Environ., 83, 287–302.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fritz, S., and L. See, 2005: Comparison of land cover maps using fuzzy agreement. International Journal of Geographical Information Science, 19, 787–807.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fu, C., 2003: Potential impacts of human-induced land cover change on east asia monsoon. Global and Planetary Change, 37, 219–229.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hou, X. Y., 2001: Vegetation Atlas of China (1:1000000). Science Press, Bei**g, 280pp. (in Chinese)

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Pflugmacher, D., and Coauthors, 2011: Comparison and assessment of coarse resolution land cover maps for northern eurasia. Remote Sens. Environ., 115, 3539–3553.

    Article  Google Scholar 

  • Pielke, R. A., 2005: Land use and climate change. Science, 310, 1625–1626.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sertel, E., A. Robock, and C. Ormeci, 2010: Impacts of land cover data quality on regional climate simulations. Int. J. Climatol., 30, 1942–1953.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wu, L., and X. Li, 2004: China Glacier Information System. Ocean Press, Bei**g, China, 135pp. (in Chinese)

    Google Scholar 

  • Zhang, S., 2002: An introduction of wetland-science database in China. Scientia Geographica Sinica, 22, 188–189. (in Chinese)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

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Correspondence to Gensuo Jia  (贾根锁).

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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

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