Assessment of Coarse-Resolution Land Cover Products Using CASI Hyperspectral Data in an Arid Zone in Northwestern China

The accuracy of different coarse-resolution land cover products is an important consideration for product users at the regional or global scale, and different evaluation methods inevitably result in discrepancies in accuracy for the same land cover product. The remote sensing community has responded to this increased interest by improving methodologies for more accurately evaluating the correctness of land cover information. In this study, a pixel-based hierarchical classification strategy followed by an object-based classification method was applied to compact airborne spectrographic imager (CASI) hyperspectral data in order to produce highly accurate, high spatial resolution classification reference data. Some aspects of the fuzzy/conventional evaluation of MODIS land cover (MODISLC) (500 m) and GlobCover (300 m) data based on sub-pixel class fractions derived from high spatial resolution reference data at different thematic resolutions are also discussed. Relationships between homogeneity and fuzzy accuracy for two land cover products were obtained at different thematic resolutions. Additionally, the influences on the relationship resulting from the thematic resolution were also studied, and these are reported in this paper. Attempts were made to establish fuzzy/conventional evaluation rules for fuzzy classes, and the different performances of the fuzzy and conventional evaluations for hard/fuzzy labels were compared. The adjusted GlobCover accuracy after theoretical removal of the effect caused by spatial resolution was calculated based on the relationship between homogeneity and accuracy; the result was a higher accuracy than for MODISLC at the same thematic resolution. In addition, the different performance characteristics of the relationships between homogeneity and adjusted GlobCover accuracy/MODISLC accuracy at different thematic resolutions were compared and analyzed over the area where the CASI transects were obtained.

[1]  Xin Li,et al.  Evaluation of four remote sensing based land cover products over China , 2010 .

[2]  Chad J. Shuey,et al.  Validating MODIS land surface reflectance and albedo products: methods and preliminary results , 2002 .

[3]  S. Goetz,et al.  Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005 , 2008 .

[4]  Guoqing Sun,et al.  Hierarchical mapping of Northern Eurasian land cover using MODIS data , 2011 .

[5]  Wenwu Zhao,et al.  Accuracy assessments of the GLOBCOVER dataset using global statistical inventories and FLUXNET site data , 2012 .

[6]  R. Dickinson,et al.  The land surface climatology of the community land model coupled to the NCAR community climate model , 2002 .

[7]  George P. Petropoulos,et al.  Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data , 2014, Int. J. Digit. Earth.

[8]  Peng Gong,et al.  Land cover assessment with MODIS imagery in southern African Miombo ecosystems , 2005 .

[9]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[10]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[11]  E. Lambin,et al.  Dynamics of Land-Use and Land-Cover Change in Tropical Regions , 2003 .

[12]  M. Hansen,et al.  A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products , 2000 .

[13]  Frédéric Achard,et al.  GLOBCOVER : The most detailed portrait of Earth , 2008 .

[14]  Dirk Pflugmacher,et al.  Comparison and assessment of coarse resolution land cover maps for Northern Eurasia , 2011 .

[15]  Armel Thibaut Kaptué Tchuenté,et al.  Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[17]  Francisco Javier García-Haro,et al.  Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe , 2012 .

[18]  C. Steele,et al.  Mapping land cover in urban residential landscapes using very high spatial resolution aerial photographs , 2012 .

[19]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[20]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[21]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[22]  Rasim Latifovic,et al.  Thematic mapper (TM) based accuracy assessment of a land cover product for Canada derived from SPOT VEGETATION (VGT) data , 2003 .

[23]  Jean-François Mas,et al.  Land cover mapping applications with MODIS: a literature review , 2012, Int. J. Digit. Earth.

[24]  J. San-Miguel-Ayanz,et al.  A methodology to generate a synergetic land-cover map by fusion of different land-cover products , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[25]  D. C. Robertson,et al.  MODTRAN cloud and multiple scattering upgrades with application to AVIRIS , 1998 .

[26]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[27]  Faith R. Kearns,et al.  Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography , 2008, Comput. Environ. Urban Syst..

[28]  N. Trodd,et al.  Monitoring and modelling open savannas using multisource information: analyses of Kalahari studies , 1999 .

[29]  Rasim Latifovic,et al.  Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data , 2004 .

[30]  G. Meehl,et al.  The Importance of Land-Cover Change in Simulating Future Climates , 2005, Science.

[31]  R. Lucas,et al.  Classification of Australian forest communities using aerial photography, CASI and HyMap data , 2008 .

[32]  R. Fernandes,et al.  Approaches to fractional land cover and continuous field mapping: A comparative assessment over the BOREAS study region , 2004 .

[33]  Alan H. Strahler,et al.  Pixel- and site-based calibration and validation methods for evaluating supervised classification of remotely sensed data , 2002 .

[34]  Weicheng Wu,et al.  Using remote sensing to assess impacts of land management policies in the Ordos rangelands in China , 2013 .

[35]  Bicheron Patrice,et al.  GlobCover - Products Description and Validation Report , 2008 .

[36]  R. Cowling,et al.  One Hundred Questions of Importance to the Conservation of Global Biological Diversity , 2009, Conservation biology : the journal of the Society for Conservation Biology.

[37]  Christiane Schmullius,et al.  Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring , 2011 .

[38]  James D. Wickham,et al.  Designing a multi-objective, multi-support accuracy assessment of the 2001 National Land Cover Data (NLCD 2001) of the conterminous United States , 2008 .

[39]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .