Integrating global land cover products for improved forest cover characterization: an application in North America

Six widely used coarse-resolution global land cover data-sets – Global Land Cover Characterization (GLCC), Global Land Cover 2000 (GLC2000), GlobCover land cover product (GlobCover), MODIS land cover product (MODIS LC), the University of Maryland land cover product (UMD LC), and the MODIS Vegetation Continuous Fields tree cover layer (MODIS VCF) disagree substantially in their estimates of forest cover. Employing a regression tree model trained on higher-resolution, Landsat-based data, these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America. Evaluated using a withheld test sample, the integrated percent forest cover (IPFC) data-set has a root mean square error of 11.75% – substantially better than the 17.37% of GLCC, 17.61% of GLC2000, 17.96% of GlobCover, 15.23% of MODIS LC, 19.25% of MODIS VCF, and 15.15% of UMD LC, respectively. Although demonstrated for forest, this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.

[1]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[2]  J. Wickham,et al.  Thematic accuracy of the NLCD 2001 land cover for the conterminous United States , 2010 .

[3]  Christopher Justice,et al.  The impact of misregistration on change detection , 1992, IEEE Trans. Geosci. Remote. Sens..

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

[5]  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 .

[6]  George Xian,et al.  Assessments of urban growth in the Tampa Bay watershed using remote sensing data , 2005 .

[7]  Ruth S. DeFries,et al.  Estimation of tree cover using MODIS data at global, continental and regional/local scales , 2005 .

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

[9]  Thomas R. Loveland,et al.  The IGBP-DIS global 1 km land cover data set , 1997 .

[10]  R. D. Ramsey,et al.  Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south‐western USA , 2005 .

[11]  J. Townshend,et al.  Urban growth of the Washington, D.C.–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover , 2013 .

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

[13]  Aaron Moody,et al.  The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing , 1995, Landscape Ecology.

[14]  Danielle J. Marceau,et al.  Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem , 1994 .

[15]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

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

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

[18]  J. Fry,et al.  Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods , 2009 .

[19]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[20]  Paul E. Geissler,et al.  Glacier Changes in Southeast Alaska and Northwest British Columbia and Contribution to Sea Level Rise , 2007 .

[21]  David P. Roy,et al.  Generating a long-term land data record from the AVHRR and MODIS Instruments , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[22]  Chengquan Huang,et al.  Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error , 2013, Int. J. Digit. Earth.

[23]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[24]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[25]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

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

[27]  C. Justice,et al.  The 1 km resolution global data set: needs of the International Geosphere Biosphere Programme† , 1994 .

[28]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[29]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[30]  T. Chase,et al.  Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0) , 2007 .

[31]  Guoqing Sun,et al.  MODIS tree cover validation for the circumpolar taiga-tundra transition zone , 2009 .

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

[33]  Steffen Fritz,et al.  Cropland for sub‐Saharan Africa: A synergistic approach using five land cover data sets , 2011 .

[34]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[35]  Stephen V. Stehman,et al.  International Journal of Applied Earth Observation and Geoinformation: Time-Series Analysis of Multi-Resolution Optical Imagery for Quantifying Forest Cover Loss in Sumatra and Kalimantan, Indonesia , 2011 .

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

[37]  Steffen Fritz,et al.  A new hybrid land cover dataset for Russia: a methodology for integrating statistics, remote sensing and in situ information , 2011 .

[38]  C. Justice,et al.  Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM + data , 2012 .

[39]  M. Hansen,et al.  Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia , 2011 .

[40]  Matthew C. Hansen,et al.  The global Landsat imagery database for the FAO FRA remote sensing survey , 2011, Int. J. Digit. Earth.

[41]  W. Cohen,et al.  North American forest disturbance mapped from a decadal Landsat record , 2008 .

[42]  Chengquan Huang,et al.  An assessment of global forest cover maps using regional higher-resolution reference data sets , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.