Abstract
Recent developments of 30 m global land characterization datasets (e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover mapping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC (Finer Resolution Observation and Monitoring-Global Land Cover) and FROM-GLC-seg (Segmentation), with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area (NL-ISA) and MODIS urban extent (MODIS-urban), to produce an improved 30 m global land cover map—FROM-GLC-agg (Aggregation). It was post-processed using additional coarse resolution datasets (i.e., MCD12Q1, GlobCover2009, MOD44W etc.) to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion aggregation approaches were employed to create a multi-resolution hierarchy (i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map (at 30 m) and the three maps subsequently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.
Similar content being viewed by others
References
Arino O, Bicheron P, Achard F, et al. 2008. GLOBCOVER The most detailed portrait of Earth. ESA Bull-Euro Space Agency, 136: 24–31
Bartholome E, Belward A S. 2005. GLC2000: A new approach to global land cover mapping from Earth observation data. Int J Remote Sens, 26: 1959–1977
Bontemps S, Defourny P, Bogaert E V, et al. 2010. GLOBCOVER2009 Products Description and Validation Report. http://due.esrin.esa.int/globcover/LandCover2009/GLOBCOVER2009_Validation_Report_2.2.pdf
Bontemps S, Herold M, Kooistra L, et al. 2012. Revisiting land cover Biogesciences, 9: 2145–2157
Carroll M L, DiMiceli C M, Sohlberg R A, et al. 2004. 250 m MODIS Normalized Difference Vegetation Index, 250ndvi28920033435, Collection 4. Maryland: University of Maryland
DeFries R S, Townshend J R G. 1994. NDVI-derived land cover classification at a global scale. Int J Remote Sens, 15: 3567–3586
Elvidge C D, Tuttle B T, Suttle P C, et al. 2007. Global distribution and density of constructed impervious surfaces. Sensor, 7: 1962–1979
Franks S, Masek J G, Headley R M K, et al. 2009. Large area scene selection interface (LASSI): Methodology of selecting Landsat imagery for the Global Land Survey 2005. Photogramm Eng Remote Sens, 75: 1287–1296
Friedl M A, Sulla-Menashe D, Tan B, et al. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens Environ, 114: 168–182
Fritz S, You L, Bun A, et al. 2011. Cropland for sub-Saharan Africa: a synergistic approach using five land cover data sets. Geophys Res Lett, 38: L04404
Gardner R H, Lookingbill T R, Townsend P A, et al. 2008. A New Approach for Rescaling Land Cover Data. Landsc Ecol, 23: 513–526
Gong P, Howarth P J. 1990. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogramm Eng Remote Sens, 56: 67–73
Gong P, Howarth P J. 1992a. Frequency-based contextual classification and grey-level vector reduction for land-use identification. Photogramm Eng Remote Sens, 58: 423–437
Gong P, Howarth P J. 1992b. Land-use classification of SPOT HRV data using a cover-frequency method. Int J Remote Sens, 13: 1459–1471
Gong P, Liang S, Carlton E, et al. 2012. Urbanization and health in China. Lancet, 379: 843–852
Gong P, Wang J, Yu L, et al. 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int J Remote Sens, 34: 2607–2654
Gupta R K, Prasan T S, Krishna R, et al. 2000. Problems in upscaling of high resolution remote sensing data to coarse spatial resolution over land surface. Adv Space Res, 26: 1111–1121
Hansen M C, DeFries R S, Townshend J R G, et al. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens, 21: 1331–1364
Hansen M C, Potapov P V, Moore R, et al. 2013. High-resolution global maps of 21s-centrury forest cover change. Science, 342: 850–853
He H S, Ventura S J, Mladenoff D J. 2002. Effects of spatial aggregation approaches on classified satellite imagery. Int J Geogr Inf Sci, 16: 93–109
Hijmans R J, Cameron S E, Parra J L, et al. 2005. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol, 25: 1965–1978
Jensen J R, Zown D C. 1999. Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens, 65: 611–622
Jung M, Henkel K, Herold M, et al. 2006. Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sens Environ, 101: 534–553
Kitron U, Clennon J A, Cecere M C, et al. 2006. Upscale or downscale: applications of fine scale remotely sensed data to Chagas disease in Argentina and schistosomiasis in Kenya. Geospatial Health, 1: 49–58
Kovalskyy V, Roy D P. 2013. The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30m Landsat data product generation. Remote Sens Environ, 130: 280–293
Liang L, Xu B, Chen Y L, et al. 2010. Combining spatial-temporal and phylogenetic analysis approaches for improved understanding on global H5N1 transmission. PLoS One, 5: e13575
Loveland T R, Reed B C, Brown J F, et al. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens, 21: 1303–1330
Matthews E. 1983. Global vegetation and land use: New high resolution data bases for climate studies. J Clim Appl Meteorol, 22: 474–487
Mauser W, Schadlich S. 1998. Modelling the spatial distribution of evapotranspiration on different scales using remote sensing data. J Hydrol, 212–213: 250–267
Moody A, Woodcock C E. 1994. Scale-dependent errors in the estimation of landcover proportions—Implications for global land-cover datasets. Photogramm Eng Remote Sens, 60: 585–594
Moody A, Woodcock C E. 1995. The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing. Landsc Ecol, 10: 363–379
Olson D M, Dinerstein E, Wikramanayake E D, et al. 2001. Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience, 51: 933–938
Olson J S, Watts J A. 1982. Major World Ecosystem Complex Map Oak Ridge. TN: Oak Ridge National Laboratory
Quattrochi D A, Goodchild M F. 1997. Scale in Remote Sensing and GIS, 1–72. Boca Raton, FL: Lewis
Raj R, Hamm N, Kant Y. 2013. Analysis the effect of different aggregation approach on remotely sensed data. Int J Remote Sens, 34: 4900–4916
Ramirez-Villegas J, Jarvis A. 2010. Downscaling global circulation model outputs: The Delta method decision and policy analysis working paper No.1. Available: WWW document] URL: http://www.ccafs-climate.org/downloads/docs/Downscaling-WP-01.pdf [Accessed: 2 January 2013]
Sexton J O, Song, X, Feng M, et al. 2013. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS Vegetation Continuous Fields with lidar-based estimates of error. Int J Dig Earth, 6: 427–448
Schneider A, Friedl M A, Potere D. 2009. A new map of global urban extent from MODIS data. Environ Res Lett, 4: 044003
Schneider A, Friedl M A, Potere D. 2010. Monitoring urban areas globally using MODIS 500 m data: New methods and datasets based on urban ecoregions. Remote Sens Environ, 114: 1733–1746
Small C. 2005. A global analysis of urban reflectance. Int J Remote Sens, 95: 335–344
Sterling S, Ducharne A. 2008. Comprehensive data set on global land cover change for land surface model applications. Glob Biogeochem Cycle, 22: GB3017
Tateishi R, Uriyangqai B, Al-Bilbisi H, et al. 2011. Production of global land cover data—GLCNMO. Int J Dig Earth, 4: 22–49
Turner M G, Dale V H, Gardner, R H. 1989a. Predicting across scales: Theory development and testing. Landsc Ecol, 3: 235–252
Turner M G, O’Neill R V, Gardner R H, et al. 1989b. Effects of changing spatial scale on the analysis of landscape pattern. Landsc Ecol, 3: 153–162
Vancutsem C, Marinho E, Kayitakire F, et al. 2013. Harmonizing and combining existing land cover/land use datasets for cropland area monitoring at the African continental scale. Remote Sensing, 5: 19–41
Verburg P H, Neumann K, Nol L. 2011. Challenges in using land use and land cover data for global change studies. Glob Change Biol, 19: 974–989
Wang J, Li C, Yu L, et al. 2014. Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution, ISPRS-J Photogramm Remote Sens, doi: 10.1016/j.isprsjprs.2014.03.007
Wang L, Gong P, Ying Q, et al. 2010. Settlement extraction over North China Plain with Landsat and Beijing-1 data using an improved watershed segmentation algorithm. Int J Remote Sens, 31: 1411–1426
Wang L, Li C, Ying Q, et al. 2012. China’s urban expansion from 1990 to 2010 determined with satellite remote sensing. Chin Sci Bull, 57: 2802–2812
Wessel P, Smith W H F. 1996. A global self-consistent, hierarchical, high-resolution shoreline database. J Geophys Res, 101: 8741–8743
Wilby R L, Wigley T M L. 1997. Downscaling general circulation model outputs: A review of methods and limitations. Prog Phys Geogr, 21: 530–548
Wilson M F, Henderson-Sellers A. 1985. A global archive of land cover and soils data for use in general circulation climate models. Int J Climatol, 5: 119–143
Woodcock C E, Strahler A H. 1987. The factor of scale in remote sensing. Remote Sens Environ. 21: 311–332
Yu L, Holden E J, Dentith M C, et al. 2012. Towards the automatic selection of optimal seam line locations when merging optical remote sensing images. Int J Remote Sens, 33: 1000–1014
Yu L, Wang J, Gong P. 2013a. Improving 30 meter global land cover map FROM-GLC with time series MODIS and auxiliary datasets: A segmentation based approach. Int J Remote Sens, 34: 5851–5867
Yu L, Wang J, Clinton N, et al. 2013b. FROM-GC: 30 m global cropland extent derived through multi-source data integration. Int J Dig Earth, 6: 521–533
Zhao S Q, Liu S, Li Z, et al. 2010. A spatial resolution threshold of land cover in estimating terrestrial carbon sequestration in four counties in Georgia and Alabama, USA. Biogeosciences, 7: 71–80
Zhong L, Gong P, Biging G S. 2012. Phenology-based crop classification algorithm and its implications on agriculture water use assessments in California’s Central Valley. Photogramm Eng Remote Sens, 78: 799–819
Zhong L, Gong P, Biging G S. 2014. Efficient corn and soybean mapping with temporal extendability: A muti-year experiment using Landsat imagery. Remote Sens Environ, 140: 1–13
Zhu P, Gong D. 2014. Suitability mapping of global wetland areas and validation with remotely sensed data. Sci China Earth Sci, doi: 10.1007/s11430-014-4925-1
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Yu, L., Wang, J., Li, X. et al. A multi-resolution global land cover dataset through multisource data aggregation. Sci. China Earth Sci. 57, 2317–2329 (2014). https://doi.org/10.1007/s11430-014-4919-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11430-014-4919-z