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Global Land Cover Mapping: Current Status and Future Trends

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Land Use and Land Cover Mapping in Europe

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 18))

Abstract

The observation of global-scale land cover (LC) is of importance to international initiatives such as the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto protocol, governments, and scientific communities in their understanding and monitoring of the changes affecting the environment, and the coordination of actions to mitigate and adapt to global change. As such, reliable and consistent global LC (GLC) datasets are being sought. For instance, GLC datasets are used as an input for many Global Circulation Models, Earth Systems Models and Integrated Assessment Models used for global and regional climate simulations, dynamic vegetation modelling, carbon (stock) modelling, ecosystem modelling, land surface modelling, and impact assessments (Hibbard et al., 2010; Herold et al., 2011).

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References

  • Anonymous (2011) Global forest land-use change from 1990 to 2005 – initial results from a global remote sensing survey. Rome, Italy

    Google Scholar 

  • Arino O, Gross D, Ranera F, Bourg L, Leroy M, Bicheron P, Latham J et al (2007) GlobCover: ESA service for global land cover from MERIS. In: Geoscience and remote sensing symposium – IGARSS 2007, 23–28 July 2007, Barcelona, Spain. IEEE International, Piscataway, pp 2412–2415

    Google Scholar 

  • Aschbacher J, Milagro-Pérez MP (2012) The European Earth monitoring (GMES) programme: status and perspectives. Remote Sens Environ 120(2012):3–8

    Article  Google Scholar 

  • Astrium (2012) SPOT-6, -7 programme. http://www.astrium-geo.com/en/147-spot-6-7

  • Bartholomé E, Belward AS (2005) GLC2000: a new approach to global land cover mapping from Earth observation data. Int J Remote Sens 26(9):1959–1977

    Article  Google Scholar 

  • Benítez P, McCallum I, Obersteiner M, Yamagata Y (2004) Global supply for carbon sequestration: identifying least-cost afforestation sites under country risk consideration. International Institute for Applied System Analysis, Laxenburg, Austria

    Google Scholar 

  • Berger M, Moreno J, Johannessen JA, Levelt PF, Hanssen RF (2012) ESA’s sentinel missions in support of Earth system science. Remote Sens Environ 120:84–90

    Article  Google Scholar 

  • Bontemps S, Defourny P, van Bogaert E, Arino O, Kalogirou V, Perez JR (2011) GlobCover 2009, products description and validation report. European Space Agency, Frascati, Italy, and UniversitÕ Catholique de Louvain, Louvain-la-Neuve, Belgium.

    Google Scholar 

  • Bontemps S, Herold M, Kooistra L, van Groenestijn A, Hartley A, Arino O, Moreau I, Defourny P (2012) Revisiting land cover observation to address the needs of the climate modeling community. Biogeosciences 9(6):2145–2157

    Article  Google Scholar 

  • Bork EW, Su JG (2007) Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: a meta analysis. Remote Sens Environ 111(1):11–24

    Article  Google Scholar 

  • Carroll ML, DiMiceli CM, Townshend JRG, Sohlberg RA, Hansen MC, DeFries RS (2006) Vegetative cover conversion MOD44A, deforestation. In: Burned vegetation – collection 4, ed. University of Maryland, College Park, Maryland. http://glcf.umiacs.umd.edu/data/vcc/

  • Chander G, Xiong X, Choi T, Angal A (2010) Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM + sensors using pseudo-invariant test sites. Remote Sens Environ 114:925–939

    Article  Google Scholar 

  • Chen J (2012) China 30 m-resolution Global Land Cover Map in 2012. GIM International. http://www.gim-international.com/issues/articles/id1838-China_mresolution_Global_Land_Cover_Map_in.html

  • CNES (2012) ORFEO Pleiades. http://smsc.cnes.fr/PLEIADES/index.htm

  • Defourny P, Bontemps S, Martin B, Brockman C, Fomferra N, Grit K, Kruger O (2011a) CCI land cover project – product specification document, version 1.2. www.esa-landcover-cci.org

  • Defourny P, Bontemps S, Schouten L, Bartalev S, Cacetta P, De Wit A, Di Bella CM et al (2011b) GLOBCOVER 2005 and GLOBCOVER 2009 validation: learnt lessons. In: GOFC-GOLD global land cover & change validation workshop, Laxenburg, Austria

    Google Scholar 

  • Defourny P, Mayaux P, Herold M, Bontemps S (2012) Global land-cover map validation experiences: toward the characterization of quantitative uncertainty. In: Giri C (ed) Remote sensing of land use and land cover – principles and applications. CRC Press – Taylor and Francis, Boca Raton, pp 207–224

    Chapter  Google Scholar 

  • DeFries RS, Los SO (1999) Implications of land-cover misclassification for parameter estimates in global land-surface models: an example from the simple biosphere model (SiB2). Photogramm Eng Remote Sens 65(9):1083–1088

    Google Scholar 

  • DeFries RS, Townshend JRG (1994) NDVI-derived land cover classifications at a global scale. Int J Remote Sens 15(17):3567–3586

    Article  Google Scholar 

  • DeFries RS, Hansen MC, Townshend JRG, Sohlberg R (1998) Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Int J Remote Sens 19:3141–3168

    Article  Google Scholar 

  • Di Gregorio A, Jansen LJM (2005) Land cover classification system: classification concepts and user manual: software Version 2. Food and Agriculture Organization of the United Nations, Rome

    Google Scholar 

  • Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B et al (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120(May):25–36

    Article  Google Scholar 

  • Food and Agriculture Organization (2007) FAO website. www.fao.org/gtos/topcECV.html

  • Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83(1–2):287–302

    Article  Google Scholar 

  • Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang XM (2010) MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114(1):168–182

    Article  Google Scholar 

  • Fritz S, McCallum I, Schill C, Perger C, Grillmayer R, Achard F, Kraxner F et al (2009) Geo-wiki.Org: the use of crowdsourcing to improve global land cover. Remote Sens 1(3):345–354

    Article  Google Scholar 

  • Fritz S, See L, McCallum I, Schill C, Obersteiner M, van der Velde M, Boettcher H, Havlík P, Achard F (2011) Highlighting continued uncertainty in global land cover maps for the user community. Environ Res Lett 6:44005

    Article  Google Scholar 

  • GCOS (2010) Implementation plan for the global observing system for climate in support of the UNFCCC, GCOS-138, vol 138

    Google Scholar 

  • GCOS (2012) Global climate observing system. http://www.wmo.int/pages/prog/gcos/index.php?name=AboutGCOS

  • Ge J, Qi J, Lofgren BM, Moore N, Torbick N, Olson JM (2007) Impacts of land use/cover classification accuracy on regional climate simulations. J Geophys Res 112(D5), D05107

    Google Scholar 

  • GEO (2010) GEO 2009–2011 work plan – revision 3

    Google Scholar 

  • GEO (2011) GEO 2012–2015 work plan – revision 1

    Google Scholar 

  • GEO (2012) Group on earth observations. http://www.earthobservations.org/about_geo.shtml

  • GEOSS (2005) The Global Earth Observation System of Systems GEOSS 10-year implementation plan. Available at: www.earthobservations.org

  • Gerrand AM, Lindquist EJ, Wilkie M, Shimabukuro Y, Cumani R, Hansen MC, Potapov P, Achard F (2009) The 2010 global forest resource assessment remote sensing survey. In: Proceedings of the 33rd international symposium on remote sensing of environment ISRSE, 2–5, Stresa, Italy

    Google Scholar 

  • Giri C, Zhiliang Z, Reed B (2005) A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sens Environ 94(1):123–132

    Article  Google Scholar 

  • GLCA (2009) Toward a post-2012 agreement on climate change: recommendations of global leadership for climate action. Global leadership for climate action. http://www.globalclimateaction.com/images/pdf/glca_recomm_post2012_agreement_climatechange.pdf

  • GOFC-GOLD (2013) Third GOFC-GOLD symposium. Wageningen University, Wageningen, The Netherlands, 15–19 April 2013. http://www.gofcgold.wur.nl/sites/Gofcgold_Symposium2013.php

  • Göhmann H, Herold M, Jung M, Schultz M, Schmullius CC (2009) Prototyping a probability-based Best Map Approach for global land cover datasets at 1 km resolution using MODIS, GLC2000, UMD and IGBP. In: 33rd ISRSE, Stresa, Italy

    Google Scholar 

  • Gong P, Wang J, Yu L, Zhao Y, Zhao Y, Liang L, Niu Z 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(7):2607–2654

    Article  Google Scholar 

  • Goodwin NR, Collett LJ, Denham RJ, Flood N, Tindall D (2013) Cloud and cloud shadow screening across Queensland, Australia: an automated method for Landsat TM/ETM + time series. Remote Sens Environ 134:50–65

    Article  Google Scholar 

  • Gutman G, Masek JG (2012) Long-term time series of the Earth’s land-surface observations from space. Int J Remote Sens 33(15):4700–4719

    Article  Google Scholar 

  • Gutman G, Justice C, King LA (2012) The NASA land-cover and land-use change program – research agenda and progress (2005–2011). In: Giri C (ed) Remote sensing of land use and land cover – principles and applications. CRC Press – Taylor and Francis, Boca Raton, pp 379–396

    Chapter  Google Scholar 

  • Hansen MC, Reed BC (2000) A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products. Int J Remote Sens 21(6–7):1365–1373

    Article  Google Scholar 

  • Hansen MC, DeFries RS, Townshend JRG, Sohlberg R (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21(6/7):1331–1364

    Article  Google Scholar 

  • Hansen MC, Stehman SV, Potapov PV (2010) Quantification of global gross forest cover loss. Proc Natl Acad Sci 107(19):8650–8655

    Article  Google Scholar 

  • Harris NL, Brown S, Hagen SC, Saatchi SS, Petrova S, Salas W, Hansen MC, Potapov PV, Lotsch A (2012) Baseline Map of carbon emissions from deforestation in tropical regions. Science 336(6088):1573–1576. doi:10.1126/science.1217962

    Article  Google Scholar 

  • Herold M, Mayaux P, Woodcock CE, Baccini A, Schmullius CC (2008) Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sens Environ 112(5):2538–2556

    Article  Google Scholar 

  • Herold M, Woodcock CE, Cihlar J, Wulder MA, Arino O, Achard F, Hansen MC et al (2009) Assessment of the status of the development of the standards for the terrestrial essential climate variables – land cover. Rome

    Google Scholar 

  • Herold M, van Groenestijn A, Kooistra L, Kalogirou V, Arino O (2011) Land Cover CCI user requirements document. Louvain-la-Neuve, Belgium

    Google Scholar 

  • Herold M, Kooistra L, van Groenestijn A, Defourny P, Schmullius CC, Kalogirou V, Arino O (2012) Building saliency, legitimacy, and credibility towards operational global and regional land cover observations and assessments in the context of international processes and observing Essential Climate Variables (ECV’S). In: USGS/Earth Resources Observation and Science (EROS) Center, Giri CP (eds) Remote sensing of land use and land cover: principles and applications. CRC Press, Sioux Falls, pp 397–414

    Chapter  Google Scholar 

  • Hibbard K, Janetos A, van Vuuren DP, Pongratz J, Rose SK, Betts R, Herold M, Feddema JJ (2010) Research priorities in land use and land-cover change for the Earth system and integrated assessment modelling. Int J Climatol 30(13):2118–2128

    Article  Google Scholar 

  • Huang C, Wylie B, Yang L, Homer CG, Zylstra G (2002) Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. Int J Remote Sens 23(8):1741–1748

    Article  Google Scholar 

  • IPCC (2006) Guidelines for national greenhouse gas inventories, vol 4 AFOLU (Agriculture, Forestry and Other Land Use). Kanagawa, Japan

    Google Scholar 

  • Jung M, Henkel K, Herold M, Churkina G (2006) Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sens Environ 101(4):534–553

    Article  Google Scholar 

  • Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — temporal segmentation algorithms. Remote Sens Environ 114(12):2897–2910

    Article  Google Scholar 

  • Lamard JL, Frecon L, Bailly B, Gaudin-Delreeu C, Kubk P, Laherrere JM (2008) The high resolution optical instruments for the pleiades HR Earth observation satellites. In: International Astronautical Federation (ed) 59th International Astronautical Congress. Glasgow, pp 2650–2662

    Google Scholar 

  • Lee-Ashley M, Moody J (2010) United States launches new global initiative to track changes in land cover and use. Lee-Ashley M, Moody J (ed). US. Department of the Interior

    Google Scholar 

  • Li G, Lu D, Moran E, Dutra L, Batistella M (2012) A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. ISPRS J Photogramm Remote Sens 70(06):26–38. doi:10.1016/j.isprsjprs.2012.03.010

    Article  Google Scholar 

  • Liao A (2013) Global land surface water product at 30 m resolution. ISPRS/GEO workshop on high resolution global land cover mapping, 24 April 2013, Beijing, China

    Google Scholar 

  • Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L, Merchant JW (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21(6/7):1303–1330

    Article  Google Scholar 

  • Lu D, Li G, Moran E, Dutra L, Batistella M (2011) A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. GISci Remote Sens 48(3):345–370. doi:10.2747/1548-1603.48.3.345

  • Lucas RM, Cronin N, Moghaddam M, Lee A, Armston J, Bunting P, Witte C (2006) Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia. Remote Sens Environ 100(3):388–406

    Article  Google Scholar 

  • Mayaux P, Eva H, Gallego J, Strahler AH, Herold M, Agrawal S, Naumov S et al (2006) Validation of the Global Land Cover 2000 map. IEEE Trans Geosci Remote Sens 44(7):1728–1739

    Article  Google Scholar 

  • McCallum I, Obersteiner M, Nilsson S, Shvidenko A (2006) A spatial comparison of four satellite derived 1 km global land cover datasets. Int J Appl Earth Obs Geoinf 8(4):246–255

    Article  Google Scholar 

  • Nakaegawa T (2011) Uncertainty in land cover datasets for global land-surface models derived from 1-km global land cover datasets. Hydrol Process 25(17):2703–2714

    Article  Google Scholar 

  • NASA (2012) CEOS LPV website. http://lpvs.gsfc.nasa.gov/

  • Olofsson P, Stehman SV, Woodcock CE, Sulla-Menashe D, Sibley AM, Newell JD, Friedl MA, Herold M (2012) A global land-cover validation data set, part I: fundamental design principles. Int J Remote Sens 33(18):5768–5788

    Article  Google Scholar 

  • Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen–Geiger climate classification. Hydrol Earth Syst 11:1633–1644

    Article  Google Scholar 

  • Scepan J, Menz G, Hansen MC (1999) The DISCover validation image interpretation process. Photogramm Eng Remote Sens 65(9):1075–1081

    Google Scholar 

  • Sertel E, Robock A, Ormeci C (2010) Impacts of land cover data quality on regional climate simulations. Int J Climatol 30(13):1942–1953

    Article  Google Scholar 

  • Sexton JO, Song X-P, Feng M, Noojipady P, Anand A, Huang C 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 Digit Earth 6(5):427–448

    Article  Google Scholar 

  • Stone R (2010) Earth-observation summit endorses global data sharing. Science 330(6006):902. http://www.sciencemag.org/content/330/6006/902.short

    Google Scholar 

  • Strahler AH, Boschetti L, Foody GM, Friedl MA, Hansen MC, Herold M, Mayaux P, Morisette JT, Stehman SV, Woodcock CE (2006) Global land cover validation: recommendations for evaluation and accuracy assessment of global land cover maps. Luxembourg

    Google Scholar 

  • Tateishi R, Uriyangqai B, Al-Bilbisi H, Ghar MA, Tsend-Ayush J, Kobayashi T, Kasimu A et al (2011) Production of global land cover data – GLCNMO. Int J Digit Earth 4(1):22–49

    Article  Google Scholar 

  • Townshend JR, Masek JG, Huang C, Vermote EF, Gao F, Channan S et al (2012) Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. Int J Digit Earth 5(5):373–397

    Article  Google Scholar 

  • Verbesselt J, Zeileis A, Herold M (2012) Near real-time disturbance detection using satellite image time series. Remote Sens Environ 123:98–108

    Article  Google Scholar 

  • Verburg PH, Neumann K, Nol L (2011) Challenges in using land use and land cover data for global change studies. Glob Chang Biol 17(2):974–989

    Article  Google Scholar 

  • Wu W, Shibasaki R, Yang P, Ongaro L, Zhou Q, Tang H (2008) Validation and comparison of 1 km global land cover products in China. Int J Remote Sens 29(13):3769–3785

    Article  Google Scholar 

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Correspondence to Brice Mora .

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Mora, B., Tsendbazar, NE., Herold, M., Arino, O. (2014). Global Land Cover Mapping: Current Status and Future Trends. In: Manakos, I., Braun, M. (eds) Land Use and Land Cover Mapping in Europe. Remote Sensing and Digital Image Processing, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7969-3_2

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