Plant functional type (PFT) is a crucial variable needed in studies of global climate, carbon cycle and ecosystem change. Using remote sensing techniques to extract PFTs is a relatively recent field of research. To date, only a very few methods for mapping PFTs have been reported. This chapter provides an overview of recent developments in this evolving field and discusses future research needs. A brief survey of existing methods for mapping PFTs is presented, followed by a discussion of several methodological issues pertaining to the development of robust remote sensing techniques for mapping of PFTs at regional to global scales. The chapter also outlines a multisource data fusion framework for improved mapping of PFTs from satellite observations.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. Geosci. Remote Sens. 28:540–552
Bonan GB (1993) Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sens. Environ. 43:303–314
Bonan GB (1995) Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. J. Geophys. Res. 100:2817–2831
Bonan GB (1996) A land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: technical description and user’s guide. NCAR Technical Note NCAR/TN-417 + STR. National Center for Atmospheric Research, Boulder, Colorado, 150pp
Bonan GB, Levis S, Kergoat L, Oleson KW (2002) Landscapes as patches of plant functional types: an integrating concept for climate and ecosystem models. Global Biogeochem. Cycles 16 (2), doi:10.1029/2000GB001360
Box EO (1995) Factors determining distributions of tree species and plant functional types. Vegetatio 121(1/2):101–116
Box EO (1996) Plant functional types and climate at the global scale. J. Veg. Sci. 7:309–320
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont, CA.
Brown JF, Loveland TR, Merchant JW, Reed BC (1993) Using multisource data in global land cover characterization: concepts, requirements and methods. Photogramm. Eng. Remote Sens. 59:977–987
Campbell JB (1978) A geographical analysis of image interpretation methods. Prof. Geogr. 30:264–269
DeFries RS, Hansen M, Townshend JGR, Sohlberg R (1998) Global land cover classification 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
DeFries RS, Townshend JRG, Hansen MC (1999) Continuous fields of vegetation characteristics at the global scale at 1-km resolution. J. Geophys. Res. 104D:16911–16923
DeFries RS, Hansen MC, Townshend JRG (2000a) Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data. Int. J. Remote Sens. 21:1389–1414
DeFries RS, Hansen MC, Townshend JRG, Janetos AC, Loveland TR (2000b) A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biol. 6:247–254
Dempster AP (1967) Upper and lower probabilities induced by multivalued mappings. Ann. Math. Stat. 38:325–329
Denning AS, Collatz GJ, Zhang C, Randall DA, Berry JA, Sellers PJ, Colello GD, Dazlich DA (1996) Simulations of terrestrial carbon metabolism and atmospheric CO2 in a general circulation model. Part 1: surface carbon fluxes, Tellus 48B:521–542
Dickinson RE, Shaikh M, Bryant R, Graumlich L (1998) Interactive canopies for a climate model. J. Climate 11:2823–2836
Fang H, Liang S (2005) A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sens. Environ. 94:405–424
Feddema JJ, Oleson KW, Bonan GB, Mearns LO, Buja LE, Meehl GA, Washington WM (2005) The importance of land-cover change in simulating future climates. Science 310:1674–1678
Foley JA, Prentice CI, Ramankutty N, Levis S, Pollard D, Sitch S, Haxeltine A (1996) An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochem. Cycles 10:603–628
Friedl MA, Brodley CE (1997) Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61:399–409
Friedl MA, Brodley CE, Strahler AH (1999) Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans. Geosci. Remote Sens. 37:969–977
Friedl MA, McIver DK, Hodges JCF, Zhang X, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schnieder A, Cooper A, Baccini A, Gao F, Schaaf C (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83(1-2):287–302
Gamon JA, Huemmrich KF, Peddle DR, Chen J, Fuentes D, Hall FG, Kimball JS, Goetz S, Gu J, McDonald KC, Miller JR, Moghaddam M, Rahman AF, Roujean JL, Smith EA, Walthall CL, Zarco-Tejada P, Fernandes R, Cihlar J (2004) Remote sensing in BOREAS: lessens learned. Remote Sens. Environ. 89:139–162
Hansen M, Dubayah R, DeFries R (1996) Classification trees: an alternative to traditional land cover classifiers. Int. J. Remote Sens. 17:1075–1081
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. 26:1331–1364
Justice CO, Townshend JRG, Vermote E, Masuoka E, Wolfe R, Saleous N, Roy D, Morisette J (2002) An overview of MODIS land data processing and product status. Remote Sens. Environ. 83:3–15
Kimes DS, Harrison PR, Ratclife PA (1991) A knowledge-based expert system for inferring vegetation characteristics. Int. J. Remote Sens. 12:1987–2020
Kucharik CJ, Foley JA, Delire C, Fisher VA, Coe MT, Lenters JD, Young-Molling C, Ramankutty N, Norman JM, Gower ST (2000) Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure. Global Biogeochem. Cycles 14:795–825
Lawton RO, Nair US, Pielke Sr. RA, Welch RM (2001) Climatic impact of tropical lowland deforestation on nearby montane cloud forest. Science 294:584–587
Le Hégarat-Mascle S, Richard D, Ottl é C (2003) Multi-scale data fusion using Dempster-Shafer evidence theory. Int. Comput.-Aid. Eng. 10:9–22
Lein JK (2003) Applying evidential reasoning methods to agriculture land cover classification. Int. J. Remote Sens. 24:4161–4180
Liang S (2001) Land cover classification methods for multiyear AVHRR data. Int. J. Remote Sens. 22:1479–1493
Liang S (2003) A direct algorithm for estimating land surface broadband albedos from MODIS Imagery. IEEE Trans. Geosci. Remote Sens. 41:136–145
Liang S (2004) Quantitative remote sensing of land surfaces. Wiley, New York, 534pp
Lloyd D (1990) A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int. J. Remote Sens. 11:2269–2279
Loveland TR, Merchant JW, Ohlen DO, Brown JF (1991) Development of a land cover characteristics database for the counterminous US. Photogramm. Eng. Remote Sens. 57:1453–1463
Loveland TR, Merchant JW, Reed BC, Brown JF, Ohlen DO, Olson P, Hutchinson J (1995) Seasonal land cover regions of the United States. Ann. Assoc. Am. Geogr. 85:339–355
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. 21:1303–1330
Marland G, Pielke Sr. RA, Apps M, Avissar R, Betts RA, Davis KJ, Frumhoff PC, Jackson ST, Joyce LA, Kauppi P, Katzenberger J, MacDicken KG, Neilson RP, Niles JO, Niyogi DS, Norby RJ, Pena N, Sampson N, Xue Y (2003) The climatic impacts of land surface change and carbon management, and the implications for climate-change mitigation policy. Climate Policy 3:149–157
McIntyre S, Diaz S, Lavorel S, Wolfgang C (1999) Plant functional types and disturbance dynamics - introduction. J. Veg. Sci. 10:604–608
Myneni RB, Maggion S, Iaquinta J, Privette JL, Gobron N, Pinty B, Verstraete MM, Kimes DS, Williams DL (1995) Optical remote sensing of vegetation: modeling, caveats and algorithms. Remote Sens. Environ. 51:169–188
Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981-1991. Nature 386:698–702
Nair US, Lawton RO, Welch RM, Pielke Sr. RA (2003) Impact of land use on Costa Rican tropical montane cloud forests: sensitivity of cumulus cloud field characteristics to lowland deforestation. J. Geophys. Res. 108:D7, 4206, doi:10.1029/2001JD001135
Nemani R, Running SW (1996) Implementation of a hierarchical global vegetation classification in ecosystem function models. J. Veg. Sci. 7:337–346
Oleson KW, Bonan GB (2000) The effects of remotely sensed plant functional type and leaf area index on simulations of boreal forest surface fluxes by the NCAR land surface model. J. Hydrometeorol. 1:431–446
Olson JS, Watts JA, Allison LJ (1983) Carbon in live vegetation of major world ecosystems. ORNL-5862, Oak Ridge National Laboratory, Oak Ridge TN
Peddle DR (1995a) Knowledge formulation for supervised evidential classification. Photogramm Eng. Remote Sens. 61:409–417
Peddle DR (1995b) MERCURY: an evidential reasoning image classifier. Comput. Geosci. 21:1163–1176
Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomon AM (1992) A global biome model based on plant physiology and dominance, soil properties, and climate. J. Biogeogr. 19:117–134
Prince SD, Goward SN (1995) Global primary production: a remote sensing approach. J. Biogeogr. 22:815–835
Roughgarden J, Running SW, Matson PA (1991) What does remote sensing do for ecology. Ecology 72:1918–1922
Running SW, Coughlan JC (1988) A general model of forest ecosystem processes for regional applications. I. Hydrological balance, canopy gas exchange and primary production processes. Ecol. Model. 42:125–154
Running SW, Loveland TR, Pierce LL, Nemani RR, Hunt ER Jr. (1995) A remote sensing based vegetation classification logic for global land cover analysis. Remote Sens. Environ. 51:39–48
Sellers PJ, Schimel DS (1993) Remote sensing of the land biosphere and biogeochemistry in the EOS era: science priorities, methods and implementation - EOS land biosphere and biogeochemical cycles panels. Global Planet. Change 7:279–297
Sellers PJ, Mintz Y, Sud YC, Dalcher A (1986) A simple biosphere model (SIB) for use within general circulation models. J. Atmos. Sci. 43:505–531
Sellers PJ, Meeson BW, Hall FG, Asrar G, Murphy RE, Schiffer RA, Bretherton FP, Dickinson RE, Ellingson RG, Field CB, Huemmrich KF, Justice CO, Melack JM, Roulet NT, Schimel DS, Try PD (1995) Remote sensing of the land surface for studies of global change: models -algorithms - experiments. Remote Sens. Environ. 51:3–26
Sellers PJ, Dickinson RE, Randall DA, Betts AK, Hall FG, Berry JA, Collatz GJ, Denning AS, Mooney HA, Nobre CA, Sato N, Field CB, Henderson-Sellers A (1997) Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275:502–509
Semenova GV, van der Maarel E (2000) Plant functional types - a strategic perspective. J. Veg. Sci. 11:917–922
Shafer G (1976) A Mathematical theory of evidence. Princeton University Press, Princeton, NJ
Smith TM, Shugart HH, Woodward FI (Eds.) (1997) Plant functional types: their relevance to ecosystem properties and global change. Cambridge University Press, New York, 369 pp
Strahler A, Coauthors (1999) MODIS Land Cover Product Algorithm Theoretical Basis Document (ATBD). Version 5.0. Boston University
Steyaert LT, Hall FG, Loveland TR (1997) Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data. J. Geophys. Res. 102:29581–29598
Soh L, Tsatsoulis C, Gineris D, Bertoia C (2004) ARKTOS: an intelligent system for SAR sea ice image classification. IEEE Trans. Geosci. Remote Sens. 42(1):229–247
Sun W, Heidt V, Gong P, Xu G (2003) Information fusion for rural land-use classification with high resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 41:883–890
Sun, W. (2004) Land-Use Classification Using High Resolution Satellite Imagery: A New Infor mation Fusion Method - An Application in Landau, Germany. University of Mainz Press, 95pp
Sun W, Liang S, Xu G, Fang H (2005) Improving MODIS PFT product using multisource evidential reasoning. Proceedings of the 9th International Symposium on Physical Measurements and Signatures in Remote Sensing 1, pp. 225-227
Sun W, Liang S, Xu G, Fang H, Townshend, JR, Dickinson R (2007) Mapping plant functional types from MODIS data using multisource evidential reasoning. Remote Sensing of Environment (in press)
Tian Y, Dickinson RE, Zhou L, Myneni RB, Friedl M, Schaaf CB, Carroll M, Gao F (2004) Land boundary conditions from MODIS data and consequences for the albedo of a climate model. Geophys. Res. Lett. 31:L05504, doi:10.1029/2003GL019104
Townshend JRG, Justice CO (1981) Information extraction from remotely sensed data - a user view. Int. J. Remote Sens. 2:313–329
Townshend JRG, Justice CO, Kalb V (1987) Characterization and classification of South American land cover types. Int. J. Remote Sens. 8:1189–1207
Townshend JRG, Justice CO, Li W, Gurney C, McManus J (1991) Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sens. of Environ. 35:243–255
Woodward FI, Cramer W (1996) Plant functional types and climatic changes: Introduction. J. Veg. Sci. 7:306–308
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V
About this chapter
Cite this chapter
Sun, W., Liang, S. (2008). Methodologies for Mapping Plant Functional Types. In: Liang, S. (eds) Advances in Land Remote Sensing. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6450-0_14
Download citation
DOI: https://doi.org/10.1007/978-1-4020-6450-0_14
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-6449-4
Online ISBN: 978-1-4020-6450-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)