Cookies Policy

This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies.

I accept this policy

Find out more here

Evaluating vegetation indices for assessing productivity along a tropical rain forest chronosequence in Western Amazonia

No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.

Brill’s MyBook program is exclusively available on BrillOnline Books and Journals. Students and scholars affiliated with an institution that has purchased a Brill E-Book on the BrillOnline platform automatically have access to the MyBook option for the title(s) acquired by the Library. Brill MyBook is a print-on-demand paperback copy which is sold at a favorably uniform low price.

Access this article

+ Tax (if applicable)
Add to Favorites
You must be logged in to use this functionality

image of Israel Journal of Plant Sciences

Tropical deforestation is leading not only to losses of biodiversity but also to regional losses of vegetation productivity. However, in many areas the deforestation process is usually accompanied by a fast forest regeneration that produces a mosaic of forest patches in different successional stages. These successional stages have different productivities owing primarily to differences in species composition and soil nutrients. In order to assess the long-term consequences of deforestation and forest regeneration on atmospheric carbon dynamics, it is imperative to analyze the spatio-temporal patterns of forest productivity along different successional stages. This study evaluates the suitability of five different vegetation indices for assessing productivity along a tropical rain forest chronosequence located in Western Amazonia. Among the indices tested, the Red Edge Chlorophyll Index (CIRed Edge) and the Wide Dynamic Range Vegetation Index (WDRVI) proved to be the most suitable for this purpose. However, due to the current low availability of multi-spectral imagery acquired by spaceborne sensors with a band in the red edge region of the electromagnetic spectrum, the WDRVI serves as the most practical index. The dynamics of the regional productivity in the study area during the 2000s were assessed using the WDRVI and proved to be consistent with observed land cover dynamics.

Affiliations: 1: Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University


Full text loading...


Data & Media loading...

1. Aguilar-Amuchastegui, N., Henebry, G.M. 2006. Monitoring sustainability in tropical forests: how changes in canopy spatial pattern can indicate forest stands for biodiversity surveys. IEEE Geosci. Remote Sens. Lett. 3: 329-333.
2. Aguilar-Amuchastegui, N., Henebry, G.M. 2008. Characterizing tropical forest spatio-temporal heterogeneity using the Wide Dynamic Range Vegetation Index (WDRVI). Int. J. Remote Sens. 29: 7285-7291.
3. Chander, G., Markham, B.L., Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113: 893-903.
4. Chen, W.R., Henebry, G.M. 2010. Spatio-spectral heterogeneity analysis using EO-1 Hyperion imagery. Comput. Geosci. 36: 167-170.
5. Ciganda, V., Gitelson, A., Schepers, J. 2008. Vertical profile and temporal variation of chlorophyll in maize canopy: quantitative "crop vigor" indicator by means of reflectance-based techniques. Agron. J. 100: 1409-1417.
6. Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37: 35-46.
7. DeFries, R., Achard, F., Brown, S., Herold, M., Murdiyarso, D., Schlamadinger, B., de Souza, C. 2007. Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environ. Sci. Policy 10: 385-394.
8. Ewusie, J.Y. 1992. Phenology in tropical ecology. Accra: Ghana Universities Press.
9. Feldpausch, T.R., Rondon, M.A., Fernandes, E.C.M., Riha, S.J., Wandelli, E. 2004. Carbon and nutrient accumulation in secondary forests regenerating on pastures in central Amazonia. Ecol. Appl. 14: S164-S176.
10. Garbulsky, M.F., Penuelas, J., Papale, D., Filella, I. 2008. Remote estimation of carbon dioxide uptake by a Mediterranean forest. Glob. Change Biol. 14: 2860-2867.
11. Gentry, A.H. 1992. Tropical forest biodiversity: distributional patterns and their conservational significance. Oikos 63: 19-28.
12. Gibbs, H.K., Brown, S., Niles, J.O., Foley, J.A. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett. 2: DOI: 10.1088/1748-9326/1082/1084/045023.
13. Gitelson, A.A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161: 165-173.
14. Gitelson, A.A., Gritz, Y., Merzlyak, M.N. 2003a. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160: 271-282.
15. Gitelson, A.A., Verma, S.B., Viña, A., Rundquist, D.C., Keydan, G., Leavitt, B., Arkebauer, T.J., Burba, G.G., Suyker, A.E. 2003b. Novel technique for remote estimation of CO2 flux in maize. Geophys. Res. Lett. 30:486, doi:410.1029/2002GL016543.
16. Gitelson, A.A., Viña, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G., Leavitt, B. 2003c. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 30:1248, doi: 1210.1029/2002GL016450.
17. Gitelson, A.A., Viña, A., Ciganda, V., Rundquist, D.C., Arkebauer, T.J. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32: L08403, doi:08410.01029/02005GL022688.
18. Gitelson, A.A., Keydan, G.P., Merzlyak, M.N. 2006a. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett. 33: DOI: 10.1029/2006GL026457.
19. Gitelson, A.A., Viña, A., Verma, S.B., Rundquist, D.C., Arkebauer, T.J., Keydan, G., Leavitt, B., Ciganda, V., Burba, G.G., Suyker, A.E. 2006b. Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res.-Atmos. 111: D08S11, doi: 10.1029/2005JD006017.
20. Gitelson, A.A., Viña, A., Masek, J.G., Verma, S.B., Suyker, A.E. 2008. Synoptic monitoring of gross primary productivity of maize using Landsat data. IEEE Geosci. Remote Sens. Lett. 5: 133-137.
21. Henebry, G.M., Viña, A., Gitelson, A.A. 2004. The Wide Dynamic Range Vegetation Index and its potential utility for Gap Analysis. Gap Analysis Program Bulletin 12: 50-56.
22. Hernández-Camacho, J.A., Hurtado-Guerra, A., Ortiz-Quijano, R., Walschburger, T. 1992. Unidades biogeográficas de Colombia. In: Halffter, G., ed. La diversidad biológica de Iberoamérica. Instituto de Ecologia, Mexico D.F., Mexico.
23. Holdridge, L.R. 1967. Life zone ecology. Tropical Science Center, San José, Costa Rica.
24. Huete, A.R., Justice, C., van Leeuwen, W. 1996. MODIS vegetation index (MOD13). Algorithm Theoretical Basis Document. Version 2. NASA Goddard Space Flight Center, Greenbelt, Maryland 20771. USA.
25. Huete, A.R., Liu, H.Q., Batchily, K., vanLeeuwen, W. 1997. A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens. Environ. 59: 440-451.
26. Huete, A.R., Restrepo-Coupe, N., Ratana, P., Didan, K., Saleska, S.R., Ichii, K., Panuthai, S., Gamo, M. 2008. Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia. Agr. Forest Meteorol. 148: 748-760.
27. IPCC. 2007. Climate Change 2007: The Physical Science Basis. Cambridge University Press, New York, NY, USA.
28. Jensen, J.R. 1996. Introductory digital image processing, a remote sensing perspective. Second edition. Prentice Hall., New Jersey:.
29. Khurshid, K.S., Staenz, K., Sun, L.X., Neville, R., White, H.P., Bannari, A., Champagne, C.M., Hitchcock, R. 2006. Preprocessing of EO-1 Hyperion data. Can. J. Remote Sens. 32: 84-97.
30. Mayaux, P., Holmgren, P., Achard, F., Eva, H., Stibig, H., Branthomme, A. 2005. Tropical forest cover change in the 1990s and options for future monitoring. Philos. T. Roy. Soc. B. 360: 373-384.
31. McGuffie, K., Henderson-Sellers, A., Zhang, H., Durbridge, T.B., Pitman, A.J. 1995. Global climate sensitivity to tropical deforestation. Global and Planetary Change 10: 97-128.
32. Myneni, R.B., Nemani, R.R., Running, S.W. 1997. Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans. Geosci. Electron. 35: 1380-1393.
33. Nakaji, T., Ide, R., Takagi, K., Kosugi, Y., Ohkubo, S., Nasahara, K.N., Saigusa, N., Oguma, H. 2008. Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agr. Forest Meteorol. 148: 776-787.
34. Rouse, J.W., Haas Jr., R.H., Schell, J.A., Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. pp 309-317 In: Third ERTS-1 Symposium. NASA, Washington, DC.
35. Ruimy, A., Kergoat, L., Bondeau, A., Intercomparison, P.P. N.M. 1999. Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light-use efficiency. Glob. Change Biol. 5: 56-64.
36. SAS. 2004. SAS/STAT User's Guide Version 9.1. SAS Institute Inc., Cary, NC, USA.
37. Suyker, A.E., Verma, S.B., Burba, G.G., Arkebauer, T.J., Walters, D.T., Hubbard, K.G. 2004. Growing season carbon dioxide exchange in irrigated and rainfed maize. Agr. Forest Meteorol. 124: 1-13.
38. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127-150.
39. Verma, S.B., Dobermann, A., Cassman, K.G., Walters, D.T., Knops, J.M., Arkebauer, T.J., Suyker, A.E., Burba, G.G., Amos, B., Yang, H.S., Ginting, D., Hubbard, K.G., Gitelson, A.A., Walter-Shea, E.A. 2005. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agri. Forest Meteorol. 131: 77-96.
40. Viña, A., Gitelson, A.A. 2005. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophys. Res. Lett. 32: L17403.
41. Viña, A., Echavarria, F.R., Rundquist, D.C. 2004a. Satellite change detection analysis of deforestation rates and patterns along the Colombia-Ecuador border. Ambio 33: 118-125.
42. Viña, A., Henebry, G.M., Gitelson, A.A. 2004b. Satellite monitoring of vegetation dynamics: Sensitivity enhancement by the wide dynamic range vegetation index. Geophys. Res. Lett. 31: L04503.
43. Viña, A., Gitelson, A.A., Nguy-Robertson, A.L., Peng, Y. 2011. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ.: DOI: 10.1016/j.rse.2011.1008.1010.
44. Walker, R., Moore, N.J., Arima, E., Perz, S., Simmons, C., Caldas, M., Vergara, D., Bohrer, C. 2009. Protecting the Amazon with protected areas. Proc. Natl. Acad. Sci. U.S.A. 106: 10582-10586.
45. Xiao, X.M., Hagen, S., Zhang, Q.Y., Keller, M., Moore, B. 2006. Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images. Remote Sens. Environ. 103: 465-473.

Article metrics loading...



Can't access your account?
  • Tools

  • Add to Favorites
  • Printable version
  • Email this page
  • Subscribe to ToC alert
  • Get permissions
  • Recommend to your library

    You must fill out fields marked with: *

    Librarian details
    Your details
    Why are you recommending this title?
    Select reason:
    Israel Journal of Plant Sciences — Recommend this title to your library
  • Export citations
  • Key

  • Full access
  • Open Access
  • Partial/No accessInformation