• Anderson, L., , Y. Shimabukuro, , R. DeFries, , and D. Morton. 2005a. Assessment of land cover changes in the Brazilian Amazon using multitemporal fraction images derived from Terra MODIS: Examples from the state of Mato Grosso. IEEE Trans. Geosci. Remote Sens. in press.

    • Search Google Scholar
    • Export Citation
  • Anderson, L. O., , Y. E. Shimabukuro, , and E. Arai. 2005b. Multitemporal fraction images derived from Terra MODIS data for analysing land cover change over the Amazon region. Int. J. Remote Sens. in press.

    • Search Google Scholar
    • Export Citation
  • Asner, G. P. 2001. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote Sens. 22:38553862.

  • Braswell, B. H., , S. C. Hangen, , S. E. Frolking, , and W. A. Salas. 2003. A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODS: Application in the Brazilian Amazon region. Remote Sens. Environ. 87:243256.

    • Search Google Scholar
    • Export Citation
  • Carroll, M., , C. Dimiceli, , J. R. G. Townshend, , R. A. Sohlberg, , and M. C. Hansen. 2004. User guide for MOD44A Vegetation Cover Conversion (VCC). University of Maryland, 6 pp. [Available online at http://glcf.umiacs.umd.edu/pdf/VCCuserguide.pdf.].

  • Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37:3546.

  • DeFries, R., , R. A. Houghton, , M. Hansen, , C. Field, , D. L. Skole, , and J. Townshend. 2002. Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 90s. Proc. Natl. Acad. Sci. 99:22,. 14 25614 261.

    • Search Google Scholar
    • Export Citation
  • Fearnside, P. M. 2003. Deforestation control in Mato Grosso: A new model for slowing the loss of Brazil’s Amazon forest. Ambio 32:343345.

    • Search Google Scholar
    • Export Citation
  • Fearnside, P. M., and R. I. Barbosa. 2003. Accelerating deforestation in Brazilian Amazonia: Towards answering open questions. Environ. Conservation 31:710.

    • Search Google Scholar
    • Export Citation
  • Hall, F. G., , Y. E. Shimabukuro, , and F. Huemmrich. 1995. Remote sensing of biophysical structure in boreal stands of picea mariana using mixture decomposition and geometric reflectance models. Ecol. Appl. 5:9931013.

    • Search Google Scholar
    • Export Citation
  • Hansen, M. C., and R. S. DeFries. 2004. Detecting long term global forest change using continuous fields of tree cover maps from 8 km AVHRR data for the years 1982–1999. Ecosystems doi:10.1007/s10021-004-0243-3.

    • Search Google Scholar
    • Export Citation
  • Hansen, M. C., , R. S. DeFries, , J. R. G. Townshend, , M. Carroll, , C. Dimiceli, , and R. A. Sohlberg. 2003. Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS Vegetation Continuous Fields algorithm. Earth Interactions 7.[Available online at http://EarthInteractions.org.].

    • Search Google Scholar
    • Export Citation
  • Hess, L. L. Coauthors 2002. Geocoded digital videography for validation of land cover mapping in the Amazon basin. Int. J. Remote Sens. 23:15271556.

    • Search Google Scholar
    • Export Citation
  • Houghton, R. A., , D. L. Skole, , C. A. Nobre, , J. L. Hackler, , K. T. Lawrence, , and W. H. Chomentowski. 2000. Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon. Nature 403:301304.

    • Search Google Scholar
    • Export Citation
  • Huete, A. R., , C. Justice, , and W. Van Leeuwen. 1999. MODIS vegetation index (MOD13). Algorithm Theoretical Basis Document (ATBD), 122 pp. [Available online at http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf.].

  • Justice, C. O., , J. R. G. Townshend, , E. F. Vermote, , E. Masouka, , R. E. Wolfe, , S. Saleous, , D. P. Roy, , and J. T. Morisette. 2002. An overview of MODIS Land data processing and product status. Remote Sens. Environ. 83:315.

    • Search Google Scholar
    • Export Citation
  • Laurence, W. H., , A. K. M. Albernaz, , P. M. Fearnside, , H. L. Vasconcelos, , and L. V. Ferreira. 2004. Deforestation in Amazonia. Science 304:1109.

    • Search Google Scholar
    • Export Citation
  • Malingreau, J. P., , F. Achard, , G. D’Souza, , H. J. Stibig, , J. D’Souza, , C. Estreguil, , and H. Eva. 1995. AVHRR for global tropical forest monitoring: The lessons of the TREES project. Remote Sens. Rev. 12:2940.

    • Search Google Scholar
    • Export Citation
  • Nelson, R., and B. Holben. 1986. Identifying deforestation in Brazil using multiresolution satellite data. Int. J. Remote Sens. 7:429448.

    • Search Google Scholar
    • Export Citation
  • Nelson, R., , N. Horning, , and T. A. Stone. 1987. Determining the rate of forest conversion in Mato Grosso, Brazil, using Landsat and AVHRR data. Int. J. Remote Sens. 8:17671784.

    • Search Google Scholar
    • Export Citation
  • Roberts, D. A., , I. Numata, , K. W. Holmes, , G. Batista, , T. Krug, , A. Monteiro, , B. Powell, , and O. Chadwick. 2002. Large area mapping of landcover change in Rondônia using multitemporal spectral mixture analysis and decision tree classifiers. J. Geophys. Res. 107.8073, doi:10.1029/2001JD000374.

    • Search Google Scholar
    • Export Citation
  • Sader, S. A., , D. J. Hayes, , J. A. Hepinstall, , M. Coan, , and C. Soza. 2001. Forest change monitoring of a remote biosphere reserve. Int. J. Remote Sens. 22:19371950.

    • Search Google Scholar
    • Export Citation
  • Salati, E., and C. A. Nobre. 1991. Possible climatic impacts of tropical deforestation. Climate Change 19:177196.

  • Shimabukuro, Y. E., and J. A. Smith. 1995. Fraction images derived from Landsat Thematic Mapper images of the Amazon Region. Can. J. Remote Sens. 21:6774.

    • Search Google Scholar
    • Export Citation
  • Shimabukuro, Y. E., , B. N. Holben, , and C. J. Tucker. 1994. Fraction images from NOAA AVHRR for studying the deforestation in the Brazilian Amazon. Int. J. Remote Sens. 15:517520.

    • Search Google Scholar
    • Export Citation
  • Shimabukuro, Y. E., , V. Duarte, , E. M. K. Mello, , and J. C. Moreira. 1999. RGB shade fraction images derived from multitemporal Landsat TM data for studying deforestation in Brazilian Amazon. Int. J. Remote Sens. 20:643646.

    • Search Google Scholar
    • Export Citation
  • Siqueira, P., , B. Chapman, , and G. McGarragh. 2003. The coregistration, calibration, and interpretation of multiseason JERS-1 SAR data over South America. Remote Sens. Environ. 87:389403.

    • Search Google Scholar
    • Export Citation
  • Skole, D. L., and C. Tucker. 1993. Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978–1988. Science 260:19051910.

    • Search Google Scholar
    • Export Citation
  • Stibig, H-J., , J. P. Malingreau, , and R. Beuchle. 2001. New possibilities of regional assessment of tropical forest cover in insular Southeast Asia using SPOT-VEGETATION satellite image mosaics COVER. Int. J. Remote Sens. 22:503505.

    • Search Google Scholar
    • Export Citation
  • Venables, W. N., and B. D. Ripley. 1994. Modern Applied Statistics with S-Plus. Springer-Verlag, 512 pp.

  • Werth, D., and R. Avissar. 2002. The local and global effects of Amazon deforestation. J. Geophys. Res. 107.8087, doi:10.1029/2001JD000717.

    • Search Google Scholar
    • Export Citation
  • Wessels, K. J., , R. S. DeFries, , J. Dempewolf, , L. O. Anderson, , A. J. Hansen, , S. L. Powell, , and E. F. Moran. 2004. Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA, and Pará State, Brazil. Remote Sens. Environ. 92:6783.

    • Search Google Scholar
    • Export Citation
  • Zhan, X., , R. Sohlberg, , J. R. G. Townshend, , C. DiMiceli, , M. Carroll, , J. C. Eastman, , M. C. Hansen, , and R. S. DeFries. 2002. Detection of land cover changes using MODIS 250 m data. Remote Sens. Environ. 83:336350.

    • Search Google Scholar
    • Export Citation
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Rapid Assessment of Annual Deforestation in the Brazilian Amazon Using MODIS Data

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  • 1 University of Maryland, College Park, College Park, Maryland
  • | 2 Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, Brazil
  • | 3 South Dakota State University, Brookings, South Dakota
  • | 4 University of Maryland, College Park, College Park, Maryland
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Abstract

The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90%) and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the total area of deforestation and identify small clearings.

* Corresponding author address: Douglas C. Morton, University of Maryland, College Park, College Park, MD 20742 morton@geog.umd.edu

This article included in the Large-Scale Biosphere-Atmosphere (LBA) Experiment special collection.

Abstract

The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90%) and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the total area of deforestation and identify small clearings.

* Corresponding author address: Douglas C. Morton, University of Maryland, College Park, College Park, MD 20742 morton@geog.umd.edu

This article included in the Large-Scale Biosphere-Atmosphere (LBA) Experiment special collection.

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