International Journal of Remote Sensing Vol. 26, No. 11, 10 June 2005, 2251-2257
Cover Multitemporal fraction images derived from Terra MODIS data for analysing land cover change over the Amazon region L. O. ANDERSON*, Y. E. SHIMABUKURO and E. ARAI Instituto Nacional de Pesquisas Espaciais, Divisao de Sensoriamento Remoto, Av. dos Astronautas, 1758, CEP 12227-010, Sao Jose dos Campos, SP, Brazil The Moderate Resolution Imaging Spectroradiometer (MODIS) instrtiment onboard Earth Observing System (EOS) Terra plataform has been designed to provide improved information for monitoring land, ocean, and atmosphere conditions. The design combined characteristics oi' the Advanced Very High Resolution Radiometer (AVHRR) and the Landsat Thematic Mapper (TM), adding spectral channels in the middle and thermal infrared wavelength and providing data in 250 m. 500 m and 1 km spatial resolutions. Spectral channels for attnospheric and cloud characterization have been included to permit both the removal of atmospheric effects on surface observations and the provision of atmospheric measurements (Justice et al. 1998). This work utilized the land product M0D13Q1, which is a vegetation index product with 250m spatial resolution and is a composite of 16 days of observation over Mato Grosso State (figure 1). This product contains the composite of NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), red reflectance band {250m spatial resolution, 620-670 nm bandwidth), near infrared reflectance band (250 m spatial resolution, 841-876 nm bandwidth), blue reflectance band (500 m spatial resolution, rearranged to 250m, 459-479nm bandwidth), medium infrared reflectance band (500m spatial resolution, rearranged to 250m, 2105-2155nm bandwidth), quality assurance data for NDVI and EVI. view zenith angle, sun zenith angle, and relative azimuth angle average parameters (Lozar and Balbach 2002). The composites of NDVI and EVI were performed using the Constrained View Maximum Value Composite (CV-MVC) algorithm. The pixel of moderate resolution sensor images, due to its spatial resolution, generally includes more than one type of terrain cover. When these sensors observe the Earth, the measured radiance is the integration of the radiance of all the objects that are contained within the pixel, implying the existence of the so-called mixture problem (Aguiar el al. 1999). The linear mixing model has been used to analyse the mixture of signatures of vegetation, soil, and shade in each pixel for either high (Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). TM, etc.) and coarse (AVHRR and Systeme Pour l'Observation de la Terre (SPOT-Vegetation)) resolution data. The available methods estimate the proportion of each component inside the pixel by minimizing the sum of squares of the errors. The proportion values must be non-negative, and they also must equal one (Shimabukuro and Smith 1991). In this work, we accept that it is possible to find pure pixels of land cover type in the 250 m spatial resolution data, and the purest signatures for vegetation, soil, and shade endmembers can be seen in figure 2. For the coming work to investigate *Corresponding author. Email:
[email protected] Iiiicrniiiiiinal Journal oj Remote Sen.ting ISSN 014.1-1 Ifil print/ISSN n66-5901 online r 2005 Taylor & Francis Group Lid hltp://www.lundf.co.uk/joiirnals IX>I: 10,1080/01431160310001620795
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Wavelength (nm) Figure 2. Endmember spectral signatures (D, soil; O, vegetation; A. shade) used as input for the linear spectral mixture model applied to MODIS data acquired August 2002 over the State of Mato Grosso. Brazil. the land cover change iti Mato Grosso State, Brazilian Amazon, the endmember signatures for MODIS data will be estimated from Landsat ETM+ fraction images adapting the procedures described by Holben and Shimabukuro (1993) and Shimabukuro and Smith (1995). The fraction images derived from SPOT-Vegetation data were shown to be a good and consistent source of information for evaluating the land cover change (Carreiras el a!. 2002). The composite fraction images derived from MODIS acquired in January and August 2002. over the Mato Grosso State, Brazil, show the improvement of this information, due to the better spatial resolution (250 m). These fraction images were generated using the four reflectance (Blue, Red, NIR, and MIR) channels provided by M0DI3Q1 land products. Figure 3 shows the fraction images of soil (a), vegetation (/?). and shade (() derived from August 2002 MODIS data. Each one of these images enhances some specific information about the land cover: the soil fraction image highlights mainly non vegetated areas (clear cuts, bare soil, etc.); the vegetation fraction image like NDVI and EVI shows the vegetation cover condition; and the shade fraction image enhances the water bodies, burned areas, and also the vegetation cover types. These images are being used in the ongoing research at the Brazilian Institute for Space Research to detect deforestation increment and to evaluate the burned areas. In figure 4 (cover figure) and figure 5 the RGB colour composites of the fraction images derived from January and August 2002. respectively are presented, showing the soil fraction in red (R). the vegetation fraction in green (G) and the shade fraction in blue (B). Comparing these two figures, the land cover change between the two dates is very clear: contrast between dense forest and savannah ('cerrado') borders (August image), due to phenology of 'cerrado" vegetation cover; and identification of agricultural areas, due to the difference in the growing season (January image) and bare soil (August image). Figure 6 shows in detail a central west
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Figure 3. Fraction images (a) soil. (/)) vegetation, and (c) shade derived from MODIS data, acquired August 2002 over the Mato Grosso Slate, Brazil.
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Figure 4. Colour composite of soil (R), vegetation (G), and shade (B) fraction images derived from MODIS data acquired August 2002 over the State of Mato Grosso, Brazil.
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Figure 5. Colour composite of soil (R), vegetation (G), and shade (B) fraction images derived from MODIS data acquired January 2002 over the State of Mato Grosso, Brazil.
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Figure 6. MODIS colour composite MIR (R). NIR (G). and Red (B) acquired {u) January 2002, (b) August 2002 and the corresponding vegetation fraction images acquired (c) January 2002, ((/) August 2002 of a central west area in State of Mato Grosso. Brazil.
area o{ Mato Grosso State in RGB (MIR. NIR. Red bands) MODIS composites (figure 6(a) and (/?)) and the corresponding vegetation fraction (figure 6(() and {d)) images for January and August. The agricultural areas over the "cerrado" region are very distinct in the January image and easy detected by the derived vegetation fraction image. The vegetation fraction image from the August data shows these areas as bare soil with no distinction from 'cerrado" areas. Then this figure shows the advantages of MODIS data, due to its moderate spatial resolution and its continuous acquisition, for the regional and global monitoring of land use and land cover changes. Therefore, the results show that MODIS sensor data is a useful source of information for monitoring land use and land cover changes over large areas using spectral mixing analysis. References AGUIAR, A.P.D., SHIMABUKURO, Y.E. and MASCARENHAS, N.D.A., 1999. Use of synthetic
bands derived from mixing models in the multispectral classification of remote sensing images. Internuiional Jmirnal of Rcmoie Sensing. 20. pp. 647-657. CARREIRAS. J.M.B.. SHIMABUKURO. Y.E. and PEREIRA, J.M.C.. 2002, Fraction images derived from SPOT-4 VEGETATION data to assess land-cover change over the State of Mato Grosso, Brazil. Jnlernational Journal of Remote Sensing, 23. pp. 4979-4983.
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HoLBEN, B.N. and SHIMABUKURO. Y.E.. 1993, Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors. International Journal of Remote Sen.sing. 14. pp. 2231 2240. JUSTICE, C O . , VERMOTE, E . . TOWNSHEND, J.R.G.. DEFRIES, R . , ROY. D.P., HALL. D.K.. SALOMONSON,
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MYNENI, R.B.. KNYAZIKHIN, Y . , RUNNING. S.W.. NEMANr. R.R., W A N . Z . . HUETE. A.R., VAN LEEUWEN. W . . WOLFE, R.F.. GiGLIO. L.. ML'LLER. J.-P.,
LEWIS, P. and BARNSLEY. M.J., 1998. The Moderate Resolution imaging Spectroradiometer (MODIS): land remote sensing for global change researeh. IEEE Transactions on Geoscieme ami Remote Sensing, 36, pp. 1228-1249. LOZAR. R.C. and BALB.ACH, H.E., 2D02. NASA MODIS Products for Military Land Monitoring and Management. Engineer Research Liiid Development Center. ERDC/ CERLTR-02-31. 75p. SHIMABUKURO. Y . E . and SMITH, J.A.. 1991. The least-squares mixing models to generate fraction images derived from remote sensing muitispectral data. IEEE Transactions on Geo.scienie and Remote Sensing, 29, pp. 16-20. SHIMABUKURO. Y.E. and SMITH. J.A.. 1995. Fraction images derived from Landsat TM and MSS data for monitoring reforested areas. Canadian Journal of Remote Sensing. 21. pp. 67-74.
