amazon river mainstem floodplain landsat tm digital mosaic

int. j. remote sensing, 2002, vol. 23, no. 1, 57–69

Amazon River mainstem � oodplain Landsat TM digital mosaic

Y. E. SHIMABUKURO, E. M. NOVO

National Institute for Space Research, Remote Sensing Division, Av. dosAstronautas, 1758, 12227-010 Sao Jose dos Campos, SP, Brazil;e-mail: [email protected] and [email protected]

and L. K. MERTES

University of California, Geography Department, Santa Barbara CA 93106,USA; e-mail: [email protected]

(Received 1 June 1999; in � nal form 9 June 2000)

Abstract. This paper presents the methodology used to build a Landsat ThematicMapper (TM) digital mosaic for the Amazon River mainstem � oodplain. Twenty-nine almost cloud-free TM Landsat scenes covering a period from 1985 to 1995were selected from the National Institute for Space Research (INPE) archive.Most of the scenes were acquired from July to September, a period that beginswith high waters and ends with receding water up to about the beginning of thelow waters. Radiometric recti� cation was applied to the images to reduce variabil-ity of environmental conditions during Landsat TM data acquisition. Theradiometric recti� cation applied had a good performance for bands 3, 5, and 7,for most of the scenes. For bands 1 and 2 the radiometric recti� cation was limited,especially for scenes with intense haze. Nevertheless, the overall performance ofradiometric normalization allowed the production of a uniform dataset for theentire Brazilian Amazon River mainstem � oodplain.

1. IntroductionVariations in amplitude and timing of the seasonal cycle of the Amazon River

over� ow determine a highly dynamic � oodplain system. The ecological features ofthis system are largely dependent on temporal and spatial variations of the seasonalinundation. Passive remote sensing provided insights to the temporal patterns ofinundation (Sippel et al. 1998). According to these authors most of the � oodplainreaches depict a unimodal peak in the � ooded area in spite of inter-annual variability.

Results from Sippel et al. (1998) suggest that the maximum � ooded area occursbetween May and August. Therefore, remote sensing images acquired during thisperiod should be useful to map diVerent � oodplain habitats. Information on the areaoccupied by the various � oodplain environments is scarce (Goulding et al. 1996 )and in urgent need given that this region is undergoing increasing ecological disturb-ance. According to Goulding et al. (1996), the Amazon River � oodplain has under-gone more environmental changes in the last three decades than in all of previoushuman history.

Lack of information on the area occupied by diVerent habitats has caused an

Internationa l Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2002 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160010029165

Y. E. Shimabukuro et al.58

incomplete assessment of the environmental impact caused by human actions suchas agriculture, � shing and logging on the Amazon River � oodplain. Information on� oodplain habitat area is also fundamental to understand and model hydrologicaland biogeochemical � uxes in the Amazon River basin. According to Mertes (1997)� oodplains encompass many boundaries since they are inherently transitionalbetween terrestrial and river systems. Those boundaries relate to various degrees ofbiodiversity as a response to spatial variability in river water composition, mixingand permanence in the � oodplain.

The role of the � oodplain as a source of methane to the atmosphere is welldocumented (Bartlett and Harris 1993). However, there is a large variability in the� ux magnitude from diVerent habitats as a function of primary productivity andwater dynamics. Therefore, a deeper understanding of methane � ux depends on abetter knowledge of the number and extent of � oodplain habitats.

There were various attempts to identify and estimate the area of various habitatsin the Amazon � oodplain (Sippel et al. 1992, Novo et al. 1997) based on partialassessment of selective � oodplain reaches. While relying on remote sensing data,those results are controversial, mainly due to the spatial variability of the � oodplain.The need for an assessment based on a complete survey of the � oodplain becameclear. Therefore, several research groups decided to combine their eVorts to performan assessment of � oodplain habitats in the entire Brazilian Amazon River mainstem� oodplain. This eVort involved the University of California at Santa Barbara, theUniversity of Washington at Seattle, and the National Institute for Space Researchat Sao Jose dos Campos. The � rst step of this eVort was to build a uniform remotesensing dataset for the entire Brazilian Amazon River mainstem � oodplain. Thesecond step was to develop a proper classi� cation procedure that could compensatefor remote sensing data shortcomings. This paper focuses on the radiometricrecti� cation method used to build the TM Landsat digital mosaic.

The assemblage of overlapping images into a single picture faces two problems:(1) the de� nition of a reference system or projection on to which the scenes will beprojected; and (2) the blending of the diVerent images into a single radiometricallybalanced set. The de� nition of a reference system is a function of the area coveredby the mosaic and the application of the information derived from it. Geographiccoordinates are indicated for large areas. The blending process used to match theradiometry of the images is also determined by the application of the resulting mosaic.

The most commonly used method for radiometric blending is histogram match-ing. Histogram matching involves the equalization of the histogram of a given imageto another or various images to a reference image. It is a statistical method basedon the cumulative distribution function of the data and does not assure the in-betweenband relationship. A more comprehensive approach (Jensen et al. 1995) consists ofthe use of regression equations among scenes. This approach is quite useful fornormalizing images from the same region acquired at diVerent dates. Those scenes,in general, present a large overlapping area where it is possible to identify referencetargets with assumed constant re� ectance. The brightness of reference targets isregressed against the same targets in the images to be normalized. This approach,however, is not useful for assembling several images into a mosaic since they do nothave common constant re� ectance targets.

Another approach to radiometric normalization is the use of pseudo-invariantfeatures (Schott et al. 1998). This approach uses a stochastic solution to estimatethe probability distributions of re� ectivity of objects with near invariant re� ectivity.

L andsat T M digital mosaic of the Amazon River 59

It is also a good approach for normalizing multiple date images acquired over thesame geographic area. It is not useful, however, for normalizing multiple imagesacquired in diVerent areas under diVerent atmospheric and ground conditions. Forthis case, the best solution is the radiometric recti� cation proposed by Hall et al.(1991) because it does not require the existence of reference targets common to allimages. Therefore, this method was adopted in the Amazon � oodplain mosaicradiometric recti� cation approach.

2. Landsat TM dataTable 1 presents the TM Landsat scenes used to build the Amazon River digital

mosaic and their main features such as date, sun angles, and ancillary informationused in the geo-referencing process. The available data show two problems: (1) theycover diVerent years and are subject to inter-annual changes in river stage (estimatesavailable from Sippel et al. (1998) indicate that the inundated � oodplain varied from50 000 km2 to 80 000 km2 ) that cannot be removed from the data, but must be taken

Table 1. Landsat TM scenes used in the Amazon River digital mosaic: path/row, date,topographic chart used for geometric correction, number of control points, imageazimuth and zenith angles.

Topographic data Control points Azimuth ZenithTM scene MIR used in angle angle(path-row) Date 1 250 000 georeferencing (°) (°)

001-61 24 Aug 1995 56, 57, 73, 74, 91, 92 51 119 43001-62 2 Oct 1992 91, 92, 112, 113 52 92 34002-61 21 May 1995 55, 56, 72, 73, 90, 91 52 98 33002-62 7 Oct 1991 91, 92, 110, 111 50 95 33003-62 11 Aug 1991 89, 90, 109, 110 54 63 42003-63 2 Aug 1988 109, 110, 133, 134 43 52 46004-62 4 Aug 1986 88, 89 25 70 38004-63 13 Sep 1989 109, 132, 133 50 76 36223-61 24 June 1987 67, 68, 84, 85 54 53 43224-60 14 Aug 1988 51, 52, 66, 67 35 63 42224-61 19 Sep 1995 66, 67, 83, 84, 101, 102 52 51 41225-60 2 Jul 1987 50, 51, 65, 66 49 54 43225-61 15 Jul 1986 65, 66, 82, 83, 100, 101 64 56 44226-60 11 Jul 1988 49, 50, 64, 65 55 54 42226-61 4 Aug 1985 64, 65, 81, 82, 99, 100 56 57 43226-62 20 Jul 1991 99, 100, 120, 121 50 56 44227-61 22 Aug 1989 63, 64, 80, 81, 98, 99 43 59 38227-62 3 Aug 1988 98, 99, 119, 120 46 59 41228-61 6 Sep 1992 62, 63, 79, 80, 97, 98 46 78 37228-62 18 Jun 1992 97, 98, 118, 119 38 52 45229-61 21 Sep 1989 61, 62, 78, 79, 96, 97 51 — 35229-62 10 Aug 1991 96, 97, 117, 118 35 62 42230-62 2 Jul 1992 95, 96, 116, 117 36 72 45231-62 2 Aug 1989 94, 95, 115, 116 42 59 42231-63 25 Jul 1992 115, 138, 139, 164 56 56 46232-62 28 Aug 1992 114, 137, 138 50 71 40232-63 1 Aug 1992 114, 137, 138, 163 46 58 44233-61 24 Jun 1987 57, 74, 75, 83, 93 52 52 44233-62 24 Aug 1992 92, 93, 113 58 69 40233-63 25 Sep 1992 113, 136, 137 46 86 35


into consideration during the analyses; (2) they cover a wide range of monthsincluding high water and receding water conditions. Figure 1 shows the frequenciesof scenes acquired over diVerent months against the average water level alongManaus reach (representing upstream conditions) and Obidos reach (representingdownstream conditions). Upstream, the majority of the scenes were acquired underhigh water conditions, whereas in the lower � oodplain the scenes represent variousconditions.

3. MethodsThe mosaic was built only for the Brazilian portion of the Amazon River main

stem (� gure 2). The � rst step in building the mosaic was to choose a cartographicprojection system. An accurate assessment of the area occupied by the various� oodplain ecosystems has to rely on precise geometric recti� cation. The problemsfaced during the geometric correction process were:

1. small number of good ground control points for each scene;2. poor quality of the cartographic data (1 250 000 topographic charts); and3. shape (2750 km wide and 875 km long) and area represented by the mosaic.

The Geographical Co-ordination System was adopted as a solution to the thirdproblem since it provides a general reference system which can be transformed intothe required projection.

The radiometric recti� cation of the mosaic was based on the method developedby Hall et al. (1991). This method assumes that the digital counts are � rst convertedinto re� ectance according to procedures proposed by Markham and Barker (1986).The re� ectance dataset is then submitted to the Kauth-Thomas transformationalgorithm (Kauth and Thomas 1976) to generate the greenness and brightness ‘bands’for each Landsat TM scene. Those bands are used to build a two-dimensionalscatterplot for each scene used for selecting a radiometric control set with ‘stable’

Figure 1. Frequencies of Landsat TM scenes acquired under diVerent water level conditions.


Figure 2. Landsat TM scenes distribution along the Brazilian Amazon River � oodplain.

Figure 3. Greenness and brightness scatterplot for the reference scene (red) and for a sceneto be recti� ed (yellow).


Figure 4. Greenness and brightness scatterplot for the reference scene (red) and for therecti� ed scene (yellow).

Table 2. Digital counts for forest samples.

Reference Recti� ed To-be-recti� ed

Aver- Mini- Maxi- Aver- Mini- Maxi- Aver- Mini- Maxi-Band age mum mum age mum mum age mum mum

1 53.757 50 58 57.186 53 61 76.807 72 812 21.087 18 25 22.583 19 25 31.583 28 343 17.329 14 21 20.537 18 22 29.344 26 324 60.195 40 81 61.476 45 83 63.798 52 795 46.124 25 72 55.188 37 74 49.546 36 637 8.467 5 17 10.217 6 15 9.187 6 13

targets. The selection of ‘stable’ reference pixels is based on the comparison betweenthe scatterplot of each scene to be recti� ed and the selected reference scene as shownin � gure 3.

Theoretically, if there are no environmentally driven changes between dataacquisitions, the scatterplots for both scenes should be overlain. The recti� cationprocess, therefore, consists of selecting ‘dark’ and ‘bright’ intervals in the two-dimensional plot. Those intervals are used to identify the pixel positions in the scene.These pixels are used to compute the mean re� ectance of ‘dark’ and ‘bright’ targetsin each original band for both the reference (r) and the ‘subject-to-recti� cation’dataset (s). The average re� ectances of these targets in each band are used todetermine the coeYcients for linear transformation of the ‘subject-to-recti� cation’image in relation to the reference image. The model used in the recti� cation processis given by a set of linear transformations as follows:

T=mi+b

i(1)


Figure 5. Colour composites of landsat TM bands 1 (blue), 2 (green), 3 (red) for both originaland recti� ed datasets.

where

mi=

Bri Dr

iBs

i Ds

i(2)

bi=

DriBs

i Ds

iBr

iBs

i Ds

i(3)

and Bri

is the average of the bright reference dataset, Dri

is the average of the darkreference dataset, Bs

iis the average of the bright ‘subject-to-recti� cation’ dataset,

and Dsi

is the average of the dark ‘subject-to-recti� cation’ dataset, for all i bands.


Figure 6. Colour composites of Landsat TM bands 3 (red), 4 (green) and 5 (blue) for bothoriginal and recti� ed datasets.

After this recti� cation, the resulting image was submitted to the KT transforma-tion algorithm and a new scatterplot produced and examined. If the pixel distributionfor the reference and corrected dataset overlays, the next step is to examine thescatterplots of each band for both the reference and the recti� ed dataset. If theydisplay a linear distribution, the coeYcients are accepted and the process stops. Ifthe distribution is not linear then the process of selecting new dark and brightintervals starts again. This means that the recti� cation method is iterative andempirical relying on a great deal of human power to be carried on.

Figure 4 shows the scatterplot derived from the reference image (red) and thecorrected image (yellow). The pixel distribution suggests that there is a good agree-


Figure 7. Colour composites of Landsat TM bands 4 (green), 5 (red) and 7 (blue) for bothoriginal and recti� ed datasets.

ment between the reference image and the corrected image. The scatterplots for thepair of bands from the reference and recti� ed images were also analysed. Theagreement between the reference and recti� ed bands indicates that the recti� cationprocess was successful. The last test was to examine the numerical values from thereference and recti� ed images (table 2). As seen in table 2, the numerical results alsosupport the success of the recti� cation process. However, this process was not alwayssuccessful, mainly when cloud and atmospheric eVects on the scene were high.

After selecting the best set of recti� ed data for each scene, colour composites of:(a) bands TM1, TM2, TM3; (b) bands TM3, TM4, TM5; and (c) bands TM4, TM5,TM7 were generated and assembled for both the original and the recti� ed datasets.The results were visually examined and each scene was classi� ed according to the


Table 3. Recti� cation performance for diVerent band combinations and scenes.

Recti� cation performanceTM scene(path-row) Date TM1TM2TM3 TM3TM4TM5 TM4TM5TM7

001-61 24 Aug 1995 Better Better Worse001-62 2 Oct 1992 Better Worse Worse002-61 21 May 1995 Worse Worse Worse002-62 7 Oct 1991 Better Better Better003-62 11 Aug 1991 Better Better Worse003-63 2 Aug 1988 Better Worse Worse004-62 4 Aug 1986 Worse Better Worse004-63 13 Sep 1989 Better Better Worse223-61 24 Jun 1987 Worse Better Better224-60 14 Aug 1988 Worse Worse Worse224-61 19 Sep 1995 Worse Worse Worse225-60 2 Jul 1987 Worse Worse Worse225-61 15 Jul 1986 Better Worse Worse226-60 11 Jul 1988 Worse Worse Worse226-61 4 Aug 1985 Worse Worse Worse226-62 20 Jul 1991 Better Better Better227-61 22 Aug 1989 Worse Worse Worse227-62 3 Aug 1988 Worse Worse Worse228-61 6 Sep 1992 Better Better Worse228-62 18 Jun 1992 Worse Better Worse229-61 21 Sep 1989 Better Better Worse229-62 10 Aug 1991 Better Better Worse230-62 2 Jul 1992 Worse Better Worse231-62 2 Aug 1989 Reference Reference Reference231-63 25 Jul 1992 Worse Better Worse232-62 28 Aug 1992 Worse Worse Worse232-63 1 Aug 1992 Worse Worse Worse233-62 24 Aug 1992 Better Better Worse233-63 25 Sep 1992 Better Worse Worse

degree of improvement in data quality provided by the recti� cation processes.Figures 5 to 7 show the original and recti� ed dataset for each of the three colour

composites. They show that in some cases the recti� cation improved data quality.

The worst performance was observed for the compositions including the visiblebands due to atmospheric eVects. The analysis in table 3 allows the classi� cation of

the scenes into four groups regarding the eVectiveness of the radiometric recti� cation:

(a) all band combinations were improved; (b) only visible band combinations wereimproved; (c) only visible–near infrared combinations were improved; and (d) only

near infrared combinations were improved.

The radiometric recti� cation deteriorated the image quality for eight scenes. Thishappened in two cases: (a) scenes with a digital count distribution similar to the

reference image; (b) scenes partially degraded by cloud cover. These scenes shall be

replaced in the future when a better acquisition will be available.Based on this analysis the � nal set of images used to build the mosaic include:

(a) recti� ed scenes (six bands recti� ed); (b) partially recti� ed scenes (one or moreoriginal bands); and (c) original scenes.


Table 4. Landsat TM data used to build the mosaic (ORI, original data; RET, recti� ed data).

TM scene(path-row) Date Bands

001-61 24 Aug 1995 TM1RET, TM2RET, TM3RET, TM4ORI, TM5RET, TM7ORI001-62 2 Oct 1992 TM1RET, TM2RET, TM3RET, TM4RET, TM5ORI, TM7ORI002-61 21 May 1995 TM1RET, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI002-62 7 Oct 1991 TM1RET, TM2RET, TM3RET, TM4RET, TM5RET, TM7RET003-62 11 Aug 1991 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI003-63 2 Aug 1988 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI004-62 4 Aug 1986 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI004-63 13 Sep 1989 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI223-61 24 Jun 1987 TM1RET, TM2ORI, TM3RET, TM4ORI, TM5RET, TM7RET224-60 14 Aug 1988 TM1ORI, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI224-61 19 Sep 1995 TM1ORI, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI225-60 2 Jul 1987 TM1RET, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI225-61 15 Jul 1986 TM1ORI, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI226-60 11 Jul 1988 TM1ORI, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI226-61 4 Aug 1985 TM1ORI, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI226-62 20 Jul 1991 TM1RET, TM2RET, TM3RET, TM4RET, TM5RET, TM7RET227-61 22 Aug 1989 TM1RET, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI227-62 3 Aug 1988 TM1ORI, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI228-61 6 Sep 1992 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI228-62 18 Jun 1992 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI229-61 21 Sep 1989 TM1RET, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI229-62 10 Aug 1991 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI230-62 2 Jul 1992 TM1RET, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI231-62 2 Aug 1989 REFERENCE231-63 25 Jul 1992 TM1RET, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI232-62 28 Aug 1992 TM1RET, TM2ORI, TM3ORI, TM4ORI, TM5ORI, TM7ORI232-63 1 Aug 1992 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI233-62 24 Aug 1992 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI233-63 25 Sep 1992 TM1RET, TM2RET, TM3RET, TM4ORI, TM5ORI, TM7ORI

4. Results and discussionTable 4 presents the dataset used to build the digital mosaic. It shows that for

the majority of scenes the recti� cation was eVective for the visible bands whereatmospheric eVects produced the largest environmental disturbances in the radiancemeasurements.

The � nal mosaic can be observed in � gure 8 for the colour composite TM3(blue), TM4 (green), TM5 (red). As mentioned previously, this mosaic assembles thebest image set for each scene, including the recti� ed, partially recti� ed and originaldataset. It shows that the overall result of the proposed methodology can be usedfor regional scale mapping of � oodplain habitats using high resolution optical data.

A detailed assessment of the recti� cation process along the � oodplain mosaicshows that even for the true colour composites, TM1 (blue), TM2 (green) and TM3(red), the results are good. Figure 9 shows the Solimoes–Negro con� uence and partof Careiro Island covered by two diVerent scenes (231/62 and 230/62, respectively)acquired three years apart. It can be observed that the colour matching between thescenes is almost perfect. The diVerences observed in the white water colour can berelated to actual changes in the sediment load from one year of acquisition to theother. For targets less susceptible to changes such as the forest itself, the transitionbetween the two scenes is not detectable.


Figure 8. Brazilian Amazon � oodplain mosaic 3 (blue), 4 (green) and 5 (red). Source: Novoand Shimabukuro (1998).

Figure 9. Colour composite 1 (blue), 2 (green) and 3 (red) for the Manaus and CareiroIsland.

In fact, Mertes et al. (2000) could map nine cover types applying a mixing modelapproach to the mosaic data. A series of applications are being tested with thisdataset including the development of an empirical model for estimating the sedimentconcentration in the Amazon River (Vieira da Silva et al. 1999 ).


5. ConclusionsThe approach used to build the Amazon River � oodplain mosaic proved able to

cope with various problems presented by the original dataset: spatial and temporalchanges in the atmospheric optical properties; and time changes in the radiometricperformance of the Landsat TM sensor during the 10 years of data acquisition.

It represents a useful dataset which is fully georeferenced and available in twospatial resolutions: 100 m×100 m and 30 m×30 m. The full potential of the mosaicis still to be accomplished.

AcknowledgmentsThe authors thank the National Institute for Space Research, the University of

California at Santa Barbara and the University of Washington at Seattle for sup-porting this research. The authors thank Dr Bernardo RudorV and Dr GetulioT. Batista for revising the text and for making valuable suggestions. The authorsalso thank the anonymous reviewers for their valuable suggestions for improvingthe manuscript.

ReferencesBartlett, K. B., and Harris, R. C., 1993, Review and assessment of methane emissions from

wetlands. Chemosphere, 26, 261–298.Goulding, M., Smith, N. J. H., and Mahar, D. J., 1996, Floods of Fortune, Ecology and

Economy along the Amazon (New York: Columbia University Press).Hall, F. G., Strebel, D. E., Nickeson, J. E., and Goetz, S. J., 1991, Radiometric recti� cation:

toward a common radiometric response among multidate, multisensor images. RemoteSensing of Environment, 35, 11–27.

Jensen, J. R., Rutchey, K., Koch, M. S., and Narumalani, S., 1995, Inland wetland changedetection in the Everglades Water Conservation Area using a time series of normalisedremotely sensed data. Photogrammetri c Engineering and Remote Sensing, 61, 199–209.

Kauth, R. J., and Thomas, G. S., 1976, The tasselled cap—a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of theSymposium on Machine Processing of Remotely Sensed Data, Purdue University, Indiana,pp. 623–627.

Markham, B. L., and Barker, J. L., 1986, Radiometric properties of U.S. processed LandsatMSS data. Remote Sensing of Environment, 22, 39–71.

Mertes, L. A. K., 1997, Documentation and signi� cance of the peripheric zone on inundated� oodplains. Water Resources Research, 33, 1749–1762.

Mertes, L. K., Novo, E. M., and Shimabukuro,Y. E., 2000, Land cover heterogeneity of riverine� oodplains of the Amazon Basin from a remote sensing perspective. InternationalAssociation of Ecology, 6th International Symposium, August 6–12, Quebec, p. 391–392.

Novo, E. M. L. M., and Shimabukuro,Y. E., 1998, O rio Amazonas em Mosaico. Ciencia Hoje,24,, No. 144, 59–61.

Novo, E. M. L. M., Leite, F. A., Avila, J., Ballester, M. V., and Melack, J. M., 1997,Assessment of Amazon � oodplain habitats using TM/Landsat data. Ciencia e Cultura,49, 280–284.

Schott, J. R., Salvaggio, C., and Volchok, W. J., 1988, Radiometric Scene Normalisation usingpseudoinvariant features. Remote Sensing of Environment, 25, 1–16.

Sippel, S. J., Hamilton, S. K., and Melack, J. M., 1992, Inundation area and morphometry oflakes on the Amazon River � oodplain, Brazil. Archives of Hydrobiology, 123, 385–400.

Sippel, S. J., Hamilton, S. K., Melack, J. M., and Novo, E. M. M., 1998, Passive microwaveobservations of inundation area and the area/stage relation in the Amazon River� oodplain. International Journal of Remote Sensing, 19, 3055–3074.

Vieira da Silva, R. C., Novo, E. M. L. M., and Pecly, J. O. G., 1999, Potencialidade do usode satelites para o monitoramento da concentracao de sedimentos no Rio Amazonas.Manaus ’99. Hydrological and Geochemical Processes in Large Scale Basins, November15–19, Manaus, Brazil, HiBam: Hydrology and Geochemistry of the Amazon Basin(http://www.unb.br/ig/hibam/hibam.htm).

amazon river mainstem floodplain landsat tm digital mosaic

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