Use of SAR satellites for mapping zonation of vegetationcommunities in the Amazon floodplain
M. P. F. COSTA
National Institute for Space Research, Av. Dos Astronautas, 1758, Sao Josedos Campos, SP 12 227-010, Brazil; e-mail: [email protected] University of Victoria, Department of Geography, P.O. Box 3050,Victoria, BC, Canada V8W 3P5; e-mail: [email protected]
(Received 6 September 2001; in final form 29 November 2002 )
Abstract. Radarsat and JERS-1 imagery were used for mapping zonation ofvegetation communities in the Amazon floodplain. Imagery analysis indicatesthat at periods of minimum water level the backscattering values of both C andL bands are the lowest and as the water level rises, so do the backscatteringvalues. JERS-1 imagery exhibits a larger dynamic range of backscattering inresponse to the ground cover for the two extremes of water level (10 dB) com-pared to Radarsat imagery. The backscattering differences from different groundcover allowed the use of a region-based classification that produced seasonalmaps with accuracies higher than 95% for vegetated areas of the floodplain.These seasonal maps were used to estimate the spatial distribution and time ofinundation and the vegetation cover of the floodplain. It was possible to deter-mine that semi-aquatic vegetation, tree-like aquatic plants, and shrub-like treescolonize regions flooded for at least 300 days year21. Secondary colonizers, suchas tall well-developed floodplain forest, cover regions flooded for approximately150 days year21, and floodplain climax forest colonize regions inundated forapproximately 60 days year21.
1. Introduction
The most important plant communities of the Amazonian floodplains are algae,
aquatic and semi-aquatic herbaceous plants, and forest. These communities have
adapted to survive in an environment that changes by season, year, decade, and
century, because of the ‘flood pulse’ of the Amazon (Junk 1997). In the Amazonian
varzea (flooded by ‘white water’ rivers) the number of days that a region is flooded
controls the zonation of vegetation communities. Generally, grass communities
tolerate 300 days of flooded conditions, shrubs and low biomass trees tolerate 260
flooded days, and tall-high biomass climax forests tolerate 230 to 150 flooded days
(Junk and Piedade 1997, Worbes 1997).
The determination of the spatial distribution and time of inundation and,
therefore, the zonation of floodplain vegetation communities at the Amazon scale is
only possible using remotely sensed data. Optical satellites are limited because of
intense cloud cover over the Amazon region. For instance, Novo et al. (1997) had
to acquire 10 years of Landsat data to produce a cloud-free mosaic of part of the
Amazon floodplain. Therefore, Synthetic Aperture Radar (SAR) satellites are
International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2004 Taylor & Francis Ltd
http://www.tandf.co.uk/journalsDOI: 10.1080/0143116031000116985
INT. J. REMOTE SENSING, 20 MAY, 2004,VOL. 25, NO. 10, 1817–1835
currently the most suitable systems to study the Amazon floodplain because of their
all-weather functionality and their independence from the Sun as an illumination
source. Moreover, microwave radiation interacts differently with distinct plant
communities of the floodplain and surrounding areas (Costa 2000). The charac-
teristics of the plant (density, distribution, orientation, shape of the foliage, dielec-
tric constant, height, and branches), the ground (dry, moist, and flooded), and the
sensor (polarization, incidence angle, and wavelength) are important in determining
the radiation backscattered towards the radar antenna (Dobson et al. 1996).
Using both C and L band imagery and field observations of a specific site in the
lower Amazon floodplain, this study investigated (1) the temporal variability of
radar backscattering of different vegetation communities of the floodplain and
surrounding areas and (2) the use of radar imagery for mapping spatial distribution
and time of inundation, and zonation of vegetation communities in the floodplain.The spatial distribution and time of inundation associated with the vegetation
cover constitutes key information to understand annual autochthonous carbon
production (Junk and Piedade 1997, Melack and Forsberg 2000) and methane
emission (Wassmann and Martius 1997, Forsberg et al. 2000), to evaluate pre-
ferential fish habitat (Junk 1997), and to define ecological sound management of the
Amazon floodplain.
2. Test site and data set
2.1. Site description
The study area, at 2‡00’ S/54‡00’W to 2‡30’ S/54‡30’W (figure 1), is a sedi-
mentary basin located in the northeast of the Brazilian Amazon, at the border of
the central and the lower Amazon regions. The predominant floodplain vegetation
communities are large homogeneous stands of herbaceous semi-aquatic plants,
pioneer shrubs, and various forest types. The predominant upland vegetation
is savanna (‘cerrado’), secondary forest, grasslands (pasture), and dense forest
(RADAMBRASIL 1976). The northern part of this floodplain is located in a small
rural setting, where some areas of floodplain forest have been converted to pasture
land. In the high water period, the southern part of the floodplain is connected to
the Amazon main channel.
The Amazon River water levels at Obidos (200 km northwest of the study
region) are shown in figure 2. Generally, the Amazon River presents a monomodal
cycle in which high water levels occur in May/June and low water levels in October/
November. The curves of water level fluctuation show that a similar amplitude of
variations occurred during the three years of study. Dissimilarities higher than 1 m
are mostly observed in 1997 when water levels were lowest from September to
November but recovered to similar values in January. The similar timing and
amplitude of water level variation allowed the assumption that field and satellite
data from different hydrological cycles can accurately portray the system over one
hydrological cycle. This was an important assumption because timing and amp-
litude of water variation are of great influence on zonation of vegetation in the
floodplain (Junk and Piedade 1997).
2.2. Ground data
Five field campaigns were conducted at different phases of three hydrological
cycles: high water (May 1996), falling water (August 1996), low water (November
1996), rising water (April 1997), and, again, high water (June 1999). Ground data
1818 M. P. F. Costa
included structural characterization and photographic surveying (hand-held 35 mm
camera) of floodplain forest, aquatic and semi-aquatic vegetation, upland forest,
and agriculture areas. Aerial photographs of selected areas were acquired at high
Figure 2. Water level fluctuation of the Amazon River at Obidos. Arrows represent theacquisition dates of JERS-1 and RADARSAT imagery.
Figure 1. Study area.
Mapping vegetation communities 1819
and rising water stages. These materials were used as a reference for characteriza-
tion and identification of major habitats such as floodplain forest, aquatic vege-
tation, upland forest, savanna forest, and pasture/agriculture.
2.3. Satellite data
Radarsat and JERS-1 data were acquired in 1996, 1997, and 1999, coinciding
with the field campaigns. The characteristics of the imagery are: ground range,
16 bit unsigned, and geocoded standard resolution. Radarsat images were processed
by the Canada Center of Remote Sensing and JERS-1 images by the Japanese
Space Agency. The major characteristics of the dataset are outlined in table 1.
The SAR imagery was radiometrically and geometrically calibrated according to
the procedures described in Costa et al. (2002). The test of radiometric stability
showed that the Radarsat and JERS-1 imagery was stable both temporally and
within the scene. Figure 3 shows the multi-temporal variation of backscattering (so)
across the range of acquisition for upland forested areas. For the study area,
upland forest was considered to have the most stable so values. The small dif-
ferences across the image range were associated with small variations in the terrain
topography. The small differences in the average backscattering between imagery
were associated with the slightly different environmental conditions at the time of
imagery acquisition. For instance, for Radarsat imagery, the lowest backscattering
values occurred in November (dry month), when the precipitation and the relative
humidity were the lowest and the temperature was the highest (approximately
20 mm, 70%, and 30‡C, respectively) compared with May (300 mm, 87%, and 26‡C,
respectively). The same pattern was observed for JERS-1 imagery, i.e. the image
acquired in December (dry month) showed lower backscattering values than the
imagery acquired in wet months, such as March and May. It is known that for a
given surface, if the roughness remains constant the so decreases when the moisture
content of the material decreases (lower dielectric constant) (Dobson et al. 1996).
The calibrated SAR imagery was submitted to two distinct procedures. (1)
Extraction of so (dB) from intensity imagery of known sites of plant communities;
details of this procedure can be found in Costa et al. (2002). (2) Classification of the
floodplain according to a region-based algorithm.
2.3.1. Classification procedureThe classification procedure consisted of the following steps: (1) filtering of SAR
imagery, (2) scaling from 16 to 8 bit, (3) applying water and upland masks over the
imagery, (4) segmentation, and (5) imagery classification. Details of the first three
steps are found in Costa et al. (2002).
The seasonally paired imagery (Radarsat and JERS-1 for the same season –
total of five pairs) was submitted to an automatic segmentation procedure (region
growing algorithm), and a region-based classification. For the automatic segmen-
tation procedure, a threshold of similarity for each paired data was required. The
definition of the threshold of similarity was critical for the success of the classi-
fication, since it defined the rules for merging regions. Figure 4 is an enlargement of
an area of the image overlaid with the regions built by using three different
similarity thresholds (in digital numbers): 20, 30 and, 40. The threshold 30 was
defined by the Least Significance Difference method (LSD) at 95% confidence level
(Snedecor and Cochran 1980). Note that when the thresholds were larger, fewer
regions were built, i.e. less detail was separated. Conversely, at lower thresholds,
1820 M. P. F. Costa
Table 1. Characteristics of the remotely sensed data.
Satellite Acquisition Incidence angle (‡) Coverage (km) Swath mode* Band/Polarization Pixel spacing (m) Resolution (m) Number of looks
Radarsat 27 May 1996 y43 1006100 S6-D C/HH 12.5612.5 26627 1647 August 1996
11 November 19965 April 19978 June 1999
JERS-1 16 May 1996 y35 75675 D L/HH 12.5612.5 26627 312 August 1996
22 December 199620 March 1997
*D stands for descending.
Mappingveg
etatio
ncommunities
18
21
more regions were built. However, more regions were not synonymous with better
results for the classification. In SAR imagery, this sometimes is caused by over
segmentation due to the speckle (Dong et al. 1999). A visual inspection of several
sectors of segmented imagery generated by the different thresholds showed that,
indeed, a threshold of 30 (the calculated LSD value) gave better results in terms of
defining regions. Table 2 presents the calculated threshold of similarities for each
pair of images.
The segmented imagery was submitted to a region-based supervised classifica-
tion according to the Battacharrya distance algorithm (Richards 1986). Digital
mask files of training and testing sites were created and overlaid over the paired
imagery to ensure that the selected regions were approximately the same for all the
seasonally paired imagery. Table 3 presents the number of training and testing
regions for each pair of images.
Figure 3. Multi-temporal variation of backscattering of upland forest across the rangedirection (near to far range). (a) Radarsat S6 and (b) JERS-1.
1822 M. P. F. Costa
3. Analysis and results
3.1. Temporal backscattering variability of different vegetation communities
The multi-temporal mean, lower and upper bound of so values at 95% con-
fidence level for aquatic vegetation, floodplain flooded forest, upland forest, pas-
ture, and savanna are presented in table 4. Generally, for Radarsat imagery, the
mean so values of a specific ground cover did not change significantly (pv0.05)
between seasons, except for November (dry season) when the so average values
decreased by approximately 5 dB (aquatic plants), 1 dB (floodplain flooded forest),
2 dB (pasture), 2 dB (savanna), and 1 dB (upland forest). For JERS-1 imagery,
likewise, the mean so values were only significantly different (pv0.05) for
November when the so values on average decrease by approximately 5 dB (aquatic
Table 2. Calculated threshold of similarities for each pair of imagery.
Period Combined images Threshold of similarity
May (high water) Radarsat S6 and JERS-1 30June (high water) Radarsat S6 and JERS-1 30August (falling water) Radarsat S6 and JERS-1 25November (low water) Radarsat S6 and JERS-1 25April (rising water) Radarsat S6 and JERS-1 25
Figure 4. Example of segmentation results using the region-growing algorithm withdifferent threshold values. (a) Threshold of 20, (b) threshold of 30, (c) thresholdof 40, and JERS-1 image enlarged in the background, and (d ) enlarged section of theaerial photograph of the same location showing the different ground covers (originalphotograph at 1:20 000 scale). Red box in figure 6(a) shows the location of selectedsub-region.
Mapping vegetation communities 1823
Table 3. Number of training and test regions.
Classified images
Number of samples
Aquaticvegetation
Floodplainflooded forest
Floodplainunflooded forest
Training Testing Training Testing Training Testing
Radarsat S6zJERS, November 48 41 10 8 60 29Radarsat S6zJERS, April 57 49 100 67 20 8Radarsat S6zJERS, May 122 72 109 71 – –Radarsat S6zJERS, June 76 54 120 64 – –Radarsat S6zJERS, August 83 59 97 72 12 8
Table 4. Multitemporal mean, lower, and upper bound of backscattering coefficients (dB) at95% confidence interval.
Month Ground cover
Radarsat S6 JERS-1
Mean Lower Upper Mean Lower Upper
November/December
Floodplain forest n~41Upland forest n~41
28.229.9
29.0210.1
27.529.7
27.428.0
28.328.2
26.627.8
Pasture n~41 212.0 212.6 211.5 212.6 213.3 212.1Savanna n~33 213.9 214.4 213.5 211.0 211.5 210.5
Aquatic plants n~11 211.9 212.4 211.3 213.6 214.1 213.4
March/April Floodplain forest 26.9 27.7 26.3 25.5 26.3 24.8Upland forest 29.6 29.9 29.4 27.4 27.4 27.1
Pasture 29.9 210.5 29.5 210.8 211.4 210.1Savanna 212.4 212.9 212.0 29.6 210.1 29.2
Aquatic plants n~16 27.4 27.8 27.0 210.5 211.1 210.1
May Floodplain forest 26.6 27.4 26.0 24.4 25.2 23.7Upland forest 28.6 28.9 28.4 26.8 27.1 26.6
Pasture 29.7 210.2 29.2 210.2 210.9 29.7Savanna 211.8 212.3 211.4 29.5 210.0 29.1
Aquatic plants n~12 27.1 27.5 26.7 28.8 29.2 28.4
June Floodplain forest 26.6 27.4 25.9Upland forest 29.3 29.5 29.1
Pasture 29.7 210.2 29.2Savanna 211.9 212.4 211.5
Aquatic plants n~35 26.9 27.5 26.3
August Floodplain forest 26.9 27.7 26.3 24.9 25.8 24.2Upland forest 29.1 29.4 28.8 27.4 27.7 27.2
Pasture 210.0 210.6 29.5 211.8 212.5 211.2Savanna 212.5 213.3 212.0 210.2 210.7 29.7
Aquatic plants n~15 26.7 27.0 26.4 29.0 29.5 28.5
n is the number of polygons sampled for each ground cover. The same polygons wereused to estimate the backscattering values monthly, with the exception of aquatic vegetation.Mean so and its confidence interval were calculated assuming that the samples have a normaldistribution due to the large number of pixels in each sampled polygon. Laur et al. (1996)calculated that a minimum number of 100 pixels per sampled polygon are required to yield a95% confidence with an error boundary of the estimated so at ¡1 dB. The average numberof pixels per sampled polygon was 215, 132, 666, 245, and 159 for aquatic vegetation,floodplain forest, upland forest, pasture, and savanna, respectively.
1824 M. P. F. Costa
plants), 3 dB (floodplain flooded forest), 2.4 dB (pasture), 1.5 dB (savanna), and
1 dB (upland forest). Nonetheless, for both Radarsat and JERS-1 imagery, the
highest so dynamic range was found for aquatic plants and floodplain flooded
forest, i.e. biotopes that were highly dependent on the water level variation. The
results showed that at minimum water levels, the so values for vegetation com-
munities of the floodplain were the lowest at both C and L bands. As the water
level rose so did the so values, until they reached a maximum value.
The multi-temporal so values of the distinct ground covers were a result of the
different scattering mechanisms, which in turn were dependent on the temporal
variability of the ground cover. Table 5 summarizes some of the main structural
characteristics of the vegetation communities and the predominant scattering
mechanisms. The following sections describe these scattering mechanisms for
different vegetation communities.
3.1.1. Floodplain forest
Regions of floodplain forest that were seasonally flooded showed a large
temporal so variation. At the low water period, the so values were very similar to
those observed for upland forest, which were a result of the interaction of the
radiation with the canopy elements. At the high water period, L band back-
scattering was higher than C band backscattering (2 dB difference) because of
differences between incidence angle and radiation wavelength. At an incidence
angle of 35‡ (JERS-1), the long wavelength of L band (23 cm) penetrated deep into
the tree canopy and interacted with the trunk and the water underneath; the reverse
pathway could also happen (Hess et al. 1995, Wang et al. 1995, Proisy et al. 2000).
This interaction, called double bounce mechanism, caused an average so value of
24 dB. At a 45‡ of incidence angle (Radarsat S6), the short C band wavelength
(5.6 cm) interacted mostly with the upper canopy layer (volume scattering
mechanism). The result was an average so value of 27 dB. Figure 5(a) illustrates
the dominant scattering mechanisms of C and L bands with floodplain flooded
forest.
During flooded conditions, the so values also varied with the degree of defo-
liation of the floodplain trees. Pseudobombax munguba (30 m height above water)
and Courupita guianensis (2 m height above water) are examples of floodplain trees
that lose their leaves during flooding conditions. For these trees, the so values were
25 dB (C band) and 23 dB (L band). The radiation penetrated deeper into the
canopy (no interference of leaves) and therefore a pronounced double bounce effect
between the tree trunk and water surface occurred.
3.1.2. Semi-aquatic vegetation
The areas covered with grass-like aquatic vegetation exhibited the greatest
temporal variation of so values. At maximum growing stage, when the water was at
its highest level, stands of Hymenachene amplexicaulis, the most common species of
aquatic vegetation in the study area, showed the following characteristics: density of
approximately 111 stems m22, grass-like structure, one stem of 0.85 m high and
0.4 cm of diameter, five leaves of 25 cm long and 2 cm wide at an angle to the
structure of roughly 45‡, and a vertical span occupied by the canopy structures of
approximately 40 cm. The interaction of microwave radiation with these plants
resulted in specular scattering, volume scattering, and double bounce scattering
Mapping vegetation communities 1825
Table 5. General discription of some important vegetation found in the study site (adapted from Dobson et al. 1996).
Ground cover
Non woody vegetation Woody vegetation
Pasture land Aquatic vegetation Upland Seasonally flooded
Surface Soil Water Soil Soil/water
Growth form Blade-like Shrubs (savanna- sparsevegetation)
Decurrent(savanna denseand secondary
forest)
Decurrent Tree-likeaquatic vegetation
Structuralcharacteristics
Trunk None Many small trunkswith randomorientation
Cylindrical Cylindrical Cylindrical
Branches Non-woody stalks or stems Many small branches Branches forkedand randomly
oriented
Branches forkedand randomly oriented
None
Foliage Blade-like erectophile -short
Blade-likeerectophile - tall
Broad leaves Broad leaves Broadleaves
Defoliated Blade-likeclump at top
of trunkScatteringmechanism
C-band (45‡) Volume scattering Volume scattering Surface-scattering(quasi-specular)
Volume-scattering Volumescattering
Doublebounce
Double bounce
L-band (35‡) Surface scattering(quasi-specular)
Volume scattering andspecular reflection
Surface scattering(quasi-specular)
Surface andvolume scattering
Doublebounce
Doublebounce
Double bounce
18
26
M.P.F.Costa
mechanisms. Figure 5(b) illustrates these mechanisms, which were a function of both
the sensor characteristics and the plant’s biophysical properties.
The interaction of the fully developed canopy elements with radiation of 5.6 cm
of wavelength at incident angle of 45‡ (Radarsat S6) resulted in the volume
scattering mechanism. The total backscattering was on average 27 dB. At this
incidence angle, the path of the radiation through the canopy was increased. Thus,
more attenuation by the scattering elements and less penetration of the radiation
occurred (Ulaby et al. 1982). In comparison, data acquired over Amazonian aquatic
vegetation with C-HH band and steeper incidence angle (v33‡) showed higher so
(approximately 24 dB) (Hess et al. 1995, Costa 2000, Novo et al. 2002). The steeper
incidence angle facilitated the deeper penetration of radiation into the vegetation
canopy, which might have resulted in a water/vegetation double bounce mechanism.
Le Toan et al. (1997) published similar so (approximately 28 dB) compared to the
values of this study for data acquired with steep incidence angle, same wavelength,
but VV polarization (ERS-1 configuration) over Indonesian flooded rice fields. We
Figure 5. Schematic representation of the scattering mechanisms at C and L bands for (a)floodplain flooded forest and (b) aquatic vegetation. The thickness of the returningarrows (1, 2, and 3) represents relative magnitude of scattered radiation.
Mapping vegetation communities 1827
are speculating that the vertically polarized radiation was strongly attenuated by
vertically oriented rice plants, while horizontally polarized radiation, such is the
case of Radarsat, penetrated deeply into the canopy.
At a wavelength of 23 cm and 35‡ incidence angle (JERS-1), deeper penetration
of the radiation within the canopy of the aquatic vegetation occurred. This resulted
in low so values (29 dB). At this wavelength, the structures of the aquatic vege-
tation were mostly transparent to the radiation (quasi-specular reflection), except
when dense canopies occurred (volume scattering) (figure 5(b)). Among the aquatic
vegetation, isolated high so values (27 dB) were observed at this wavelength. The
higher values were related to areas colonized by Echinochloa polystachya and Paspalum
fasciculatum. The higher so values suggested that canopy–water interaction (double
bouncemechanism)mayhaveoccurredwhentheradiationwas interactingwithtallgrass-
like plants (1.5 m), with large leaves (80 cm long and 3 cm wide), thick stems (2 cm), and
larger canopy gaps (27 plants m22) compared with H. amplexicaulis. Hess et al. (1995)
published comparable so values at L band for Amazonian aquatic vegetation.
Montrichardia arborescens is a tree-like semi-aquatic vegetation that colonizes
areas where moisture is retained even during the low water period. The general
structural characteristics of this species are as follows: dense elongated or round
patches of plants, 3 m height above water or 7 m total height, cylindrical vertical
trunk of 4 cm diameter, and a clump of approximately four broad leaves of 50 cm
long and 40 cm wide at an angle to the trunk of roughly 45‡. The average so was
26 dB and 25 dB at Radarsat and JERS-1, respectively. These values were a result
of the same type of interaction (double bounce) observed between microwave
radiation and floodplain defoliated trees. At the low water period, the so from
M. arborescens remained high due to the high moisture content of the areas colonized
by this species. At this period, average so values were 28 dB (C band) and 27 dB
(L band).
3.1.3. PastureShort grass-like vegetation, a few random short non-woody shrubs, and bare
soil covers the pastureland of the study area. The grass-like vegetation was equi-
valent in size to the wavelength of C band when compared with L band. Con-
sequently, at L band, relatively higher transmissivity of the radiation through this
vegetation occurred and, therefore, the radiation interacted with the ground surface.
This interaction resulted in low backscattering values for the driest (November) and
wettest (May) periods, 213 dB and 210 dB, respectively. Conversely, C band radia-
tion was expected to interact with the volume of the vegetation due to the small size
of the scattering elements. However, the observed backscattering values (212 dB
for the driest and 210 dB for the wettest period) suggested that the interaction was
mostly a result of the roughness of ground surface. Note that at both C and L
bands the lowest values occurred during the dry season due to the decrease of
moisture content of the ground.
3.1.4. SavannaA similar interaction, i.e. interaction with the roughness of the ground surface,
was evident for savanna areas where the vegetation (3 m height) was sparse and the
ground surface was composed of bare sandy soil and short grass. During the dry
season, so values were on average 214 dB for C and 211 dB for L band. During the
wet season, savanna areas showed values of 212 dB for C and 210 dB for L band.
1828 M. P. F. Costa
Again, the slightly increased values were due to the higher moisture content of the
surface during the wet season. In general, for both seasons, the low values of so
suggested that radiation at both wavelengths interacted mostly with large patches of
bare soil and grass (surface scattering) than with the shrubs.
3.1.5. Upland forestUpland forest showed the lowest temporal variation of the backscattering
values. In the study area, upland forest is not a typical dense rain forest; it is mostly
composed of secondary forest and dense savanna (RADAMBRASIL 1976). The so
values were on average 29 dB for C and 27 dB for L band, and the lowest values
(less than 1 dB difference) were for the dry season imagery. For C and L bands,
regardless of the period of imagery acquisition, the so values were primarily a result
of the interaction of the radiation with the structures of the canopy (volume
scattering).
3.2. Zonation of the floodplain plant communities according to the time of
inundation
The previous section helped to understand the temporal scattering mechanisms
that occur due to the interaction of microwave radiation and vegetation communities
and ground surface. This knowledge was applied to define the classification scheme.
The classification procedure resulted in temporal maps of the floodplain with the
following classes: floodplain unflooded forest, floodplain flooded forest, aquatic
vegetation, upland vegetation (upland forest, savanna, and pasture), and water. The
upland vegetation class was a pre-defined mask applied over the imagery. The water
class was also a pre-defined seasonal mask applied over the imagery (details of these
procedures can be found in Costa et al. 2002).Overall, the classification accuracy of the multi-wavelength, C and L band
composite, exceeded 95%. The seasonal thematic maps are shown in figure 6, and
the matrix of error of the classification is shown in table 6. Aquatic vegetation areas
were accurately classified (approximately 95%) at any season. Floodplain flooded
forest showed an average classification accuracy of 90%. The main classification
error occurred between floodplain unflooded forest and flooded forest, for the low
water period imagery (November). When water levels rise, the different forested
areas within the floodplain present three distinct conditions of ground surface: dry,
moist, and flooded. It is speculated that the observed misclassification happened
because moist and flooded surfaces show similar strong backscattering due to the
high moisture content of the ground.
Clearly, the different backscattering values, which were a result of the inter-
action of C and L bands with the distinct ground covers, explain the high classi-
fication accuracies. That is, different scattering mechanisms occurred from the
interaction of short and long wavelengths with the canopy of the vegetation and
the undermeath surface. Other researchers have shown similar results in which
multi-wavelength SAR combinations improved the general classification of vege-
tated areas to values higher than 90% for forested areas (Pierce et al. 1994, Dobson
et al. 1996, Bergen et al. 1998, Kellndorfer and Pierce 1998), agriculture areas
(Lobo et al. 1996), and wetlands (Hess et al. 1995, Costa et al. 1998). Costa et al.
(1998) classified individual and combined sub-scenes from a set of original images
acquired in May for the same study area in the Amazon. The classified maps were
compared with a ground truth map that was generated from visual interpretation of
Mapping vegetation communities 1829
the aerial photography acquired for the same period. The comparison showed that
the classification of Radarsat images alone yielded confusion among the vegetated
ground cover. The JERS-1 images alone yielded confusion between floodplain
flooded forest and upland forest and pasture and aquatic vegetation and, in addition,
(a) (b)
(c)
(e)
(d )
Figure 6. Thematic classification maps. Cyan~aquatic vegetation; yellow~floodplainflooded forest; orange~floodplain unflooded forest; green~upland forest (forest,pasture and savanna); blue~water. Red box in 6 (a) represents the area of figure 4.(a) November – low water, (b) April – rising water, (c) May – high water, (d ) June –high/falling water, (e) August – falling water.
1830 M. P. F. Costa
did not separate narrow water channels as well as Radarsat. Generally, the multi-
wavelength combination provided better classification of the ground cover.In summary, the analyses of our multi-temporal results and the results of Costa
et al. (1998) show that a combination of different wavelengths yield better classi-
fication than individual C or L bands.
The seasonal calculated area of aquatic vegetation and floodplain flooded forest
and unflooded forest of the floodplain are shown in table 7. By November/
December, the Amazon River water level, as measured in Obidos, was already high
by approximately 2.5 m, and field observations showed that regions on the margins
of the central lake and nearest to the Amazon channel were starting to flood. In
these regions, large areas of aquatic vegetation at the beginning of the growth cycle
were observed. As a result, the measured total area covered by aquatic vegetation in
November was 342 km2. By April, the measured area decreased to 217 km2. At this
month, the Amazon River water level was 4 m higher than in November, causing a
2 m increase in the water depth of regions colonized by aquatic vegetation (Costa
et al. 2002). Possibly, the flood exterminated colonies of plants (established in
November) that could not keep pace with the water level change, as mentioned by
the local villagers. Further, during the rising water period, buffalo and cattle graze
large areas of freshly growing aquatic vegetation. This might also partially explain
the larger area occupied by aquatic vegetation in November than in April.
Maximum coverage of aquatic vegetation occurred in May (397 km2), during high
water. After May, the water started to recede and some aquatic vegetation com-
munities detached from the bottom and were carried away towards the Amazon
River. This was commonly observed during the field campaigns. The occupied area
Table 6. Matrices of confusion for the classifications.
% Classified as
True class
Aquaticvegetation
Floodplainflooded forest
Floodplainunflooded forest
Radarsat S6zJERS-1, November – overall accuracy~97.02%Aquatic vegetation 99.1 0 0.91Floodplain flooded forest 0 58.9 41.Floodplain unflooded forest 3.3 11.3 85.
Radarsat S6zJERS-1, April – overall accuracy~96.94%Aquatic vegetation 94.4 5.6 0Floodplain flooded forest 0.9 97.8 1.2Floodplain unflooded forest 0 2.9 97.1
Radarsat S6zJERS-1, May – overall accuracy~97.39%Aquatic vegetation 95.3 4.5 –Floodplain flooded forest 0 100 –Floodplain unflooded forest – – –
Radarsat S6zJERS-1, June – overall accuracy~95.47%Aquatic vegetation 97.4 2.6 –Floodplain flooded forest 0.6 99.36 –Floodplain unflooded forest – – –
Radarsat S6zJERS-1, August – overall accuracy~93.85%Aquatic vegetation 93.4 5.5 1.11Floodplain flooded forest 4.6 95.5 0Floodplain unflooded forest 0 8.1 91.9
Mapping vegetation communities 1831
decreased to 296 km2 (June) and 282 km2 (August) following the decrease of water
levels.
The area classified as floodplain flooded forest increased from November to
May, and decreased again from June to August, clearly reflecting the annual water
level variation. The areas classified as floodplain unflooded forest decreased from
November (333 km2) to April (87 km2) and May/June (0 km2), and started to
increase again by August (85 km2), once again following the water level. Large areas
of floodplain forest were not flooded in November when the water level was on
average 2.5 m high. This area increased considerably in April when the water level
variation was 6 m. By May/June, the areas of floodplain forest were completely
flooded. By August, when the water level receded, the areas of floodplain flooded
forest decreased.
Four distinct areas of zonation were characterized based on (i) the knowledge
that the time of inundation period is the primary force of zonation in the
Amazonian varzea (Junk and Piedade 1997, Worbes 1997), (ii) the understanding of
the interaction of microwave radiation with distinct ground covers, and (iii) the
seasonal thematic maps that were produced.
Two different ground covers were classified as flooded for at least 300 days
year21. The first ground cover contained aquatic vegetation continuously from the
beginning of rising water in November until August. In the Amazon, aquatic vege-
tation tolerates flood conditions for more than 300 days year21 as well as high rates
of sedimentation. These plants are one of the pioneer species that fix their roots
(Junk and Piedade 1997). In the study region, H. amplexicaulis is the dominant first
settler. In areas occupied by this species, both sedimentation and accumulation of
decaying organic material are greatly increased, which facilitate the formation of
habitats that are colonized by pioneer tree-like communities. These pioneer tree-like
communities comprise the second ground cover that was flooded continuously from
November to August. M. arborescens (aninga), a tall tree-like aquatic plant, Salix
sp. (oierana), and C. guianenses (castanha de macaco), both shrub-like trees,
tolerate flood conditions of 300 days year21 (Junk and Piedade 1997, Worbes 1997).
Early and late secondary settlers colonized the areas of floodplain forest that
were flooded at least from April to August (these areas were not flooded in
November), i.e. approximately 150 days year21. The most common species in the
floodplain forest in the study area are Cecropia latiloba (imbauba), Pseudobombax
munguba (munguba), and Astrocaryum jauari (jauarı). Unfortunately, the temporal
frequency in which the satellite imagery was acquired did not allow a separation of
areas of early and secondary colonizers. It is not known when these areas started to
flood due to the lack of image acquisition in January and February. Furthermore, it
Table 7. Total area (km2) per class.
MonthAquatic
vegetationFloodplain
flooded forestFloodplain
unflooded forest Upland*Open
water*
November 342 93 333 844 1045April 217 156 87 844 1247May 397 304 – 844 1112June 296 290 – 844 1227August 282 238 85 844 1208
*Area of upland represents the upland mask; areas of open water represent the openwater mask for each period.
1832 M. P. F. Costa
is not known when the flood receded from these areas due to the lack of imagery of
September and October. It is only known that these areas were flooded from April
to August, i.e. at least 150 days year21, and that they were not flooded in November,
which suggests that these colonizers do not tolerate flooded conditions all yeararound.
Finally, climax forest, composed of very tall dense-wood species (Worbes 1997),
colonized areas that were flooded only in May and June (possibly July, but the data
was not available), totalling approximately 60 days year21. These areas were not
flooded from August to November, i.e. these species were restricted to shorter flood
conditions. They were mostly seen in the northern areas of the classified maps
(figure 6(a) and (e)).
4. Summary and conclusions
This investigation examined the temporal variation of SAR backscattering and
the use of such data for mapping the spatial distribution and time of inundation
and zonation of vegetation communities in the Amazon floodplain.
In the first part of the analysis, so values indicated that at periods of minimum
water level the backscattering coefficients of both C and L bands were the lowest.
As the water level rose, so did the backscattering values. JERS-1 imagery exhibited
a larger dynamic range of backscattering in response to the ground cover for thetwo extreme periods of water level (10 dB) and within the scene (6 dB) compared
with Radarsat imagery. This suggests that the longer L band wavelength was more
sensitive to thickness and size of the vegetation scattering elements compared with
the C band shorter wavelength. The sensitivity of C and L bands to the scattering
elements provoked different scattering mechanisms. The possible scattering mechan-
isms for aquatic vegetation were as follows: at C band (45‡ of incidence angle),
volume scattering was dominant and at L band (35‡ of incidence angle), volume
scattering and specular reflection were dominant, however, for some species, doublebounce occurred. For defoliated flooded forest, double bounce occurred for C and
L bands. For foliated flooded forest, a combination of double bounce and volume
scattering prevailed at L band and volume scattering prevailed at C band. For
upland forest, volume scattering occurred for both C and L bands. For pasture and
savanna areas, surface scattering prevailed at both C and L bands.
In the second part of the analysis, the sensitivity of SAR imagery to temporal
changes and different ground covers was used for mapping the floodplain vege-
tation communities. The analysis of the backscattering differences between the flood-
plain and the surrounding upland areas suggested that a combination of Radarsatand JERS-1 was the optimal choice for mapping the seasonal habitats within the
floodplain according to a multi-wavelength region-based classification. The tem-
poral mapping achieved an accuracy of approximately 95%. These maps allowed
the determination of the spatial distribution and time of inundation and zonation of
different vegetated areas in the floodplain. Grass-like semi-aquatic vegetation, tree-
like semi-aquatic plants, and some floodplain shrub-like trees colonize regions flooded
for at least 300 days year21. Secondary settlers such as well-developed floodplain
forest, colonize regions flooded for approximately 150 days year21. Floodplainclimax forest colonizes regions flooded for approximately 60 days year21. The
zonation according to the time of flooding is a result of the different adaptations of
the vegetation for tolerating flood stress (Junk and Piedade 1997).
The method presented here, based on multi-temporal C and L band SAR
imagery, can provide quantitative information on spatial distribution and time of
Mapping vegetation communities 1833
inundation and vegetation cover of the Amazon floodplain. This method can be
further applied to the scale of the whole Amazon basin and used to further under-
stand vegetation adaptations to flood conditions, to map preferential habitats for
fish and human use of the floodplain, and to further elaborate the carbon budget of
the floodplain.
Acknowledgments
The Canadian International Space Agency and Fundacao de Amparo a
Pesquisa do Estado de Sao Paulo funded this research. Financial contributions for
field campaigns from Dr John Melack under the LBA project is also acknowledged.
CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nıvel Superior), Brazil,
provided the graduate scholarship to the author. The National Space Agency of
Japan and the Canadian Space Agency provided JERS-1 and Radarsat imagery,
respectively. The support of several colleagues and friends during fieldwork is also
acknowledged, especially Dr Novo, Dr Mantovani, and Gilson.
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