Remote Sensing of Environment 87 (2003) 404 – 428
www.elsevier.com/locate/rse
Dual-season mapping of wetland inundation and vegetation for
the central Amazon basin
Laura L. Hess a,*, John M. Melack a, Evlyn M.L.M. Novo b, Claudio C.F. Barbosa c, Mary Gastil a
a
Institute for Computational Earth System Science, University of California, Santa Barbara, CA 93106, USA
Remote Sensing Division, National Institute for Space Research (INPE), C.P. 515-CEP 12201-970, São José dos Campos, Brazil
c
Image Processing Division, National Institute for Space Research (INPE), C.P. 515-CEP 12201-970, São José dos Campos, Brazil
b
Received 5 November 2002; received in revised form 28 March 2003; accepted 29 April 2003
Abstract
Wetland extent was mapped for the central Amazon region, using mosaicked L-band synthetic aperture radar (SAR) imagery acquired by
the Japanese Earth Resources Satellite-1. For the wetland portion of the 18 8j study area, dual-season radar mosaics were used to map
inundation extent and vegetation under both low-water and high-water conditions at 100-m resolution, producing the first high-resolution
wetlands map for the region. Thematic accuracy of the mapping was assessed using high-resolution digital videography acquired during two
aerial surveys of the Brazilian Amazon. A polygon-based segmentation and clustering was used to delineate wetland extent with an accuracy
of 95%. A pixel-based classifier was used to map wetland vegetation and flooding state based on backscattering coefficients of two-season
class combinations. Producer’s accuracy for flooded and nonflooded forest classes ranged from 78% to 91%, with lower accuracy (63 – 65%)
for flooded herbaceous vegetation. Seventeen percent of the study quadrat was occupied by wetlands, which were 96% inundated at high
water and 26% inundated at low water. Flooded forest constituted nearly 70% of the entire wetland area at high water, but there are large
regional variations in the proportions of wetland habitats. The SAR-based mapping provides a basis for improved estimates of the
contribution of wetlands to biogeochemical and hydrological processes in the Amazon basin, a key question in the Large-Scale Biosphere –
Atmosphere Experiment in Amazônia.
D 2003 Elsevier Inc. All rights reserved.
Keywords: Dual-season mapping; Central Amazon Basin; Vegetation; Wetland mapping; Amazon floodplain; Wetland remote sensing; Wetland inundation;
JERS-1; LBA; Image segmentation
1. Introduction
Riverine floodplains and other wetlands are common
features of the Amazon basin, where they alter flood waves
(Richey, Mertes, et al., 1989), store sediments (Dunne,
Mertes, Meade, Richey, & Forsberg, 1998), and provide
important ecological habitats (Junk, 1997). Measurements in
floodplains have shown the significance of these environments to regional carbon biogeochemistry (Melack & Forsberg, 2001; Richey et al., 1990). For example, outgassing of
CO2 from water to the atmosphere, extrapolated over the
whole basin, is at least 10 times the fluvial export of organic
carbon to the ocean (Richey, Melack, Aufdenkampe, Ballester, & Hess, 2002). The outgassing is fueled primarily by
* Corresponding author. Tel.: +1-805-893-8339; fax: +1-805-8932578.
E-mail address: lola@icess.ucsb.edu (L.L. Hess).
0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2003.04.001
organic carbon from riparian and flooded forests and floating macrophytes and fluctuates seasonally with changes in
inundation. Emission of CH4 fluctuates seasonally as well,
and accurate estimation of methane emissions from Amazonian wetlands requires knowledge of seasonal changes in
vegetation (Devol, Richey, Forsberg, & Martinelli, 1990)
and inundation (Rosenqvist, Forsberg, Pimentel, Rauste, &
Richey, 2002). In addition to biogeochemical applications,
information on the seasonal extent of floodplain habitats is
also required for effective management of Amazon fisheries,
since many key fish species harvested in the Amazon basin
obtain nutrition in flooded forests (Goulding, 1980) or from
organic matter derived from floodplain algae (Forsberg,
Araujo-Lima, Martinelli, Victoria, & Bonassi, 1993).
Mapping of wetlands extent and inundation is thus
essential to the Large-Scale Biosphere – Atmosphere Experiment in Amazônia (LBA), which focuses on understanding
the ecological functioning of the region with particular
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
emphasis on the carbon cycle (Avissar & Nobre, 2002).
Although estimates of wetland area in the Amazon basin
exceed 1 million km2 (Junk, 1997), this value is based on
actual measurements in few places. The quantitative analysis
of inundation and vegetation dynamics of wetlands in the
Amazon basin requires appropriate remotely sensed data,
acquired basin-wide. Optical sensors such as Landsat Thematic Mapper have been used to map wetland vegetation in
the Amazon (Mertes et al., 1995; Novo & Shimabukuro,
1997), but are limited by the fact that vegetation often covers
underlying waters, and clouds or smoke frequently obscure
the ground. Global or continental maps of land cover derived
from coarse-resolution optical data have emphasized nonwetland cover: they have either omitted wetlands as a class
(Hansen, Fries, Townshend, & Sohlberg, 2000; Stone, Schlesinger, Houghton, & Woodwell, 1994) or have significantly
underestimated wetland area (Loveland et al., 2000).
Passive and active microwave sensors, which are much
less influenced by clouds and smoke and can penetrate
vegetation at some wavelengths, have also been employed
for wetlands mapping (Melack & Hess, 1998; Prigent,
Matthews, Aires, & Rossow, 2001; Sippel, Hamilton, Melack, & Choudhury, 1994). Many studies (reviewed by
Lewis, 1998) have successfully used synthetic aperture
radar (SAR) sensors to map inundation and wetlands
vegetation. When specular reflections from an underlying
water surface interact with vegetation via double-bounce or
multiple scattering, SAR backscattering is enhanced (Hess,
Melack, & Simonett, 1990). Although C-HH sensors (Cband, horizontal send polarization, horizontal receive polarization) such as RADARSAT are able to delineate subcanopy inundation in some types of floodplain forests
(Townsend, 2001), the longer wavelength of L-HH sensors
such as the Japanese Earth Resource Satellite-1 (JERS-1)
maximizes canopy penetration and discrimination between
flooded and nonflooded forest (Hess, Melack, Filoso, &
Wang, 1995). Flooding in forested wetlands of the Amazon
has been successfully mapped using L-HH imagery from
JERS-1 (Melack & Wang, 1998; Rosenqvist et al., 2002;
Saatchi, Nelson, Podest, & Holt, 2000) and Shuttle Imaging
Radar-C (SIR-C) (Hess, 1999). The combination of JERS-1
L-band and RADARSAT C-band data is preferable to either
sensor alone for studying biomass and species composition
of aquatic macrophyte communities of Amazon lakes and
reservoirs (Costa, Niemann, Novo, & Ahern, 2002; Novo,
Costa, Mantovani, & Lima, 2002). However, the sites for
which near-simultaneous JERS-1 and RADARSAT data
have been acquired are few.
SAR images of the Amazon basin acquired by JERS-1
during low- and high-water periods (Rosenqvist et al., 2000)
provide the first data set suitable for regional mapping of
Amazon wetlands. We present here our approaches for
mapping wetland vegetation and inundation for the central
Amazon at approximately 100-m resolution, and for validating these products using high-resolution digital videography. In addition to producing the first high-resolution
405
wetlands map for the region, our study addresses general
questions relevant to wetlands mapping and to land cover
mapping of large regions at high resolution, such as:
How useful is image segmentation for delineating the
complex spatial patterns of riverine wetlands, and for
increasing the ease with which they can be distinguished
from spectrally similar nonwetland land cover types?
Is it practical to apply image segmentation to large data
sets having both complex land cover patterns and
significant radiometric uncertainties?
Given the inherent limitations of using a single wavelength and polarization to map a broad range of
vegetation types, how can dual-season data, timed to
high- and low-water periods, be used to best advantage in
a classifier?
In remote, cloud-covered regions, what is an appropriate
validation method for high-resolution maps of dynamic
processes such as flooding?
2. Methods
2.1. SAR mosaics
The Amazon wetlands mapping was carried out using
mosaics of SAR images acquired by JERS-1 as part of the
Global Rain Forest Mapping (GRFM) Project. Begun in
1995, the GRFM project is an ongoing effort led by the
National Space Development Agency of Japan (NASDA) to
produce spatially and temporally contiguous SAR data sets
over Earth’s tropical regions (Rosenqvist et al., 2000). Since
the primary scientific objective for the Amazon basin acquisitions was to determine seasonal patterns of inundation, two
basin-wide data sets were acquired, timed to low- and highwater stages of the Amazon River (Freeman, Chapman, &
Siqueira, 2002). The GRFM Amazon data were processed at
NASA’s Alaska SAR Facility and NASDA’s Earth Observation Center into images of normalized radar backscatter (rj),
which were mosaicked and radiometrically calibrated at the
Jet Propulsion Laboratory as part of NASA’s JERS-1 Amazon Multi-Season Mapping Study (Chapman, Siqueira, &
Freeman, 2002; Siqueira et al., 2000).
Characteristics of the GRFM Amazon mosaics are
reviewed by Chapman et al. (2002) and summarized in
Table 1. Pixel dimensions were increased from the origTable 1
Data characteristics of GRFM Amazon mosaics
JERS-1 SAR frequency and polarization
JERS-1 SAR incidence angle range
Mosaic pixel dimensions
Acquisition period, low-water mosaic
Acquisition period, high-water mosaic
Noise equivalent rj
Calibration uncertainty
a
With exceptions noted in text.
L-band (1275 MHz), HH-pol
34 – 43j
3 arcsec (approx. 100 m)
August – September 1995
May – August 1996
20 to 15 dB
F 0.2 dBa
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inal 12.5 m pixel spacing by averaging over 8 8 pixel
windows, and the mosaics were generated in a geographic
format with pixel dimensions of 3 arcsec. This spatial
averaging reduced the mosaics to a size of 1.9 Gbyte per
season and ameliorated the effects of radar speckle. At
the equator, the resulting pixel dimensions are 93 m. In
this study, when classified mosaics were converted to an
equal area projection for calculation of areas, a 100-m
pixel size was used. In addition to the mosaics of rj at
low- and high-water seasons, a texture mosaic was
generated for the low-water season in which each pixel
digital number (DN) is equal to the standard deviation of
texture of its constituent 8 8 window (Freeman et al.,
2002).
Although radiometric calibration succeeded in reducing
range-dependent and processing-dependent uncertainty in
rj to within F 0.2 dB, three additional sources of calibration error in the backscattering mosaics were not
correctable (Chapman et al., 2002). First, some along-track
calibration errors result from a lag time in adjustment of
the instrument’s automatic gain control when passing from
a high-return target such as flooded forest, or a low-return
target such as water, to an intermediate target such as
rainforest (M. Shimada, personal communication). Since
this sequence is common in regions with large rivers, these
radiometric anomalies occur often enough to impact the
accuracy of the Amazon mosaic classification. Second,
because high-resolution terrain data were not available
for the Amazon region at the time of processing, backscatter values could not be corrected for terrain-induced
distortions (see Siqueira, Chapman, & McGarragh, this
issue). Finally, the thermal noise equivalent rj for the
JERS-1 SAR ranges from 20 to 15 dB, compressing
the dynamic range for low-return targets such as open
water and bare ground.
This study focused on a subset of the basin-wide
mosaics, extending 18j in longitude by 8j in latitude
and corresponding to the central Amazon region (Fig. 1).
With an area of 1.77 million km2, the study quadrat covers
about one third of the lowland Amazon basin (defined as
areas less than 500 m in elevation), and includes the main
stem of the Amazon River from slightly downstream of the
Napo River confluence to slightly downstream of the
Tapajós River, the Negro River south of the Uaupés River
Fig. 1. Global Rain Forest Mapping Project radar mosaic showing central Amazon study quadrat (black) and boundary of the Amazon basin (white). The
quadrat extends from 72jW, 0jN to 54jW, 8jS.
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
confluence, and the lower reaches of the Tapajós, Trombetas, Madeira, Purus, Japurá, Juruá, and Icßá rivers. The
quadrat received complete coverage during the low-water
imaging; at high-water stage, scenes covering approximately 12,000 km2 were not imaged. For the quadrat area, the
co-registered mosaics were warped to video-based and
map-based ground control points using Delaunay triangulation, to correct geolocation errors found in the original
mosaic products.
The inundation patterns imaged on the GRFM mosaics
must be considered in relation to long-term river stage
records. Timing of the annual flood wave varies little, but
the wave amplitude is affected by regional climatic factors
such as ENSO events (Richey, Nobre, & Deser, 1989). For
the main stem floodplain near Manaus, the high-water
mosaic images were acquired at river stages approx. 20 cm
higher than the long-term mean high-water stage and the
low-water mosaic images were acquired at river levels about
3.5 m below mean low-water stage. The recurrence interval
for river stage as low as that captured on the JERS-1 lowwater mosaic is 10 years. Thus, the GRFM mosaics represent
average high-water conditions, and lower than average lowwater conditions, for the central main stem floodplain.
Timing of the mosaics relative to inundation periodicity for
the entire study area is analyzed in Section 4.
2.2. Videographic surveys
Four aerial videographic surveys were carried out in
order to acquire data sets suitable for training of classification algorithms and assessment of classification accuracy.
These Validation Overflights for Amazon Mosaics
(VOAM) surveys, described in detail by Hess et al.
(2002), made it possible to obtain ground documentation
of temporally dynamic inundation status over a large,
remote region with persistent cloud cover. The initial
surveys, VOAM95 and VOAM96, were flown during the
GRFM imaging periods for the central Amazon and were
limited to areas within 600 km of Manaus. Video data were
acquired by handheld analog camcorder aimed obliquely
from light aircraft, at flight altitudes of 300– 500 m. Handrecorded global positioning system (GPS) coordinates and
times allowed rough geolocation by comparison with the
time display on the recorded videotape. Acquired nearly
simultaneously with the JERS-1 mosaic scenes covering the
survey flight lines, the 25 h of VOAM95 and VOAM96
data provide a permanent record of the floodplain conditions captured by the GRFM Amazon mosaics, and were
used primarily for locating training sites for classification
algorithms.
In order to expand the ground data set to a more
extensive region and to improve the geolocation accuracy
and resolution of the video data, follow-up surveys were
flown in 1997 and 1999 from an aircraft operated by
Brazil’s Instituto Nacional de Pesquisas Espaciais (INPE),
timed to correspond in season with the previous surveys and
407
with the GRFM mosaics. The video systems used for these
surveys, developed in collaboration with JPL, INPE, and the
University of Massachusetts, Amherst (UMass), used digital
camcorders and a Horita time code generator to encode GPS
location on the audio track of the videotapes. The VOAM99
system, operated by UMass, incorporated further features
including real-time differentially corrected GPS, an attitude
reference system, a laser altimeter, and a tandem wide-angle
and zoom camcorder setup.
Automated video mosaicking and geolocation software
developed by UMass allowed efficient processing of the
VOAM97 and VOAM99 data sets. After transferring the
video data to computer disk using a digital video capture
card, individual frames were extracted, automatically labeled with GPS timecode, and mosaicked into georeferenced images using auxiliary flight log data. This
allowed precise location of training and test samples on
the GRFM mosaics.
The suitability of the VOAM data sets for validation
depends on their correspondence to GRFM mosaic conditions. The timing of the GRFM and video mosaics relative
to Amazon river stage levels is shown in Fig. 2. Because the
VOAM95 and VOAM96 surveys were carried out during
the JERS-1 imaging periods, floodplain inundation conditions recorded during those surveys correspond to those on
the mosaics. Stage levels during the VOAM99 survey were
0 –1 m higher than during the GRFM high-water imaging,
and those during the VOAM97 survey were 1 –2 m higher
than low-water GRFM levels. Given the 13-m difference in
Amazon River stage between the low- and high-water
mosaics, the VOAM97 and VOAM99 surveys adequately
represent water levels on the GRFM mosaics for the central
Amazon.
Each validation sample comprised a 100 100-m area
on a VOAM97 or VOAM99 video mosaic (Fig. 3), and the
accompanying video clips. Center points of these 1-ha
samples were selected by random sampling of flight times
along flight tracks within the study quadrat. Three constraints were placed on the sample selection: samples could
not be located within 10 flight seconds of one another, to
avoid overlapping samples; a maximum of two samples was
allowed within each cell of a 5 5-km grid superimposed
on the mosaics, to avoid oversampling areas with multiple
flight lines; and areas used for classifier training were
excluded from consideration. Positioning of sample center
points on the GRFM mosaics incorporated uncertainty from
both the GRFM mosaics and the video mosaics. Geolocation certainty was estimated to be F 150 m for VOAM97
and F 10 m for VOAM99 along the center third of the
swath.
2.3. Creation of wetlands mask
The first step in the wetlands mapping was creation of a
wetlands mask, a binary classification denoting wetland
and nonwetland areas. For this study, wetlands are defined
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Fig. 2. Timing of GRFM mosaic acquisitions and VOAM surveys relative to long-term stage records for the Amazon River at Manacapuru (60.6jW, 3.3jS).
Manacapuru data courtesy of Agência Nacional de Energia Elétrica. Long-term maximum and minimum calculated from Manaus stage levels given by Smith
(1981) converted to Manacapuru levels using relationship given by Richey, Mertes, et al. (1989).
as (1) areas that were inundated during either or both
JERS-1 mosaic acquisition periods and (2) areas not
flooded on either date, but which are adjacent to or
surrounded by flooded areas and which display landforms
consistent with wetland geomorphology. For the central
Amazon, the high-water mosaic was acquired concurrent
with main stem stage levels 0 – 1 m higher than long-term
mean high water stage, and it can reasonably be assumed
that nearly all wetland areas were in fact inundated. Most
of the mapped wetlands thus fell under the objective
criterion of condition 1. Condition 2, which requires
human interpretation, allowed for the inclusion as wetlands
of features such as high levees, which may not be
inundated every year. This prevented the labeling as nonwetland of features that are situated on and genetically
related to a floodplain, while allowing upland (terra firme)
outliers that are surrounded by floodplain to be correctly
labeled as nonwetland.
Creation of a wetlands mask accomplished two goals:
calculation of total wetland area and spatial distribution,
and elimination from the classification process of nonwetland areas with backscattering statistics similar to those of
wetlands. Nonwetland targets with low L-HH returns such
as deforested areas, dry savannas, and northwest-facing
hillslopes are not spectrally distinct from wetland targets
such as open water or exposed lake beds, and the bright
returns from urban areas and southeast-facing hillslopes are
nearly equivalent to those from flooded forests. Although
such features cannot be discriminated based on the back-
scattering coefficients of individual pixels, they are recognizable to a human interpreter by their spatial patterns.
Because of the large size of the quadrat (21,600 9600
pixels), delineating these spatial patterns by hand was not
feasible. A hybrid machine and human-interpreted approach
was therefore employed, in which homogeneous regions
were created using an automated image segmentation and
polygon clustering, with the resulting polygons edited by
human interpreters.
Although classification of images into land cover units
is perhaps the most common remote sensing task, classification procedures are typically based on the properties of
individual pixels, the boundaries of which are imposed
during data processing and lack an intrinsic relationship to
the boundaries of landscape elements. Pixel-based classifications can be problematic for ecological applications
because low statistical separability of classes results in low
accuracy or the use of very generalized classes, and the
spatial distributions of the classes are not depicted with
high accuracy (Lobo, 1997). An alternative approach is to
segment the image into more or less homogeneous regions
which become the objects to be classified, based on
aggregate statistics of the pixels within the region. Shimabukuro, Duarte, Mello, and Moreira (1999) concluded
that the complex spatial patterns of ‘‘fish-bone’’ deforestation in the Brazilian Amazon could be accurately
mapped by a procedure of segmentation of the shade
fraction of Landsat data, followed by a polygon-based
classification. Image segmentation is of particular interest
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
409
Fig. 3. Digital videography used for validation. (a) Flightlines for VOAM97 (timed to low-water stage; shown in yellow) and VOAM99 (high-water stage; red)
digital video surveys. (b) Mosaicked low-water video segment for várzea (white floodplain) site near Parintins and (c) same segment at high-water stage. (d)
Enlargement of blue inset from (c), showing projected ground track of laser altimeter (red) and 100-m UTM grid (white). A 1-ha validation sample (black
square) is centered on randomly selected point along flightline (marked by X). This sample had mixed cover of flooded forest and open water at high water
stage. Smooth green patches at lower left of inset are floating grasses at high water, grass and bare soil at low water.
for classification of SAR images because of the need to
reduce the noise-like effects of radar speckle (Caves,
Quegan, & White, 1998; Dong, Milne, & Forster, 2001).
Costa et al. (2002) used image segmentation to map
Amazon floodplain communities with RADARSAT and
JERS-1 data, and Sgrenzaroli, Grandi, Eva, and Achard
(2002) found that wavelet segmentation yielded better
results than pixel-based clustering for mapping spatially
fragmented forest landscapes at three Amazon sites with
JERS-1 imagery.
For the GRFM Amazon mosaics, the speckle problem
had already been reduced by the multilooking carried out
when generating the mosaics, but the non-uniqueness of
wetland backscattering coefficients combined with the distinctiveness of wetland spatial patterns made segmentation
and polygon-based classification a promising method for
accurate wetlands delineation. The wetlands delineation
procedure, described in detail by Barbosa, Hess, Melack,
and Novo (2000), was performed using the Sistema de
Processamento de Informacßões Georeferenciada (SPRING),
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
a freeware geographical information and image processing
system with an object-oriented data model allowing the
integration of raster and vector data formats in a single
environment (Câmara, Souza, Freitas, & Garrido, 1996).
The steps of the procedure are:
1. Region-growing segmentation and attribute extraction. The SPRING segmentation uses a region-growing
process in which regions are grown from individual
pixels and iteratively merged according to similarity and
minimum area thresholds set by the user (Bins, Fonseca,
Erthal, et al., 1996). If the mean difference in DNs
between adjacent pixels or sets of pixels is less than
the similarity threshold, the pixels belong to the same
region. Preliminary tests evaluated the use of a difference
image between the low- and high-water mosaics as input
to the segmentation. While the difference image was
excellent for separating static features such as hillslopes,
urban areas, and clearings from seasonally changing
wetland areas, wetlands with static signatures (e.g., permanently flooded forests and river channels) were misidentified as nonwetlands. The segmentation was
therefore carried out on the co-registered high- and
low-water mosaics. Both dates were input to the segmentation, to allow for the possibility of inundation seasonality contrary to the general regional pattern and to allow
at least partial delineation in the small regions lacking
high-water data. Because of the large data volume, the
study quadrat was divided into six 4j 6j tiles for
segmentation. Following segmentation, the SPRING software calculates statistical attributes (mean and variance
for each region), generating a list of regions and attributes, sorted by region size.
2. Unsupervised classification and merging of classes.
An iterative clustering algorithm based on Mahalanobis
distance (Bins, Erthal, & Fonseca, 1993) is applied to the
region and attribute list. A user-defined percentage acceptance threshold controls the total number of classes. Following this, the user labels the resulting classes as either
wetland or nonwetland, generating a preliminary wetlands
mask.
3. Editing. The preliminary mask is superimposed on the
original mosaics and edited to (1) remove features such as
hillslopes and upland clearings from the wetlands class; (2)
outline low-contrast features such as small floodplains that
have been omitted from the wetlands class; and (3) verify
that small ‘‘outliers’’ of nonwetland within larger wetland
features have been correctly classified.
For segmentation, the mosaic DN values were transformed to decibel scale, so that the increment in decibel
between successive DNs was constant throughout the range
of DNs. A similarity threshold of 0.6 dB and area threshold
of 50 pixels (approx. 50 ha) were selected following
preliminary tests to determine parameters that would give
adequate but not excessive delineation of landscape units.
For the six tiles of the study area, segmentation resulted in
43,000 – 78,000 polygons per tile.
2.4. Mapping of vegetation and inundation
Following wetlands delineation, areas within the wetlands mask were classified into ‘‘cover states’’ for both
seasons. The 10 possible cover-state classes consisted of 5
vegetation cover classes, combined with inundation state
(flooded or nonflooded) at the time of imaging. The five
vegetation classes (Table 2) correspond to physiognomic
classes of the National Vegetation Classification Standard
(NVCS; Federal Geographic Data Committee, 1997). This
vegetative-hydrologic classification scheme meets the criteria for a ‘‘functional parameterization’’ of wetlands
(Sahagian & Melack, 1998), with classes suitable for
biogeochemical modeling of wetland functions. For Amazonian wetlands, emission rates for trace gases such as
methane are primarily a function of flooding status and
vegetative cover (Melack & Forsberg, 2001). Since inundation periodicity and vegetation structure are key determinants of fish habitat quality (Henderson & Robertson,
1999), occurrence of economically significant tree species
(Parolin, 2000), and agricultural potential (Gutjahr, 2000),
the classes are also applicable to a variety of resource
management applications.
In contrast to the polygon-based procedure used for
wetlands delineation, cover-state mapping used a parallelpiped classifier applied to the two-season backscattering
coefficients of individual pixels. The change in method
follows from the different spatial scales of the land cover
units targeted during the two phases of the mapping. Wetlands delineation identified landscape units larger than 50
Table 2
Relation of vegetation cover classes to National Vegetation Classification
Standard
Vegetation
cover class
National Vegetation Classification Standard
Nonvegetated
Nonvegetated ( < 1% vegetation cover).
Sparsely vegetated (1 – 10% vegetation cover)
Herbaceous (dominated by nonwoody plants, with < 25%
cover by trees or shrubs; herbaceous cover is usually
z 25% but may be less if herbaceous cover exceeds that
of other life forms)
Shrubland (dominated by shrubs,a with individuals or
clumps not touching to interlocking; shrub cover is
usually z 25% but may be less if shrub cover exceeds
that of other life forms)
Open tree canopy (dominated by trees with crowns not
touching, generally forming 25 – 60% cover, but may be
less if tree cover exceeds that of other life forms)
Closed tree canopy (dominated by trees with interlocking
crowns, generally forming 60 – 100% of crown cover)
Herbaceous
Shrub
Woodland
Forest
Source: National Vegetation Classification Standard, Appendix III (Federal
Geographic Data Committee, 1997).
a
Shrubs are defined as woody plants taller than 0.5 m with multiple
stems and a bushy appearance; trees are defined as generally having a single
main stem and a more or less definite crown. Where it is difficult to
distinguish trees from shrubs based on these characteristics, woody plants
less than 5 m tall are defined as shrubs.
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
pixels (approx. 50 ha), within which all wetland classes
were merged. The cover-state mapping sought to retain as
far as possible the spatial patterns of common floodplain
features such as meander scrolls, which are on the order of
tens to hundreds of meters wide. At the spatial resolution of
the GRFM mosaics, the small minimum area parameter
required to capture such areas during segmentation results
in over-segmentation of larger landscape units and an
excessively large number of polygons for regional-scale
mapping.
It was known from prior work (Hess, 1999) that some
floodplain vegetation types that cannot be discriminated
using a single L-HH image are identifiable by a distinctive
two-date backscattering response resulting from changes in
the amount of canopy emergent above water (including the
case of complete submersion). Furthermore, if an a priori
assumption can be made regarding the directionality of such
change, the total number of possible classes is constrained,
increasing classification accuracy for the remaining classes.
The first phase in developing a classification algorithm was,
therefore, extensive viewing of videography in conjunction
with the co-registered mosaics to attempt to locate any areas
that deviated from the general pattern of higher water levels
during the high-water mosaic imaging relative to the lowwater imaging.
This ‘‘high-water assumption’’ is known to be valid for
seasonally inundated floodplains of the Amazon main stem
and major tributaries (those with watersheds >100,000
km2), for which inundation is driven primarily by regional
precipitation patterns, generating a predictable flood pulse
(Richey, Mertes, et al., 1989). For smaller streams and
interfluvial wetlands, however, inundation levels are influenced to a greater extent by local precipitation and are thus
less predictable. Prance (1979) used the predictability of
inundation to classify forests inundated by regular annual
cycles, and those inundated by irregular rainfall (generally
in the upper reaches of rivers) as distinct types. As described
in a later section, examination of precipitation and river
stage records showed the high-water assumption to be valid
for nearly the entire study area.
Given the assumption that water levels cannot be higher
on the low-water mosaic than on the high-water mosaic, the
number of possible two-date combinations of the 10 coverstate classes can be reduced from 100 to 13. For example,
the transition from forest at low water to flooded forest at
high water is allowed, but the reverse transition is not. The
total number of combinations is further reduced by eliminating those that do not occur because of height restrictions
or ecological constraints. For example, forests are too tall to
transition to open water (though shrubs may), and shrubs do
not transition to macrophytes because canopy shading discourages macrophyte growth (Junk & Piedade, 1993).
Transitions related to human activities during the 8 months
between mosaic acquisitions, such as forest conversion to
pasture, were assumed to be negligible for the wetland
areas, as were transitions involving creation and destruction
411
of floodplain features; land use change, sedimentation, and
bank erosion are important ongoing processes on the floodplain, but cannot be studied adequately at the time and
spatial scale of the GRFM mosaics. For the 13 remaining
two-date cover-state combinations, decision boundaries
were derived by analysis of backscattering response for
training polygons and of values reported in the literature
for these communities from other studies (Costa et al., 2002;
Hess et al., 1995).
3. Results
3.1. Wetlands mask
The wetlands mask for the central Amazon is shown in
Fig. 4a. Seventeen percent of the quadrat, approximately
300,000 km2, is occupied by wetlands. This figure represents the maximum inundatable area, not the area actually
flooded on the high-water mosaics. Because of regional
differences in the timing of inundation, the entire wetland
area would not be flooded on a single date. Moreover, the
highest elevations on floodplains may not flood every
year. Channels and seasonally inundated floodplains of
large rivers are the predominant wetland features in the
quadrat (Fig. 4b), accounting for 51% of the wetland area.
The floodplain of the Solimões– Amazon River constitutes
28% of the total wetland area, and channels and floodplains of eight major tributaries which arise beyond the
bounds of the quadrat (the Icßá, Juruá, Japurá, Purus, Negro,
Branco, Madeira, Trombetas, and Tapajós) occupy a further
23%. The remaining 49% of the wetland area comprises
minor tributaries, interfluvial wetlands, and the Balbina
reservoir.
For the wetlands mask, an appropriate validation sample
size was selected using the tables provided by Ginevan
(1979), which give optimal sample sizes (N) and associated
critical values (X, the maximum number of misclassifications allowed before rejecting the map as inaccurate), based
on the binomial distribution. The optimal level is one that
balances the defined acceptable user’s risk (b, the risk of
accepting an inaccurate map) and producer’s risk (a, the risk
of rejecting an accurate map), for chosen accuracy proportions Q2 (the accuracy level one wishes to reject with
probability of 1 b) and Q1 (the level one wishes to avoid
rejecting with probability a). For a desired 5% risk of
accepting a map that is less than 90% accurate (b = 0.05,
Q2 = 0.90) and a 2.5% risk of rejecting a map that is more
than 95% accurate (a = 0.025, Q1 = 0.95), the optimal sample size N is 356 and X, the critical number of errors for
rejecting the map, is 26.
Nine of the 365 video samples examined could not be
unambiguously labeled. Of the remaining 356 samples, 339
(95.2%) were mapped correctly on the wetlands mask. With
less than 21 misclassified samples, the map therefore is
accepted according to the specified standard of a less than
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Fig. 4. (a) Wetlands mask for the central Amazon; wetlands (white) occupy 17% of the total area. (b) Channels and floodplains of Amazon River main stem and
eight major tributaries (black). Floodplains of large seasonally inundated rivers occupy 51% of the area mapped as wetland.
5% chance that the map is less than 90% accurate. The 17
misclassified samples were evenly divided between errors
of commission (wrongly mapped as wetlands) and errors of
omission (wrongly mapped as nonwetland). Misclassified
samples were examined to determine the source of the
error, and categorized as classification errors, registration
errors, size-based errors, and errors related to apparent
disparities in flooding between video flight and mosaic.
Most errors of commission were nonwetland clearings and
second growth adjacent to rivers or lakes. Registration
errors involved samples within 1.5 pixels of a wetland/
nonwetland boundary, and size-based errors resulted when
a land cover unit smaller than the 50-ha minimum mapping
was identifiable on the videography. While classification
errors identified problem areas that can be targeted in future
revisions of the wetlands masking, registration and sizebased errors are determined by the geolocation accuracy
and resolution of the mosaics, and are not correctable by
mask revision.
3.2. Dual-season backscattering signatures
Fig. 5 shows probability density function (PDF) plots
for backscattering coefficients of pixels from the 10 coverstate classes (excepting nonflooded woodland, for which a
sufficiently large training sample could not be identified)
and for 2 additional vegetation classes of interest (flooded
palms and stands of the aroid Montrichardia arborescens).
Table 3 lists commonly used descriptive terms corresponding to the plotted classes. For all classes except
flooded shrub, PDFs were derived from the high- and
low-water GRFM mosaics based on 112 training polygons,
with 4 to 12 polygons for each class and polygon sizes
ranging from 15 to 100 pixels. For flooded shrub, training
data were derived from SIR-C data obtained in October
1994 (Hess, 1999).
The median, range, and various quantiles were used as
descriptive statistics, since several of the distributions are
non-normal. Comparisons with SIR-C data refer to swaths
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
413
Fig. 5. Probability density functions for backscattering coefficients of training pixels. Distributions are plotted for 9 of the 10 cover-state classes and for 2
distinctive wetland vegetation types common in the study area. Separate plots are shown for backscattering values lower than 10 dB (a) and greater than
10 dB (b); the shrub-nonflooded and woodland-flooded classes extend across both plots.
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Table 3
Common terminology corresponding to cover-state classes and specific
wetland vegetation types
Cover state classes
Equivalent terms (English, Portuguese)
Nonvegetated-flooded
Open water; água, rio, lago, paraná,
igarapé, furo
Aquatic macrophyte, floating meadow,
marsh; macrófitas aquáticas, capim
flutuante, campos inundáveis
Shrub swamp; vegetacßão arbustiva
inundada, campinas inundadas
Woodland, savanna; chavascal, pântano,
savanas inundadas
Floodplain forest, swamp, gallery forest;
mata de várzea, várzea estacional, igapó
(estacional ou permanente), restinga,
floresta inundada, floresta de pântanos
(permanente)
Bare soil, sand bar, mud bank; solo nu,
terreno desnudo, bancos de areia,
margens lamosas
Grassland, pasture; campo, campo limpo,
campos de várzea
Shrubland; vegetacßão arbustiva, campina
Forest; floresta, mata (many subtypes)
Herbaceous-flooded
Shrub-flooded
Woodland-flooded
Forest-flooded
Nonvegetated-nonflooded
Herbaceous-nonflooded
Shrub-nonflooded
Forest-nonflooded
Specific vegetation types
Montrichardia arborescens
Palms-flooded
aningal
Palm swamp; buritizal
Sources: Ayres (1993), Junk and Piedade (1997), Prance (1980).
46.7 and 110.5 (Hess, 1999; Hess et al., 1995). The
distribution for open water areas (nonvegetated-flooded)
reflects the high noise floor of the JERS-1 sensor. The
thermal noise equivalent, ranging from 20 to 15 dB,
contributes to a higher median ( 16.7 dB) and a broader
range than would normally be the case for water; median
SIR-C rjLHH for open water was 22 to 19 dB, depending on the roughness of the water surface.
The PDF for bare soil (nonvegetated-nonflooded) is
bimodal, with peaks at 14.6 dB for dry soil and 11.9
dB for wet soil. Wet soils occurred primarily at the lowest
elevations in lakebeds recently exposed by the very low
water levels of October 1995, or in deep swales of scroll-bar
topography. As shown in Fig. 5a, the bare soil distribution
overlaps that of open water. With a median backscattering
of 12.6 dB, the herbaceous-nonflooded class (dry or
moist ground with green grass, sedges, or forbs) is largely
coincident with the bare soil class. This is expected, since at
the L-band wavelength of 23.5 cm there are no significant
scatterers in either cover type to yield strong returns.
Although in some cases there are abrupt boundaries on
the floodplain between herbaceous cover, dry soil, and wet
soil, these classes also may grade continuously or occur in
complex mosaics; the classes also grade temporally,
depending on length of exposure following inundation.
Nonvegetated-nonflooded and herbaceous-nonflooded were
therefore combined into a single class for cover-state
mapping.
The forest-nonflooded class median of 7.4 dB is
typical of values reported for tropical forest (Rignot, Salas,
& Skole, 1997; Salas, Ducey, Rignot, & Skole, 2002;
Santos, Lacruz, Araujo, & Keil, 2002) and is 2.1 dB lower
than the forest-flooded median of 5.3 dB, owing to
increased double-bounce returns caused by flooding. These
values are similar to those reported by Rosenqvist et al.
(2002) who, in a study of the inundation patterns in the Jaú
river basin using a time series of JERS-1 data, found mean
rjLHH of 7.3 dB for terra firme forest, 7.1 dB for
nonflooded igapó forest (seasonally or permanently flooded
forest of blackwater rivers), and 4.6 dB for flooded igapó
forest. The highest backscattering returns for natural land
cover come from flooded palms (median = 3.1 dB). The
depth of wetland palm canopies is small relative to total tree
height, and stand densities tend to be low, with few interlocking crowns. These characteristics reduce canopy attenuation, allowing ample signal penetration and strong
double-bounce interactions with tree trunks. Because the
distribution for flooded palms overlaps that for flooded nonpalm forest, however, it cannot reliably be distinguished as a
subtype.
Median returns for aquatic macrophytes (herbaceousflooded) are 8.3 dB, a value comparable to the mean
JERS-1 backscattering of 8.8 dB reported by Costa et al.
(2002) for floating meadow stands near Santarém in May
1996. The upper quartile of the distribution for macrophytes
coincides with the lower quartile for nonflooded forest. The
curve shown for herbaceous-flooded in Fig. 5b does not
include stands of the shrubby aroid M. arborescens, a
conspicuous species forming monospecific stands (aninngais) at semi-permanently flooded sites along the central
and lower main-stem floodplain. Because these stands reach
heights of up to 4 m, with semi-woody stems, L-HH
backscattering from Montrichardia is nearly identical to
that from nonflooded forest.
Woodland-flooded (median backscattering = 6.8 dB)
has the largest dynamic range of any class, with nearly 7
dB separating the 5% quantile ( 11.5 dB) and 95%
quantile ( 4.6 dB). Variability is high because flooded
woodland sites encompass a wide range of stand densities,
have macrophyte understories varying from dense to absent, include flood-deciduous tree species, and include
both seasonally flooded and semi-permanently to permanently flooded regimes. The remaining two classes, nonflooded and flooded shrub, coincide spectrally with other
classes described above: shrub-nonflooded (median = 8.8
dB) with herbaceous-flooded, and shrub-flooded (median =
4.5 dB) with forest-flooded, woodland-flooded, and
palm-flooded.
From Fig. 5, it is clear that the 10 cover-state classes
cannot be distinguished solely on the basis of the single-date
backscattering coefficient. Other studies have found multiscale texture measures useful for mapping land cover types
from JERS-1 data (Podest & Saatchi, 2002). Texture values
from the low-water texture mosaic (Freeman et al., 2002)
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
were extracted for the same training areas, to test whether
that texture measure increased the ability to discriminate
among cover-state classes. For the classes and scale used in
the current study, the standard deviation of texture mosaic
did not aid in class discrimination.
3.3. Classification into cover states
Fig. 6 shows the decision boundaries for mapping the
two-date vector for each pixel into 1 of the 13 classes. The
principal reasons for using a simple parallel-piped-type
classifier are that (a) only two decision variables are
available (the two dates); (b) classifiers assuming normal
or other distribution functions are not suitable; (c) the
decision boundaries are transparent, without any ‘‘blackbox’’ components that limit the ability to test it on other data
sets; and (d) the classifier can easily be modified, since
modification of a decision boundary affects only the adjacent classes, not the entire classification.
The collapsing of the transition matrix from 100 to 13
classes allows the identification of some classes that cannot
be discriminated using a single date. For example, the
backscattering distribution for shrub-flooded is similar to
that of forest-flooded (Fig. 5b). However, because forest
trees are assumed to be too tall to be completely submerged
at high water, there is no class transitioning from open water
415
to flooded forest; the decision space for pixels with very low
returns at high water, followed by very high returns at low
water, is occupied only by the nonvegetated-flooded/shrubflooded class (Fig. 6).
On both dates, pixels with rjLHH < 14 dB are classified as nonvegetated-flooded (open water). The herbaceousflooded class covers a broader range at high water ( 7.8 to
14 dB) than at low water ( 8.5 to 11 dB). The upper
limit is higher during high water, since floating grasses such
as Hymenachne amplexicaulis, Echinochloa polystachya,
and Paspalum repens are close to peak growth then (Costa
et al., 2002). At high water, the lower range has been
extended below what is shown in Fig. 5b, to include areas
with sparse macrophyte canopies, patches of macrophytes
interspersed with open water, or macrophyte species such as
Oryza perennis, which were observed to have begun senescing at the time of the high water acquisition. A boundary
of 6.5 dB separates flooded from nonflooded forest. The
class at the upper left of the diagram, which transitions from
very high to very low returns, is a mixed class that does not
correspond to any training category. It occurs along the
borders of waterways mainly in a few areas of the mosaic
where there is local misregistration between the high- and
low-water scenes.
Fig. 7 shows the classified high- and low-water
mosaics. Two hundred thousand square kilometers, two-
Fig. 6. Decision rules for cover-state classification. Pixels are mapped into 1 of 13 dual-season cover-state classes, labeled in figure as ‘‘high-water class’’/
‘‘low-water class’’.
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Fig. 7. Mapping of wetlands vegetation and inundation at (a) low-water stage (September – October 1995), and (b) high-water stage (May – June 1996).
Floodplain reaches pictured in Fig. 8 are shown in red.
thirds of the total wetland area, is forested (Table 4); of
this, the flooded portion decreases from 72% at high water
to 22% at low water. Woodland, occupying 8% of the
wetland area, does not change in extent or in flooding
status between high and low water, because the classifier
maps semi-permanently to permanently flooded woodland;
only the depth of flooding changes between seasons. The
third woody category, shrub, constitutes 3% of the wetland
area; one-third of the shrub area is completely submerged
at high water, and the remainder is flooded but emergent.
Fourteen percent of the wetland area is open water at highwater stage, decreasing to 12% at low water. Most of the
2% difference results from the transition to bare ground or
to nonflooded herbaceous vegetation in shallow lakes. The
8% of the wetland area occupied by aquatic macrophytes
at high-water stage drops to 5% at low water; some areas
remain as macrophyte, while some transition to bare
ground, nonflooded herbaceous, or open water. Consider-
ing all cover types together, 80.5% of the wetland area is
flooded in the high-water mosaic, decreasing to 39% in the
low water mosaic. Of the area flooded at low water, 30%
is open water, 36% is flooded forest, and 20% is flooded
woodland. Considering the entire study area of 1.77
million km2, flooded area decreases from 14% to 7%
between high- and low-water seasons.
3.4. Accuracy of cover-state mapping
Video-based accuracy assessment of the low-water and
high-water cover-state maps omitted the shrub-nonflooded
and shrub-flooded classes, owing to insufficient survey
coverage of these cover types. The mixed class, an artifact
of mosaic generation, was also omitted. For the remaining
five high-water and six low-water classes, a precision of
5% was targeted at the 90% confidence level. The sample
sizes required for these error estimates were calculated
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Table 4
Areas of wetland cover states for central Amazon quadrat as mapped from
high- and low-water JERS-1 mosaics
Nonvegetated-flooded
Nonvegetated or
herbaceous-nonflooded
Herbaceous-flooded
Shrub-nonflooded
Shrub-flooded
Woodland-flooded
Forest-nonflooded
Forest-flooded
Mixed
Total
Area (103 km2)
% of wetland area
High
Low
High
Low
43
0
35
14
14
0
12
5
26
0
6
24
59
144
1
303
16
10
0
24
160
43
1
303
8
0
2
8
20
48
0
100
5
3
0
8
53
14
0
100
based on the multinomial distribution. Following the
formulation of Congalton and Green (1999), for a given
number of classes k, desired precision b, and desired
confidence level 1 a, the required sample size n is
given by
n ¼ BPð1 PÞ=b2 ;
ð1Þ
where B is equal to the upper (a/k) 100th percentile of
the v2 distribution with 1 degree of freedom and Pi is
the proportion of the population in the ith category, for
i = 1,. . .,k. P was set to equal 0.5, since both mosaics
had classes covering nearly 50% of the wetland area.
Eq. (1) thus simplifies to n = B/4b2 (Congalton & Green,
417
1999). The required sample sizes for error matrices are
542 for the high-water map and 573 for the low-water
map. Producer’s and user’s accuracies calculated from
error matrices are suitable measures of thematic map
accuracy only if the set of test pixels represents true
class proportions in the scene (Richards, 1996). In this
study, it was assumed that the mapped class proportions
were representative of actual class proportions. Randomly selected video samples were therefore labeled and
accumulated until the target proportion for each class
was achieved. Approximately 30% of the 100 100-m
video samples contained two or more classes. Samples
visually estimated not to have at least two-thirds coverage by a single class were not used in the accuracy
assessment; this represented about 16% of the initial
sample set.
Accuracy results are given in Tables 5 and 6. Since the
shrub categories are not included as test samples, errors of
commission into one of the shrub classes are included with
the mixed class in the error matrix. As would be expected
for an L-band SAR, and based on the backscattering
distributions shown in Fig. 5, accuracies are high (greater
than 90%) for open water and low (54 – 84%) for herbaceous
and woodland categories. Producer’s accuracies for the
forest and flooded forest classes were 78% and 87%,
respectively, at high water, and 88% and 81% at low water.
User’s accuracies showed a greater disparity between the
two forest classes, as the relative proportions of nonflooded
and flooded forest shifted between high and low water.
Although errors for the forest classes were mostly the result
of confusion between flooded and nonflooded states, addi-
Table 5
Error matrix, producer’s accuracy, and user’s accuracy, high-water classification
Validation sample label
Map label
Nonvegetated-flooded
Nonvegetated +
herbaceousnonflooded
Herbaceous-flooded
Woodland-flooded
Forest-nonflooded
Forest-flooded
Mixed
Nonvegetated-flooded
Nonvegetated +
herbaceousnonflooded
Herbaceous-flooded
Woodland-flooded
Forest-nonflooded
Forest-flooded
Mixed
Total
Nonvegetatedflooded
Nonvegetatednonflooded
72
0
0
0
2
0
2
0
0
0
0
0
0
0
76
0
3
1
0
3
0
79
0
0
0
0
0
0
30
3
0
6
5
46
2
24
5
10
0
43
5
3
85
16
0
109
2
6
22
230
5
265
0
0
0
0
0
0
42
37
112
265
10
542
Producer’s accuracy
User’s accuracy
91.1
NA
94.7
NA
65.2
55.8
78.0
86.8
NA
71.4
64.9
75.9
86.8
NA
Herbaceousflooded
Woodlandflooded
Forestnonflooded
Forestflooded
Mixed
Total
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Table 6
Error matrix, producer’s accuracy, and user’s accuracy, low-water classification
Validation sample label
Map label
Nonvegetated-flooded
Nonvegetated +
herbaceousnonflooded
Herbaceous-flooded
Woodland-flooded
Forest-nonflooded
Forest-flooded
Mixed
Nonvegetatedflooded
Nonvegetatednonflooded
Nonvegetated-flooded
Nonvegetated +
herbaceousnonflooded
Herbaceous-flooded
Woodland-flooded
Forest-nonflooded
Forest-flooded
Mixed
Total
62
3
3
22
2
3
0
0
0
0
0
0
0
0
67
28
2
0
0
2
0
69
3
1
0
0
4
34
25
0
10
0
0
40
0
25
10
11
0
46
4
0
268
26
6
304
0
4
11
65
0
80
0
0
0
0
0
0
34
30
299
104
10
573
Producer’s accuracy
User’s accuracy
89.9
64.5
92.5
78.5
62.5
54.3
88.2
81.3
NA
73.1
83.5
89.6
62.5
NA
tional errors were caused by confusion with aquatic macrophytes (mostly M. arborescens), woodland, shrub (especially for deeply flooded stands), and open water. Confusion
with open water occurred where video samples fell on
floodplain channels 100 –200 m wide, bordered by forest.
As with the wetlands mask assessment, error sources
include classification error, registration error, size-based
error, and error related to apparent disparities between
video and mosaic. All types of errors were higher for
the cover-state classification than for the wetlands mask,
owing to the higher number of classes and the finer spatial
scale of the mapped features. In addition, the wetlands
masking involved human interpretation that allowed spatial
pattern and context to inform the classification, while the
cover-state mapping was based only on backscattering
characteristics.
For some applications, knowledge of flooding status
alone is required. When vegetation types are grouped
together to create only flooded and nonflooded classes,
user’s accuracy at high water is 94% for the flooded
class and 76% for the nonflooded class. At low water,
user’s accuracy is 84% for flooded and 89% for
nonflooded.
4. Discussion
4.1. Mapping methodology
Although L-HH SAR is the best single-band sensor for
mapping central Amazon wetlands, the backscattering
Herbaceousflooded
Woodlandflooded
Forestnonflooded
Forestflooded
Mixed
Total
response from nonflooded forest—the most common nonwetland cover—overlaps those of common wetland vegetation such as forest and aquatic macrophyte. The
difficulty of class discrimination is compounded by residual radiometric anomalies in the mosaics. A classification
based on backscattering alone would thus result in misclassified flooded forest or macrophyte pixels scattered
throughout the surrounding upland, and blocks of misclassified pixels related to radiometric artifacts. The polygons created by an image segmentation, however, can be
correctly assigned as wetland or nonwetland based on the
combination of backscattering response, spatial pattern,
and context. The process is largely automated, but correct
assignment during the editing phase requires a trained
human interpreter knowledgeable about the region’s vegetation, landforms, and patterns of human disturbance.
Although the editing phase is labor-intensive, for this
study the time and labor expenditure was justified since
it yielded a highly accurate product that will be used for
multiple applications and which does not need to be
updated frequently. The complex spatial patterns and
radiometric artifacts present in the data set contributed to
large numbers of polygons per segmented tile, but these
were readily processed on an ordinary desktop computer.
The wetlands delineation thus demonstrates the practicality
of applying image segmentation to moderately high-resolution data sets covering areas on the order of millions of
square kilometers.
The masking out of nonwetland areas eliminated a
large potential source of misclassification error, increasing the accuracy of the pixel-based classifier applied to
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
419
Fig. 8. Mapped classes at three 110 90-km sites on the Solimões – Amazonas River; locations shown in Fig. 7, class statistics in Table 7. (a) Mamirauá, near
Tefé, upper floodplain; (b) Cabaliana, near Manacapuru, middle floodplain; (c) Curuaı́, near Óbidos, lower floodplain. Red lines on high-water scenes mark
floodplain boundaries. Features identified on low-water scenes are Solimões (1) and Japurá (2) rivers and Cabaliana (3), Padre (4), Manacapuru (5), and Curuaı́
lakes (6).
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
the wetlands. Use of decision rules that incorporated
knowledge of the wetland habitats at high- and lowwater stages allowed the delineation of some vegetation
types that could not be distinguished on a single date,
such as shrubs. (Nonflooded and flooded shrubs were
omitted during accuracy assessment due to insufficient
validation samples, but accuracies based on training data
were greater than 80% for both classes.) The primary
advantage of using a dual-season classifier that incorporated an assumed directionality of water level change
was that it constrained the number of possible classes
and precluded the mapping of pixels into illogical twodate combinations.
Validation of land-cover classifications in remote,
cloudy regions is challenging. Airborne digital videography—flown much of the time below cloud cover—made
it possible to validate the wetlands delineation and
habitat mapping using independent, randomly selected
samples acquired across the region. Random sampling
ensured that heterogeneous and edge areas were included
in the validation, giving a more realistic result than with
subjectively chosen samples which tend to be biased
toward homogeneous patches. The high resolution of
the video samples permitted direct visual interpretation,
avoiding the biases introduced when maps or classified
satellite images, with unknown accuracies, are used for
validation.
4.2. Regional patterns of floodplain habitats
While Table 4 provides a regional average of seasonal
changes in wetland habitats, conditions at specific sites
vary considerably. We therefore present classified images
for three representative reaches along the Amazon floodplain (Fig. 8), and accompanying statistics on the proportions of the wetland cover types for each reach (Table 7).
Reach statistics are for the Amazon floodplain only (delineated on Fig. 8), and exclude tributary floodplains and
wetlands.
Fig. 8a– c illustrates geomorphic features described by
Mertes, Dunne, and Martinelli (1996) as typical of the
Amazon floodplain in its upstream, middle, and downstream reaches, respectively. Upstream reaches generally
exhibit greater channel sinuosity and higher rates of bank
erosion and channel migration, resulting in a floodplain
surface dominated by alternating meander scrolls and
swales. Lakes and islands tend to be small and short-lived.
Traces of numerous scroll lakes and forested scroll bars are
visible in the Mamirauá scene (Fig. 8a). Moving downstream along the main stem, rates of channel migration are
lower, channel banks are relatively higher and more stable,
and overbank deposition plays a greater role. Along the
middle main stem, the distinct ridge and swale topography
of the upper reaches is increasingly modified by deposition
of fine sediments, forming depressions occupied by larger,
rounder lakes such as those at the eastern end of the
Table 7
Generalized wetland cover states as percentage of floodplain area at highand low-water stages for three Amazon floodplain reaches
Reach
Class
High (%)
Low (%)
Mamirauá (Fig. 8a)
Nonvegetated-flooded
Nonwoody-nonflooded
Nonwoody-flooded
Woody-nonflooded
Woody-flooded
Nonvegetated-flooded
Nonwoody-nonflooded
Nonwoody-flooded
Woody-nonflooded
Woody-flooded
Nonvegetated-flooded
Nonwoody-nonflooded
Nonwoody-flooded
Woody-nonflooded
Woody-flooded
12
0
6
11
71
23
0
11
10
56
56
0
20
5
19
10
3
4
59
24
14
10
8
53
15
46
16
12
17
9
Cabaliana (Fig. 8b)
Curuaı́ (Fig. 8c)
Reach locations are shown in Fig. 7a. Statistics apply to main river
floodplains only (Fig. 8); tributaries are excluded. Nonvegetated-nonflooded and herbaceous-nonflooded cover states are combined here into
nonwoody-nonflooded; herbaceous flooded and mixed are combined into
nonwoody-flooded; shrub, woodland, and forest are combined into woodynonflooded and woody-flooded.
Cabaliana reach (Fig. 8b). As documented by Sippel,
Hamilton, and Melack (1992), downstream reaches are
increasingly dominated by large, dish-shaped floodplain
lakes, which may be formed by local subsidence of the
floodplain (Mertes et al., 1996). Secondary distributary
channels draining into such lakes deposit internal deltas
that ultimately may partition the lake into a series of subbasins (Sternberg, 1960), as can be seen within Curuaı́
Lake (Fig. 8c).
The contrasting geomorphic features of the upper,
middle, and lower main stem are reflected in statistics
showing cover states as a percentage of floodplain area
(Table 7). Open water area, and the decrease in that area
between high and low water, are greater for the middle
and downstream reaches because of extensive lakes such
as Cabaliana, Padre, and sub-basins of Curuaı́. On the
low-water mosaic, mud flats, green grass, and shrubs
were exposed on beds and low-lying internal levees in
these lakes. The relative areas covered by aquatic macrophytes at the three sites parallel the trend for open water,
with higher values along downstream reaches. Favored
habitats for macrophyte growth are channel edges, lake
edges, and non-forested portions of depositional features
within lakes. Conversely to herbaceous vegetation, the
percentage of woody vegetation decreases between Mamirauá and Curuaı́.
In addition to scroll lakes and dish-shaped lakes, ria or
blocked-valley lakes are prominent features of the central
Amazon. These formed when valleys scoured deeply
during periods of lowered sea levels were subsequently
flooded (Irion, 1984). The Manacapuru lake (Fig. 8b)
illustrates the vegetation pattern seen in many ria lakes.
Table 8
Timing and height of river stages observed on GRFM Amazon mosaics, relative to means
Gauging station
Timing (day of year)
Stage height (cm)
High water
Low water
High water
Observationa
Mean
Differenceb
Observationc
Mean
Differenceb
Low water
Observationa
Mean
Differenced
Solimões
Solimões
Solimões
Solimões
Amazonas
162
155
151
147
138
132
164
168
173
149
30
9
17
26
11
308
301
297
293
284
261
285
289
304
311
47
16
8
11
27
1315
1366
1579
1863
743
1323
1346
1574
1844
704
8
20
5
19
39
Major tributaries
6. Ipiranga Velho
7. Vila Bittencourt
8. Acanaui
9. Ipixuna
10. Santos Dumont
11. Gaviao
12. Seringal Fortaleza
13. Labrea
14. Canutama
15. Aruma-Jusante
16. SG da Cachoeira
17. Tapuruquara
18. Barcelos
19. Manaus
20. Caracarai
21. Humaita
22. Manicore
23. Novo Aripuana
24. Borba
25. Cach. Da Porteira
26. Barra S. Manoel
27. Jatoba
28. Itaituba
Ica
Japura
Japura
Jurua
Jurua
Jurua
Purus
Purus
Purus
Purus
Negro
Negro
Negro
Negro
Branco
Madeira
Madeira
Madeira
Madeira
Trombetas
Tapajos
Tapajos
Tapajos
164
164
159
166
160
158
157
153
153
149
160
156
152
146
150
150
147
146
145
141
141
140
138
154
184
188
83
89
110
84
94
106
154
189
186
191
172
196
100
109
111
120
154
74
79
105
10
20
29
83
71
48
73
59
47
5
29
30
39
26
46
50
38
35
25
13
67
61
33
310
310
305
312
306
304
303
299
299
295
306
302
298
292
296
296
293
292
291
287
287
286
284
NAe
27
33
242
265
264
266
270
267
290
52
58
36
303
71
262
269
286
300
344
253
261
306
NAe
82
93
70
41
40
37
29
32
5
111
121
103
11
140
34
24
6
9
57
34
25
22
1326
1145
1300
444
1349
1347
1293
1791
2293
2111
997
738
806
2797
603
1732
2103
2245
1974
1240
619
665
775
1381
1250
1322
1297
2044
1413
2188
2025
2388
2051
1058
728
838
2761
641
2278
2330
2138
2047
995
901
817
807
55
105
22
853
695
66
895
234
95
60
61
10
32
36
38
546
227
107
73
245
282
152
32
Mean
Differenced
603
112
380
567
46
502
446
758
929
176
101
334
378
362
130
880
843
792
512
811
NA
760
457
816
643
665
256
271
1536
140
957
1048
1027
812
210
333
426
173
972
585
756
277
641
146
750
511
905
965
623
315
324
1836
111
1076
1050
966
1098
289
341
419
251
92
258
36
235
170
NA
10
54
89
322
42
59
53
300
29
119
2
61
286
79
8
7
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Mainstem
1. S.P. de Olivenca
2. Tefe
3. Itapeua
4. Manacapuru
5. Obidos
Observationc
Stage data was obtained from ANEEL HidroWeb site (http://hidroweb.aneel.gov.br). Lengths of daily stage records range from 17 to 97 years. Gauge locations are shown in Fig. 9.
a
Day of year or stage height observed by JERS-1 satellite for the high-water (1996) GRFM Amazon mosaic scene in which each river gauge is located.
b
Difference in days between date observed by JERS-1 satellite and mean date of maximum or minimum stage.
c
Day of year or stage height observed by JERS-1 satellite for the low-water (1995) GRFM Amazon mosaic scene in which each river gauge is located.
d
Difference in cm between stage height observed by JERS-1 satellite and mean maximum or minimum stage.
e
Ipiranga Velho hydrograph has two major troughs: 972 cm on day 32, and 1028 cm on day 282.
421
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
Upstream from the lowest reaches of the lake, which
remains year-round as open water, is shrubland which is
completely submerged or barely emergent at high-water
stage; further upstream, this transitions to seasonally
flooded igapó forest.
Junk (1989) related the distribution of plant communities
on the Amazon floodplain to the average annual inundation
period. For sites near Manaus, he found that the inundation
limit for established woody communities was about 9
months for shrubs and 6 months for trees. Hess (1999)
extended this range to include semi-permanently to permanently flooded woodlands, inundated more than 9 months
per year; these communities were found in lake beds and
appeared to be successional. The vegetation patterns seen in
Fig. 8 are consistent with the hypothesis of inundation
duration as the primary determinant of vegetation structure
on the floodplain. Mamirauá, 38% of which remained
flooded on the low water mosaic, has 3.5 times more woody
cover than Curuaı́, 67% of which remained flooded at low
water. Other factors that influence the relative amounts of
woody and nonwoody vegetation include fire frequency,
soil – water deficits during the dry season, and human
activities such as logging, cattle ranching, and farming.
Logging on the central várzea is primarily selective (Albernaz & Ayres, 1999), seldom resulting in conversion to
nonwoody vegetation. The practice of clearing várzea forest
for creation of artificial pasture has grown over the past few
decades (Ohly, 2000), and may have reduced the amount of
várzea forest in areas adjacent to terra firme, such as along
the south shore of Curuaı́ lake, or along levees of the largest
floodplain channels. Within the study quadrat, farming of
crops such as banana, papaya, jute, and manioc occurs
mainly in small plots along high levees, covering a negligible area; these areas are mapped mainly as shrub or forest
on the mosaics.
4.3. Hydrologic assessment of mosaics
To what degree are the GRFM high- and low-water
mosaics representative of inundation extent under average
high- and low-water conditions in various parts of the
study quadrat? Knowledge of the relationship of mosaic
conditions to long-term averages is required for appropriate use of the classified mosaics for biogeochemical
modeling and other applications. We analyzed two types
of long-term hydrologic data. For floodplains of the main
stem and major tributaries (51% of the wetland area),
river stage data provide the best indicator of inundation
extent. For smaller floodplains and interfluvial wetlands,
423
local runoff is most important. Precipitation data were
used to assess whether the seasonality of local runoff was
likely to be consistent with the high- and low-water
mosaic imaging dates. Daily hydrologic data were
obtained from the HidroWeb site of Brazil’s Agência
Nacional de Energia Elétrica (ANEEL), http://hidroweb.
aneel.gov.br.
In Table 8, imaging dates and stage levels observed on
the high- and low-water mosaics are compared with mean
dates and stage levels for maximum and minimum river
stage at 28 gauging stations on the main stem and 9 major
tributaries (tributaries with watersheds >100,000 km2).
Station locations are shown in Fig. 9a. Averaged hydrographs were monomodal for all stations except Ipiranga
Velho on the Icßá. Manaus provides the longest stage
record within the study area. Because of strong backwater
effects on the lower Negro, the Manaus gauge can be
used as a proxy for Amazon River stage levels (Richey,
Nobre, et al., 1989). Based on the Manaus record from
1903 to 1999, the GRFM mosaics imaged a typical
annual maximum stage (36 cm higher than the long-term
mean) and a lower than normal minimum stage (300 cm
lower than the long-term mean). The observed maximum
of 2797 cm was reached in 46 of the past 97 years, 1.7 m
below the record maximum of 2969 cm, recorded in
1953. The recurrence interval for the observed minimum
of 1536 cm is about 10 years, with a range of recurrence
from 1 to 32 years.
Along the Amazon main stem, the annual flood wave
peak takes about 40 days to travel from São Paulo de
Olivencßa to Manacapuru. Because the GRFM imaging
proceeded from east to west, scenes were acquired about
1 month before the average peak high water at Manacapuru and 1 month after the average peak at S.P. de
Olivencß a. Main stem stages observed on the high-water
mosaic were thus similar to average high-water conditions.
On the low-water mosaic, stage levels at Tefé, Itapeua,
and Manacapuru were at least 3 m lower than the average
annual minimum. Levels were 130 cm below the average
minimum at Óbidos, and about one meter above the
average minimum at São Paulo de Olivencßa. The lowwater mosaic is therefore less consistent than the highwater mosaic in its relationship to average conditions for
the main stem.
In Fig. 9b and c, the differences between observed and
mean stage levels are shown geographically. Stations in the
southern and northwestern portions of the study quadrat
show significant deviation from average high- and lowwater conditions. High-water stage was not adequately
Fig. 9. River stage along main stem and major tributaries. (a) Locations of 28 stream gauges for which timing and levels of river stage are presented in Table 8,
excepting station 20 (Caracarai, located on Branco River at 61.1jW, 1.8jN). (b) Difference in cm between stage height recorded on high-water mosaic imaging
date and mean stage at high water (Table 8); positive value indicates that stage level on high-water mosaic imaging date was higher than mean high-water stage
level. (c) Difference in cm between stage height recorded on low-water mosaic imaging date and mean stage at low water; positive value indicates that stage
level on low-water mosaic imaging date was higher than mean low-water stage level.
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
observed for areas south of about 6jS, and low-water
observation dates were problematic in the southwest and
northwest of the study area. The ‘‘high-water assumption’’
of the classification algorithm was valid for all stations
except Ipixuna on the lower Juruá.
Fig. 10 plots average monthly precipitation at 12
stations distributed across the study area and lists mean
statistics for each station. Mean annual precipitation ranges
from 3010 mm in the northwest of the study region to
1833 mm in the north central and 1843 mm in the central
east. Seasonality of precipitation increases from north to
south and from west to east, and a northwest –southeast
gradient is seen in the timing of the wet and dry seasons.
For all stations, the GRFM high-water mosaic was acquired either during the wet season or within 6 weeks of
the end of the wet season. For all stations except station 3,
the low-water GRFM mosaic was acquired toward the end
of the dry season, or early in the transition period from dry
to wet season.
We examined the total monthly precipitation received at
the 12 stations during the 3-month period preceding each
GRFM acquisition in relation to mean precipitation for
those periods. Short-term records at a particular station
must be treated with caution since rainfall events are
episodic and spatial variability of precipitation is high.
For most stations, rainfall during the period preceding the
high-water mosaic imaging was within F 25% of mean
values, and rainfall during the period preceding the lowwater imaging was near or below average. We conclude
that conditions in wetlands influenced mainly by local
precipitation were likely to be similar to those during the
average wet season on the high-water mosaic, and similar
to or drier than those during the average dry season on the
low-water mosaic.
4.4. Extension of the mapping
The approach used here to map central Amazon wetlands—delineation of wetland extent using image segmentation, followed by mapping of vegetation and inundation
based on dual-season backscattering characteristics—can
be applied to other wetlands having similar vegetation
structure and strong seasonality of inundation, provided
that appropriately timed L-HH imagery is available. We
are currently applying segmentation-based masking to the
remainder of lowland Amazonian wetlands, and the dualseason mapping of cover states to the portion of those
wetlands for which the cover-state mapping assumptions
are valid. Regions where the assumptions are not valid or
where wetland landscapes are dominated by vegetation
structures for which the current algorithm performance is
425
poor include tidally inundated areas such as Marajó
Island, areas with large expanses of M. arborescens along
the lower Amazon floodplain, interfluvial white-sand wetlands with low-biomass shrubs and sedges, and wetlands
within large savannas such as Roraima-Rupununi, where
aquatic macrophytes are typically low-biomass and emergent rather than high-biomass and floating. For such
cases, means of improving the accuracy of cover-state
mapping include (a) ‘‘regionalizing’’ the classifier by
modifying decision boundaries in favor of dominant
classes; (b) including additional dates of archived JERS1 imagery or planned acquisitions from the Advanced
Land Observing System (ALOS) L-band SAR, scheduled
for launch in 2004; (c) merging C-band data from sensors
such as RADARSAT or Envisat with the JERS-1 imagery;
and (d) incorporating optical data such as Landsat. C-band
or optical data would be effective particularly for improving classifier performance for the herbaceous, shrub, and
woodland classes.
Beyond the Amazon, GRFM mosaics of JERS-1 images
have been generated for equatorial Africa (Rosenqvist &
Birkett, 2002) and Southeast Asia (Shimada & Isoguchi,
2002). For Southeast Asia, a single mosaic was constructed, primarily from data acquired in January and
February 1997 but including several passes that were
disjunct in time. Use of the Southeast Asia mosaic for
lowland wetlands mapping is complicated by the lack of
dual-season data timed to high- and low-water periods, the
widespread proximity of wetlands to agriculture and human settlements, and by possible differences in absolute
values of rj compared with the other mosaics (suggested
by differences for well-characterized targets such as upland
rainforest Shimada & Isoguchi, 2002). Nevertheless, the
Southeast Asia mosaic provides an important regional data
set with which flooding of major rivers such as the
Mekong and Irrawaddy and large lakes such as Tonle
Sap can be studied.
Evaluating the dual-season GRFM Africa mosaics for
hydrological applications in the Congo river basin, Rosenqvist and Birkett (2002) concluded that the mosaics were
suitable for mapping of flooding extent for some sub-basins,
but that three observations within a year would be required
to adequately capture high- and low-water stages for this
region. Combining the GRFM Africa mosaics with a European Remote Sensing Satellite-1 mosaic, Simard, Grandi,
Saatchi, and Mayaux (2002) found that the merged L-HH
and C-VV data could map woody and herbaceous wetlands
of coastal Gabon. That study also confirmed earlier observations that unlike most seasonally flooded forest, tall
mangrove forest may not show enhanced L-HH backscattering when flooded.
Fig. 10. Mean monthly precipitation (mm) for 12 precipitation gauges within or near the study area. Precipitation data were obtained from ANEEL HidroWeb
site (http://hidroweb.aneel.gov.br). Lengths of records range from 8 to 30 years. Listed below each station name are mean annual rainfall, mean rainfall during
wettest month, mean rainfall during driest month, and mean range between wettest and driest month (all in mm).
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L.L. Hess et al. / Remote Sensing of Environment 87 (2003) 404–428
4.5. Application to biogeochemical modeling
The suitability of the classification approach used in this
study for regional biogeochemical modeling was demonstrated by Richey et al. (2002). In that study, the dual-season
cover-state mapping described here was combined with field
measurements of carbon dioxide concentrations in surface
waters to calculate outgassing of CO2 from water to the
atmosphere for the central Amazon basin. For the main stem
Amazon and its floodplain, seasonal inundation patterns
derived from low-resolution passive microwave data (Sippel, Hamilton, Melack, & Novo, 1998) were combined with
the high-resolution cover-state mapping to extrapolate dualseason flooding patterns throughout the annual cycle. Mean
monthly river stage data from tributaries to the main stem
Amazon were used to extrapolate the temporal flooding
patterns for these rivers.
For the 1.77 million km2 quadrat (Fig. 1), the summation
for all rivers and floodplains results in the region being most
flooded in May with 350,000 km2 inundated, or 20% of the
quadrat. This large inundated area is of importance to
outgassing of methane and carbon dioxide and for models
of energy and water vapor at a regional scale. Extrapolated
over the whole basin, CO2 flux from flooded areas was
found to be of comparable magnitude to some estimates of
carbon sequestration by upland forests. Hence, the overall
carbon budget of intact mature Amazon forests, summed
across terrestrial and aquatic environments, may be closer to
being balanced than would be inferred from studies of
uplands alone. Current work using the wetlands mosaics
as input to regional models of methane emissions will
further document the role of wetlands in the carbon cycle
of the Amazon basin, and contribute a key element to the
LBA goal of understanding how Amazônia functions as a
regional entity.
5. Summary
Using the Global Rain Forest Mapping Project dualseason mosaics of JERS-1 SAR data, we have mapped
wetland habitats at 100 m resolution for an 18 8j quadrat
of the central Amazon region. Wetlands were defined for
this purpose as inundatable areas. A mask showing wetland
extent was created by a procedure of image segmentation
and clustering, followed by human interpretation and
editing of clustered polygons. Within the wetlands portion
of the study area, the radar mosaics were classified into
two-season combinations of vegetative-hydrologic cover
states on the basis of pixel backscattering coefficients.
The dual-season approach automatically generated classifications of both the high- and low-water mosaics, without
illogical cover-state transitions between the two dates. The
maps were validated using high-resolution, geocoded digital videography collected during aerial surveys at highand low-water periods. Thematic accuracy of the wetlands
mask was estimated to be 95%. For the open water,
nonflooded forest, and flooded forest cover-state classes,
producer’s accuracies were good (78 – 91%) at both highand low-water stages. Producer’s accuracies were low
(about 65%) for aquatic macrophytes and very low (about
55%) for flooded woodland, both of which have L-HH
backscattering responses partly coinciding with those of
forest and flooded forest. The results show that by using a
hybrid machine- and human-interpreted procedure, image
segmentation can be used to accurately map large regions
at high resolution. The mapping approach presented has
general applicability to seasonally inundated tropical wetlands. C-band or combined C- and L-band would yield
higher accuracies for wetlands with primarily herbaceous
vegetation.
Seventeen percent of the 1.77 million km2 study quadrat
is occupied by wetlands, which were 96% inundated at high
water and 26% inundated at low water. About half of the
wetland area was accounted for by floodplains and channels
of the Amazon main stem and major tributaries. Flooded
forest constituted nearly 70% of the overall wetland area,
but proportions of the wetland habitats showed large regional variations related to floodplain geomorphology and
between whitewater and blackwater environments. Analysis
of historic river stage and precipitation data shows that the
GRFM Amazon mosaics depict average high-water conditions, and below-average low-water conditions, for most of
the study area. The mapped classes, which are based on
vegetation physiognomy and flooding status, can be used in
conjunction with field measurements for regional estimation
of trace gas emissions and are also suitable for applications
such as fisheries management.
Acknowledgements
We thank Dana Slaymaker, Ake Rosenqvist, Bruce
Chapman, Andre Monteiro, and Ryan Ashker for their
contributions in acquiring and processing the radar and
videographic data sets. Support was provided by NASA
grants to the GRFM-JAMMS project at UCSB and to LBAECO investigation LC-07. Acquisition of the JERS-1
imagery was made possible by NASDA’s Global Rain
Forest Mapping Project.
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