Use of SAR satellites for mapping zonation of vegetation
communities in the Amazon floodplain
M. P. F. COSTA
National Insti...
currently the most suitable systems to study the Amazon floodplain because of their
all-weather functionality and their ind...
included structural characterization and photographic surveying (hand-held 35 mm
camera) of floodplain forest, aquatic and ...
and rising water stages. These materials were used as a reference for characteriza-
tion and identification of major habita...
Table 1. Characteristics of the remotely sensed data.
Satellite Acquisition Incidence angle (‡) Coverage (km) Swath mode* ...
more regions were built. However, more regions were not synonymous with better
results for the classification. In SAR image...
3. Analysis and results
3.1. Temporal backscattering variability of different vegetation communities
The multi-temporal me...
Table 3. Number of training and test regions.
Classified images
Number of samples
Aquatic
vegetation
Floodplain
flooded fore...
plants), 3 dB (floodplain flooded forest), 2.4 dB (pasture), 1.5 dB (savanna), and
1 dB (upland forest). Nonetheless, for bo...
Table 5. General discription of some important vegetation found in the study site (adapted from Dobson et al. 1996).
Groun...
mechanisms. Figure 5(b) illustrates these mechanisms, which were a function of both
the sensor characteristics and the pla...
are speculating that the vertically polarized radiation was strongly attenuated by
vertically oriented rice plants, while ...
Again, the slightly increased values were due to the higher moisture content of the
surface during the wet season. In gene...
the aerial photography acquired for the same period. The comparison showed that
the classification of Radarsat images alone...
did not separate narrow water channels as well as Radarsat. Generally, the multi-
wavelength combination provided better c...
decreased to 296 km2
(June) and 282 km2
(August) following the decrease of water
levels.
The area classified as floodplain fl...
is not known when the flood receded from these areas due to the lack of imagery of
September and October. It is only known ...
inundation and vegetation cover of the Amazon floodplain. This method can be
further applied to the scale of the whole Amaz...
multisensor data: per-pixel versus per-object statistics and image segmentation.
International Journal of Remote Sensing, ...
Use of sar satellites for mapping zonation of vegetation communities in the amazon floodplain
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Use of sar satellites for mapping zonation of vegetation communities in the amazon floodplain

  1. 1. Use of SAR satellites for mapping zonation of vegetation communities in the Amazon floodplain M. P. F. COSTA National Institute for Space Research, Av. Dos Astronautas, 1758, Sa˜o Jose´ dos Campos, SP 12 227-010, Brazil; e-mail: maycira@ltid.inpe.br and University of Victoria, Department of Geography, P.O. Box 3050, Victoria, BC, Canada V8W 3P5; e-mail: maycira@office.geog.uvic.ca (Received 6 September 2001; in final form 29 November 2002 ) Abstract. Radarsat and JERS-1 imagery were used for mapping zonation of vegetation communities in the Amazon floodplain. Imagery analysis indicates that at periods of minimum water level the backscattering values of both C and L bands are the lowest and as the water level rises, so do the backscattering values. JERS-1 imagery exhibits a larger dynamic range of backscattering in response to the ground cover for the two extremes of water level (10 dB) com- pared to Radarsat imagery. The backscattering differences from different ground cover allowed the use of a region-based classification that produced seasonal maps with accuracies higher than 95% for vegetated areas of the floodplain. These seasonal maps were used to estimate the spatial distribution and time of inundation and the vegetation cover of the floodplain. It was possible to deter- mine that semi-aquatic vegetation, tree-like aquatic plants, and shrub-like trees colonize regions flooded for at least 300 days year21 . Secondary colonizers, such as tall well-developed floodplain forest, cover regions flooded for approximately 150 days year21 , and floodplain climax forest colonize regions inundated for approximately 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 va´rzea (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 Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2004 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/0143116031000116985 INT. J. REMOTE SENSING, 20 MAY, 2004, VOL. 25, NO. 10, 1817–1835
  2. 2. 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 O´ bidos (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
  3. 3. 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 O´ bidos. Arrows represent the acquisition dates of JERS-1 and RADARSAT imagery. Figure 1. Study area. Mapping vegetation communities 1819
  4. 4. 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 procedure The 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
  5. 5. 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 164 7 August 1996 11 November 1996 5 April 1997 8 June 1999 JERS-1 16 May 1996 y35 75675 D L/HH 12.5612.5 26627 3 12 August 1996 22 December 1996 20 March 1997 *D stands for descending. Mappingvegetationcommunities1821
  6. 6. 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 range direction (near to far range). (a) Radarsat S6 and (b) JERS-1. 1822 M. P. F. Costa
  7. 7. 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 30 June (high water) Radarsat S6 and JERS-1 30 August (falling water) Radarsat S6 and JERS-1 25 November (low water) Radarsat S6 and JERS-1 25 April (rising water) Radarsat S6 and JERS-1 25 Figure 4. Example of segmentation results using the region-growing algorithm with different threshold values. (a) Threshold of 20, (b) threshold of 30, (c) threshold of 40, and JERS-1 image enlarged in the background, and (d ) enlarged section of the aerial photograph of the same location showing the different ground covers (original photograph at 1:20 000 scale). Red box in figure 6(a) shows the location of selected sub-region. Mapping vegetation communities 1823
  8. 8. Table 3. Number of training and test regions. Classified images Number of samples Aquatic vegetation Floodplain flooded forest Floodplain unflooded forest Training Testing Training Testing Training Testing Radarsat S6zJERS, November 48 41 10 8 60 29 Radarsat S6zJERS, April 57 49 100 67 20 8 Radarsat 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) at 95% confidence interval. Month Ground cover Radarsat S6 JERS-1 Mean Lower Upper Mean Lower Upper November/ December Floodplain forest n~41 Upland forest n~41 28.2 29.9 29.0 210.1 27.5 29.7 27.4 28.0 28.3 28.2 26.6 27.8 Pasture n~41 212.0 212.6 211.5 212.6 213.3 212.1 Savanna 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 24.8 Upland forest 29.6 29.9 29.4 27.4 27.4 27.1 Pasture 29.9 210.5 29.5 210.8 211.4 210.1 Savanna 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 23.7 Upland forest 28.6 28.9 28.4 26.8 27.1 26.6 Pasture 29.7 210.2 29.2 210.2 210.9 29.7 Savanna 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 25.9 Upland forest 29.3 29.5 29.1 Pasture 29.7 210.2 29.2 Savanna 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 24.2 Upland forest 29.1 29.4 28.8 27.4 27.7 27.2 Pasture 210.0 210.6 29.5 211.8 212.5 211.2 Savanna 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 were used 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 normal distribution 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 a 95% confidence with an error boundary of the estimated so at ¡1 dB. The average number of 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
  9. 9. 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
  10. 10. 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- sparse vegetation) Decurrent (savanna dense and secondary forest) Decurrent Tree-like aquatic vegetation Structural characteristics Trunk None Many small trunks with random orientation Cylindrical Cylindrical Cylindrical Branches Non-woody stalks or stems Many small branches Branches forked and randomly oriented Branches forked and randomly oriented None Foliage Blade-like erectophile - short Blade-like erectophile - tall Broad leaves Broad leaves Broad leaves Defoliated Blade-like clump at top of trunk Scattering mechanism C-band (45‡) Volume scattering Volume scattering Surface-scattering (quasi-specular) Volume-scattering Volume scattering Double bounce Double bounce L-band (35‡) Surface scattering (quasi-specular) Volume scattering and specular reflection Surface scattering (quasi-specular) Surface and volume scattering Double bounce Double bounce Double bounce 1826M.P.F.Costa
  11. 11. 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 returning arrows (1, 2, and 3) represents relative magnitude of scattered radiation. Mapping vegetation communities 1827
  12. 12. 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)mayhaveoccurredwhentheradiationwasinteractingwithtallgrass- 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. Pasture Short 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. Savanna A 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
  13. 13. 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 forest Upland 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
  14. 14. 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~floodplain flooded 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
  15. 15. 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 O´ bidos, 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 Aquatic vegetation Floodplain flooded forest Floodplain unflooded forest Radarsat S6zJERS-1, November – overall accuracy~97.02% Aquatic vegetation 99.1 0 0.91 Floodplain 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 0 Floodplain flooded forest 0.9 97.8 1.2 Floodplain 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 1.11 Floodplain flooded forest 4.6 95.5 0 Floodplain unflooded forest 0 8.1 91.9 Mapping vegetation communities 1831
  16. 16. 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 va´rzea (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 (imbau´ba), 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. Month Aquatic vegetation Floodplain flooded forest Floodplain unflooded forest Upland* Open water* November 342 93 333 844 1045 April 217 156 87 844 1247 May 397 304 – 844 1112 June 296 290 – 844 1227 August 282 238 85 844 1208 *Area of upland represents the upland mask; areas of open water represent the open water mask for each period. 1832 M. P. F. Costa
  17. 17. 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 year around. 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 the two 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, double bounce 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 Radarsat and 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 . Floodplain climax 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
  18. 18. 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 Fundac¸a˜o de Amparo a Pesquisa do Estado de Sa˜o Paulo funded this research. Financial contributions for field campaigns from Dr John Melack under the LBA project is also acknowledged. CAPES (Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nı´vel 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. References BERGEN, K. M., DOBSON, M. C., PIERCE, L. E., and ULABY, T., 1998, Characterizing carbon in a northern forest by using SIR-C/X-SAR imagery. Remote Sensing of Environment, 63, 24–39. COSTA, M. P. F., 2000, Net Primary Productivity of Aquatic Vegetation of the Amazon Floodplain: A multi-SAR satellite approach. PhD thesis, University of Victoria, Victoria, Canada. COSTA, M. P. F., NOVO, E. M. L. M., AHERN, F., MITSUO II, F., MANTOVANI, J. E., BALLESTER, M. V., and PIETSCH, R. W., 1998, The Amazon floodplain through radar eyes: Lago Grande de Monte Alegre case study. Canadian Journal of Remote Sensing, 24, 339–349. COSTA, M., NIEMANN, O., NOVO, E., AHERN, F., and MANTOVANI, J., 2002, Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and RADARSAT. International Journal of Remote Sensing, 23, 1401–1426. DOBSON, M. C., PIERCE, L. E., and ULABY, F. T., 1996, Knowledge-based land-cover classification using ERS-1/JERS-1 SAR composites. IEEE Transactions on Geoscience and Remote Sensing, 34, 83–99. DONG, Y., FORESTER, B. C., and MILNE, A. K., 1999, Segmentation of radar imagery using the Gaussian Markov random field model. International Journal of Remote Sensing, 20, 1617–1639. FORSBERG, B. R., ROSENQVIST, A., PIMENTEL, T. P., and RICHEY, J. E., 2000, Modeling of flooding patterns and methane emissions in the Jau River floodplain (Central Amazon) using JERS-1 imagery. Resume. In Wetlands 2000, Quebec. p. 131. HESS, L. L., MELACK, J. M., FILOSO, S., and WANG, Y., 1995, Delineation of inundated area and vegetation along the Amazon Floodplain with SIR-C synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 33, 896–903. JUNK, W. J., 1997, General aspects of floodplain ecology with special reference to Amazonian floodplains. In The Central Amazon Floodplain: Ecology of a pulsing system, edited by W. J. Junk (Berlin: Springer), pp. 3–17. JUNK, W. J., and PIEDADE, M. T., 1997, Plant life in the floodplain with special reference to herbaceous plants. In The Central Amazon Floodplain: Ecology of a pulsing system, edited by W. J. Junk (Berlin: Springer), pp. 147–181. KELLNDORFER, J. M., and PIERCE, L. L., 1998, Towards consistent regional-to-global-scale vegetation characterization using orbital SAR systems. IEEE Transactions on Geoscience and Remote Sensing, 36, 1396–1411. LE TOAN, T., RIBBES, F., WANG, L., FLOURY, N., DING, K., KONG, J. A., and FUJITA, M., 1997, Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Transactions on Geoscience and Remote Sensing, 35, 41–55. LOBO, A., CHIC, O., and CASTERAD, A., 1996, Classification of Mediterranean crops with 1834 M. P. F. Costa
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