Repeat-pass multi-temporal interferometric SAR

International Journal of Remote Sensing Vol. 31, No. 4, 20 February 2010, 881–901

Repeat-pass multi-temporal interferometric SAR coherence variations with Amazon floodplain and lake habitats HAHN CHUL JUNG* and DOUG ALSDORF School of Earth Sciences, The Ohio State University, Columbus, OH 43210, USA (Received 12 October 2007; in final form 13 August 2008)

Downloaded By: [Jung, Hahn Chul] At: 14:55 9 March 2010

We have analysed interferometric coherence variations in Japanese Earth Resources Satellite (JERS-1) L-band synthetic aperture radar (SAR) data at three central Amazon sites: Lake Balbina, Cabaliana and Solimo ˜ es-Puru´s. Because radar pulse interactions with inundated vegetation typically follow a double-bounce travel path that returns energy to the antenna, coherence will vary with vegetation type as well as with physical and temporal baselines. Lake Balbina consists mostly of upland forests and inundated trunks of dead, leafless trees whereas Cabaliana and Solimo ˜ es-Puru´s are dominated by flooded forests. Balbina has higher coherence values than either Cabaliana or Solimo ˜ es-Puru´s probably because the dead, leafless trees support strong double-bounce returns. The mean coherences of flooded woodland are 0.28 in Balbina and 0.11 in both Cabaliana and Solimo ˜ es-Puru´s. With increasing temporal baselines, flooded and nonflooded wetland habitats show a steadily decreasing trend in coherence values whereas terra-firme and especially open-water habitats have little variation and remain lower in value. Flooded and nonflooded wetland coherence varies with the season whereas terra-firme and open water do not have similarly evident seasonal variations. For example, flooded habitats in all three study regions show annual peaks in coherence values that are typically 0.02 greater than coherence values from temporal baselines 180 days later, yet open water shows no variation with time. Our findings suggest that, despite overall low coherence values, repeat-pass interferometric coherence of flooded habitats is capable of showing the annual periodicity of the Amazon flood wave.

1.

Introduction

Wetlands, lakes and rivers cover 5–8 million km2 globally, blanketing up to 6% of the Earth’s land surface (Matthews and Fung 1987, Mitchell 1990, Matthews 1993). The flow of water through these environments is a control on both biogeochemical and sediment fluxes. Water storage in floodplains is also a key governing parameter in continental-scale hydrological models (e.g. Richey et al. 1989, Vorosmarty et al. 1989, Coe 1998). Monitoring discharge in the main channels of rivers and upland tributaries as well as storage changes in floodplain lakes is necessary for understanding flooding hazards, methane production, sediment transport and nutrient exchange. An understanding of the flooding dynamics and hydrological exchange between rivers and related floodplains relies on measurements of water levels recorded at gauging stations along a main channel. For nearly all wetlands, however, the lack of floodplain stage recording devices results in poorly constrained estimates of floodplain water storage. *Corresponding author. Email: [email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2010 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431160902902609


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H. C. Jung and D. Alsdorf

Given the vast size and remote location of large tropical basins such as the Amazon and Congo, satellite observations remain a viable approach to constraining and validating basin-scale hydrological models. For example, modelling efforts have begun to rely on remotely sensed observations that either directly record water surface elevations using satellite radar altimetry (e.g. Koblinsky et al. 1993, Birkett 1998, Maheu et al. 2003, Leon et al. 2006) or infer stage and discharge from relationships between main channel gauge data and remotely sensed inundated areas (e.g. Smith et al. 1995, 1996, Vorosmarty et al. 1996, Smith 1997, Sippel et al. 1998). Interferometric synthetic aperture radar (InSAR) has recently been used to measure water level changes with time (dh/dt) (Alsdorf et al. 2000, 2001a,b, Kim et al. 2005, Lu et al. 2005) and has been coupled with model-based understanding of storage changes (Alsdorf 2003) and flow hydraulics (Alsdorf et al. 2005). Previous investigations have used InSAR for forest mapping (Askne et al. 1997, Engdahl and Hyyppa 1997), forest change detection (Wegmuller et al. 1995, 2000) and flood water studies (Alsdorf et al. 2000). Numerous studies have demonstrated the value of SAR amplitude (radar backscatter) for delineation of wetland ecosystems (e.g. Harris and Digby-Arbus 1986, Richards et al. 1987, Hess and Melack 1994, Hess et al. 2003) and especially flooding beneath the forest canopy (Hess et al. 1990, Wang et al. 1995). Recently, InSAR phase coherence was found to be more effective than radar backscatter for differentiating willow-alder (broadleaf tree), spruce (needle-leaf tree), ice and open water (Hall-Atkinson and Smith 2001). The phase coherence between repeat-pass SAR observations of a forest region was investigated to estimate the growing-stock volume (Luckman et al. 2000, Eriksson et al. 2003). However, few studies focus on InSAR coherence variations with vegetation and with seasonal water fluctuations, particularly related to using the related phase for measuring dh/dt. We examined the relationships between InSAR coherence and physical and temporal baselines as measured across various Amazon floodplain habitats (e.g. terrafirme, open water, flooded and nonflooded forest). We used repeat-pass Japanese Earth Resources Satellite (JERS-1) SAR data, which is L-band and has an HH polarization. Because the Amazon has a strong, seasonal flood wave, we also investigated the association between coherence and seasonal flooding. 2. 2.1

Study area and classification Study location

The central Amazon floodplain contains the confluence of the Amazon and Puru´s Rivers (upstream of the confluence with the Negro River, the Amazon River is referred to as the Solimo ˜ es River, whereas the combined Amazon–Solimo ˜ es River is referred to as the Amazon mainstem). The Amazon Basin contains about 750 000 km2 of annually inundated area (Melack and Forsberg 2001). The alluvial floodplains of the Amazon River and major tributaries in Brazil are thought to cover over 300 000 km2 (Klinge et al. 1990). The alluvial deposits along just the mainstem Amazon River in Brazil cover approximately 92 000 km2 (Sippel et al. 1992). The floodplain can be divided into the varzea, which is flooded by sediment- and nutrient-rich water (white water), and igapo, which is flooded by sediment- and nutrient-poor water (black water) (Sioli 1968). The floodplains of these large


Repeat-pass multi-temporal interferometric SAR coherence

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Figure 1. Location map of the Amazon Basin study areas: A, Balbina (JERS-1 path–row 414–303); B, Cabaliana (416–306); C, Solimo ˜ es-Puru´s (417–307). Dark blue indicates rivers and lakes that do not completely drain, light blue depicts annually flooded areas that drain, and green indicates terra-firme or upland areas that never flood.

lowland rivers are ,20 to ,50 km wide, with low topographic relief ranging from 20 to 30 m above mean sea level. Terra-firme uplands, which are never flooded, are developed in river terrace sediments and border the floodplains with elevations ranging from 30 to 50 m above mean sea level. Figure 1 shows the location and geographical coordinates of the three study areas. Lake Balbina is a man-made reservoir created to supply hydroelectric power to the city of Manaus. The reservoir is located on the Uatuma River and drains a 19 100 km2 basin of mostly upland topography, where the relief extends from 30 to 200 m in elevation (Fearnside 1989). The lake includes a cluster of about 1500 islands separated by submerged, shallow valleys within a flooded water-surface area of 2400 km2 (Melack and Wang 1998). Prior to dam closure on 1 October 1987, the annually averaged flow on the river was about 450 m3 s-1. Water depths in the full reservoir average 7.4 m whereas the average water level fluctuations have a range of about 3 m year-1. Because the vegetation was not cleared before filling, the lake consists mostly of forest and inundated trunks of dead, leafless trees. The other study areas are the Cabaliana floodplain on the Solimo ˜ es River and the confluence of the Puru´ s and Solimo˜ es Rivers. The annual rise and fall of the Solimo˜ es River averages about 10 m on this reach and inundates large areas of floodplain (i.e. varzea). Amazonian varzea forests have stand densities comparable to upland (terra-firme) forests but tend to have lower species diversity (Campbell et al. 1992). The Puru´ s River drains the sediments of the sub-Andean trough and of the central plain. It is a southern tributary of the Solimo ˜ es River with an intermediate composition between black and white water (Hedges et al. 1986). The floodplain area between Itapeau and Manacapuru along the Solimo˜ es River is about 12 000 km2 (Alsdorf 2003).

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2.2

H. C. Jung and D. Alsdorf Classification scheme

Hess et al. (2003) conducted dual-season mapping of wetland inundation and vegetation for the central Amazon basin under both low-water and high-water conditions at 3 arcsec resolution. Mosaics of JERS-1 SAR images were created as part of the Global Rain Forest Mapping Project (GRFM; Rosenqvist et al. 2000) and four validation overflights for Amazon mosaics (VOAM) surveys were conducted. For GRFM, the entire Amazon Basin was acquired in a series of orbital passes during the generally low flood season of the Amazon River in September to December 1995. The same area was covered again in May to August 1996, during the high flood period. A pixel-based classifier was used by Hess et al. (2003) to map wetland vegetation and flooding states based on SAR backscattering coefficients of two-season class combinations. The two initial VOAM surveys were flown in 1995 and 1996 during the GRFM imaging periods for the central Amazon and were limited to areas within 600 km of Manaus. The aerial video graphic surveys covered regions of Balbina, Cabaliana and Solimo˜ es-Puru´s. To expand the ground data set to a more extensive region, follow-up surveys were flown in 1997 and 1999. VOAM95 and VOAM97 surveys are timed to low-water whereas VOAM96 and VOAM99 correspond to high-water stages of the Amazon River. Based on the VOAM surveys, the accuracy for flooded and nonflooded forest classes ranged from 78% to 91%, with lower accuracy (63–65%) for flooded herbaceous vegetation (Hess et al. 2003). Because VOAM97 and VOAM99 surveys were performed in all three study areas, the classification scheme used in this research has higher accuracy compared to the regional accuracy. The Amazon floodplain was classified by Hess et al. (2003) in terms of both inundation state (flooded or nonflooded) and vegetation covers (nonvegetated, herbaceous, shrub, woodland or forest) at the time of imaging. The five vegetation classes correspond to physiognomic classes of the National Vegetation Classification Standard (NVCS; Federal Geographic Data Committee 1997). Herbaceous is defined as nonwoody plants as compared to woody plants of shrub, woodland and forest. Shrub is dominated by individuals or clumps, woodland is dominated by trees with crowns (i.e. open tree canopy), and forest is dominated by trees with interlocking crowns (i.e. closed tree canopy). Nine hydrologic-vegetative categories include: terra-firme, open water, flooded-herbaceous, flooded-shrub, flooded-woodland, flooded-forest, nonfloodedherbaceous (or bare soil), nonflooded-shrub, and nonflooded-forest (figure 2). Table 1 presents areas and their percentages of the entire interferometric JERS-1 frame for these hydrologic-vegetation habitats as mapped from high- and low-water seasons in Balbina, Cabaliana and Solimo ˜ es-Puru´s. Balbina has 72% terra-firme and shows little change in the area of flooded (range from 22% to 20%) and nonflooded (3% to 8%) classes from high- to low-water seasons, respectively. By contrast, Cabaliana and Solimo ˜ es-Puru´s indicate significant change in the area of flooded (range from 37% to 12% and 60% to 10%) and nonflooded (7% to 37% and 8% to 59%) classes from high- to low-water seasons, respectively. The flooded shrub area disappears during the lowwater season whereas none of the nonflooded herbaceous and nonflooded shrub classes exist during the high-water season. 3. 3.1

SAR data and processing Interferometric processing

JERS-1 scenes total 23 for Balbina, 21 for Cabaliana and 18 for the Solimo˜ es-Puru´s, thus permitting a variety of interferometric pairs. With the spatially coregistered

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Figure 2. Classification maps for high- and low-water seasons: (a) Balbina; (b) Cabaliana; (c) Solimo ˜ es-Puru´s (source: Hess et al. 2003).

scenes, all possible combinations of interferometric pairs were generated. The Balbina site has 253 pairs, Cabaliana has 210 and Solimo ˜ es-Puru´s has 153. Figure 3 presents the distribution of temporal and perpendicular baselines for all interferometric pairs used in this study. The repeat orbital period of JERS-1 is 44 days; thus the minimum

Nonflooded

Terra-firme Open water Flooded

Nonvegetated Herbaceous Shrub Woodland Forest Herbaceous (or bare soil) Shrub Forest

*Shortage of data to compute statistics of the classes.

Total

Upland Wetland

Class 3036 / 72 140 / 3 235 / 6 3/0 479 / 11 215 / 5 0 / 0* 0 / 0* 106 / 3 4214 / 100

High 3036 / 72 16 / 0 222 / 5 0 / 0* 479 / 11 171 / 4 101 / 3 39 / 1 150 / 4 4214 / 100

Low

Balbina (km2 / %)

2043 / 45 523 / 11 297 / 7 72 / 2 177 / 4 1077 / 24 0 / 0* 0 / 0* 316 / 7 4505 / 100

High

2043 / 45 257 / 6 220 / 5 2 / 0* 177 / 4 137 / 3 267 / 6* 146 / 3* 1256 / 28 4505 / 100

Low

Cabaliana (km2 / %)

1071 / 23 412 / 9 251 / 6 94 / 2 104 / 2 2280 / 50 0 / 0* 0 / 0* 367 / 8 4579 / 100

High

1071 / 23 351 / 8 165 / 4 0 / 0* 104 / 2 182 / 4 127 / 3 114 / 2 2465 / 54 4579 / 100

Low

Puru´s (km2 / %)

Table 1. Upland and wetland areas and their percentages in Balbina, Cabaliana and Solimo ˜ es-Puru´s as mapped from high- and low-water seasons (source: Hess et al. 2003).


886 H. C. Jung and D. Alsdorf



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Figure 3. Physical and temporal baseline distributions for each study location: (a) Balbina; (b) Cabaliana; (c) Solimo ˜ es-Puru´s.

temporal baseline in the three study areas is 44 days. Table 2 presents the temporal and spatial characteristics of the interferometric pairs considered. Some time-spans are greater than 4 years (data were acquired between 1993 and 1997). The maximum baselines are all below the JERS-1 theoretical critical baseline of 5.7 km, where the correlation between the interferometric signals received by the two radar antennae drops to zero (Eriksson 2004). The R2 values of the perpendicular baselines with respect to the temporal baselines in all three study regions are less than 0.04, indicating the low strength of a linear relationship between the two variables. Overall, the interferometric dataset is randomly distributed with respect to both temporal and perpendicular baselines such that there is no preferential sampling of any particular time-span or physical baseline that might skew the resulting coherence data set. Over open water, the transmitted radar pulse specularly reflects away from the offnadir imaging JERS-1 SAR antenna, yielding low-amplitude returns, poor interferometric coherence, and unreliable interferometric phase values. Over inundated

Scenes

23 21 18

Location

Balbina Cabaliana Puru´s

253 210 153

Pairs 23 February 1993 to 9 August 1997 25 February 1993 to 11 August 1997 26 February 1993 to 29 June 1997

Acquisition period 44 44 44

Minimum

1628 1628 1584

Maximum

Temporal baseline (days)

Table 2. The temporal and spatial parameters of acquired SAR images.


128 3 156

Minimum

4967 4586 4231

Maximum

Perpendicular baseline (m)

888 H. C. Jung and D. Alsdorf


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vegetation, an L-HH radar pulse follows a path that penetrates the vegetation canopy, reflects specularly from the underlying water surface, backscatters from the vegetation trunks, and returns to the antenna (Richards et al. 1987, Hess et al. 1995). Multilook amplitude images were generated by averaging two looks in range and six looks in azimuth to reduce speckle noise. The ground size of a pixel is 28 m in range and 27 m in azimuth. The classification schemes were coregistrated to the amplitude images with third-order polynomials and nearest-neighbour interpolation. 3.2

Coherence variations


We produced coherence images for all interferometric pairs. Coherence is a measure of the phase consistency in returned radar energy between two SAR acquisitions and is defined by equation (1) (Zebker and Villasenor 1992): S1 S2

¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (1) S1 S1 S2 S2 where is coherence, s1 and s2 are complex values in SAR image 1 and SAR image 2, respectively, and s* is the conjugate of s. The braces indicate local spatial averaging around an individual multi-looked pixel; we used a 5 5 window (i.e. a 135 m spatial resolution) with a decreasing linear weighting scheme for pixels located away from the centre of the window. In most literature coherence refers to the magnitude of the complex coherence and takes values between 0 and 1. Total observed coherence is composed of spatial, temporal and thermal components, and is described by equation (2) (Zebker and Villasenor 1992):

¼ spatial temporal thermal

(2)

where spatial is spatial baseline decorrelation, which is a function of the perpendicular baseline, temporal is temporal decorrelation, which depends on changes in the scattering centres between the two image acquisitions, and thermal is thermal decorrelation due to radar sensor noise. Coherence is also affected by vegetation type (Hall-Atkinson and Smith 2001) and backscatter amplitude (Luckman et al. 2000). We analysed coherence variations of the hydrologic-vegetation classes on a pixel-by-pixel basis, albeit weighted by the 5 5 window. Coherence is assessed with respect to baseline components, vegetation type and inundation state. Six classes (terra-firme, open water, flooded herbaceous, flooded woodland, flooded forest and nonflooded forest) exist during both high- and low-water seasons and were used to estimate coherence variations. 4. 4.1

Results and discussion Coherence variations with perpendicular baselines

A perpendicular baseline is the distance, measured perpendicular to the radar lookangle, between two orbits of an interferometric pair. Figure 4 shows mean interferometric coherence variations of terra-firme, open water, flooded herbaceous, flooded woodland, flooded forest and nonflooded forest with respect to perpendicular baselines. Mean coherence is the average of all multi-look pixels within a given hydrologicvegetation class of a single interferogram. Only those pixels that are in the same class during both high and low water are used in this mean coherence measure (figure 2). This selection is necessary to isolate the influence of the physical baseline on


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Figure 4. Mean coherence values of various vegetation-hydrologic habitat classes (i.e. terrafirme, open water, flooded herbaceous, flooded woodland, flooded forest and nonflooded forest) compared to interferometric perpendicular baselines for each study location: (a) Balbina; (b) Cabaliana; (c) Solimo ˜ es-Puru´s. Note that open water is consistently separate from the other classes.

coherence from that of vegetation, otherwise there would be a mixing of vegetation influence in figure 4. All statistics reported below and in the tables are calculated from this mean coherence. For each study location, there are over 150 interferometric pairs, thus for display purposes the mean coherence values for each class were further averaged into 50 m bins of incremental perpendicular baseline size and plotted in figure 4. Short perpendicular baselines yield more topographic relief per phase cycle than long baselines, thus providing a more reliable estimate of surface change (Zebker and Villasenor 1992). Assuming the scattering centres do not change substantially between acquisitions, short perpendicular baselines typically should yield better coherence than long baselines because the imaging geometry is more closely parallel (e.g. Kim et al. 2005). In our study area over the Amazon floodplain, however, short perpendicular baselines at the L-band do not benefit in yielding a quality coherence value (figure 4). Furthermore, coherence values for all three locations are generally



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low, with Balbina yielding higher values than either Cabaliana or Solimo˜ es-Puru´s. Coherence varies randomly as the perpendicular baseline increases; no trends are readily apparent within the individual habitat classes. The high coherence peaks (e.g. at perpendicular baseline ,4800 m over Balbina and ,4600 m over Cabaliana) are probably a result of the short temporal baseline of those particular interferometric pairs. Importantly, the flooded and nonflooded wetland classes have higher coherence values compared to terra-firme or to open water at all three study locations. Terra-firme has lower backscatter amplitudes than wetlands because it does not produce a double-bounce radar return. Over open water the transmitted radar pulse specularly reflects away from a side-looking SAR antenna and thus the open water class has the lowest coherence. Lake Balbina has stronger coherence values for each class and a different distribution of coherence values among the classes. The individual mean coherence values of the six habitat classes range from 0.28 to 0.14 in Balbina compared to 0.12 to 0.09 in Cabaliana and 0.11 to 0.09 in Solimo ˜ es-Puru´s (table 3). In Balbina, flooded woodland and flooded herbaceous habitats have higher coherence values than flooded forest whereas in Cabaliana and Solimo ˜ es-Puru´s this distinction is not clear, with these habitats having slightly lower coherence values than flooded forest. Use of the nonparametric Mann–Whitney test of population distribution (Davis 1986, Hirsch et al. 1993) and a 99% probability level suggest that some habitat classes are identically distributed; that is, flooded forest and nonflooded forest in Balbina; flooded herbaceous, flooded woodland and nonflooded forest in Cabaliana; and flooded herbaceous, flooded woodland, flooded forest and nonflooded forest in Solimo ˜ es-Puru´s (table 4). At a 99% probability (i.e. a ¼ 0.01, or 1% chance of a type I error), the critical z value for failure in all tests is 2.33. Flooded herbaceous, flooded woodland, flooded forest and nonflooded forest classes have coherence variations that are statistically separate in Balbina, but they cannot be similarly separated in Cabaliana and Solimo ˜ es-Puru´s. It is important to note that open water is statistically distinct from all other classes. We suggest that the stronger coherence values for Lake Balbina, compared to Cabaliana and Solimo ˜ es-Puru´s, result from the difference in vegetation. The dominant vegetation of the inundated areas of Lake Balbina consists mostly of the flooded trunks of dead, leafless trees that are identified as the flooded woodland class (table 1). The radar pulse may more easily penetrate this open canopy and subsequently support strong double-bounce returns. Cabaliana and Solimo ˜ es-Puru´s, conversely, are dominated by living, flooded forests with a thicker canopy, where leaves help to prohibit a strong radar return (compared to Balbina) and thus diminish the strength of the coherence. Overall, the coherence values at all three study locations are lower than typically used in conventional repeat-pass interferometry, for example in the study of earthquakes in dry lands (e.g. Massonnet et al. 1993, Zebker et al. 1994). Nevertheless, despite the low Table 3. The mean plus or minus the standard deviation of coherences of terra-firme, open water, flooded herbaceous, flooded woodland, flooded forest, and nonflooded forest in figure 4. Terrafirme

Open water

Balbina 0.140.017 0.130.018 Cabaliana 0.100.008 0.090.006 Puru´s 0.100.007 0.090.006

Flooded herbaceous

Flooded woodland

Flooded forest

0.250.059 0.110.008 0.110.007

0.280.067 0.180.033 0.110.010 0.120.013 0.110.010 0.110.013

Nonflooded forest 0.180.034 0.110.010 0.110.009

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coherence values, we suggest that they are greater than the noise floor. Open water represents this noise floor because it is mostly a specular reflector at the L-band. Open water consists of constantly changing scattering centres, a result of wave action, which produce a noncoherent repeat-pass interferometric phase. Although, occasionally, waves within any given pixel may produce a similar scattering centre structure for both SAR acquisitions in a repeat-pass interferogram, a spatial average of these is an indication of the noise floor. In figure 4 and table 4 all wetland classes are statistically distinct from open water and thus have coherence values all greater than noise.


4.2

Coherence variations with temporal baseline

The temporal baseline indicates the time interval between two image acquisitions that form an interferometric pair. Over land surfaces where changes in soil moisture, vegetation and freeze/thaw cycling cause random changes in the structure and dielectric properties of the scattering elements, interferometric coherence typically diminishes with increasing time between SAR acquisitions. Figure 5 shows mean temporal baseline coherence variations of terra-firme, open water, flooded and nonflooded habitat classes. Mean coherence is the average of all multi-look pixels within a given hydrologicvegetation class of a single interferogram and only those pixels that are in the same class during both high and low water are used in this mean coherence measure. We further average together coherence values from the three flooded classes to better estimate the influence of flooding on coherence. All statistics reported below and in the tables are calculated from this mean coherence. The repeat cycle of JERS-1 is 44 days, thus for plotting in figure 5 the mean coherence values at each 44-day increment were averaged. As the temporal baseline increases for the three study areas, the mean coherence values of open water generally remain constant whereas all the other classes show a decrease. The mean coherence values plotted with the temporal baseline are greatest for the flooded classes (green lines), of intermediate values for the nonflooded wetland classes (red lines), lower for terra-firme (black line), and least for open water (blue line). The flooded and nonflooded wetland habitat classes show anomalous increases in temporal coherence values, which occur annually, whereas terra-firme and open water classes do not have similar annual signals (figures 5 and 6). Normalized power spectrums, calculated after removal of the trends shown in figure 5, demonstrate a strong annual periodicity for flooded habitats in all three study locations. Nonflooded wetland habitats have a similar or slightly weaker periodicity. By contrast, open water and terra-firme do not show this strong annual amplitude. Note that the annual periodicity in coherence results from temporal baselines stretching across one or multiple years, connecting two low-water seasons (or connecting two mid-rising, two high, two mid-recessional, etc., seasons) and that these yield higher coherence values than temporal baselines connecting two differing seasons. This implies that the interferometric phase for measuring dh/dt is more reliable with both a 1-year and same season encompassed in the temporal baseline rather than with a different combination such as 6 months and different seasons in the temporal baseline. We suggest that this annual periodicity of coherence values is a direct result of the annual Amazon flood. In the central Amazon region, the flood wave peak arrives in late June to early July whereas the trough can arrive over a slightly broader time

4.69

O

14.27 14.57

FH

15.08 15.25 2.97

FW

10.42 11.61 8.34 10.66

FF 11.02 12.14 7.57 10.06 1.01

NF

T 15.88

O 4.26 16.57

FH 5.36 16.46 1.79

FW

Cabaliana

8.18 16.85 5.22 3.34

FF 5.67 16.70 1.92 0.04 3.35

NF

T

13.76

O

4.92 14.34

FH

T, terra-firme; O, open water; FH, flooded herbaceous; FW, flooded woodland; FF, flooded forest; NF, nonflooded forest. z-values less than 2.33 (in bold) in the study indicate that the corresponding two classes are statistically identical.

T O FH FW FF

T

Balbina

Table 4. Absolute z values of the Mann–Whitney statistical test.


5.38 14.35 1.20

FW

Puru´s

4.47 14.20 0.57 0.44

FF

3.99 14.25 0.77 1.76 1.10

NF

Repeat-pass multi-temporal interferometric SAR coherence 893


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Figure 5. Mean coherence values of various vegetation-hydrologic habitat classes (i.e. terrafirme, open water, flooded and nonflooded) compared to interferometric temporal baselines for each study location: (a) Balbina; (b) Cabaliana; (c) Solimo ˜ es-Puru´s. Solid lines are least-squares fit, second-order polynomials. Annual periodicity is found in the flooded and nonflooded habitats but absent in the open water and terra-firme classes.

range, but generally in October (Richey et al. 1989). Although this timing from peak to peak (or trough to trough, etc.) is not exactly 365 days, it is within the sampling accuracy represented by the 44-day repeat-pass cycle of JERS-1. Seasonal growth cycles in wetland vegetation are also timed with this flood pulse (Junk 1999, Middleton 2002). Thus, the two primary hydrogeomorphic determinants of coherence (i.e. vegetation type and inundation state) are also annual. Conversely, terrafirme does not exhibit an annual inundation state and, likewise, does not exhibit a strong annual signal in temporal coherence (figures 5 and 6). Finally, we note that all of the spectra for the flooded and nonflooded wetland classes exhibit more power than the noise floor represented by open water (figure 6). Thus, despite the overall low values of interferometric coherence for the various wetland classes, the values remain greater than noise for all temporal baselines (figure 5).



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Figure 6. Power spectra of mean coherence values (see figure 7) calculated after subtraction of polynomial trends for (a) Balbina, (b) Cabaliana and (c) Solimo ˜ es-Puru´s. Clear, annual periodicity is always found in flooded classes and never in open water.

Using 253 interferometric SAR coherence images in Balbina, 210 in Cabaliana and 153 in Solimo ˜ es-Puru´s, two-dimensional coherence variation plots were constructed with kriging interpolation (figure 7). These plots show the spatial and temporal variation in coherence for the various habitat classes. Importantly, open water has the lowest coherence values for all combinations of spatial and temporal baselines whereas the flooded and nonflooded wetland classes show the highest coherence values. 4.3

Coherence variations within high- and low-water seasons

The low- and high-water seasons represent significant contrasts of inundation state on the Amazon floodplain. Lake Balbina, however, does not have similarly sharp contrasts in habitat with water level fluctuations (e.g. figure 2(a), compare the percentages of flooded and nonflooded habitats between low- and high-water seasons in table 1). We use these two seasons to investigate the seasonal similarities and differences of InSAR coherence variations. High water occurs during May, June and July whereas


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Figure 7. Variation in coherence values with temporal and physical interferometric baselines for (a) Balbina, (b) Cabaliana and (c) Solimo ˜ es-Puru´s. Point locations are noted in figure 2 and kriging is used to contour the coherence values.

low water occurs in October, November and December. The GRFM mosaics and their classifications are also based on scene acquisitions from these months. Interferometric pairs constructed from scenes acquired during these months total 15 (high water) and 15 (low water) for Balbina, 19 (high) and 6 (low) for Cabaliana, and 10 (high) and 10 (low) for Solimo ˜ es-Puru´s. High-water coherence values are greater than those of low water for each habitat in Cabaliana and Solimo ˜ es-Puru´s, with the exception of open water, which shows essentially the same coherence values for both seasons (figures 8 and 9). During high water and over an approximately 1-year time-span, both Cabaliana and Solimo ˜ es-Puru´s display coherence values for flooded and nonflooded habitats that are distinct from terra-firme and open water classes. For temporal baselines longer than about 1 year at high water, only open water remains distinct from the other Cabaliana and Solimo ˜ es-Puru´s classes. During low water in Cabaliana and Solimo ˜ esPuru´s, coherence values for the flooded, nonflooded and terra-firme habitats are not well delineated from each other, with only open water remaining clearly separate. Balbina, however, displays fairly similar coherence values for each individual habitat between high- and low-water seasons. Flooded habitats have higher coherence values and are distinct from nonflooded habitats in both seasons whereas open water has the lowest coherence values for both seasons. 5.

Conclusions

Several key conclusions may be drawn from our analyses. (1) Interferometric phase coherence does not vary with physical baseline, but does show a decrease with increasing temporal baseline for wetland habitats. (2) Interferometric coherence



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Figure 8. Mean coherence values for interferometric pairs where both dates occur during the high-water season. Coherence values of various vegetation-hydrologic habitat classes (i.e. terrafirme, open water, flooded and nonflooded) are compared to interferometric temporal baselines for each study location, (a) Balbina, (b) Cabaliana and (c) Solimo ˜ es-Puru´s. Dots are the habitat mean coherence and circles are the average of these mean coherence values.

values in wetland habitats show annual periodicity with temporal baseline, probably because of the hydrogeomorphic effects of the annual flood wave. Temporal coherence from baselines spanning annual periods (e.g. 1 year, 2 years, etc.) or from much shorter time-spans of less than 2 months (i.e. 44 days) are greater than those of intervening 6-month time-spans. For example, the mean coherences of flooded habitats in Balbina from 44, 176 and 352 days in temporal baseline are 0.39, 0.27 and 0.32. Essentially, the interferometric coherence is timed with the seasonal variations due to the inundation state. (3) Because of the ‘double-bounce’ phenomenon, as radar backscatter amplitudes increase, interferometric coherence also tends to increase. Flooded habitats are represented by stronger backscatter amplitude and, in general, also by higher coherence values compared to open water, terra-firme and nonflooded habitats. Taken together, these analyses suggest that, despite the fairly low coherence values, the interferometric phase of flooded habitats is a reliable measurement of temporal changes in water levels because these flooded habitats all have coherence


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Figure 9. Mean coherence values for interferometric pairs where both dates occur during the low-water season. Coherence values of various vegetation-hydrologic habitat classes (i.e. terrafirme, open water, flooded, and nonflooded) are compared to interferometric temporal baselines for each study location, (a) Balbina, (b) Cabaliana and (c) Solimo ˜ es-Puru´s. Dots are the habitat mean coherence and circles are the average of these mean coherence values.

values that are greater than open water and have temporal periodicity that most probably results from the annual flood pulse. The low values, however, indicate the need for spatial averaging across areas larger than a single multi-look SAR pixel. Acknowledgements Funding for this research was provided by the Terrestrial Hydrology Program and Solid Earth and Natural Hazards Program at the National Aeronautics and Space Administration (NASA). JERS-1 SAR data were provided by the National Space Development Agency of Japan (NASDA). This work was also supported by a Korean Science and Engineering Foundation Grant (No. C00131). References ALSDORF, D.E., 2003, Water storage of the central Amazon floodplain measured with GIS and remote sensing imagery. Annals of the Association of American Geographers, 93, pp. 55–66.



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Repeat-pass multi-temporal interferometric SAR

Repeat-pass multi-temporal interferometric SAR

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