Ó Springer 2006
Wetlands Ecology and Management (2006) 14:401–408
DOI 10.1007/s11273-005-6237-x
-1


Assessing fine beam RADARSAT-1 backscatter from a white mangrove
(Laguncularia racemosa (Gaertner)) canopy

John M. Kovacs1,*, Casey V. Vandenberg2 and Francisco Flores-Verdugo3
1
Department of Geography, Nipissing University, North Bay, Ontario, P1B 8L7, Canada; 2Department of
Geography, University of Lethbridge, Lethbridge, Alberta, T1K 3M4, Canada; 3Instituto del Ciencias del Mar
y Limnologı´a, UNAM, 82000 Mazatla´n, Sinaloa, Mexico; *Author for correspondence (e-mail: johnmk@
nipissingu.ca; phone: +705-474-3461; fax: +705-474-1947)

Received 31 October 2005; accepted in revised form 21 December 2005



Key words: Backscatter coefficients, Biophysical parameters, LAI, Mangroves, Mexico, RADARSAT-1,
SAR


Abstract

To determine whether spaceborne Synthetic Aperture Radar (SAR), specifically fine beam RADARSAT-1
C-band, could be used to provide quantitative data on white mangrove (Laguncularia racemosa (Gaertner))
forests, backscatter coefficients (r°) were examined in relation to structural parameter data collected from
plots located in a mangrove forest of the Mexican Pacific. Significant coefficients of determination were
recorded between the backscatter coefficients and the logarithms of both Leaf Area Index (LAI) and mean
stem height at two incident angles for both the dry and wet seasons. The highest coefficients of determi-
nation for LAI (r2 = 0.60) and mean stem height (r2 = 0.72) were observed using the shallower ($40°) and
steeper ($47°) incident angles, respectively. No significant relationships were recorded between the back-
scatter coefficients and either stem density, basal area or mean DBH. Given the results of this investigation,
it is recommended that for cloud covered regions, fine beam RADARSAT-1 data could be used by resource
managers when they require a quick method for surveying structural damage to mangroves resulting from
both natural and anthropogenic causes.




                                 parameters such as Leaf Area Index (LAI). The
Introduction
                                 results of these investigations do suggest that, in
Numerous studies have been conducted in the use         conjunction with field data, these satellite sensors
of remotely sensed data from satellites for moni-        can provide sufficiently accurate estimations of
toring mangrove forests but few have examined          many of these structural parameters. For example,
the use of these data for estimating quantitative        Kovacs et al. (2005) recently mapped estimated
measures of these forested wetlands (Green et al.        mangrove LAI at the species level using a combi-
1998). Consequently, there has been a recent           nation of very high resolution IKONOS data and
interest (Jensen et al. 1991; Ramsey and Jensen         in situ LAI collected with a LAI-2000 Plant Can-
1996; Green et al. 1997; Kovacs et al. 2004a) in         opy Analyzer.
determining whether these sensors can be used as a         Although the results of the aforementioned
method for extracting mangrove forest biophysical        remote sensing studies on mangroves are quite
402

promising, the sensors they have examined are     Materials and methods
limited to optical (passive) satellite platforms. In
many tropical and subtropical regions persistent    Study area
cloud cover is the norm and thus the availability
                            The Agua Brava Lagoon is located along the
of useful optical data of many mangrove regions
                            Pacific Coast of Mexico within the State of Nayarit
is quite limited. One alternative to optical data is
                            (Figure 1). This mangrove forest belongs to the
Synthetic Aperture Radar (SAR). Unhampered
                                         ´
                            much greater Teacapan–Agua Brava Lagoon–
by cloud cover, SAR imagery can also provide
                            Estuarine System, which is considered one of the
unique information on the surface targets based
                            largest mangrove wetlands of the Pacific coast of
on the interaction with the active energy and the
                            the Americas (Flores-Verdugo et al. 1990). This
geometry and dielectric constants of the ground
                            system experiences sharp seasonal variation in
features imaged (Ulaby et al. 1986). Moreover,
                            precipitation, typical of the tropical sub-humid
due to characteristic interactions of SAR with
                            climatic zone, with distinct wet and dry seasons
persistently flooded forests and relatively flat
                            occurring in the months of June–October and
terrain, mangroves are considered ideal forest
                            November–May, respectively. Although consid-
canopies for SAR investigations (Hess et al.
                            ered a very important ecological reserve for
1990). As with other forested wetlands, the
                            numerous organisms (Flores-Verdugo et al. 1997)
backscatter from SAR is enhanced due to the
                            and an important local source of renewable prod-
smooth water below which enhances scattering
                            ucts (Kovacs 1999), recent investigations (Flores-
mechanisms within the forest components (e.g.
                            Verdugo et al. 1997; Kovacs 2000; Kovacs et al.
stems, branches). Although limited in number,
                            2001a, 2001b, 2004a, 2004b) have indicated that
recent studies (Mougin et al. 1999; Proisy et al.
                            the system is experiencing considerable degrada-
2000; Proisy et al. 2002) of a mangrove forest in
                            tion, resulting primarily from anthropogenic cau-
French Guiana have shown the utility of air-
                            ses. Consequently, a recent study of estimated
borne SAR for estimating structural parameters
                            mangrove LAI (Kovacs et al. 2005) has identified,
of mangrove forest. Their results, based on 12
                            based on state of degradation and species compo-
sample stands, revealed significant correlations
                            sition, four types of mangroves located within the
between radar backscatter coefficients and
                            Agua Brava Lagoon. These mangrove classes in-
numerous stand parameters (e.g. basal area, stem
                            clude dead white mangrove (Laguncularia race-
density) at various SAR frequencies and polar-
                            mosa (Gaertner)), poor condition white mangrove,
izations, including C-band HH polarization.
                            healthy condition white mangrove and healthy red
  The purpose of this investigation is to determine
                            mangrove (Rhizophora mangle (L.)). The latter
whether C-band HH polarization data, captured
                            class, red mangrove, does not appear to be affected
from a satellite platform (RADARSAT-1 fine
                            but is limited in distribution to the main edge of the
beam), can provide similar success in estimating
                            Agua Brava Lagoon as well as along the numerous
structural parameter data of mangrove forest. In
                            tidal channels that extend further inland.
comparison to airborne SAR, spaceborne SAR
data may be more applicable for mangrove mon-
itoring systems as the data are available on a
                            Field data collection
repetitive basis and cover much larger areas. Un-
like the previous SAR studies, this investigation
                            Fourteen circular plots (0.03 ha) were laid out in
will also focus on potential differences associated
                            the Agua Brava Lagoon system during the month
with multiple incident angles and multitemporal
                            of May 2004. Within each plot, all trees of a DBH
data (i.e. seasonal acquisitions). Although using
                            greater than or equal to 2.5 cm were measured and
standard beam RADARSAT-1 and focusing on
                            the central location recorded at a sub-meter
non-mangrove forested wetlands, the results of
                            accuracy using a differential post-processing GPS
Townsend (2002) suggest that the accuracy of the
                            unit. The selection of the stands was chosen at
estimation of the mangrove biophysical parame-
                            random from homogeneous white mangrove areas
ters are likely to vary according to these two
                            that represented the three conditions that persist in
variables.
                                                      403




                Figure 1. The Agua Brava Lagoon of the Mexican Pacific.



this basin mangrove forest (Kovacs et al. 2005).       Remote sensing data acquisition & processing
Specifically, four of the plots were laid out in poor
                               Two fine beam mode RADARSAT-1 (C-band)
condition white mangrove stands and seven of the
                               scenes of the study area, representing two incident
plots in healthy condition sites. The remaining
                               angles, were acquired for both the dry and wet
three plots were laid out in dead tree stands. All
                               seasons. Specifically, the two band positions cho-
fourteen plots were located in areas where the
substrate is completely saturated or flooded. An       sen, SAR F2Near and SAR F5Far, represent
                               incident angles of approximately 40° and 47°,
average stem height, an average DBH and both
                               respectively. At present RADARSAT-1 fine beam
stem density and basal area was determined for
                               has the highest spatial resolution for a SAR based
each location (Table 1). In addition, to confirm the
                               satellite platform with a spatial resolution of
homogeneity of the locations, average tree heights
                               approximately 8 m and a pixel spacing of 6.25 m.
and quick estimates of basal areas, calculated
                               Meteorological records from an adjacent town
using a forest prism (BAF2), were determined at a
                               were used to determine if any precipitation events
20 m distance from the centre of the plot in all four
cardinal directions. Using the technique described      occurred prior to or during the SAR acquisitions.
                               For each scene, radar backscatter coefficients (r°)
by Kovacs et al. (2004a), estimated LAI maps for
                               were then recorded, in decibels, by using the ori-
the two seasons, dry and wet, were then derived
                               ginal brightness values, the georeference segments,
from an April 2004 and an October 2004 IKONOS
                               the orbital segments and arrays provided. All four
scene, respectively. As a result, using the estimated
                               of the scenes were then co-registered to a geo-
LAI maps, an average LAI value was then deter-
                               metrically corrected 2004 IKONOS scene. Using
mined for each plot, for both the dry and wet
                               the sub-meter GPS data collected, a series of
seasons.
404

Table 1. Biophysical parameter data for the Laguncularia racemosa dominated plots.

                          Basal area (m2/ha)
Plot #   Vegetated  Stem density (stems/ha)                 Mean DBH  Mean stem   LAI dry   LAI wet
      condition  total (L.r/R.m.)      total (L.r/R.m.)       (cm)    height (m)   season   season

1     dead     3633            9.2             5.5    0.5      0.20    0.01
2            4367            13.5             6.1    0.5      0.10    0.31
3            4533            17.5             6.7    1.2      0.01    0.01
4     poor     4633            15.6             6.1    3.7      1.10    1.41
5            5133            24.6             7.1    3.5      1.16    1.28
6            7067            19.9             5.3    2.6      0.87    1.81
7            2902            4.7             4.2    3.0      1.10    1.63
8     healthy   6833            24.3             6.1    2.5      2.00    3.36
9            5133            19.5             6.2    3.0      2.57    3.95
10            9767            29.5             5.7    5.5      2.02    3.44
11            3712  (3666/46)      16.0  (15.93/0.02)     6.5    4.5      2.27    3.66
12            3237  (3116/121)     11.2  (10.93/0.29)     6.0    7.0      2.78    4.12
13            4282  (4068/214)     11.1  (10.88/0.22)     5.3    3.5      2.57    4.10
14            4988  (4926/62)      8.6  (8.54/0.05)      4.2    4.5      2.38    4.06

L.r. = Laguncularia racemosa and R.m. = Rhizophora mangle.

masks, to approximate the area of sampling, were           the mean RADARSAT-1 backscattering coeffi-
then created for each plot. Consequently, a mean           cient (Table 2), only two parameters, LAI and
backscatter coefficient, in decibels, was then de-           mean stem height, were identified as having a sig-
rived from the pixels located under the masks. The          nificant relationship (p < 0.01) for both incident
masks were slightly larger than the actual plot           angles and for both seasons. Specifically, lower
areas but given the homogeneity of the areas,            mean backscatter coefficients were recorded from
determined from the IKONOS maps and prism              white mangrove plots having lower LAI and lower
sweeps, this was deemed appropriate. The size of           mean stem heights (Figure 2). It must be noted
the masks was selected to reduce radiometric res-          that although both of these parameters were
olution errors originating from speckle for the           identified separately as statistically significant they
homogeneous targets (Ulaby et al. 1986). Finally,          are interrelated with one another and thus should
simple linear regression techniques were employed          not be expected to respond separately or uniquely
to examine whether the radar backscatter coeffi-            in relation to the total backscatter observed from
cients could be used to predict any of the bio-           each plot. Specifically, according to Table 1, LAI
physical parameters.                         and mean stem height are highly correlated.
                                   However, given the characteristic properties of the
                                   radar wavelength selected (C-band), it is postu-
                                   lated that LAI plays the dominant role in the
Results and discussion
                                   contribution of the observed backscatter. Specifi-
Based on the linear coefficients of determination           cally, it is commonly agreed (Leckie and Ranson
between the logarithm of the stand parameters and          1998) that the primary contributor to backscatter

Table 2. Linear coefficients of determination (r2) between the logarithm of structural parameters of white mangrove plots (n = 14)
and RADARSAT-1 fine beam backscattering coefficients (dB) at two incident angles (F2N=40°; F5F=47°). Boldface numbers
represent significant relationships at p < 0.01.

Parameter                  Dry season                      Wet season

                       F2N             F5F           F2N             F5F

LAI                     0.60             0.56          0.48            0.45
Mean stem height (m)             0.68             0.55          0.61            0.72
Stem density (stem/ha)            0.04             0.16          0.10            0.05
Basal area (m2/ha)              0.01             0.06          0.14            0.00
Mean DBH (cm)                0.06             0.02          0.01            0.13
                                                          405




Figure 2. Fine beam RADARSAT-1 backscattering coefficients, at two incident angles (F2N=40°; F5F=47°), vs. Leaf Area Index
(LAI) and mean stem height for 14 white mangrove plots located in a mangrove forest of the Mexican Pacific.



from forest canopies in the shorter wavelengths        tributions. In contrast, the interactions of SAR
(e.g. K, X, C) is from the interactions with the        with trunks and main branches are considered the
leaves and smaller canopy elements (i.e. volume        main contributors of forest stand backscatter for
scattering). Consequently, little or no backscatter      the longer SAR wavelengths (e.g. L and P). As a
should be expected from dead mangrove areas          result, for such SAR wavelengths, the backscatter
(Figure 3) which contain few, if any, leaves (i.e.       coefficients from the dead areas, where trunks
LAI $ 0), especially with the flooded regime of the       remain, should be significant. This would be
mangroves which limits ground reflectance con-         especially true in these flooded forests where the
406




Figure 3. Comparison of RADARSAT-1 F5F backscatter from a white mangrove forest with an enhanced optical IKONOS data false
colour composite (4, 3, 2). Note the large expanse of dead mangroves in the top portions of the images. Refer to Figure 1 for
geographic position.


double-bounce effect would result from the inter-         SAR backscatter coefficients and other man-
actions between the standing water and the trunks        grove structural parameters. For example, in these
which act as dihedral corner reflectors.             studies, in addition to biomass, coefficients as high
  Although LAI and the corresponding back-           as 0.90 and 0.88 were recorded for basal area and
scatter coefficients were deemed significant, the          mean DBH in relation to AIRSAR C-HH back-
data plotted in Figure 2 indicate that the discrep-       scatter. The much higher spatial resolution of their
ancy between no leaf content (i.e. LAI $ 0) and         SAR platform and, possibly, the greater range of
some leaf content (i.e. low LAI) is more apparent        the parameters recorded in their study plots may
than between low LAI and high LAI for the RA-          explain the discrepancy. For example, in this
DARSAT-1 data. This circumstance could explain          study, although the LAI varied considerably
the lower coefficients of determination recorded          among the sample plots, this was not the case for
for the wet season data, when the range of the LAI        the basal areas and stem densities (Table 1).
increased dramatically. The limited ability to dis-         Enhanced backscatter from flooded areas is
cern LAI values for the healthier mangroves may         angle dependent and thus an important consider-
be due to saturation of the SAR signal occurring         ation for mangrove areas. However, with regards
much earlier than that which might be expected          to the examination of two incident angles, the
from longer SAR wavelengths (Dwivedi et al.           results of this study indicate that the use of the
1999). Specifically, shorter wavelengths are known        shallower incident angle (40°) only slightly im-
to be constrained in their ability to penetrate         proves the relationship between LAI and the cor-
closed forest canopies.                     responding backscatter coefficients. At the fine
  Unlike the previous work of others using air-         beam mode, RADARSAT-1 data selection is
borne SAR (Mougin et al. 1999; Proisy et al. 2000;        restricted to an incident angle range of approxi-
Proisy et al. 2002), the results of this study do        mately 38°–47°. At the standard beam mode, the
not indicate significant relationships between          range of incident angles available does improve
                                                        407

(24°–47°) but at the expense of a lower spatial    anticipated that with the planned launch of
resolution (25 m).                   RADARSAT-2 the extraction of mangrove bio-
  With regard to seasonality of the data, the     physical parameter data from spaceborne SAR
coefficients of determination for LAI and SAR      will improve significantly. The launch of RA-
backscatter coefficients were greater for the dry    DARSAT-2 will ensure the continuity of all
season. LAI values for the plots did, however, vary  existing RADARSAT-1 beam modes, which
quite dramatically as a result of the phenological   would allow for long term change detection stud-
changes associated with the white mangroves of     ies. Moreover, RADARSAT-2 will provide new
this study area. In this investigation no precipita-  capabilities for improving mangrove forest struc-
tion events were recorded three days prior to or    tural parameter extraction including the addition
during the acquisition of the wet season imagery.   of an ultra fine beam mode (3 m resolution) and
Consequently, it was not possible to determine     quad-polarization.
whether surface moisture, which increases the
dielectric constant of the leaves, would have
altered the relationship between LAI and the
                            Acknowledgements
backscatter coefficients.
                            J.M. Kovacs wishes to acknowledge financial sup-
                            port (grant # 249496-02) of the Natural Sciences
Conclusion
                            and Engineering Research Council of Canada. The
                            Canada Space Agency provided the RADARSAT-
The results of this investigation suggest that
                            1 data to J.M. Kovacs and F. Flores-Verdugo as
spaceborne SAR, specifically fine beam RADAR-
                            part of the Data for Research Use (DRU) program
SAT-1, can be used to some extent for extracting
                            (project # 02-04). The authors would also like to
biophysical parameter data from mangrove for-
                            extend their thanks to Lance P. Aspden, Francisco
ests. Specifically, in this investigation it was pos-
                            Flores de Santiago and Neil Latour for their
sible to discern dead white mangrove stands from
                            assistance in the field data sampling.
healthy ones due to a significant relationship be-
tween LAI and corresponding RADARSAT-1 fine
beam backscatter coefficients. The results also
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