Radar modeling of mangal forest stands, in the Sundarbans area of Southern Bangladesh, was developed. The modeling employs radar system parameters such as wavelength, polarization, and incidence angle, with forest data on tree height, spacing, biomass, species combinations, and water content (including slightly conductive water) both in leaves and trunks of the mangal. For Sundri and Gewa tropical mangal forests, five model components are proposed, which are required to explain the contributions of various forest species combinations in the attenuation and scattering of mangal vegetated nonflooded or flooded surfaces. Statistical data of simulated images (HH components only) were compared with those of SIR-B images both to refine the modeling procedures and to appropriately characterize the model output. The possibility of delineation of flooded or non-flooded boundaries is discussed

Simonett, David S.

Wang, Yong

English

NASA Technical Reports Server

Final Reports
Under
Contract _ NAG - 5 - 1010
National Aeronautics and Space Administration
/ Y -- -c
Improvement and extension of a radar forest backscattering model
Prepared by:
David S. Simonett
Yong Wang
I /
DEPARTMENT OF GEOGRAPHY
UNIVERSITY OF CALIFORNIA SANTA BARBARA
October 1989
(NASA-CR-IB&Q83) IMPROVEMENT AND EXTENSION
OF A RADAQ FOREST BACKSCATTFgTNG MODEL Final
Keport (ca]ifornia dniv.) 1_ P CSCL OZF
63/43
N90-12973
Unclas
0263080
https://ntrs.nasa.gov/search.jsp?R=19900003657 2020-03-19T23:45:36+00:00Z
1. Introduction
Radar modeling of mangal forest stands, in the Sundarbans area of Southern Bangladesh,
has been developed. The modeling employs radar system parameters such as wavelength, polari-
zation, and incidence angle, with forest data on tree height, spacing, biomass, species combina-
tions, and water content (including slightly conductive water) both in leaves and trunks of the
mangal. For Sundri and Gewa tropical mangal forests, five model components are proposed,
which are required to explain the contributions of various forest species combinations in the
attenuation and scattering of mangal vegetated nonflooded or flooded surfaces. Statistical data of
simulated images (HH components only) have been compared with those of SIR-B images both to
refine the modeling procedures and to appropriately characterize the model output. The possibil-
ity of delineation of flooded or non-flooded boundaries is discussed.
2. Characteristics of Mangal Forests in Southern Bangladesh
2.1. Gewa and Sundri Trees
Gewa (Exeoeearia agallocha) and Sundri (Heritiera minor Syn. H. forme,) are mangrove
/
species. The shoots are mostly orthotropic with infrequent diffuse branches, which sometimes
may branch from the base and shrub-like form. Leaves are simple in shapes; and broad in size
(Tomlinson, 1986). Gewa lacks any elaborated aerial part or pneumatophores, which function to
supply oxygen to the roots at high tide. Sundri does have pneumatophores.
A mature Oewa tree grows intermittently and irregularly, up to 20 meters high for a mature
tree. Branching is diffuse, irregular, and by prolepsis. The canopy is dense. A mature Sundri
tree is about 10 to 25 meters tall, and low-branched. The branches are thick and crooked, and
the canopy is very dense.
2.2. Gewa and Sundrl Forest Stands
Gewa and Sundri forests occur dominantly at the tidal mouth of the Baleswar River in
southern Bangladesh, which belong to closed broadleaved woody tropical rain forests (FAO, 1981).
Most of the mouth areas are frequently flooded by fresh water during the monsoon rains (May -
June to October - November, Chapman, 1977), with salt water intrusion during the rest of the
year. Gewa and Sundri forests are very difficult of access with soft footing, and have extremely
high canopy and stem densities. The mangrove forests in this area are estimated at about 590,000
-2-
hectares(FAO,1982),whichcontributeveryimportant forest resources and environmental protec-
tion for Bangladesh: they provide marketable products, and also play roles as buffers for storm
surges, and tidal waves coming from the Bay of Bengal (Imhoff et al., 1986).
4
2.3. Ground Surfaces
The Delta soil is a clay loam lying over alternating layers of clay and sand, highly saturated
with exchangeable bases (Na +, Ca ++, K +, etc.). The salinity of the soil varies seasonally as a
result of the changing balance of fresh water and salt water. Chapman (1977) pointed out that
there is a very high salinity at the beginning of the dry season, and this factor considerably limits
the development, maintenance, or regeneration of the less halophilous species such as Sundri,
which is on the way to extinction because of excessive salt water intrusion. During the rainy sea-
son (when the SIR-B images were obtained), the salinity will be low.
3. Analysis of Ground Truth Data
Ground truth data of Sundri and Gewa mangal forest in our southern Bangladesh research
site were originally collected by a NASA and Bangladesh science team concurrently with the SIR-
B Mission in October, 1984; and subsequently by Chaffey, et al., (1985). All these data were
prepared by averaging measurements made for inventory plots (100 * 100 meters in size), consist-
ing of:
a. a digitized forest stand map which was used to locate and extract each forest stand data
from the SIR-B images. The ground surface is clarified as either flooded or nonflooded sur-
face, based on field observations.
b. DBH count distributions by DBH segments, employed to generate random aerial root (pneu-
matophore), and tree trunk distributions with varying DBHs in simulated stand areas.
c. tree heights and canopy depths by their DBHs were used to compute each tree height and
mean canopy depth in each simulated pixel in a forest stand based on the DBH counts for
each DBH segment in that pixel.
d. canopy biomass of in each DBH segment was employed to calculate attenuation coefficients
of canopies.
e. gravimetric moisture content of canopies and trunks was used to derive the relative dielec-
tric constants of the leaves and the tree trunks.
-3-
4. Radar Models of Sundrl and Gewa Forest Stands
Based on the above analysis, five components are proposed, as shown in Figure 1 (see also
Wang, et al., 1989):
° - 1 Direct backscattering from various surfaces (a_e),
2 volume scattering from canopy (a_),
3 double-bounce specular reflection of tree trunk-surface (_0),
4 double-bounce specular reflection of aerial roots-surface (ad°ra), and
5 multiple scattering of surface-canopy (a°0_).
Thus, the total radar return is
0 0 O •
atot a,_o + a_o_ + a_, + ad,, + arnse
It should be noted that all these model components may or may not be attenuated by the canopy,
by the aerial root layer, and/or by the tree trunk layer, depending on their presence or not (see
Figure 1.).
5. Discussion :
5.1. The Fit of the Models to the SIR-B Images (HH polarization only)
Three forest stands were extracted from SIR-B images (DT 120, incidence angle 26°; and
DT 104, incidence angle 46 ° ; see also Imhoff et al. 1986), and the image DNs were converted to
relative radar backscattering coefficients (dB) by
a ° _10 _ Iog(DN 2- C1_)- C2_
where i _ 1, 2, corresponding to DT 120 and DT 104. C1; are the noise levels, C2i constants
for calibration. Both C1_" and C2; were provided by Jet Propulsion Laboratory (JPL), as shown
in Table 5.1.
The radar returns for each stand at the two incidence angles, given in Table 5.2, show that
there are differences in the absolute values in that the returns from SIR-B images are lower than
those of our model results. The mean (p) and standard deviation (a) of the differences, in dB, are
p2_o _ -10.9, a260 _ 0.90; and p46o _- -9.4, a4¢o _ 0.86. The reason for the differences may be
that the models are theoretical solutions and each model component is incoherently added, which
will almost certainly produce higher values than the actual coherent summation of the
-4-
electromagneticfield,whichwe cannotperformbecauseof the complexityof the environment.
Thesameevidenceof higherincoherentsummationwasalsofoundby SunandSimonett(1988).
Also,SIR-Bhadsomemajorcalibrationproblems,whichmayproduceuncertaintyasto thetrue
4
values of C1¢ and G2i (Wall and Curlander, 1988). Because the standard deviations of the
differences are relatively small, after the model results are shifted down (/_2_0 and V4_o for 26 °
and 46 ° incidence angles, respectively), the model results match the SIR-B images well in disper-
sion.
In order to better assess the model's ability to predict differences in returns from difference
stands, stand Gn was selected as a reference, and a ° for each stand relative to that for Gn was
calculated by subtraction (equivalent to a ratio, after logl0 operation). There is good agreement
between the model and SIR-B results (Table 5.3): all inter-stand comparisons at both angles
agreed in sign except for (SGn - Gn) and (SGn -Gn) at 26°; and (GSn - Gn) and (SGn- Gn) at
46 ° . The magnitudes of the differences agreed very well for some stands (Gf - Gn) but poorly for
others (SGn - Gn, and SGf- Gn).
5.2. Radar Returns from Nonflooded and Flooded Areas
It is well understood that some types of flooded forest yield brighter L-band returns than
unflooded stands (Stone and Woodwell, 1985; Richards et al., 1987). SIR-B and model results
given in Table 5.4 show that although flooded stands yield higher returns than nonflooded stands
in all but one case, the difference was not very great. The differences ranged from 0.5 to 3.2 dB
for SIR-B images; 0.9 to 1.7 dB for model results at HH polarization; and 0.1 to 1.2 clB for model
results at W polarization. Most were less than 1.5 dB. The lack of a marked difference may
result from strong attenuation by the dense canopy, and also from the uncertainty in the locations
of the flooded and nonflooded areas due to changing tidal inundation, when the areas were imaged
at 20:31 at GMT (3:31 am local time) for DT 120 and at 20:48 GMT (3:48 am local time) (Cimino
et al., 1988). The difference in returns between flooded and nonflooded stands was, on average,
greatest for GS, and least for SG, at both incidence angles. SG was the only stand for which
SIR-B flooded returns were less than nonfloodcd returns (a 0.4 dB reduction). Due to uncertainty
in calibration of a ° , these small differences cannot be considered significant; in most cases they
are less than one standard deviation of the stand means (Table 5.6). The differences are probably
not large enough to delineate the flooded boundaries with confidence. Also, there are no obvious
visual boundaries on the SIR-B images between those stand areas and surrounding areas, because
-5-
of the high intrinsic variance in SIR-B images and the patchiness of the flooded areas.
5.3. Differences in Radar Returns between Different Stands
o _- For both flooded and nonflooded stands, there are only small differences (ranging -1.8 to 0.8
dB) (Table 5.5). This may be explained as follows: a) each stand is a mix of Sundri and Gewa in
varying proportions; b) the number of stems is very large and the canopy is very dense, which are
of importance both in enhancing a_0 and a_° and in attenuation; and e) the structure of Sundri
and Cewa are similar to each other (Tomlinson, 1986).
5.4. Radar Returns as a Function of Different Incidence Angles
The mean returns at different incidence angles are almost the same, but with greater vari-
ance at the smaller incidence angle, as tabulated in Table 5.6. At the smaller incidence angle,
there is a shorter path length through the canopy, yielding less attenuation for the cr_ a and the
a_0 terms, which are the main component producing the variance for mangal: a similar conclu-
sion was reached by Sun and Simonett (1988) for pine forest.
5.5. Relative Importance of Model Components
The dominant model components are a_0 at HH polarization. At W polarization, a_°,
a_0, and a,_ c are roughly equal to each other for nonflooded and flooded surfaces (Table 5.7).
For flooded surfaces a_, and a_e are increased, and a,_c is unchanged. In areas where there are
some natural clearings which lack the dense canopy layer, a_, and a_, are clearly dominant.
The high returns from forest edges facing the radar illumination direction on the SIR-B images
are notable.
6. Publication Produced under this Contract
Wang, Y., M. L. Imhoff, and D. S. Simonett, (1989), "Radar modeling of mangal forest stands",
The Proceedings of the IGARSS'89, voh 4, pp. 2497-2500. Vancouver, Canada, July, 1989.
-6-
Table5.1.ConversionconstantsfromDNsto dBfor SIR-Bimages(HH)
Cx_ C2i
4
- -" DT 120 (26 ° incidence angle) 3157.5 55.5
DT 104 (46 ° incidence angle) 4389.1 56.4
Formulas for conversion of SIR-B image DNs to ¢r° in dBs is:
a ° = lO*log[DN2-Cl_] - C_i
where i _-- I, 2, corresponding to DT 120 and DT 104 of SIR-B images. These data were pro-
vided Jet Propulsion Laboratory.
Table 5.2. Mean a ° (dB) of SIR-B images (HI-I) and model results
r
Stands Gn Gf GSn GSf SGn SGf
26 °
46 °
SIR-B images (HH)
Model results (HH)
Differences (HtI)*
Model results (VV)
-13.5 -12.2 -14.4 -12.2 -13.6 -14.0
-3.1 -2.1 -3.5 -1.8 -2.5 -1.4
-10.4 -10.1 -10.9 -10.4 -11.1 -12.6
-7.0 -6.8 -7.0 -6.9 -7.4 -7.0
SIR-B images (HH)
Model results (HH)
Differences (HH)*
Model results (VV)
-13.4 -12.1 -14.5 -11.3 -13.6 -13.1
-4.3 -3.4 -4.0 -3.1 -3.6 -2.7
-9.1 -8.7 -10.5 -8.2 -10.0 -10.4
-7.2 -6.0 -7.5 -6.3 -7.8 -6.7
* The differences are defined as the dB values of SIR-B images minus those of model results
(HH). The means (p) and the standard deviations (a) of the differences are P260 ---_ -10.9, a2a0 -----
0.90, and P46o = -9.9, a46o _ 0.95.
-7-
Table5.3.Differencesof (meana ° , in dB) for model results and SIR-B images
Gn-Gn Gf-Gn GSn-Gn GSf-Gn SGn-Gn SGf-Gn
26 o SIR-B images (HH) 0.0 1.3 -0.9 1.3 -0.1 -0.5
, _- Model results (HH) 0.0 1.0 -0.4 1.3 0.6 1.7
Model results (VV) 0.0 0.2 0.0 0.1 -0.4 0.0
46 ° SIR-B images (HH) 0.0 1.3 -1.1 2.1 -0.2 0.3
IVfodel results (HH) 0.0 0.9 0.3 1.2 0.7 1.6
Model results (VV) 0.0 1.2 -0.3 0.9 -0.6 0.5
Stand Gn was selected as a reference. The radar returns for the other stands minus the
return of Gn were calculated for both model results (HH and VV) and SIR-B images (HH).
Table 5.4. Differences of (mean a ° , in dB) between flooded and nonflooded stands
Gf- Gn GSf- GSn SGf- SGn
26 ° SIR-B images (HH) 1.3 2.2 .-/ -0.4
Model results (HI-I) 1.0 1.7 1.1
Model results (VV) 0.2 0.1 0.4
46 ° SIR-B images (HH) 1.3 3.2 0.5
Model results (HH) 0.9 0.9 0.9
Model results (W) 1.2 1.2 1.1
-8-
Table 5.5. Differences of (mean a ° , in dB) between different stands
Nonflooded Flooded
GSn - Gn SGn - Gn GSf- Gf SGf- Gf
26 ° SIR-B images (HH) -0.9 -0.1 0.0 -1.8
* " Model results (HH) -0.4 0.6 0.3 0.7
Model results (VV) 0.0 -0.4 -0.1 -0.2
46 ° SIR-B images (HI-I) -1.1 -0.2 0.8 -1.0
Model results (I-[H) 0.3 0.7 0.3 0.7
Model results (W) -0.3 -0.6 -0.3 -0.7
Gn and Gf (for model results and SIR-B images) were selected as references for nonflooded
and flooded stands, respectively.
Table 5.6. a ° (dB) as a function of incidence angles
Mean vMues
Gn Gf GSn GSf SGn SGf
SIR-B images (HH) 26 ° -13.5 -12.2 -14.4 -12.2 -13.6 -13.4
46 ° -13.4 -12.1 -14.5 -11.3 -13.6 -13.1
Differences -0.1 -0.1 0.1 0.9 0.0 0.3
Model results (HH) 26* -3.1 -2.1 -3.5 -1.8 -2.5 -1.4
46 ° -4.3 -3.4 -4.0 -3.1 -3.6 -2.7
Differences 1.2 1.3 0.5 1.3 1.1 1.3
Model results (VV) 26 o -7.0 -6.8 -7.0 -6.9 -7.4 -7.0
46 o -7.2 -6.0 -7.5 -6.3 -7.8 -6.7
Differences 0.2 -0.8 0.5 -0.6 0.4 0.3
Standard derivations
Gn Gf GSn GSf SGn SGf
SIR-B images (HH) 26 ° 2.04 2.06 2.03 2.09 1.86 3.46
46 ° 1.41 1.75 1.45 1.18 1.33 1.27
Model results (HH) 26 ° 0.21 0.23 0.19 0.20 0.19 0.22
460 0.27 0.29 0.20 0.21 0.22 0.23
Model results (W) 26 ° 0.34 0.39 0.28 0.32 0.26 0.31
46 ° 0.19 0.15 0.16 0.14 0.19 0.15
i_ dB)
-9-
Table 5.7. Relative importance of model components (mean a ° ,
Gf
GSn
GSf
SGn
SGf
26° (un)
26° (W)
46° (nil)
46° (VV)
26° (HH)
26° (vv)
46* (HH)
46° (vv)
26° (HH)
26° (vv)
46° (HH)
46' (w)
26" (m-t)
26" (w)
46° (rot)
46° (w)
26' (HH)
26° (w)
4_o (un)
46o (w)
26° (HH)
26o (w)
46° (rot)
46' (w)
-22.3 -12.7 -5.3 -7.3 -11.2
-22.4 -12.7 -14.9 -10.7 -15.1
-50.3 -13.1 -5.4 -11.8 -12.2
-46.7 -13.1 -12.9 -8.7 -19.2
-12.9 -12.3 -7.0 -11.5 -11.9
-12.5 -12.3 -16.2 -15.1 -15.1
-24.7 -12.7 -6.0 -13.8 -13.6
-21.9 -12.7 -14.1 -11.5 -21.3
-22.9 -12.3 -4.5 -7.9 -11.0
-23.2 -12.3 -14.2 -11.4 -15.0
-51.1 -12.7 -4.9 -12.7 -12.3
]
i
-47.7 -12.7 -12.3 -9.7 -19.5
-14.7 -11.9 -5.0 -10.5 -12.8
-15.3 -11.9 -15.2 -14.4 -17.0
-26.0 -12.3 -5.2 -15.1 -14.0
-23.4 -12.3 -13.4 -13.1 -21.9
-23.9 -11.9 -3.5 -8.9 -II.I
-24.3 -11.9 -13.4 12.6 -15.2
-52.3 -12.3 -4.1 -14.0 -12.8
-49.3 -12.3 -11.6 -11.3 -20.1
- 10-
6. References
Chaffey,D. R., F. R. Miller, and J. H. Sandom, (1985), "A forest inventory of the Sundarbans,
Bangladesh", British Overseas Development Administration Project _ 140, Land Resource
Development Center, Tolworth, Surbiton, Surrey, England.
4
Chapman, V. J. (1977), " Wet coastal ecosystems, Ecosystems of the world 1", Elsevier Scientific
Publication Company.
FAO, Food and Agriculture Organization of the United Nations, (1981), Tropical resources
assessment project (in the framework of the global environment monitoring system - GEMS),
Forest resources of tropical Asia.
FAO, Food and Agriculture Organization of the United Nations, (1982), Management and utiliza-
tion of mangroves in Asia and the Pacific.
Imhoff, M. L., M. Story, C. Vermillion, F. Khan, and F. Polcyn, (1986), "Forest canopy character-
ization and vegetation penetration assessment with space-borne radar", IEEE Transactions
on Geosciencc and Remote Sensing, vol. GE-24, no. 4, pp. 535-542.
Cimino, J. B., B. Holt, and A. H. Richardson, (1988), "The shuttle imaging radar (SIR-B) experi-
ment report", JPL publication 88-2.
Richards, J. A., P. W. Woodgate, and A. K. Skidmore, (1987), An explanation of enhanced radar
backscattering from flooded forests, International Journal of Remote Sensing, vol. 8, pp.
1093 - 1100.
Stone, T. A. and G. M. Woodwell, (1985), "Analysis of deforestation in Amazonia using shuttle-
image radar", IEEE Cat. No. 85CHP162-6, Digest 1985 International Geoseicnee and
Remote Sensing Symposium, vol. II, Amherst, Ma, 574. t
Sun, C. Q. and D. S. Simonett, (1988), "Simulation of L-band HH radar backscatter from coni-
ferous forest stands: a comparison with SIR-B data", International Journal of Remote Sens-
ing, vol. 9, no. 5, pp. 907 - 925.
Tomlinson, P. B., (1986), The botany of mangroves, Cambridge University Press.
Wall, S. D. and J. C. Curlander, (1988), "Radiometric calibration analysis of SIR-B imagery",
International Journal of Remote Sensing, vol. 9, pp. 891 - 906.
Wang, Y., M. L. Imhoff, and D. S. Simonett, (1989), "Radar modeling of mangal forest stands",
The Proceedings of the IGARSS'89, vol. 4, pp. 2497-2500. Vancouver, Canada, July, 1989.
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Improvement and extension of a radar forest backscattering model

Research to-date has focused on modeling development and programming based on model components proposed during the past several months and research progress made by the Simonett team. The model components and programs (in C language under UNIX) finished to date are summarized. These model components may help explain the contributions of various vegetation structural components to the attenuation and backscattering of vegetated surfaces to extract useful data concerning forest stands and their underlying surfaces for both the seawater-on and seawater-off

Improvement and extension of a radar forest backscattering model

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