Leaf Area Index Modeling of Mangrove based on Hyperspectral ...

<strong>Leaf</strong> <strong>Area</strong> <strong>Index</strong> <strong>Modeling</strong> <strong>of</strong> <strong>Mangrove</strong> <strong>based</strong> on Hyperspectral
Remote Sensing and in-situ Hemispherical Photography
in Maipo Ramsar Site <strong>of</strong> Hong Kong
*WONG, Kwan Kit Frankie and FUNG, Tung
Department <strong>of</strong> Geography and Resource Management, The Chinese University <strong>of</strong> Hong Kong
1. Introduction
kkit@cuhk.edu.hk
<strong>Mangrove</strong>s are important primary producers in coastal ecosystem. Not only do they provide
shelters for many marine and terrestrial animals, they also help stabilizing the coastal areas
from erosion. However, mangroves are globally threatened due to unsustainable exploitation,
development and pollution (Valiela et al., 2001). Information related to spatial extent,
photosynthesis, productivity level is essential to monitor the ecological conditions <strong>of</strong> mangrove.
Given the environmental constraints in most <strong>of</strong> the mangrove ecosystems, remote sensing has
long been regarded as a cost-effective and timely tool for mangrove conservation study.
The objective <strong>of</strong> the study is to make use <strong>of</strong> the field-measured <strong>Leaf</strong> <strong>Area</strong> <strong>Index</strong> (LAI) and
spectral indices derived from hyperspectral image in order to:
- estimate LAI in Inner Deep Bay;
- evaluate the performance <strong>of</strong> vegetation indices (VIs) in LAI mapping;
- compare the LAI <strong>of</strong> different mangrove species; and
- contribute to existing research on mangrove monitoring, management and conservation.
2. Remote sensing and biophysical parameter monitoring
Remote sensing has long been used for vegetation studies. With well-developed studies on
fundamental parameters affecting vegetation reflectance as well as advancement in technology,
the scopes <strong>of</strong> remote sensing had not limited to classification and had extended to ecological
applications. Crucial ecological-related parameters such as fraction <strong>of</strong> absorbed
photosynthetically active radiation, leaf area index, albedo and gross primary vegetation
productivity can be estimated through remote sensing imageries at regional and global scale.
1

Every single material on earth has its unique spectral response light. In other words, the amount
<strong>of</strong> absorption, transmittance and reflection <strong>of</strong> different materials is wavelength dependent.
Specifically for vegetation science, factors such as species, healthiness and moisture levels
would affect their responses to light in different wavelengths. Taking this characteristics into
consideration, the advancement <strong>of</strong> hyperspectral sensors allow satellites to capture
exceptionally fine, narrow and contiguous spectral bands <strong>of</strong> different materials on earth surface.
Compared with the multispectral counterpart, the narrowband characteristic has significantly
enhanced the spectral measurement capabilities that are useful for detection and modeling <strong>of</strong>
the characteristics <strong>of</strong> terrestrial ecosystem (Kumar et al., 2001; Thenkabail et al., 2004).
Researches showed that hyperspectral data performs better in terms <strong>of</strong> vegetation mapping as
well as biophysical and biochemical parameters extraction when compared with multispectral
counterpart (Gao, 1999; Broge & Leblanc, 2000; Gong et al., 2003; Hirano et al., 2003; Pu et al.,
2005; Zhao, 2007).
The majority <strong>of</strong> vital ecosystem processes such as carbon dioxide flux, evapotranspiration,
rainfall interception, photosynthesis and so on, take place in foliage elements. <strong>Leaf</strong> area index
(LAI) is always used to quantify the amount <strong>of</strong> live green leaf materials in the canopy. It is a
dimensionless variable defined as the total one-sided area <strong>of</strong> all leaves in the canopy per unit
ground area (Pierce and Running, 1988; Gong et al., 2003; Lee et al., 2004). LAI is one <strong>of</strong> the
most important elements in ecological field and modeling studies (Chason et al., 1991). The
success <strong>of</strong> LAI estimation can simulate the ecological process and predict the response <strong>of</strong>
ecosystem (Green et al., 1997).
Measurement <strong>of</strong> LAI is generally divided into two main categories – direct methods and indirect
methods (Chason et al., 1991; Chen et al., 1997; Jonckheere et al., 2004; Welles, 1990). Direct
methods such as area harvest, litter collection, allometry, involve direct measurement <strong>of</strong> area,
shape, orientation <strong>of</strong> individual foliage. If the methodology is strictly followed, it is the most
accurate method. However, the process is extremely time-consuming, costly and labour
intensive. The feasibility is questionable when spatial and temporal scales <strong>of</strong> the study are
concerned.
Indirect method is to infer LAI from observation <strong>of</strong> another variable. The optical methods <strong>based</strong>
on radiative transfer theory are the most popular technique. Since canopy architecture directly
affects the characteristics <strong>of</strong> radiation interception, the optical methods measure the probability
<strong>of</strong> radiation not obstructed by vegetative elements from a view angle, which is called the gap
fraction (Ross, 1981; Welles, 1990; Welles and Norman, 1991). With appropriate radiative
transfer model, LAI is computed from the inversion <strong>of</strong> the exponential expression <strong>of</strong> gap fraction.
With reference to the theory <strong>of</strong> Miller 1967, the relationship between gap fraction and LAI is
expressed as:
2
L 2
ln( T(
)) cos
sind
0
2

Where L is the LAI; T(θ) is the gap fraction as a function <strong>of</strong> zenith angle θ; and cosθ indicates the
decrease <strong>of</strong> intensity <strong>of</strong> radiance with increase in path length (and zenith angle θ) through the
canopy. LAI is computed as the integration <strong>of</strong> natural logarithm <strong>of</strong> gap fraction measured at
zenith angle θ, ranging from 0 - π/2 <strong>based</strong> on the assumption that the foliage elements are
randomly distributed in terms <strong>of</strong> azimuth angle φ.
The optical methods are comparatively faster, non-destructive, amendable to automation (both
data acquisition by electronic instruments and data processing by programme and s<strong>of</strong>tware),
and therefore allow sampling <strong>of</strong> large area and with higher frequency. Common practices
include LAI-2000 Plant Canopy Analyzer, hemispherical photography, quantum sensor, DEMON
and TRAC. However, the optical methods cannot separate vegetative elements, i.e. branches
from foliage, stem from foliage, it is usually referred to ‘effective LAI (LAIe)’. Besides, the optical
methods assume that foliar elements are randomly distributed. The assumption is <strong>of</strong>ten violated
which generally underestimate LAI for about 25-50%. Clumping index is always used to
compensate the dispersion or aggregation <strong>of</strong> the canopy (Chen and Black, 1992).
The use <strong>of</strong> remote sensing supplemented with field measurements to estimate biophysical
variables at larger scale becomes feasible.
3. Methodology
The study aimed at calibrating the spectral data extracted from hyperspectral images with insitu
field measured LAI data using multivariate statistical analysis. Figure 1 shows the
methodological workflow <strong>of</strong> the study.
Figure 1. The methodological workflow
3

The Hyperspectral data from the onboard Hyperion sensor <strong>of</strong> NASA Earth Observing 1 (EO-1)
satellite was acquired, atmospherically and geometrically corrected. A number <strong>of</strong> vegetation
indices were derived from the narrowbands. In terms <strong>of</strong> LAI measurement, the hemispherical
photos were taken with locations recorded by GPS. Linear regression models were then used to
estimate the LAI <strong>of</strong> study area. The predicted LAI results were evaluated and compared among
species. The procedures were described below.
3.1 Study area
The Mai Po Nature Reserve in the Inner Deep Bay flourishing the largest mangrove stand at the
northwestern part <strong>of</strong> Hong Kong (22°29'N - 22°31'N, 113°59'E - 114°03'E) was the chosen site <strong>of</strong>
study. Figure 2 shows the distribution <strong>of</strong> mangrove stands in Hong Kong and location <strong>of</strong> study
area. It covers an area about 172 hectares and keeps extending towards Deep Bay steadily every
year. The site was designated as “Wetland <strong>of</strong> International Importance” under the Ramsar
Convention in 1995 and nurtures four native species including Acanthus ilicifolius, Aegiceras
corniculatum, Avicennia marina, and Kandelia obovata as well as two exotic species Sonneratia
caseolaris and Sonneratia apetala which propagated from the plantation along the Shenzhen
River in Futian National Nature Reserve in Shenzhen.
Deep Bay
Mai Po in Inner Deep Bay
Lantau Island
New Territories
Hong Kong Island
114°20’0”E
22°30’0”N
Figure 2. The location <strong>of</strong> Mai Po Nature Reserve and the distribution <strong>of</strong> mangrove stand in Hong
Kong
4

3.2 Field measurement – Hemispherical photography
Field measurement <strong>of</strong> LAI was conducted using hemispherical photographic technique during
October to November, 2008, which was scheduled to coincide with the Hyperion data. A Nikon
Coolpix 5400 digital camera mounted with Nikon FC-E9 fish-eye lens converter (180 degrees
field <strong>of</strong> view) was used to capture the canopy characteristics. The camera was set on a selfleveling
O-mount for automatic leveling with an electronic NorthFinder indicating the north
direction in each <strong>of</strong> the hemispherical photo taken (Regent Instruments Inc., 2008). The whole
set <strong>of</strong> instrument was mounted on a tripod and the height was adjusted to around breast height
by fully extending tripod legs and central shaft. A remote controller was used to trigger the
shutter with a view to minimize undesirable manual distortion to the horizontal leveling <strong>of</strong> the
camera.
A total <strong>of</strong> 95 plots were visited and 1,140 hemispherical photographs were taken in the field
along four different pre-defined transects. Each plot was designed to cover an area <strong>of</strong>
approximately 30 meters x 30 meters. The coordinates <strong>of</strong> the central location <strong>of</strong> each plot was
recorded with the Trimble GPS GeoXH Handheld. A Hurricane antenna was attached to ensure
signal was well-received under canopy. Within each plot, four points were chosen for the photo
acquisition covering the centre <strong>of</strong> the plot as well as the corners. During photo acquisition,
exposure bracketing was used to capture photographs with three different exposure levels, i.e.
0EV, -1.0EV and -2.0EV. The aim was to acquire photos with higher contrast level between sky
and vegetative elements. The 95 plots covered two dominant species, Avicennia marina and
Kandelia obovata.
3.3 Field LAI Extraction
The set <strong>of</strong> image files with -2.0EV were chosen from which canopy information was extracted
due to higher contrast between sky and vegetative elements. WinSCANOPY 2006a from Regent
Instruments Inc was used to covert gap fraction to LAI using two methods provided by
WinSCANOPY including calculation proposed by Bonhomme and Chartier in 1972 and a method
similar to Li-Cor’s LAI-2000 plant canopy analyzer which was abbreviated as Bonhomme’s LAI
and LAI-2000’s LAI respectively in the following article. The Bonhomme’s method measures the
gap fraction with zenith angles centered at 57.5°, which is found to be insensitive to leaf angle
distribution. LAI was computed using the simple formula LAI = -1.1In[GapFr(57.5)] (Regent
Instrument Inc., 2008). Comparatively, the LAI-2000 method measured gap fractions at five
default range <strong>of</strong> zenith rings (θ) centered at 7°, 23°, 38°, 53°, and 68° with a total field <strong>of</strong> view
148°, which was then converted to LAI using the Miller’s (1967) theorem as described previously
in Section 2. Since it is always measured at five fixed zenith angles, the expression is expressed
as:
5

5
1
L 2 ln( T ( )) cos
sind
Prior to analysis, image defects such as uneven illuminated sky and sun flares due to
unfavourable and varied sky conditions were removed. This is followed by the specification <strong>of</strong>
specific regions in terms <strong>of</strong> zenith and azimuth angles to be analyzed. Since the camera and the
lens were well calibrated by the company, pixel classification <strong>of</strong> images was then conducted to
produce a binary image. For each photo, a threshold was manually selected by visual
interpretation with a view to maximally separate the vegetative elements from the sky.
Thresholds ranged from 90 to 140 were set <strong>based</strong> on image contrast. Figure 3a shows the
original image overlaid with sky grids divided self-defined azimuthal and zenithal divisions as
well as the suntracks. Figure 3b shows the interactive threshold selection interface in deriving
the binary image. Parameters describing the canopy structure including gap fraction, LAI
(computed with different method), leaf inclination angle distribution, clumping index were
calculated automatically after the binary classification. Since the distribution <strong>of</strong> leaves was
observed to be non-random in nature, the clumping compensation described in Van Gardingen
(1999) was taken into account during LAI computation. Finally, the LAI <strong>of</strong> each plot was
calculated as the average <strong>of</strong> the four measurement points within the plot.
a) Raw image overlaid with
sky grids and suntracks
b) Binary image with chosen threshold (130)
Figure 3. The transformation from raw to binary image for LAI estimation.
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3.4 Hyperspectral Data and Processing
The satellite image used was a hyperspectral image which was acquired by the onboard
Hyperion sensor <strong>of</strong> NASA Earth Observing 1 (EO-1) satellite, covering a complete spectrum
ranged 357-2576 nm with a spectral interval <strong>of</strong> 10 nm. A Level 1R Hyperion image centered at
22.452°N, 114.014°E, was acquired on 21 st , November, 2008 at 14:48:53 GTM covering the Inner
Deep Bay. The image is <strong>of</strong> high quality with minimal effect <strong>of</strong> cloud. The study area is totally
cloud-free.
The uncalibrated bands (1-7 and 58-76) and duplicated bands (57 and 77) were removed from
the dataset. The remaining 196 calibrated band were carried forward for further processing.
Processing steps including abnormal pixel and line correction, strip removal and noise reduction
were conducted in order to rectify the artifacts created during the image correction.
Atmospheric correction was then conducted in order to extract the reflectance characteristics <strong>of</strong>
the mangroves using the Fast Line-<strong>of</strong>-sight Atmospheric Analysis <strong>of</strong> Spectral Hypercubes
(FLAASH) in ENVI. One <strong>of</strong> the important merits <strong>of</strong> FLAASH is its ability to calculate and extract
essential atmospheric parameters such as aerosol level and water amount from the absorption
features <strong>of</strong> the narrow spectral bands. Another essential feature <strong>of</strong> FLAASH is the ability to
automatically correct the ‘smile effect’ inherited in Hyperion sensor.
Geometric rectification was then preformed to match with the projection and coordinate
system <strong>of</strong> field data. 28 GCPs were collected evenly from the image with an overall root mean
square error <strong>of</strong> 0.07 pixels (2 meters). The error was within tolerance limit.
3.5 Vegetation <strong>Index</strong> Extraction
Spectral vegetation indices (VIs) derived from narrowband hyperspectral data were used to for
LAI prediction in the Inner Deep Bay. The selection <strong>of</strong> VIs is important as demonstrated by
previous researches that issues such as saturation problem, model linearity and stability were
noteworthy (Elvidge & Chen, 1995; Roujean & Breon, 1995; Broge & Leblanc, 2001; Brown et al.,
2000; Gong et al., 2003; Zhao et al., 2007). For instances, according to Zhao et al. (2007), VIs
computed from the narrowband combination <strong>of</strong> 700nm and 800nm generally produced a better
result than the band combination <strong>of</strong> 670nm and 800nm. Ray et al. (2006) found that VIs such as
NDVI and SAVI computed using 680nm and 780nm showed maximum correlation to LAI among
other band combination.
Correlation matrix was used to explore the relationship between 95 LAI measurements and 196
spectral bands. Linear correlations with reflectance were weak in the red and SWIR regions but
strong in near-infrared regions. Narrow spectral bands with highest correlation coefficients at
682nm and 825nm were used to represent red and near infrared region respectively. Due to the
fluctuation <strong>of</strong> reflectance and low correlation with LAI in the SWIR bands, the reflectance
7

etween 1550nm and 1650nm were averaged to represent SWIR band. Figure 4 shows the
selected bands covering the study area. Table 1 described the vegetation indices used for LAI
prediction in the study. The typical ones were Simple Ratio (SR) and Normalized Difference
Vegetation <strong>Index</strong> (NDVI) while Renormalized Difference Vegetation <strong>Index</strong> (RDVI), Non-Linear
Vegetation <strong>Index</strong> (NLVI), Modified Simple Ratio (MSR) and Reduced Simple Ratio (RSR) were
designed to suppress background effect. The reflectance at Red Edge Position (REP) calculated
using the linear interpolation method, is one <strong>of</strong> the important features in vegetation analysis
and correlates well with chlorophyll, LAI and healthiness as demonstrate in other studies.
Figure 4. The selected NIR (825nm), red (682nm) and SWIR (Averaged 1550-1650nm) reflectance
images <strong>of</strong> the study area (from left to right).
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Table 1. The Spectral Vegetation <strong>Index</strong> (VIs) used for LAI modeling
Simple Ratio
(SR)
<strong>Index</strong> Formula Description References
Normalized Difference
Vegetation <strong>Index</strong>
(NDVI)
Renormalized
Difference Vegetation
<strong>Index</strong>
(RDVI)
Non-Linear Vegetation
<strong>Index</strong>
(NLVI)
Modified Simple Ratio
(MSR)
Reduced Simple Ratio
(RSR) SR* 1 –
Reflectance at
Red Edge Position
(REP)
ρNIR /ρR
(ρNIR – ρR)/( ρNIR+ρR)
(ρNIR – ρR)/( ρNIR + ρR) 1/2
(ρSWIR - ρSWIRmin)
(ρSWIRmax - ρSWIRmin)
Relates the changes in green biomass, pigment content, etc.
Enhance the contrast between vegetation and soil and minimizing the effect <strong>of</strong>
illumination.
Responds to the changes in green biomass, pigment content, etc.
Effective in predicting canopy properties in moderate vegetation density.
Higher linearity with canopy parameters.
Incorporate the advantages <strong>of</strong> the NDVI and Difference Vegetation <strong>Index</strong> (DVI), which is
sensitive for high and low density vegetation covers respectively.
Tucher, 1979;
Baret & Guyot, 1991
Rouse et al., 1974
Roujean & Breon,
1995
(ρ 2 NIR – ρR)/( ρ 2 NIR+ρR) Develop linear relationships with surface parameters that tend to be nonlinear. Goel & Qin, 1994
(ρNIR / ρR – 1)
(ρNIR / ρR) 1/2 + 1
ρ 670nm + ρ780nm
2
An enhancement over RDVI by developing a linear relationship between the index and
biophysical parameters.
Chen, 1996
Modified SR by SWIR reflectance.
Compensate for differences in background reflectance and canopy closure. Brown et al., 2000
Reflectance <strong>of</strong> the point with the maximum slope along the red edge.
Relatively insensitive to soil background, atmospheric effects, and solar angle.
Danson & Plummer,
1995
Shafri et al., 2006
9

3.6 Linear Regression <strong>Modeling</strong>
Linear regression model was developed (Green et al., 1997; Kovacs et al., 2004; Kovacs et al.,
2005) using vegetation indices as independent variable to predict LAI, the dependent variable.
The 95 in-situ LAI samples were divided equally into training and testing dataset. Three sets <strong>of</strong>
training and testing data were generated through random sampling. The coefficient <strong>of</strong>
determination R 2 which is proportion <strong>of</strong> variance explained by the regression model and the
mean residuals were used to measure model performance.
The LAI map was produced using the unstandardized beta coefficient and constant given by the
regression model. Based on the known spatial distribution <strong>of</strong> species, the LAI for different
species was extracted and compared using univariate statistics.
4. Results and Discussion
4.1 Field measured LAI
LAI derived from Bonhomme’s method had a range from 1.31 to 3.56 while that derived from
LAI-2000 method had a range from 1.27 to 3.0. The mean (standard deviation) LAI was 2.27
(0.49) and 2.15 (0.45) for Bonhomme’s method and LAI-2000 method respectively. The
Bonhomme’s LAI was generally higher than LAI-2000 method. Figure 5 shows the frequency
distribution <strong>of</strong> LAI for both methods.
Figure 5. The frequency distribution <strong>of</strong> measured LAI for Bonhomme’s and LAI- 2000 methods
4.3 Regression and Validation
Table 2 shows the average performances <strong>of</strong> regression models extracted from the three
randomly-generated datasets. The RDVI model has the best performance with the highest R-
10

square (Bonhomme R 2 = 0.614; LAI-2000 R 2 = 0.707) and low mean residual (Bonhomme = 0.046;
LAI-2000 = 0.05). According to Roujean and Breon (1995), RDVI combines advantages <strong>of</strong> NDVI
(not affected by orientation and optical properties <strong>of</strong> leaves and working better under high
vegetation coverage) and DVI (less influenced by background spectral signature under sparse
vegetation, while is much affected by spectral and directional canopy properties under high
vegetation density). The REP model is the second best model though the R 2 is low (Bonhomme
R 2 = 0.0.492; LAI-2000 R 2 = 0.57). The red edge, a distinctive spectral feature <strong>of</strong> vegetation, is the
spectral region with abrupt reflectance change caused by strong chlorophyll absorption and leaf
interior scattering. Variation in factors like LAI, plant health status, ages, seasonal patterns will
cause a shift in REP position (in terms <strong>of</strong> wavelength) and change <strong>of</strong> magnitude <strong>of</strong> reflectance at
REP (Shafri et al., 2006). However, the relationship with LAI was likely to be non-linear as
suggested by Danson and Plummer (1995). Regression models using SR, NDVI, NLVI, MSR and
RSR did not provide good fitting solutions with RSR gave the worst result.
Figure 6 shows the scatterplots <strong>of</strong> field-measured and predicted LAI (using RDVI) for two
computation methods. Three sets <strong>of</strong> prediction are represented by different colors. The linear
regression models for the two methods were:
Bonhomme’s LAI = -4.785 + 0.113 (RDVI)
LAI-2000 LAI = -4.725 + 0.11 (RDVI)
Judging from the three regression models, the LAI prediction for the LAI-2000 method was more
consistent as indicated by less discrepancy among the predictions. This was also reflected by the
lower average standard deviation <strong>of</strong> residual for LAI-2000 method though the mean residual is
about the same in Table 2. The LAI model derived from LAI-2000 method was comparatively
more robust and stable.
Table 2. The average performance <strong>of</strong> regression models from the three random sets <strong>of</strong> sample
SR NDVI RDVI NLVI MSR RSR REP
Bonhomme’s method
Model R 2 - Total 0.193 0.328 0.614 0.445 0.236 0.055 0.492
Model R 2 - Training 0.140 0.280 0.561 0.397 0.179 0.048 0.444
Model R 2 - Independent 0.260 0.386 0.671 0.500 0.309 0.062 0.545
Mean Residual - Independent 0.070 0.066 0.046 0.373 0.072 0.802 0.050
S.D. Residual - Independent 0.446 0.398 0.288 0.356 0.430 0.492 0.339
LAI – 2000’s method
Model R 2 - Total 0.186 0.348 0.707 0.478 0.235 0.032 0.570
Model R 2 - Training 0.134 0.300 0.658 0.429 0.178 0.033 0.522
Model R 2 - Independent 0.253 0.404 0.759 0.533 0.306 0.032 0.620
Mean Residual - Independent 0.071 0.069 0.050 0.067 0.073 0.504 0.043
S.D. Residual - Independent 0.407 0.355 0.223 0.312 0.390 0.450 0.281
11

R 2 = 0.658
R 2 = 0.707
Figure 6. The scatterplots <strong>of</strong> measured and predicted LAI <strong>of</strong> the 95 plots
The corresponding LAI predicted maps are shown in Figure 7. The predicted Bonhomme’s LAI
was generally higher than that predicted by LAI-2000’s method. The maximum LAI is 3.97 and
3.80 for Bonhomme’s method and LAI-2000’s method respectively. However, no distinctive
difference in the spatial pattern <strong>of</strong> LAI was observed.
Sample set 1
Sample set 2
Sample set 3
Regression fit line
Figure 7 also indicates the locations <strong>of</strong> pure patches <strong>of</strong> mangrove <strong>based</strong> on previous documents
and researches. Site 1 is Kandelia obovata; site 2 is Avicennia marina; site 3 is Kandelia obovata;
and site 4 is Acanthus ilicifolius. The two K. obovata sites showed significant difference in LAI
with site 3 much higher than that <strong>of</strong> site 1. As documented in the government database,
mangroves in site 1 and the areas adjacent to the Gai Wai had appeared as early as 1945. Over
the years, the mangroves extended seawards to the Deep Bay in a steady rate. Site 3 had begun
to emerge since 1990 and not until 2005 had it grown to the current status. A number <strong>of</strong> factors,
such as the natural setting and human-related activities had affected to their growths. Although
they are <strong>of</strong> the same species, they were significantly different in terms <strong>of</strong> their growth structure
and canopy characteristics, which in turn affect the gap fraction and LAI. The LAI <strong>of</strong> site 1 is
significantly lower than that <strong>of</strong> site 3. Mediusm LAI was found in site 2 and 4. LAI computed from
both methods exhibited the same spatial pattern.
12

Deep Bay
Mudflat
3
2
4
1
Gai Wai
Figure 7. The spatial pattern <strong>of</strong> LAI modeled with regression analysis
Simple statistical measures were used to measure the variations <strong>of</strong> LAI at species levels. Table 3
shows the predicted LAI in terms <strong>of</strong> species. A. marina had the highest average LAI (Bonhomme
= 2.018; LAI-2000 = 1.909); this was followed by A. ilicifolius, K. obovata and A. corniculatum.
The standard deviation was relatively large (about 0.8) for A. corniculatum and A. ilicifolius. This
was due to the presences <strong>of</strong> comparatively large amount <strong>of</strong> sites with LAI equal to or
approaching zero. A. ilicifolius is a pioneer species, which extend towards the fringe <strong>of</strong> mangrove,
the mixed pixel problem tends to be more serious, especially when a medium resolution sensor
was used. The recorded distribution <strong>of</strong> A. corniculatum also tends to be coastal, which therefore
face the same problem. The constraint <strong>of</strong> mixed pixel also existed in the other two species but
the magnitude was less prominent.
3
2
4
1
13

Table 3. Statistical comparison <strong>of</strong> predicted LAI among species
Bonhomme's method LAI-2000’s method
*Species Ac Ai Am Ko Ac Ai Am Ko
Mean 1.661 1.951 2.018 1.879 1.583 1.852 1.909 1.772
S.D. 0.875 0.825 0.635 0.595 0.828 0.784 0.602 0.566
Minimum 0.009 0.008 0.019 0.021 0.002 0.001 0.008 0.008
Maximum 3.813 3.969 3.372 3.514 3.644 3.797 3.216 3.353
*Species: Ac - Aegiceras corrniculatum; Ai - Acanthus ilicifolius; Am - Avicennia marina; Ko - Kandelia obovata
5. Conclusion
The study demonstrated the combination <strong>of</strong> in-situ field measurement and vegetation indices
derived from narrowband hyperspectral image in mapping one <strong>of</strong> the vital biophysical
parameters – <strong>Leaf</strong> <strong>Area</strong> <strong>Index</strong> (LAI) in a mangrove site <strong>of</strong> worldwide importance. Two methods,
Bonhomme and LAI-2000, were used to calculate LAI <strong>based</strong> on the measured gap fraction.
Among the vegetation indices, Renormalized Difference Vegetation <strong>Index</strong> (RDVI) explained the
highest proportion <strong>of</strong> variability in the linear regression model, which was then used to predict
LAI <strong>of</strong> non-surveyed areas. The predicted Bonhomme’s LAI was relatively higher than that
predicted by LAI-2000 method though the spatial pattern was comparable. The final session
compared LAI difference in terms <strong>of</strong> species. Of the same species, significant LAI difference was
found due to temporal variation. Besides, species showed quite a distinctive LAI pattern.
RDVI showed relatively strong and linear relationship with LAI. The fast and simple computation
<strong>of</strong> RDVI enables effective monitoring <strong>of</strong> mangrove. However, the performance <strong>of</strong> other VIs
hindered the derivation <strong>of</strong> robust model. The poor performance was most probably due to
intrinsic property <strong>of</strong> the vegetation indices, like background or non-linearity with LAI. Another
possible cause was the selection <strong>of</strong> unrepresentative narrowbands for VIs computation. Instead
<strong>of</strong> picking the narrowbands using correlation, more robust feature selection algorithms can be
explored in terms <strong>of</strong> spectral band selection and generation <strong>of</strong> spectral derivatives having better
response to the biophysical parameter <strong>of</strong> interest in the next step. The feature selection
technique is especially essential in the SWIR spectral region when low signal-to-noise ratio was
encountered. All in all, the role remote sensing in ecological survey and monitoring has been
well-established over the past decades. The continuous emergence <strong>of</strong> new spaceborne sensors,
techniques and algorithms has continuously <strong>of</strong>fered a cost effective way in monitoring <strong>of</strong> earth
ecological resources at different scales.
14

Acknowledgement
This research is supported by a Research Grant Council General Research Grant (Project
2160368). The authors would also like to thank the Agriculture, Fisheries and Conservation
Development, HKSAR for providing full support on field data collection and validation.
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