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ANALYSIS OF IN-SITU SPECTRAL REFLECTANCE OF SAGO AND OTHER PALMS:
IMPLICATIONS FOR THEIR DETECTION IN OPTICAL SATELLITE IMAGES
J. R. Santillan*, M. Makinano-Santillan
Caraga Center for Geo-Informatics, College of Engineering and Information Technology,
Caraga State University, Ampayon, Butuan City, Agusan del Norte, Philippines – (jrsantillan, mmsantillan) @carsu.edu.ph
Commission III, WG III/1
KEY WORDS: Spectral reflectance, Spectral analysis, Sago, Palms, Detection, Optical satellite image
ABSTRACT:
We present a characterization, comparison and analysis of in-situ spectral reflectance of Sago and other palms (coconut, oil palm and
nipa) to ascertain on which part of the electromagnetic spectrum these palms are distinguishable from each other. The analysis also
aims to reveal information that will assist in selecting which band to use when mapping Sago palms using the images acquired by these
sensors. The datasets used in the analysis consisted of averaged spectral reflectance curves of each palm species measured within the
345 - 1045 nm wavelength range using an Ocean Optics USB4000-VIS-NIR Miniature Fiber Optic Spectrometer. This in-situ
reflectance data was also resampled to match the spectral response of the 4 bands of ALOS AVNIR-2, 3 bands of ASTER VNIR, 4
bands of Landsat 7 ETM+, 5 bands of Landsat 8, and 8 bands of Worldview-2 (WV2). Examination of the spectral reflectance curves
showed that the near infra-red region, specifically at 770, 800 and 875 nm, provides the best wavelengths where Sago palms can be
distinguished from other palms. The resampling of the in-situ reflectance spectra to match the spectral response of optical sensors
made possible the analysis of the differences in reflectance values of Sago and other palms in different bands of the sensors. Overall,
the knowledge learned from the analysis can be useful in the actual analysis of optical satellite images, specifically in determining
which band to include or to exclude, or whether to use all bands of a sensor in discriminating and mapping Sago palms.
* Corresponding author
1. INTRODUCTION
The Sago palm (Metroxylon sagu Rottb.; Figure 1) is considered
to be the highest starch producer among many other starch-
producing crops (Bujang, 2008). With a yield reaching 25 tons
per hectare per year, the palm is now grown commercially in
Malaysia, Indonesia and Papua New Guinea for production of
Sago starch and/or conversion to animal food or fuel ethanol
(McClatchey et al. 2006). In the Philippines, interests are gaining
to develop and sustain a large-scale Sago starch industry.
Information on the present location and distribution of Sago
palms is needed in order to ascertain whether there is enough
supply of Sago logs to drive and sustain a large scale Sago starch
industry. Therefore, mapping the location of existing Sago palms
is a necessity to determine current supply, as well as for
characterization of its habitat such that other areas suitable for
mass propagation can also be mapped out.
Clusters of Sago palms can be found in marshlands and other
wetlands of Mindanao and in some islands in the Visayas. A
thorough mapping of the locations of these clusters is expensive
especially when done using conventional field mapping
techniques, aside from being difficult due to in-accessibility. The
use of remote sensing data and techniques could lessen logistical
and practical difficulties that are often encountered especially in
inaccessible areas, and is considered to be the best alternative
compared to mapping the Sago palms through traditional
approaches.
In a study by Santillan et al. (2012), it was found that Sago
palms can be detected through Maximum Likelihood
classification of a combination Landsat 7 ETM+ multispectral
bands, Normalized Difference Vegetation Index, and Shuttle
Radar Topography Mission Digital Elevation Model (SRTM
DEM). However, the Users and Producers accuracy of the
detection were found to be less than 85%. These relatively low
classification accuracies of the Sago palm classification were
attributed to three factors: (i) the differences in the date of image
acquisition and the date of field surveys when the sago ground
truth data were collected; (ii.) the 30-m spatial resolution of the
Landsat ETM+ image may not be optimal for classifying specific
vegetation species such as the sago palms, especially in areas
where sago palms are interspersed with other land-cover types;
and (iii.) the similarities in the spectral characteristics of sago
palm with other palm vegetation, especially coconut and oil palm
(Santillan et al., 2012).
All these cited factors are few of the many challenges that are
often encountered when using remote sensing-based approaches
in vegetation mapping (Xie et al., 2008). Spectral similarity, in
particular, is an important factor to be considered when doing
image classification. Since different vegetation types may
possess similar spectra, it makes it very hard to obtain accurate
classification results either using the traditional unsupervised
classification or supervised classification (Xie et al., 2008). The
difficulty is further complicated by the fact that spectral
reflectance is affected by such dynamics as seasonal vegetation
development or plant stress (Feilhauer and Schmidtlein, 2011).
These challenges can be addressed through the use of improved
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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185
classification methods which usually focus and expand on
specific techniques or spectral features, which can then lead to
better classification results (Xie et al., 2008), or through the use
of satellite datasets with higher spatial and spectral resolution
which allows plant-level assessments such as tree species
identification (Schafer et al., 2016). Another approach employed
to improve mapping efficiency, as well as to provide satisfactory
image classification result, is by characterizing the spectral
response of every plant species being mapped through in-situ
spectral measurements and analysis, or also referred to as field
spectroscopy (Jimenez and Diaz-Delgado, 2015).
Field spectroscopy, the measurement of high-resolution spectral
radiance or irradiance in the field, is applied to retrieve the
reflectance or emissivity spectral signatures of terrestrial surface
targets. Through this method, the uniqueness of the spectral
response of individual plant species can be estimated by
comparing plant spectral signatures between and within species
and detecting differences and distances in their spectral shape
and reflectance (Jimenez and Diaz-Delgado, 2015). The method
has been used to discriminate between Mediterranean native
plants and exotic-invasive shrubs (Lehmann et al., 2015). It was
also used in examining the spectral separability of the invasive
Prosopis glandulosa from co-existent species (Mureriwa et al.,
2016). The use of in-situ spectral data also allows the
determination of significant spectral information that might be
detected by satellite sensors (Rock et al., 1998). By integrating
field-measured spectral signatures with a satellite sensor’s
Relative Spectral Response (RSR) function, band reflectance
values can be simulated as if they were measured by the satellite
sensor (Fleming, 2006). This so-called spectral resampling can
help on identifying which sensor or bands can best provide an
imagery where the different plant species can be distinguished
from each other.
2. OBJECTIVES
In the present study, we applied field spectroscopy to analyze
similarities or differences in spectral signatures of Sago and
other palms such as coconut, oil palm and nipa. Specifically, the
study aims to: (i) ascertain on which part of the visibile to near
infrared VIS-NIR) region of the electromagnetic spectrum these
palms are distinguishable from each other based on the analysis
of their in-situ spectral reflectance; and (ii.) conduct spectral
resampling of the spectral signatures to reveal information that
will assist in the interpretation and analysis of medium (ALOS
AVNIR-2, ASTER VNIR, Landsat 7 ETM+, Landsat 8 OLI)
and high resolution (Worldview-2) optical satellite images.
3. MATERIALS AND METHODS
3.1 In-situ Spectral Measurements
Spectral data used in this work consisted of reflectance spectra of
unique stands of Sago palm, coconut, oil palm and nipa (Table
1). The data was gathered from February to May 2012 in 52
sampling sites located in the provinces of Agusan del Norte,
Agusan del Sur, and Surigao del Sur in Mindanao, Philippines.
The stands of palm vegetation in the sampling site ranges from
2x2 m2 to 10x10 m2 in size.
Figure 1. Pictures of sago palms, coconut, oil palm and nipa.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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At each sampling site, the amount of electromagnetic radiation
(i.e. radiation from the sun) reflected by a stand of palm
vegetation was measured using the Ocean Optics USB4000-VIS-
NIR Miniature Fiber Optic Spectrometer. The “stand of palm
vegetation” being referred here is a cluster of palms and not as
individual trees. However, for coconut, the measurements were
done in individual trees.
Palm
Species
No. of Sites
(Samples)
No. of
Measurement
Setup/Trials
Per Site
No. of
Measurements
Per Set-up
Coconut
9
5
25
Nipa
7
5
25
Oil Palm
5
5
25
Sago Palm
31
5
25
Table 1. Number of sampling sites for reflectance measurements.
The sensor detects and records radiance or irradiance at 1 nm
resolution within the spectral range from 345 nm to 1047 nm.
The set-up (Figure 2) is composed of the sensor mounted in an
improvised pole, and is positioned at different portions of the
palm stand. The sensor is connected to the spectrometer through
a fiber optic cable. The spectrometer is connected to a laptop
computer that performs the scanning procedure, displays the plot
of the observed reflectance and stores the reflectance data. At
each site, there were five measurement setups (or trials) wherein
each setup corresponds to the measurement of reflected canopy
radiation at a particular portion of the palm stand. The five set-
ups corresponds to the top and the sides of the palm stand. At
each set-up 25 consecutive measurements of reflected canopy
radiation (Rcanopy) were conducted.
To determine the percentage of radiation coming from the sun
that has been reflected by a stand of palm vegetation, it is
necessary to measure also how much radiation is reflected by a
reference standard (Rreference). This was done by doing another set
of 25 measurements of radiation reflected by a white reference
panel before and immediately after measuring reflected radiation
by palm vegetation. The white reference panel used is the Ocean
OpticsTM WS-1-SL White Reflectance Standard with
Spectralon which approximately reflects 99% of incoming
radiation with wavelengths ranging from 400-1500 nm. The
averages of the measurements were then taken at each trial. With
this setup, the percentage reflectance of a palm vegetation is
obtained by dividing the Rcanopy with Rreference and multiplying the
result by 100. It was assumed that the incoming radiation from
the sun was the same during measurement of Rcanopy and Rreference.
At each site, the average of the five trials was then used in the
analysis.
3.2 Spectral Resampling
The in-situ reflectance curves of Sago and other palms were
resampled to simulate reflectance values as if they were
measured by the optical satellite imaging sensors namely Landsat
7 ETM+, Landsat 8 OLI, ALOS AVNIR-2, ASTER VNIR,
andWorldview-2 (Table 2). Some refer to this procedure as
resampling to match the spectral response of the four sensors
(e.g., Kooistra et al., 2004). Spectral resampling was done using
ENVI 5 software with the aid of relative spectral response
functions (RSRFs) corresponding to the VIS-NIR bands of the
five sensors. The spectral response function defines the spectral
sensitivities of a sensors band to reflected light. By
characterizing the sensor’s sensitivities, it allows the calculation
of band values from any given spectral content of light reflected
by an object back to the sensor (in this case the palm vegetation).
The band values are calculated by integrating the RSRF over the
in-situ reflectance spectra.
Figure 2. In-situ spectral reflectance measurement setup.
3.3 Spectral Characterization and Analysis
The average in-situ spectral reflectance curves of Sago, coconut,
oil palm and nipa including the 95% confidence intervals (CIs) of
the mean were plotted and visually examined to determine which
part of the visible-NIR region of the electromagnetic spectrum
these palms are distinguishable from each other. The same
approach was applied to analyse the resampled spectral
reflectance. Differences in band reflectance values of Sago from
other palms were also computed and considered in the analysis.
4. RESULTS AND DISCUSSION
4.1 In-Situ Spectral Reflectance of Sago and Other Palms
Figure 3 shows the average in-situ spectral reflectance of Sago
and other palms, including the 95% CIs of the mean. The graph
shows several portions of the electromagnetic spectrum where
Sago palm is distinguishable from other palms. The most
obvious is at 550, at 770, at 800, and at 875 nm. At these
portions, Sago palm has the lowest reflectance while nipa has the
highest. In between are coconut and oil palm. It is noticeable that
the Sago palm’s average reflectance curve cannot be considered
unique. Looking at the 95% CIs, the Sago palm’s reflectance is
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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187
slightly contaminated by those of Oil palm but not by coconut
and nipa. Based on this data, there is high discrimination of Sago
palm from nipa and coconut but relatively low discrimination
from oil palm specifically at 400-700 nm. In the NIR region,
specifically at 750-800 nm, the Sago palm’s average reflectance
is almost the same as the lower 95% CI of oil palm, while the Oil
palm’s average reflectance is almost the same as the upper 95%
CIs of Sago palm. Considering only the average reflectance, the
NIR region, specifically at 770, 800 and 875 nm, provides the
best wavelengths where Sago palm can be distinguished from
other palms.
Satellite and
Sensor
Band
No.
Band
Name
Wavelength
range, nm
Central
Wavelength,
nm
Landsat 7
ETM+
1
2
3
4
Blue
Green
Red
Near
Infrared
(NIR)
450-520
520-600
630-690
770-900
482.5
565
660
825
Landsat 8
OLI
1
2
3
4
5
Coastal
aerosol
Blue
Green
Red
NIR
433-453
450-515
525-600
630-680
854-885
443
482.6
561.3
654.6
864.6
ASTER
VNIR
1
2
3
Green
Red
NIR
520-600
630-690
760-860
556
661
825
ALOS
AVNIR-2
1
2
3
4
Blue
Green
Red
NIR
420-500
520-600
610-690
760-890
460
560
650
825
Worldview-
2
1
2
3
4
5
6
7
8
Coastal
blue
Blue
Green
Yellow
Red
Red
edge
NIR 1
NIR 2
400-450
450-510
510-580
585-625
630-690
705-745
770-895
860-1040
425
480
545
605
660
725
832.5
950
Table 2. Description of band numbers, names, wavelength
ranges, and central wavelengths of optical satellite sensors.
4.2 Simulated Sensor-specific Reflectance of Sago and
Other Palms based on Spectral Resampling
4.2.1 Landsat 7 ETM+ Resampled Reflectance Values:
Figure 4a shows the resampled in-situ reflectance values of Sago
and other palms in Bands 1-4 of Landsat 7 ETM+. The
reflectance values of Sago palm appear to be similar to that of oil
palm in Bands 2, 3 and 4. There is slight difference in reflectance
values of Sago palm to those of other palms in Bands 2 and 4.
Looking at the differences in reflectance values of Sago with
those of other palms (Figure 4b), it appears that none of the four
bands of Landsat 7 ETM+ is suitable to discriminate Sago with
other palms if they are to be used individually.
4.2.2 Landsat 8 OLI Resampled Reflectance Values: Figure
5a shows the resampled in-situ reflectance values of Sago and
other palms in Bands 1-5 of Landsat 8 OLI. The reflectance
values of Sago palm appear to be similar to those of nipa in Band
1, and to oil palm in Bands 3 and 5. There is slight difference in
reflectance values of Sago palm to those of other palms in Bands
2 and 4. Looking at the differences in reflectance values of Sago
with those of other palms (Figure 5b), it appears that Landsat 8
OLI bands, just like Landsat 7 ETM+, may not be suitable to
discriminate Sago with other palms if they are to be used
individually.
Figure 3. Average in-situ spectral reflectance of Sago and other
palms, including the 95% confidence interval of the mean.
4.2.3 ASTER VNIR Resampled Reflectance Values: Figure
6a shows the resampled in-situ reflectance values of Sago and
other palms in Bands 1-3 of ASTER VNIR. The reflectance
values of Sago palm appear to be similar to those of oil palm in
Band 1. In Bands 2 and 3, the reflectance values of Sago palm
appear to be dissimilar with the other palms. Looking at the
differences in reflectance values of Sago with those of other
palms (Figure 6b), it appears that Bands 2 and 3 are useful to
discriminate Sago with other palms with the differences greater
than 1%. In Band 3, there is relatively large separability between
reflectance values compared to the other bands implying that
Sago palm may be best discriminated in this band.
4.2.4 ALOS AVNIR-2 Resampled Reflectance Values: Figure
7a shows the resampled in-situ reflectance values of Sago and
other palms in Bands 1-4 of ALOS AVNIR-2. The reflectance
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China
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values of Sago palm appear to be similar to those of oil palm in
Bands 2 and 3. In Bands 1, the reflectance of Sago palm has
value nearer to that of Nipa. Looking at the differences in
reflectance values of Sago with those of other palms (Figure 7b),
it appears that ALOS AVNIR-2 bands 1-3 may not be suitable to
discriminate Sago with other palms if they are to be used
individually. In Band 4, there is better separability between
reflectance values implying that Sago palm can be best
discriminated in this band.
4.2.5 Worldview-2 Resampled Reflectance Values: Figure 8a
shows the resampled in-situ reflectance values of Sago and other
palms in Bands 1-8 ofWorldview-2. Compared to the other 3
sensors, there are several bands of Worldview-2 that are useful
in discriminating Sago from other palms. These bands are 1, 6, 7
and 8. The greatest difference in reflectance values can be found
in Band 8 followed by Band 6 (Figure 8b).
5. SUMMARY, CONCLUSIONS AND FUTURE WORK
In this paper, important information with regards to differences
in spectral reflectance of Sago and other palms in the visible to
near infra-red region of the electromagnetic spectrum was
revealed using field spectroscopy. In general, Sago, coconut, nipa
and oil palms have lower reflectance in the blue and red regions
but higher reflectance in the green region. The NIR region,
specifically at 770, 800 and 875 nm, provides the best
wavelengths where Sago palm can be distinguished from other
palms. However, the validity of this result when applied in
analysing optical satellite images must be evaluated since the
bands of the sensors does not actually equate to a specific
wavelength but to a range of wavelengths. Also, the conditions
during the in-situ reflectance measurements are different from the
condition when the satellite images were acquired. Moreover,
reflectance values measured by satellite sensors are also affected
by atmospheric effects which will make the in-situ spectral
reflectance different from the image-based reflectance. There is
also the issue of spectral variance in satellite image data. Satellite
images have several meters wide pixel sizes so they are not only
capturing the leaf surface but also other parts of the canopy,
stem, ground, and shadows that will all add up to the variance in
the spectral reflectance. This makes it almost impossible for the
in-situ spectral reflectance to be the same to the image-based
reflectance especially that the in-situ spectral reflectance data
were collected on just five locations in a stand of palm
vegetation, and more or less, represents only spectral reflectance
of leaf surfaces.
The resampling of the in-situ reflectance spectra to match the
spectral response of optical sensors made possible the analysis of
the differences in reflectance values of Sago and other palms in
different bands of the sensors. Results showed that both Landsat
7 ETM+ and Landsat 8 OLI bands may not be suitable to
discriminate Sago with other palms if they are to be used
individually. On the other hand, Sago palm can be best
discriminated in Band 3 of ASTER VNIR because of large
differences in reflectance values. For ALOS AVNIR-2, all of its
four bands appear to be not suitable to discriminate Sago with
other palms if used individually. This observation is the same
with that of Landsat 7 ETM+ and Landsat 8 OLI. It suggest that
if images acquired by either Landsat 7 ETM+, Landsat 8 OLI,
ALOS AVNIR-2 and even ASTER VNIR are to be used to
detect Sago palms, the use of single band may not provide good
Figure 4. (a.) Resampled in-situ reflectance values of Sago
and other palms in Bands 1-4 of Landsat 7 ETM+; (b.)
Difference in resampled in-situ reflectance of Sago with
those of other palms.
Figure 5. (a.) Resampled in-situ reflectance values of Sago
and other palms in Bands 1-5 of Landsat 8 OLI; (b.)
Difference in resampled in-situ reflectance of Sago with
those of other palms.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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189
results. The use of all bands (and maybe some derivatives such
as NDVI) may be helpful to successfully detect Sago palms by
discriminating them from other palm vegetation.
A more interesting result was obtained with the analysis of
Worldview 2 reflectance values of Sago and other palms.
Compared to the other 3 sensors, there were four bands that
appear to be useful in discriminating Sago from other palms:
Bands 1, 6, 7 and 8. It is in these bands that the large differences
in reflectance values were obtained.
In all the optical sensors, it was very evident that the resampled
reflectance of Sago palm is similar with those of Oil palm. This
is similar to what can be observed even if the spectral reflectance
curves of these two palms have not yet been resampled. As far as
spectral reflectance information is used, this similarity can
greatly affect the discrimination of Sago palm from oil palms in
any of the images such as misclassifying oil palms as Sago palm
or vice-versa. This finding can explain the low accuracy of Sago
palm classification encountered in a previous study (Santillan et
al., 2012).
The knowledge learned in this study is useful in the actual
analysis of optical satellite images, specifically in determining
which band to include or to exclude, or whether to use all bands
of a sensor in discriminating and mapping Sago palms using the
images. An important matter not discussed in this paper is
testing the statistical significance of the differences in in-situ
reflectance between the palm species. The consistency of the
patterns obtained in the analysis of in-situ reflectance values with
those obtained from the images (i.e., image-based reflectance
Figure 8. (a.) Resampled in-situ reflectance values of Sago
and other palms in Bands 1-8 Worldview-2; (b.) Difference
in resampled in-situ reflectance of Sago with those of other
palms.
Figure 6. (a.) Resampled in-situ reflectance values of Sago
and other palms in Bands 1-3 of ASTER VNIR; (b.)
Difference in resampled in-situ reflectance of Sago with
those of other palms.
Figure 7. (a.) Resampled in-situ reflectance values of Sago
and other palms in Bands 1-4 of ALOS AVNIR2; (b.)
Difference in resampled in-situ reflectance of Sago with
those of other palms.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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190
values), including the seasonal changes in the reflectance values,
must also be evaluated. Another matter not done is the
comparison between the palm reflectance values with non-palm
vegetation. Although it was shown that Sago palms are
distinguishable from other palms, it was not established if they
are also distinguishable from other types of vegetation.
ACKNOWLEDGEMENTS
This work was an output of two research projects funded by the
Philippine Council for Industry, Energy and Emerging
Technology Research and Development of the Department of
Science and Technology (PCIEERD-DOST), namely Project
II.2-Biophysical, Structural and Spectral Characterization of
the Sago Palm implemented by Caraga State University (CSU),
and Project II.3-Mapping Sago Habitats and Sago Suitable
Sites using Optical and Radar Image Analysis and Suitability
Relationships implemented by the University of the Philippines-
Diliman (UPD). The PCIEERD-DOST is also acknowledged for
the financial support provided to J.R. Santillan which made
possible the presentation of this paper in the ISPRS TC III
Symposium 2018. Majority of this work was conducted when
J.R. Santillan was still connected with the Research Laboratory
for Applied Geodesy and Space Technology (AGST Lab.) of the
Department of Geodetic Engineering & Training Center for
Applied Geodesy and Photogrammetry, UPD. The AGST Lab is
gratefully acknowledged for providing the instruments and
equipment used during the spectral measurements. We also thank
the following colleagues from CSU for their assistance during in-
situ spectral measurements and preliminary data processing
namely, Michelle V. Japitana, Arnold G. Apdohan, Arthur M.
Amora, Linbert Cutamora, Dylan Dikraven Galavia and Cherry
Bryan G. Ramirez. The insightful and helpful comments and
suggestions of three anonymous reviewers are also highly
appreciated.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-3, 2018
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-3-185-2018 | © Authors 2018. CC BY 4.0 License.
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