Forum Geografi, 31(1), 2017; DOI: 10.23917/forgeo.v31i1.2518
Temporal Decorrelation Effect in Carbon Stocks Estimation Using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) (Case Study: Southeast Sulawesi Tropical Forest)
1 Faculty of Engineering, Universitas
Halu Oleo, Jl. HEA. Mokodompit
No. 8, Kampus Hijau UHO, Anduonohu Kendari Southeast Sulawesi
(Indonesia)
2 Center for Remote Sensing Institute of Technology
Bandung (ITB) Jl. Ganesha No. 10, Bandung, West Java (Indonesia),
3 ForMIND Institute (Forum Peneliti
Muda Indonesia)
4 Indonesia National Institute of Aeronautics and
Space (LAPAN) Jl. Lapan No. 70, Pekayon
Pasar Rebo, Jakarta
(Indonesia)
5 Meteorology Department, Faculty of Earth
Science and Technology, ITB (Indonesia), Jl. Ganesha
No. 10, Bandung West Java (Indonesia)
*) Corresponding Author (e-mail: laode251@yahoo.com)
Received: 30 March 2017
/ Accepted: 13 April 2017 / Published: 01 July 2017
Abstract
This paper
was aimed to analyse the effect of temporal
decorrelation in carbon stocks estimation. Estimation of carbon stocks plays
important roles particularly to understand the global carbon cycle in the
atmosphere regarding with climate change mitigation effort. PolInSAR
technique combines the advantages of Polarimetric
Synthetic Aperture Radar (PolSAR) and Interferometry
Synthetic Aperture Radar (InSAR) technique, which is
evidenced to have significant contribution in radar mapping technology in the
last few years. In carbon stocks estimation, PolInSAR
provides information about vertical vegetation
structure to estimate carbon stocks in
the forest layers. Two coherence Synthetic Aperture Radar (SAR) images of ALOS
PALSAR full-polarimetric with 46 days temporal
baseline were used in this research. The study was carried out in Southeast
Sulawesi tropical forest. The research method was by comparing three
interferometric phase coherence images affected by temporal decorrelation and
their impacts on Random Volume over Ground (RvoG)
model. This research showed that 46 days temporal baseline has a significant impact to estimate tree heights of the forest cover where the accuracy decrease
from R2=0.7525 (standard deviation of tree heights is 2.75 meters)
to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772
(standard deviation 3.15 meters) respectively. However, coherence optimisation can provide the best coherence
image to produce a good accuracy of
carbon stocks.
Keywords: Temporal Decorrelation, Carbon Stocks, PolInSAR,
ALOS PALSAR.
Abstrak
Penelitian ini bertujuan untuk
meneliti pengaruh dekorelasi temporal dalam pendugaan cadangan karbon. Pendugaan cadangan karbon sangat penting untuk memahami daur karbon secara
global in atmosfer terkait upaya mitigasi perubahan iklim. Teknik PolInSAR menggabungkan dua buah teknik pemetaan
dengan citra Synthetic
Aperture Radar (SAR) yaitu Polarimetric
Synthetic Aperture Radar (PolSAR) dan
Interferometry Synthetic Aperture Radar (InSAR) yang terbukti berkontribusi dalam teknologi pemetaan sejak beberapa tahun terakhir. Teknik PolInSAR dapat memberikan informasi struktur tinggi vegetasi yang digunakan untuk menduga besarnya
cadangan karbon pada tutupan hutan.
Dua buah citra ALOS PALSAR yang koheren dan full-polarisasi dengan perbedaan waktu perekaman 46 hari digunakan dalam penelitian ini. Wilayah studi penelitian ini adalah hutan tropis
Sulawesi Tenggara. Metode penelitian
ini adalah membandingkan tiga buah citra fase
interferometrik yang dipengaruhi
oleh dekorelasi temporal dan dampaknya dalam
pembentukan model RVoG. Hasil penelitian ini menunjukkan bahwa adanya perbedaan
perekaman citra selama 46 hari memiliki dampak yang signifikan dalam estimasi cadangan karbon dimana akurasi
tinggi vegetasi berkurang dari R2=0.7525
(simpangan baku
2.75 meter) menjadi R2=0.4435 (simpangan baku 4.68 meter) dan R2=0.3772 (simpangan
baku 3.15 meter). Namun demikian, dengan proses optimisasi koherensi dapat menghasilkan citra dengan nilai
koherensi yang terbaik untuk menghasilkan nilai cadangan karbon dengan akurasi
yang baik.
Kata Kunci: Dekorelasi Temporal, Cadangan
Karbon, PolInSAR, ALOS
PALSAR
Introduction
Estimation of carbon stocks plays an
important role in the context of climate change mitigation. Forest
layers absorb carbon emission from the atmosphere and store it as carbon pool (IPCC, 2007). Indonesia
is one of the countries having the
largest tropical forest that contains carbon
stocks and gives impact to climate change. The mapping of forest carbon stocks in
Indonesia will be very useful to support mitigation of climate change under the scheme
of Reducing Emission from Deforestation and Forest Degradation (REDD). Some efforts have been conducted to estimate carbon
stocks of Indonesian tropical forest including by using optical remote sensing (Kustiyo et
al,
2015). However, optical remote sensing system does not generate
optimum results such as the unclear and low quality of satellite image due to
cloud cover and other meteorological conditions. Therefore, the radar remote
sensing system becomes another option and has been
widely applied for that purpose.
In the model development for carbon stocks mapping, radar polarimetric (PolSAR) has been popular
in the last few years. Radar polarimetric
uses polarisation component to develop
the relationship between forest carbon
stocks and backscatter coefficient from different frequencies (Mitchell et al,
2012; Villard & Le Toan,
2014) and different sensors (Kurvonen et al, 1999). However, this method has a problem of
saturation (Moreira et
al,
2002; Le Toan et al, 2011). Meanwhile, radar interferometric
(InSAR) has some advantages including it is not
influenced by saturation. Some researchers such as Neeff
et al, (2005)
and Kugler et
al,
(2006) have discussed the contribution of the InSAR method to assess biophysical parameters in forest areas.
Thus, the integration of radar polarimetry and interferometry (PolInSAR) can overcome saturation problem in the development
of physical object model. This method was
initially published by Cloude and Papathanassiou (1998). The PolInSAR is based on the formation of two coherence
images from two difference positions and times of acquisition. It means that
the PolInSAR is particularly vulnerable to temporal
decorrelation (Mette, 2006; Richards,
2009). The objective of this paper was to analyse
the effects of temporal decorrelation in carbon stocks estimation using ALOS PALSAR full-polarimetric
image.
Research Method
Location
This research was conducted in
Southeast Sulawesi, Indonesia. Specifically, the study are is a part of Wolasi Tropical Protection Forest (4°06’10.22”S to
4°12’15.74”S and 122°29’2.15”E to 122°30’33.4”E) and the altitude at 300-700 meter above mean sea level. The average temperature is 25 –34° and annual
precipitation is 1,469 mm. The topography is mountainous with a gradient up to 10-20%. Furthermore, 20 sample
plots were established in the study area with 20x20m2
in size. The map of the study area is displayed in Figure 1.
The dominant vegetations in this area are Eha (Castanopsis buruana), Batu-Batu (Ptemandra spp.), Dange (Dillenia sp.) and Ruruhi (Syzygium spp.). Some of trees aged between
5 (five) to older than 40 years old with a range of 5 to 40 cm in diameter. The
tree heights are between 3-35 m and the density is 200-300 trees per hectare. The
forest in the study are is displayed in Figure 2.
SAR Data
Two levels of L1.1 full polarimetric ALOS
PALSAR data were used in this research.
The polarisations were HH, HV, VH and VV,
respectively. Temporal baseline between these two images was 46 days. Data
characteristics are described in Table 1. The images were
divided into master and slave.
Description |
Master
Image |
Slave
Image |
Center Frequency |
1,25
GHz |
1,25
GHz |
Wavelength |
23 cm |
23 cm |
Polarization |
HH, HV,
VH, VV |
HH, HV,
VH, VV |
Range
Resolution |
9.4 m |
9.4 m |
Azimuth
Resolution |
3.8 m |
3.6 m |
Date of
acquisition |
2 May
2010 |
17
March 2010 |
Incidence
Angle |
23° |
21.5° |
Pass |
Ascending |
Ascending |
Mode |
Single
Look Complex (SLC) |
SLC |
SAR Data Processing
SAR data processing is a fundamental key to obtain good quality of interferogram.
Its fundamental steps consist of multilooking, polarimetric
calibration, coregistration, spectral filtering and interferogram generation. It also proceeds two coherence ALOS PALSAR images from two different periods
and two slightly different look angles, further referred as master and slave as
presented in Figure 3 in Red Green Blue (RGB) composite.
PolInSAR technique is an integration of
the advantages of PolSAR and InSAR
techniques. The coherence between two identical images in full-polarizations environment is the key of PolInSAR technique
(Cloude & Papathanossiou, 1998).
ALOS PALSAR image obtained on May 2nd, 2010 was assigned as master
image and those on March 17th, 2010 was
assigned as slave. Each image was performed as scattering matrix contains all
polarisations as displayed in equation (1).
The scattering matrix, namely the
S2 matrix, contains backscatter properties of the earth objects (Richards, 2009). The scattering matrix characterises the scattered wave for any polarisation
of incidence wave at every image pixel. The elements of the scattering matrix are
very useful in the analysis of pixels, for example, on the classification process using decomposition (Richards, 2009). The matrix component can be transformed into a vector with four elements
arranged in the form of the scattering matrix-vector,
called Pauli basis target vector (Cloude
& Papathanossiou, 1998; Richards, 2009)
as shown in Equation (2). “T” indicates
transpose matrix.
For coregistration of two full polarimetric SAR coherent images, equation
(2), was
transformed into 6x6 [T6] Hermitian matrix as shown in Equation
(3) (Richards, 2009).
Where < > represented multi-looking operator, * is Hermitian
transformation, k1 and k2 are 3D
Pauli scattering vectors. and are standard Hermitian coherency matrix containing
full-polarimetric information from each full-polarimetric image. The matrix states new complex matrix in 3x3 dimension,
which contains not only polarimetric information, but also interferometric phase
between polarimetric channels in the coherent pair
images (Richards, 2009). The T6 was then proceeded to estimate the coherence of
interferogram and to produce the RVoG
model.
Results and Discussion
Estimation of carbon stocks in Indonesia has been
carried out by several researchers by using several ways, including backscatter
radar to identify the characteristics of forest stand in tropical forest (Wuryanta, 2016), discrimination of mangrove ecosystem objects on the visible spectrum using
spectroradiometer HR-1024 (Arfan, 2015) and calculation
based on land cover changes in the Leuser Ecosystem Area (LEA) in Aceh (Hermon, 2015).
However, the efforts produced uncertainty results. In this paper, we attempted
to minimise the uncertainty by using PolInSAR method and to investigate the
impediment aspect, namely the temporal decorrelation.
In Indonesia, the usage of PolInSAR through
INDREX-II airborne campaign was initiated
in 2004 (Hajnsek et al, 2005). The campaign was
conducted to develop database of aboveground
biomass in Kalimantan forests by using L- and P-bands in polarimetric
and interferometric components using
Experimental Synthetic Aperture Radar (E-SAR) of the German Aerospace Center (DLR). Radar technology is allegedly suitable to map
carbon stocks in Indonesian forest environment due to the obstacles of cloud
cover, smoke and haze, especially in Sumatera, Kalimantan, Sulawesi and Papua.
Temporal Decorrelation is the main
problem in the PolInSAR (Mette,
2006; Richards, 2009; Meng et al,
2010). Decorrelation itself is divided
into two ways. First, it refers to the critical baseline. A larger baseline
means a greater phase shift leads to a more sensitive interferometer. However,
there is a limit at 2π phase shift for certain pixels. If the change exceeds 2π , then it is hard to recover the inter-pixel
variation in elevation (Richard, 2009). He also
suggested that the > 1000m baseline of spaceborne
will inhibit to understand the flat earth variation. In relation with airborne SAR image,
Lee and Pottier (2009) explained their
preference to use 10 m baseline pair in their study in order to avoid phase
unwrapping problems in forest height estimation as well as 20 m baseline to
gain better sensitivity in height estimation, which could be used for height
estimation of lower vegetation. Second, decorrelation comes from any
mechanism that leads to statistical differences between the signals received by
two channels (Richards, 2009). Repeat-pass interferometry will change ground
scattered in two acquisitions then the interferometric phase difference will be
affected. Shortly, temporal decorrelation will lead to uncertainty in interferogram formation. However, it cannot be avoided in repeat-pass mode of SAR system, for instance ALOS PALSAR. To reduce the impact of temporal decorrelation, coherence optimisation was
conducted.
Figure 4, 5, and 6 showed the coherence level between pair of images produced by coherence optimisation. Coherence value will be zero (0)
for minimum called incoherence (black colour)
and one (1) for maximum called fully coherence
(white colour). Indicated by the histogram of coherence, Figure 4 is identified as the
best coherence since the histogram value is near to one. The optimum coherence value was obtained by ground
topography but dropped down in the forested area because of the residual volume component which cannot be removed (Cloude & Papathanassiou, 2003). Furthermore, the figures 4,
5, and 6 indicated the effect of temporal
decorrelation on the coherence interferometric phase of a PolInSAR method to establish Random Volume over
Ground (RVoG) model. RVoG
is biophysical model of forest which is separated into two layers; top canopy layer
and underlying topography (Cloude &
Papathanassiou, 2003). Figure 4 showed the most coherent
image in compared to Figure 5 and 6,
in which Figure 5 is more coherent than
6. The effect of temporal decorrelation can be observed in forest height
inversion from RVoG model as showed in Figure 7 (a), (b) and (c).
Temporal decorrelation affected not only the inaccuracy of forest
heights but also the decline of the top phase height of forest canopy layer. In
this research, the maximum canopy layer height decreased by 22%, from 32 m to
25 m as an effect of temporal decorrelation. Furthermore, the accuracy of
forest height obtained from field survey In compared
to RVoG heights also decreased.
Figure 8 (a), (b) and (c) showed the effect of
temporal decorrelation on the accuracy of canopy layer heights. The accuracy
decreased from R2=0.7525 (standard deviation of tree heights is 2.75
meters) to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772
(standard deviation 3.15 meters), respectively. In the repeat-pass mode, the temporal decorrelation cannot be avoided (Askne & Santoro, 2005; Lee et al., 2009). Hence, spaceborne
single pass mode program, e.g., TanDEM-X has been
developed to overcome the temporal decorrelation (Kugler et al.,
2014). However, from this research, it can be suggested to consider
the effect of temporal decorrelation in PolInSAR
technique to minimise the uncertainty of
carbon estimation in the context of climate change mitigation. Consequently, coherence
optimisation must be conducted to get the
highest accuracy of tree heights estimation (Lee &
Pottier, 2009). Thus the highest accuracy
carbon stocks can be gained.
Conclusions
In this research, the effect of temporal decorrelation in carbon stocks
estimation was discussed. The result
indicates temporal decorrelation has an effect
on the uncertainty of forest heights and carbon stocks estimation. In fact, it
cannot be avoided in a repeat-pass mode such as ALOS PALSAR or other
spaceborne SAR (RADARSAT, ENVISAT, etc).
Temporal decorrelation reduces the accuracy of forest heights estimation. In
this study, 46 days temporal baseline using ALOS PALSAR resulted in about 37.72% to 75.25% accuracy with standard deviation
varies from 4.68 to 2.75 meters. The
highest value of accuracy and the lowest
standard deviation can be obtained
through coherence optimisation. The highest coherency of phase interferometric will
result in the highest accuracy of carbon stocks estimation (as shown in Figure 7 (a)
and 8 (a)). Also,
single pass spaceborne mode, e.g., TanDEM-X is developed
to obtain high coherency.
Acknowledgment
We would like to thank Prof. M Shimada from
JAXA Japan who provided ALOS PALSAR Full-Polarimetric.
Also thanks to our colleagues at Faculty
of Forestry and Faculty of Engineering Universitas Halu Oleo, CRS ITB, and ForMIND
Institute (Prof. Ketut Wikantika) for the assistances in completing this work.
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open access publication under
the terms and conditions of the Creative
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(http://creativecommons.org/licenses/by/4.0/).
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