Sains Malaysiana 44(8)(2015): 1085–1093
Estimating Biomass in Logged Tropical Forest Using L-Band
SAR (PALSAR) Data and GIS
(Penganggaran Biojisim dalam Hutan Hujan
telah Diteroka
menggunakan Data SAR Berjalur L
(PALSAR) dan GIS)
HAMDAN
OMAR1*,
MOHD
HASMADI
ISMAIL2,
KHALI
AZIZ
HAMZAH1,
HELMI ZULHAIDI MOHD
SHAFRI2
& NORIZAH KAMARUDIN2
1Forest Research Institute
Malaysia, 52109 FRIM, Kepong, Selangor Darul
Ehsan, Malaysia
2Universiti Putra Malaysia,
43400 Serdang, Selangor Darul
Ehsan, Malaysia
Diserahkan: 28 Jun 2014/Diterima: 5 Mac 2015
ABSTRACT
The use of remote sensing
imagery, to some extends geographic information system (GIS),
have been identified as the most recent and effective technologies
to assess forest biomass. Depending on the approaches and methods
employed, estimating biomass by using these technologies sometimes
can lead to uncertainties. The study was conducted to investigate
appropriate methods for estimating aboveground biomass (AGB) by using synthetic aperture radar
(SAR)
data. A total of 60187 ha in Dungun Timber Complex (DTC)
were selected as the study area. Thirty seven sample plots, measuring
30×30 m were established in early 2012 covering both natural and
logged forests. Phase Array Type L-Band SAR (Palsar)
images that were acquired in 2010 were used as primary remote sensing
input and shapefile polygons comprised logging records was used
as supporting information. By using these data, two estimation methods,
which were ‘stratify and multiply’ (SM) and ‘direct remote sensing’ (DR)
have been adopted and the results were compared. The estimated total
AGB
were about 20.1 and 22.3 million Mg, from SM and DR methods,
respectively. The study found that the images that incorporated
texture measures produced more accurate estimates as compared to
the images without texture measures. The study suggests that SM method
still a viable and reliable technique for quick assessment of AGB in
a large area. The DR method is also relevant provided that
an appropriate type and processing techniques of SAR data
are utilized.
Keywords: Biomass estimate;
GIS; L-band SAR; tropical forest
ABSTRAK
Penggunaan citra penderiaan
jauh dan seiringan dengan sistem maklumat geografi (GIS), telah
dikenal pasti
sebagai teknologi terkini yang paling efektif untuk penilaian biojisim hutan. Penganggaran biojisim
menggunakan teknologi
ini bergantung kepada pendekatan dan kaedah yang diguna pakai kerana
kadangkala ia
boleh membawa
kepada kesilapan. Kajian ini dijalankan untuk menentukan kaedah yang sesuai untuk menganggar biojisim atas tanah
(BAT)
menggunakan data bukaan
radar sintetik (SAR).
Sejumlah
60187 ha di dalam kawasan
Kompleks Kayu-Kayan Dungun (DTC)
telah dipilih
sebagai kawasan kajian. Tiga puluh tujuh plot sampel berukuran 30×30 m telah disediakan di dalam kawasan kajian
pada awal tahun 2012 merangkumi kedua-dua hutan asli dan hutan
yang telah dibalak.
Fasa tatasusunan jenis SAR berjalur
L (Palsar) merupakan
data penderiaan jauh yang dicerap pada tahun
2010 telah digunakan
sebagai input utama, manakala poligon yang mengandungi rekod pembalakan telah digunakan sebagai maklumat sokongan. Dengan menggunakan data-data tersebut,
dua kaedah penganggaran
iaitu ‘mengelas
dan mendarab’ (SM)
dan juga ‘penderiaan jauh langsung’ (DR)
telah diguna pakai
dan hasil daripada kedua-duanya dibandingkan. Kajian menganggarkan BAT yang
masing-masing 20.1 dan
22.3 Mg dijalankan melalui
kaedah SM dan DR.
Kajian juga mendapati
bahawa imej yang telah menggunakan ukuran tekstur menghasilkan anggaran yang lebih tepat berbanding
imej tanpa ukuran tekstur. Kajian ini menyarankan bahawa kaedah SM masih relevan untuk
penilaian segera
AGB
bagi kawasan yang luas. Kaedah DR juga relevan dengan syarat teknik
pemprosesan data SAR yang
sesuai digunakan.
Kata kunci: Anggaran
biojisim; GIS; hutan tropika; SAR
berjalur L
RUJUKAN
Abdul Rashid, A.M., Shamsudin, I., Ismail, P. & Fletcher, S.C. 2009. The Role of FRIM in Addressing Climate-Change Issues. Research
Pamphlet No. 128, Forest Research Institute Malaysia, Kepong.
Angelsen, A., Brown, S., Loisel, C., Peskett, C., Streck, C. & Zarin, D. 2009.
Reducing emission from deforestation and degradation (REDD): An
options assessment report. A report prepared for the government
of Norway. Meridian Institute. p. 100.
Asner, G.P. 2001. Cloud
cover in Landsat observations of the Brazilian Amazon. International
Journal of Remote Sensing 22: 3855-3862.
Franklin,
S.E., Hall, R.J., Moskal, L.M., Maudie,
A.J. & Lavigne, M.B. 2000. Incorporating
texture into classification of forest species composition from airborne
multispectral images. International Journal of Remote
Sensing 21(1): 61-79.
Gibbs,
H.K., Brown, S., O’Niles, J. & Foley,
J.A. 2007. Monitoring and estimating
tropical forest carbon stocks: Making REDD a reality. Environmental
Research Letter 2: 1-13.
Goetz,
S.J., Baccini, A., Laporte,
N.T., Johns, T., Walker, W., Kellndorfer,
J., Houghton, R.A. & Sun, M. 2009. Mapping and monitoring carbon stocks
with satellite observations: A comparison of methods. Carbon
Balance and Management 4(2): 1-7.
Hamdan, O., Khali Aziz, H.
& Abd Rahman, K. 2011. Remotely
sensed L-Band SAR data for tropical forest biomass estimation.
Journal of Tropical Forest Science 23(3): 318- 327.
Jong,
W., Chokkalingam, U., Smith, J. &
Sabogal, C. 2001. Tropical secondary forests in Asia:
Introduction and synthesis. Journal of Tropical Forest Science
13(4): 563-576.
Kandaswamy, U.,
Adjeroh, D.A. & Lee, M.C. 2005. Efficient
texture analysis of SAR imagery. IEEE Transaction on Geosciences
and Remote Sensing 43(9): 2075-2083.
Kato,
R., Tadaki, Y. & Ogawa, H. 1978. Plant biomass and growth
increment studies in Pasoh forest. Malayan
Nature Journal 30: 211-224.
Le
Toan, T., Quegan, S., Davidson,
M.W.J., Balzter, H., Paillou,
P., Papathanassiou, K., Plummer, S., Rocca, F., Saatchi, S., Shugart, H. & Ulander, L. 2011.
The BIOMASS mission: Mapping global forest biomass to better understand
the terrestrial carbon cycle. Remote Sensing of Environment 115(11):
2850-2860.
Lu, D. 2006. The potential and challenge of remote sensing-based biomass estimation.
International Journal of Remote Sensing 27: 1297-1328.
Lucas, R., Armston, J., Fairfax, R., Fesham,
R., Accad, A., Carreiras, J., Kelley,
J., Bunting, P., Clewley, D., Bray, S.,
Metcalfe, D., Dwyer, J., Bowen, M., Eyre, T., Laidlaw, M. &
Shimada, M. 2010. An evaluation of the PALSAR L-band backscatter
- Above ground biomass relationship Queensland, Autralia: Impacts of surface moisture condition and vegetation
structure. IEEE Journal of Selected Topics on Applied Earth Observations
and Remote Sensing 3(4): 576-593.
Quinones,
M.J. & Hoekman, D.H. 2004. Exploration
of factors limiting biomass estimation by polarimetric
radar in tropical forests. IEEE Transaction on Geosciences
and Remote Sensing 42: 86-104.
Robinson,
C., Saatchi, S., Neumann, M. & Gillespie, T. 2013. Impacts
of spatial variability on aboveground biomass estimation from L-band
radar in a temperate forest. Remote Sensing 5: 1001-1023.
Saatchi,
S.S., Marlier, M., Chazdon,
R.L., Clark, D.B. & Russell, A.E. 2011. Impact of spatial
variability of tropical forest structure on radar estimation of
aboveground biomass. Remote Sensing of Environment 115(11):
2836-2849.
Sandberg, G., Ulander, L., M.H., Fransson, J.E.S.,
Holmgren, J. & Le Toan, T. 2011. Land
P-band backscatter intensity for biomass retrieval in hemi boreal
forest. Remote Sensing of Environment 115(11): 2874-2886.
Sarker, L.R.,
Nichol, J., Ahmad, B., Busu, I. &
Rahman, A.A. 2012.
Potential of texture measurements of two-date
dual polarization PALSAR data for the improvement of forest biomass
estimation. ISPRS Journal of Photogrammetry 69: 146-166.
Sessa, R.
& Dolman, H. 2008.
Terrestrial essential climate variables for climate change assessment,
mitigation and adaptation. Rome: FAO GTOS-52.
Shimada,
M., Isoguchi, O., Tadono,
T. & Isono, K. 2009. PLASAR
radiometric calibration and geometric calibration. IEEE
Transaction on Geosciences and Remote Sensing 3: 765-768.
Wright, S.J. 2010. The future of tropical forests. Annals of
the New York Academy of Sciences. Ecological Conservation
and Biology 1195: 1-27.
*Pengarang untuk surat-menyurat; email: hamdanomar@frim.gov.my
|