 |
 |
 |
 |
 |
 |
Sains
Malaysiana 51(4)(2022): 959-976
http://doi.org/10.17576/jsm-2022-5104-02
Review on Remote Sensing
Technologies for Seagrass Mapping in Tropical Region
(Ulasan bagi Teknologi Penderiaan
Jauh untuk Pemetaan Rumput Laut di Wilayah Tropika)
WIWIN AMBARWULAN1,*,
RATNA SARI DEWI1 & WIDIATMAKA2
1Geospatial
Information Agency (Badan Informasi Geospasial), Jalan Raya Jakarta Bogor, KM
46, Cibinong, Indonesia
2Faculty
of Agriculture, IPB University, Jalan Meranti, Dramaga Campus, Bogor, Indonesia
Diserahkan:
6 Julai 2021/Diterima: 12 September 2021
Abstract
Seagrass ecosystems can be mapped
using RS because this technique is versatile and accurate. The
availability of seagrass information is very important for the sustainable
management of seagrass ecosystems. The use of RS technology to map
seagrass has become the focus of many researches worldwide by using various
types of platforms, sensors and various algorithms for satellite imagery
processing. In literature, there have been many review papers related to
seagrass, however, a comprehensive review on various aspects of seagrass is
limited. The objective of this review paper was to fill the gap by highlighting
the existing RS technology, seagrass biophysical property and image
processing analysis. Review results indicated that RS technology is
a powerful tool for accelerating seagrass mapping and for monitoring the
condition of seagrass ecosystems at regional scale due to the availability of
long-archived RS data and their free-access. In literature, the
empirical approaches still dominated seagrass mapping methodology compared to
the semi-analytic and analytic approaches. A clear conclusion from this review
is that the development in sensor technology and data processing algorithm is
still ongoing and has driven RS capabilities to map seagrass more
rapidly, accurately and less expensive. Future research on seagrass mapping
could be focused on a more automated classification by applying
machine-learning to handle a large amount of data to improve accuracy and to
discover robust methods for image pre-processing that is suitable for tropical
shallow waters such as those in Indonesia.
Keywords:
RS; seagrass; shallow water; tropical region
Abstrak
Ekosistem
rumput laut dapat dipetakan menggunakan penginderaan jauh kerana teknik ini
serba boleh dan tepat. Ketersediaan maklumat rumput laut sangat penting untuk
pengurusan ekosistem rumpai laut yang lestari. Penggunaan teknologi
penginderaan jauh untuk memetakan rumput laut telah menjadi tumpuan banyak
penyelidikan di seluruh dunia dengan menggunakan pelbagai jenis pentas,
sensor dan pelbagai algoritma untuk pemprosesan citra satelit. Dalam
kepustakaan, terdapat banyak makalah kajian yang berkaitan dengan rumput laut,
namun, tinjauan komprehensif mengenai pelbagai aspek rumput laut adalah
terbatas. Objektif kertas ini adalah untuk mengisi jurang dengan mengetengahkan
teknologi penginderaan jauh yang ada, harta biofisik rumput laut dan analisis
pemprosesan gambar. Hasil tinjauan menunjukkan bahawa teknologi penginderaan
jauh adalah alat yang ampuh untuk mempercepat pemetaan rumput laut dan untuk
memantau keadaan ekosistem rumput laut pada skala wilayah kerana ketersediaan
data penginderaan jauh yang diarkibkan lama dan akses bebasnya. Dalam
kepustakaan, pendekatan empirik masih mendominasi kaedah pemetaan rumput laut
berbanding dengan pendekatan separa-analitik dan analitik. Kesimpulan yang jelas
daripada tinjauan ini adalah bahawa pengembangan teknologi sensor dan algoritma
pemprosesan data masih berterusan dan mendorong keupayaan penginderaan jauh
untuk memetakan rumput laut dengan lebih cepat, tepat dan lebih murah.
Penyelidikan masa depan mengenai pemetaan rumput laut dapat difokuskan pada
pengelasan yang lebih automatik dengan menerapkan pembelajaran mesin untuk
menangani sejumlah besar data untuk meningkatkan ketepatan dan untuk menemukan
kaedah yang kuat untuk pemprosesan gambar yang sesuai untuk perairan dangkal
tropika.
Kata
kunci: Kawasan tropika; penginderaan jauh; perairan cetek; rumput laut
RUJUKAN
Adi, N.S. 2015. Characterisation of
the light environment and biophysical parameters of seagrass using remote
sensing. Ph.D. Thesis. The University of Queensland (Unpublished).
Agus, S.B., Aziizah, N.N., Subarno,
T. & Sunudin, A. 2018. The utilization of spot-7 imagery for mapping seagrass
distribution on intertidal zone and water depth estimation in Wawonii Island. Jurnal Ilmu dan Teknologi Kelautan Tropis 10(1): 197-207.
Ambarwulan, W., Verhoef, V.,
Mannaerts, C.M. & Salama, M.S. 2012. Estimating total suspended matter
concentration in tropical coastal waters of the Berau Estuary, Indonesia. International Journal of Remote Sensing 33(16): 4919-4936.
Ambarwulan, W., Salama, M.S.,
Mannaerts, C.M. & Verhoef, W. 2011. Estimating specific inherent optical
properties of tropical coastal waters using bio-optical model inversion and in
situ measurements: Case of the Berau Estuary, East Kalimantan, Indonesia. Hydrobiologia 658(1): 197-211.
Amran, M.A. 2010. Estimation of
seagrass coverage by depth invariant indices on quickbird imagery. Biotropia 17(1): 42-50.
Amri, K., Takwir, A., Asmadin &
Siregar, V.P. 2010. The accuracy assessment of QuickBird Satellite data by
using differential global positioning system method for classification of coral
reefs type on Karang Congkak and Karang Lebar, Seribu Islands. Jurnal Kelautan Nasional 5(1): 1-15.
Anggoro, A., Siregar, V.P. &
Agus, S.B. 2016. The effect of sunglint on benthic habitats mapping in Pari
Island using Worldview-2 imagery. Procedia
Environmental Sciences 33: 487-495.
Anggraeni, D., Fauzi, M.N. &
Handayani, C.N. 2019. Mapping of seagrass beds distribution using Landsat 8
satellite imagery in Tanimbar Islands Maluku Tenggara Barat regency Maluku
province. SEMNAS BIG 2018, Bogor.
Ayustina, R., Aulia, Z., Mustakin,
H., Alam, F., Amron, A., Yuwono, D., Ahmad, T., Prayogo, A. & Sari, F.
2018. Classification of shallow water seabed profile based on Landsat 8 Imagery
and in-situ data. Case study in Gili
Matra Island Lombok, Indonesia. E3S Web
of Conferences.
Aziizah, N.N., Siregar, V.P., Agus,
S.B. & Manuputty, A. 2016. Spatial analysis of seagrass cover area in Tunda
Serang Island, Banten. Omni-Akuatika 12(1): 73-80.
Barillé, L., Robin, M., Harin, N.,
Bargain, A. & Launeau, P. 2010. Increase in seagrass distribution at
Bourgneuf Bay (France) detected by spatial remote sensing. Aquatic Botany 92(3): 185-194.
Baumstark, R., Duffey, R. & Pu,
R. 2016. Mapping seagrass and colonized hard bottom in Springs Coast, Florida
using WorldView-2 satellite imagery. Estuarine,
Coastal and Shelf Science 181: 83-92.
Benfield, S.L., Guzman, H.M., Mair,
J.M. & Young, J.A.T. 2007. Mapping the distribution of coral reefs and
associated sublittoral habitats in Pacific Panama: A comparison of optical
satellite sensors and classification methodologies. International Journal of Remote Sensing 28(22): 5047-5070.
Carlson, D.F., Yarbro, L.A.,
Scolaro, S., Poniatowski, M., McGee-Absten, V. & Carlson, P.R. 2018. Sea
surface temperatures and seagrass mortality in Florida Bay: Spatial and
temporal patterns discerned from MODIS and AVHRR data. Remote Sensing of Environment 208: 171-188.
Carruthers, T.J.B. & Walker,
D.I. 1999. Sensitivity of transects across a depth gradient for measuring
changes in aerial coverage and abundance of Ruppia Megacarpa Mason. Aquatic Botany 65(1-4): 281-292.
Chavez, J.P.S. 1988. An Improved
dark-object subtraction technique for atmospheric scattering correction of
multispectral data. Remote Sensing of
Environment 24(3): 459-479.
Chayhard, S., Manthachitra, V.,
Nualchawee, K. & Buranapratheprat, A. 2018. Multi-temporal mapping of
seagrass distribution by using integrated remote sensing data in Kung Kraben
Bay (KKB), Chanthaburi Province, Thailand. International
Journal of Agricultural Technology 14(2): 161-170.
Chefaoui, R.M., Assis, J., Duarte,
C.M. & Serrao, E.A. 2016. Large-scale prediction of seagrass distribution
integrating landscape metrics and environmental factors: The case of Cymodocea
Nodosa (Mediterranean–Atlantic). Estuaries
and Coasts 39: 123-137.
Chen, C., Lau, V., Chang, N., Son,
N., Tong, P. & Chiang, S. 2016. Multi-temporal change detection of seagrass
beds using integrated landsat TM/ETM+/OLI imageries in Cam Ranh Bay, Vietnam. Ecological Informatics 35: 43-54.
Cho, H.J., Deepak Mishra
& Wood, J. 2012. Remote sensing of submerged aquatic vegetation. Chapter
12. In Remote Sensing - Applications, edited by Escalante-Ramirez, B.
Rijeka, Croatio: IntechOpen. pp. 297-308. DOI: 10.5772/35156
Collier, C.J. & Waycott, M.
2014. Temperature extremes reduce seagrass growth and induce mortality. Marine Pollution Bulletin 83(2):
483-490.
Collin, A., Etienne, S. &
Feunteun, E. 2017. VHR coastal bathymetry using WorldView-3: colour versus
learner. Remote Sensing Letters 8(11): 1072-1081.
Collings, S., Campbell, N.A. &
Keesing, J.K. 2019. Quantifying the discriminatory power of remote sensing
technologies for benthic habitat mapping. International
Journal of Remote Sensing 40(7): 2717-2738.
Conger, C.L., Hochberg, E.J.,
Fletcher, C.H. & Atkinson, M.J. 2006. Decorrelating remote sensing color
bands from bathymetry in optically shallow waters. IEEE Transactions on Geoscience and Remote Sensing 44(6):
1655-1660.
Criales, M.M., Cherubin,
L.M. & Browder, J.A. 2015. Modeling larval transport and settlement of pink
shrimp in South Florida: Dynamics of behavior and tides. Marine and Coastal
Fisheries 7(1): 148-176. https://doi.org/10.1080/19425120.2014.1001541.
Daud, M., Pin, T.G. & Handayani,
T. 2019. The spatial pattern of seagrass distribution and the correlation with
salinity, sea surface temperature, and suspended materials in Banten Bay. IOP Conference Series: Earth and
Environmental Science 243(1): 012013.
Dekker, A., Brando, V., Anstee, J.,
Fyfe, S., Malthus, T. & Karpouzli, E. 2007. Remote sensing of seagrass
ecosystems: Use of spaceborne and airborne sensors. In Seagrasses: Biology, Ecology and Conservation, edited by, Larkum,
A.W.D., Orth, R.J. & Duarte, C.M. Dordrecth: Springer. pp. 347-359.
Dierssen, H.M., Chlus, A. &
Russell, B. 2015. Hyperspectral discrimination of floating mats of seagrass
wrack and the macroalgae Sargassum in coastal waters of Greater Florida Bay
using airborne remote sensing. Remote
Sensing of Environment 167: 247-258.
Dierssen, H.M., Zimmerman, R.C.,
Drake, L.A. & Burdige, D. 2010. Benthic ecology from space: Optics and net
primary production in seagrass and benthic algae across the Great Bahama Bank. Marine Ecology Progress Series 411:
1-15.
Duarte, C.M. 2002. The future of
seagrass meadows. environ conserv. Environmental
Conservation 29(2): 192-206.
Duarte, B., Martins, I., Rosa, R.,
Matos, A.R., Roleda, M.Y., Reusch, T.B., Engelen, A.H., Serrão, E.A., Pearson,
G.A., Marques, J.C., Caçador, I., Duarte, C.M. & Jueterbock, A. 2018.
Climate change impacts on seagrass meadows and macroalgal forests: An
integrative perspective on acclimation and adaptation potential. Frontiers in Marine Science 5(190):
1-23.
Duffy, J.P., Pratt, L., Anderson,
K., Land, P.E. & Shutler, J.D. 2018. Spatial assessment of intertidal
seagrass meadows using optical imaging systems and a lightweight drone. Estuarine, Coastal and Shelf Science 200: 169-180.
Durako, M.J. & Howarth, J.F.
2017. Leaf spectral reflectance shows Thalassia
testudinum seedlings more sensitive to hypersalinity than
hyposalinity. Frontiers in Plant
Science 8(1127): 1-8.
Eggy Arya Giofandi, Yuliana
Safitri & Ahkmad Eduardi. 2020. Detection of the existence of seagrass bads
and coral reef using lyzenga algorithm and the ability of storing carbons. Jurnal
Kelautan: Indonesian Journal of Marine Science and Technology 12 (January):
165-174. https://doi.org/10.21107/jk.v12i2.5803
ERSDAC. 2003. ASTER Reference Guide,
Version 1.0. Earth Remote Sensing Data Analysis Center (ERSDAC).
Erzad, A.F., Hartoko, A. &
Muskananfola, M.R. 2020. Seagrass ecosystem as fish larvae habitat and its
biomass algorithm from Sentinel-2A satellite at Kemujan Island, Karimunjawa,
Indonesia. AACL Bioflux 13(3):
1511-1521.
Fauzan, M.A., Kumara, I.S.W.,
Yogyantoro, R., Suwardana, S., Fadhilah, N., Nurmalasari, I., Apriyani, S.
& Wicaksono, P. 2017. Assessing the capability of sentinel-2A data for
mapping seagrass percent cover in Jerowaru, East Lombok. Indonesian Journal of Geography 49(2): 195-203.
Ferwerda, J.G., De Leeuw, J.,
Atzberger, C. & Vekerdy, Z. 2007. Satellite-based monitoring of tropical
seagrass vegetation: Current techniques and future developments. Hydrobiologia 591(1): 59-71.
Foden, W.B., Mace, G.M. &
Butchart, S.H.M. 2013. Indicators of climate change impacts on biodiversity. Biodiversity Monitoring and Conservation:
Bridging the Gap between Global Commitment and Local Action, edited by
Collen, B., Pettorelli, N., Baillie, J.E.M. & Durant, S.M. Chichester,
Wiley-Blackwell. pp. 120-137.
Fortes, M.D., Ooi, J.L.S., Tan,
Y.M., Prathep, A., Bujang, J.S. & Yaakub, S.M. 2018. Seagrass in Southeast
Asia: A review of status and knowledge gaps, and a road map for conservation. Botanica Marina 61(3): 269-288.
Giardino, C., Brando, V.E., Gege,
P., Pinnel, N., Hochberg, E., Knaeps, E., Reusen, I., Doerffer, R., Bresciani,
M., Braga, F. & Foerster, S. 2019. Imaging spectrometry of inland and
coastal waters: State of the art, achievements and perspectives. Surveys in Geophysics 40(3): 401-429.
Glasby, T.M., Taylor, S.L. &
Housefield, G.P. 2015. Factors influencing the growth of seagrass seedlings: A
case study of Posidonia australis. Aquatic Botany 120: 251-259.
Green, E.P. & Short, F.T. 2003. World Atlas of Seagrasses. Berkeley:
University of California Press. pp. 1-330.
Gumusay, M.U., Bakirman, T., Tuney
Kizilkaya, I. & Aykut, N.O. 2019. A review of seagrass detection, mapping
and monitoring applications using acoustic systems. European Journal of Remote Sensing 52(1): 1-29.
Ha, N.T., Manley-Harris, M., Pham,
T.D. & Hawes, I. 2020. A comparative assessment of ensemble-based machine
learning and maximum likelihood methods for mapping seagrass using sentinel-2
imagery in Tauranga Harbor, New Zealand. Remote
Sensing 12(355): 1-16.
Hafizt, M., Manessa, M.D.M., Adi,
N.S. & Prayudha, B. 2017. Benthic habitat mapping by combining lyzenga’s
optical model and relative water depth model in Lintea Island, Southeast
Sulawesi. In IOP Conference Series: Earth
and Environmental Science 98: 012037.
Haklay, M. 2013. Citizen science and
volunteered geographic information: Overview and typology of participation. In Crowdsourcing Geographic Knowledge,
edited by Sui, D.Z. & Elwood, S. Dordrecht: Springer. pp. 105-122.
Hamsah, K., Azhar, S., Ulfah, M.
& Yunita, D.A. 2019. Analysis of shallow water benthic using of SPOT-6
imagery data on Miang Besar Island, Kutai Timur, Indonesia. In IOP Conference Series: Earth and
Environmental Science 348: 012011.
Hashim, M., Ito, S., Numata, S.,
Hosaka, T., Hossain, M.S., Misbari, S., Yahya, N.N. & Ahmad, S. 2017. Using
fisher knowledge, mapping population, habitat suitability and risk for the
conservation of dugongs in Johor Straits of Malaysia. Marine Policy 78: 18-25.
Hedley, J.D., Russell, B.J.,
Randolph, K., Perez-Castro, M.A., Vasquez-Elizondo, R.M., Enriquez, S. &
Dierssen, H.M. 2017. Remote sensing of seagrass leaf area index and species:
The capability of a model inversion method assessed by sensitivity analysis and
hyperspectral data of Florida Bay. Frontiers
in Marine Science 4(362): 1-20.
Hedley, J., Russell, B., Randolph,
K. & Dierssen, H. 2016. A physics-based method for the remote sensing of
seagrasses. Remote Sensing of Environment 174: 134-147.
Hedley, J., Roelfsema, C., Koetz, B.
& Phinn, S. 2012. Capability of the Sentinel 2 mission for tropical coral
reef mapping and coral bleaching detection. Remote
Sensing of Environment 120: 145-155.
Hedley, J.D., Harborne, A.R. &
Mumby, P.J. 2005. Simple and robust removal of sun glint for mapping
shallow‐water benthos. International
Journal of Remote Sensing 26(10): 2107-2112.
Hemminga, M.A. & Duarte, C.M.
2000. Elemental dynamics in seagrass systems. In Seagrass Ecology. Cambridge: Cambridge University Press. pp.
146-198.
Hill, V.J., Zimmerman, R.C.,
Bissett, W.P., Dierssen, H. & Kohler, D.D. 2014. Evaluating light
availability, seagrass biomass, and productivity using hyperspectral airborne
remote sensing in Saint Joseph’s Bay, Florida. Estuaries and Coasts 37(6): 1467-1489.
Hossain, M.S., Hashim, M., Bujang,
J.S., Zakaria, M.H. & Muslim, A.M. 2019. Assessment of the impact of
coastal reclamation activities on seagrass meadows in Sungai Pulai estuary,
Malaysia, using Landsat data (1994-2017). International
Journal of Remote Sensing 40(9): 3571-3605.
Hossain, M.S., Bujang, J.S.,
Zakaria, M.H. and Hashim, M., 2015a. The application of remote sensing to
seagrass ecosystems: An overview and future research prospects. International Journal of Remote Sensing 36(1): 61-114.
Hossain, M.S., Bujang, J.S.,
Zakaria, M.H. & Hashim, M. 2015b. Application of Landsat images to seagrass
areal cover change analysis for Lawas, Terengganu and Kelantan of Malaysia. Continental Shelf Research 110: 124-148.
Hossain, M.S., Sidik, B.J., Harah,
Z.M. & Hashim, M. 2015c. Landsat image enhancement techniques for subtidal
and intertidal seagrass detection and distribution mapping in the coastal
waters of Sungai Pulai estuary, Malaysia. Coastal
Marine Science 38(1): 27-41.
Huang, C., Davis, L.S. &
Townshend, J.R.G. 2002. An assessment of support vector machines for land cover
classification. International Journal of
Remote Sensing 23(4): 725-749.
Huong, N.T.T. 2017. A review of
seagrass studies by using satellite remote sensing data in the Southeast Asia:
Status and potential. Vietnam Journal of
Science and Technology 55(4C): 148-154.
Ilyas, T.P., Nababan, B., Madduppa,
H. & Kushardono, D. 2020. Seagrass ecosystem mapping with and without water
column correction in Pajenekang island waters, South Sulawesi. Jurnal Ilmu dan Teknologi Kelautan Tropis 12(1): 9-23.
Indinna, S.A. & Wicaksono, P.
2019. Seagrass species composition and above-ground carbon stock mapping in
Parang Island using Planetscope image. In Sixth
Geoinformation Science Symposium, 2019, Yogyakarta, Indonesia. SPICE
Library. pp. 1-10.
Islam, K.A., Hill, V., Schaeffer,
B., Zimmerman, R. & Li, J. 2019. Semi-supervised adversarial domain
adaptation for seagrass detection in multispectral images. In 2019 IEEE International Conference on Data
Mining (ICDM). pp. 1120-1125.
Jones, B.L., Unsworth, R.K.,
McKenzie, L.J., Yoshida, R.L. & Cullen-Unsworth, L.C. 2018. Crowdsourcing
conservation: The role of citizen science in securing a future for
seagrass. Marine Pollution Bulletin 134: 210-215.
Jordà, G., Marbà, N. & Duarte,
C.M. 2012. Mediterranean seagrass vulnerable to regional climate warming. Nature Climate Change 2(11): 821-824.
Kaewsrikhaw, R., Ritchie, R.J. &
Prathep, A. 2016. Variations of tidal exposures and seasons on growth,
morphology, anatomy and physiology of the seagrass Halophila ovalis (R. Br.) Hook. f. in a seagrass bed in Trang
Province, Southern Thailand. Aquatic
Botany 130: 11-20.
Kay, S., Hedley, J.D. &
Lavender, S. 2009. Sun glint correction of high and low spatial resolution
images of aquatic scenes: A review of methods for visible and near-infrared
wavelengths. Remote Sensing 1(4):
697-730.
Koedsin, W., Intararuang, W.,
Ritchie, R.J. & Huete, A. 2016. An integrated field and remote sensing
method for mapping seagrass species, cover, and biomass in southern
Thailand. Remote Sensing 8(4):
1-18.
Krause-Jensen, D., Quaresma, A.L.,
Cunha, A.H. & Greve, T.M. 2004. How are seagrass distribution and abundance
monitored? In European Seagrasses:
An Introduction to Monitoring and Management, edited by Krause-Jensen, D.,
Quaresma, A.L., Cunha, A.H. & Greve, T.M. California: M&MS Project. pp.
45-53.
Kumar, A., Equeenuddin, S.M.,
Mishra, D.R. & Acharya, B.C. 2016. Remote monitoring of sediment dynamics
in a coastal lagoon: Long-term spatio-temporal variability of suspended
sediment in Chilika. Estuarine,
Coastal and Shelf Science 170: 155-172.
Kuo, J. 2007. New monoecious
seagrass of Halophila sulawesii (Hydrocharitaceae) from Indonesia. Aquatic
Botany 87(2): 171-175.
Kurniawan, A., Subardjo, P. &
Pratikto, I. 2014. Spatial analysis of seagrass beds using remote sensing data
from the GeoEye-1 Satellite in the waters of Parang Island and Kumbang Island,
Karimunjawa Islands. Diponegoro Journal
of Marine Research 3(3): 374-379.
Kutser, T., Vahtmäe, E. & Praks,
J. 2009. A sun glint correction method for hyperspectral imagery containing areas
with non-negligible water leaving NIR signal. Remote Sensing of Environment 113(10): 2267-2274.
Lamit, N., Tanaka, Y. & Majid,
H.M.B.A. 2017. Seagrass diversity in Brunei Darussalam: First records of three
species. Scientia Bruneiana 16(2):
48-52.
Lee, Z.P., Darecki, M., Carder,
K.L., Davis, C.O., Stramski, D. & Rhea, W.J. 2005. Diffuse attenuation
coefficient of downwelling irradiance: An evaluation of remote sensing
methods. Journal of Geophysical
Research: Oceans 110(C2): 1-9.
León-Pérez, M.C., Armstrong, R.A.,
Hernández, W.J., Aguilar-Perera, A. & Thompson-Grim, J. 2020. Seagrass
cover expansion off Caja de Muertos Island, Puerto Rico, as determined by
long-term analysis of historical aerial and satellite images (1950-2014). Ecological Indicators 117: 1-13.
Li, F. & Xiao, B. 2011. Aquatic
vegetation mapping based on remote sensing imagery: An application to Honghu
Lake. 2011 International Conference on
Remote Sensing, Environment and Transportation Engineering. IEEE. pp.
4832-4836.
Li, S. 2018. Seagrass mapping and
human impact evaluation using remote sensing imagery at Core Banks, North
Carolina. M.E.M. Thesis. Duke University (Unpublished).
Lillesand, T., Kiefer, R.W. &
Chipman, J. 2015. Remote Sensing and
Image Interpretation. Hoboken: John Wiley & Sons Inc. pp. 1-707.
Lubis, Muhammad Zainuddin,
Dewi Puspa Sari, Titi Aprilliyanti, Ari Kurniawan Daulay, Aditya Hanafi,
Fitriya Ananda, Dhea Ayu Saputri, Siti Aminah, Muhammad Anand Pratama Zabid
& Muhammad Ma’ruf Ibrahim. 2017. Utilization of Landsat 8 Imagery to map
the distribution of seagrass in the coastal area of Batam Island. Dinamika
Maritim 6(1 SE-Dinamika Maritim). https://ojs.umrah.ac.id/index.php/dinamikamaritim/article/view/185.
Luo, Y. 2018. Mapping plant
communities in the intertidal zones of the Yellow River delta using Sentinel-2
Optical and Sentinel-1 SAR Time series data. M.Sc. Thesis. University of Twente
(Unpublished).
Lyons, M.B., Phinn, S.R. &
Roelfsema, C.M. 2012. Long term land cover and seagrass mapping using Landsat
and object-based image analysis from 1972 to 2010 in the coastal environment of
South East Queensland, Australia. ISPRS
Journal of Photogrammetry and Remote Sensing 71: 34-46.
Lyons, M., Phinn, S. &
Roelfsema, C. 2011. Integrating Quickbird multi-spectral satellite and field
data: Mapping bathymetry, seagrass cover, seagrass species and change in
Moreton Bay, Australia in 2004 and 2007. Remote Sensing 3(1): 42-64.
Lyzenga, D.R. 1981. Remote sensing
of bottom reflectance and water attenuation parameters in shallow water using
aircraft and Landsat data. International
Journal of Remote Sensing 2(1): 71-82.
Lyzenga, D.R. 1978. Passive remote
sensing techniques for mapping water depth and bottom features. Applied Optics 17(3): 379-383.
Lyzenga, D.R., Malinas, N.P. &
Tanis, F.J. 2006. Multispectral bathymetry using a simple physically based
algorithm. IEEE Transactions on
Geoscience and Remote Sensing 44(8): 2251-2259.
Manessa, M.D.M., Kanno, A., Sekine,
M., Ampou, E.E., Widagti, N. & As-syakur, A. 2014. Shallow-water benthic identification
using multispectral satellite imagery: Investigation on the effects of
improving noise correction method and spectral cover. Remote Sensing 6(5): 4454-4472.
Manik, H.M. & Apdillah, D. 2020.
Remote sensing of seagrass and seabed using acoustic technology in Bintan
Seawater, Indonesia. Pertanika
Journal of Science & Technology 28(2): 421-439.
Manuputty, A., Gaol, J.L., Agus,
S.B. & Nurjaya, I.W. 2017. The utilization of depth invariant index and
principle component analysis for mapping seagrass ecosystem of Kotok Island and
Karang Bongkok, Indonesia. In IOP
Conference Series: Earth and Environmental Science 54(1): 012083.
Marbà, N., Krause-Jensen, D.,
Alcoverro, T., Birk, S., Pedersen, A., Neto, J.M., Orfanidis, S., Garmendia,
J.M., Muxika, I., Borja, A., Dencheva, K. & Duarte, C.M. 2013. Diversity of
European seagrass indicators: Patterns within and across regions. Hydrobiologia 704(1): 265-278.
Marcello, J., Eugenio, F., Marqués,
F. & Martín, J. 2015. Precise classification of coastal benthic habitats
using high resolution Worldview-2 imagery. In 2015 IEEE International Geoscience and Remote Sensing Symposium
(IGARSS). IEEE. pp. 2307-2310.
Maritorena, S., Morel, A. &
Gentili, B. 1994. Diffuse reflectance of oceanic shallow waters: Influence of water
depth and bottom albedo. Limnology
and Oceanography 39(7): 1689-1703.
McKenzie, L.J. 2003. Guidelines for the Rapid Assessment of
Seagrass Habitats in the Western Pacific. Queensland: Department of Primary
Industries. http://www.seagrasswatch.org/Methods/Manuals/SeagrassWatch_Rapid_Assessment_Manual.pdf
McKenzie, L.J., Nordlund, L.M.,
Jones, B.L., Cullen-Unsworth, L.C., Roelfsema, C. & Unsworth, R.K. 2020.
The global distribution of seagrass meadows. Environmental Research Letters 15: 1-14.
McKenzie, L.J., Finkbeiner, M.A.
& Kirkman, H. 2001. Methods for mapping seagrass distribution. In Global Seagrass Research Methods, edited
by Short, F.T. & Coles, R.G. Amsterdam: Elsevier. pp. 101-121.
McMahon, K., van Dijk, K.J.,
Ruiz-Montoya, L., Kendrick, G.A., Krauss, S.L., Waycott, M., Verduin, J., Lowe,
R., Statton, J., Brown, E. & Duarte, C. 2014. The movement ecology of
seagrasses. Proceedings of the Royal
Society B: Biological Sciences 281: 1-9.
Mejia, A.Y., Rotini, A., Lacasella,
F., Bookman, R., Thaller, M.C., Shem-Tov, R., Winters, G. & Migliore, L.
2016. Assessing the ecological status of seagrasses using morphology,
biochemical descriptors and microbial community analyses. A study in Halophila stipulacea (Forsk.) Aschers
meadows in the northern Red Sea. Ecological
Indicators 60: 1150-1163.
Misbari, S. & Hashim, M. 2016.
Change detection of submerged seagrass biomass in shallow coastal water. Remote Sensing 8(3): 1-29.
Mumby, P.J., Clark, C.D., Green,
E.P. & Edwards, A.J. 1998. Benefits of water column correction and
contextual editing for mapping coral reefs. International Journal of Remote Sensing 19(1): 203-210.
Niroumand-Jadidi, M. & Vitti, A.
2016. Optimal band ratio analysis of WorldView-3 imagery for bathymetry of
shallow rivers (case study: Sarca River, Italy). International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences (ISPRS), Czech Republic.
Nordlund, L.M., Koch, E.W., Barbier,
E.B. & Creed, J.C. 2016. Seagrass ecosystem services and their variability
across genera and geographical regions. PLoS ONE 11(10): e0163091.
Nurdin, N., Amri, K., Djalil, A.R.,
Jaya, I., Aris, A. & AS, M.A. 2014. Geospatial dynamic of seagrass in outer
zone, Spermonde Archipelago, Indonesia using Landsat data from 1972-2013.
In Ocean Remote Sensing and
Monitoring from Space. SPICE. pp. 127-139. https://doi.org/10.1117/12.2062898
Ogawa, H., Sidik, B.J. &
Zakaria, M.H. 2011. Seagrasses:
Resource Status and Trends in Indonesia, Japan, Malaysia, Thailand and Vietnam.
Tokyo: Seizando-Shoten Publishing. pp. 1-168.
Oguslu, E., Islam, K., Perez, D.,
Hill, V.J., Bissett, W.P., Zimmerman, R.C. & Li, J. 2018. Detection of
seagrass scars using sparse coding and morphological filter. Remote Sensing of Environment 213:
92-103.
Ooi, J.L.S., Kendrick, G.A., Van
Niel, K.P. & Affendi, Y.A. 2011. Knowledge gaps in tropical Southeast Asian
seagrass systems. Estuarine, Coastal
and Shelf Science 92(1): 118-131.
Pan, Z., Glennie, C., Legleiter, C.
& Overstreet, B. 2015. Estimation of water depths and turbidity from
hyperspectral imagery using support vector regression. IEEE Geoscience and Remote Sensing Letters 12(10):
2165-2169.
Pasqualini, V., Pergent-Martini, C.,
Pergent, G., Agreil, M., Skoufas, G., Sourbes, L. & Tsirika, A. 2005. Use
of SPOT 5 for mapping seagrasses: An application to Posidonia oceanica. Remote
Sensing of Environment 94(1): 39-45.
Patty,
S.I. 2016. Mapping conditions of seaweeds in ternate - tidore waters and
surrounding areas. Jurnal Ilmiah Platax 4(1): 9-18.
Perez, D.I., Phinn, S.R., Roelfsema,
C.M., Shaw, E., Johnston, L. & Iguel, J. 2018. Primary production and
calcification rates of algae‐dominated reef flat and seagrass
communities. Journal of Geophysical
Research: Biogeosciences 123(8): 2362-2375.
Pham, T.D., Xia, J., Ha, N.T., Bui,
D.T., Le, N.N. & Tekeuchi, W. 2019. A review of remote sensing approaches
for monitoring blue carbon ecosystems: Mangroves, seagrasses and salt marshes
during 2010-2018. Sensors 19(8):
1-27.
Phinn, S., Roelfsema, C., Dekker,
A., Brando, V. & Anstee, J. 2008. Mapping seagrass species, cover and
biomass in shallow waters: An assessment of satellite multi-spectral and
airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote Sensing of Environment 112(8):
3413-3425.
Potouroglou, M., Bull, J.C., Krauss,
K.W., Kennedy, H.A., Fusi, M., Daffonchio, D., Mangora, M.M., Githaiga, M.N.,
Diele, K. & Huxham, M. 2017. Measuring the role of seagrasses in regulating
sediment surface elevation. Scientific
Reports 7(1): 1-11.
Pu, R. & Bell, S. 2017. Mapping
seagrass coverage and spatial patterns with high spatial resolution IKONOS
imagery. International Journal of
Applied Earth Observation and Geoinformation 54: 145-158.
Pu, R., Bell, S. & Meyer, C.
2014. Mapping and assessing seagrass bed changes in Central Florida's west
coast using multitemporal Landsat TM imagery. Estuarine, Coastal and Shelf Science 149: 68-79.
Pu, R.,
Bell, S.S., Meyer, C., Baggett, L. & Zhao, Y. 2012. Mapping and assessing
seagrass Purnawan, S., Aulia, F. &
Khairuman, T. 2016. Using Landsat-8 imagery data on mapping of the seagrass
distribution in Matahari Island, Pulau Banyak district, Aceh province,
Indonesia. Aceh International
Journal of Science and Technology 5(2): 63-68.
Qian, Y., Zhou, W., Yan, J., Li, W.
& Han, L. 2015. Comparing machine learning classifiers for object-based
land cover classification using very high resolution imagery. Remote Sensing 7(1): 153-168.
Roelfsema, C.M., Lyons, M., Kovacs,
E.M., Maxwell, P., Saunders, M.I., Samper-Villarreal, J. & Phinn, S.R.
2014. Multi-temporal mapping of seagrass cover, species and biomass: A
semi-automated object based image analysis approach. Remote Sensing of Environment 150: 172-187.
Sagawa, T., Boisnier, E., Komatsu,
T., Mustapha, K.B., Hattour, A., Kosaka, N. & Miyazaki, S. 2010. Using
bottom surface reflectance to map coastal marine areas: A new application
method for Lyzenga's model. International
Journal of Remote Sensing 31(12): 3051-3064.
Sari, D.P. & Lubis, M.Z.
2017. Pemanfaatan Citra Landsat 8 untuk memetakan persebaran lamun di wilayah
pesisir Pulau Batam. Jurnal Enggano 2(1): 38-45.
https://doi.org/10.31186/jenggano.2.1.38-45.
Schroeder, S.B., Dupont, C., Boyer,
L., Juanes, F. & Costa, M. 2019. Passive remote sensing technology for
mapping bull kelp (Nereocystis luetkeana):
A review of techniques and regional case study. Global Ecology and Conservation 19: e00683.
Schweizer, D., Armstrong, R.A. &
Posada, J. 2005. Remote sensing characterization of benthic habitats and
submerged vegetation biomass in Los Roques Archipelago National Park,
Venezuela. International Journal of
Remote Sensing 26(12): 2657-2667.
Scott, A.L., York, P.H., Duncan, C.,
Macreadie, P.I., Connolly, R.M., Ellis, M.T., Jarvis, J.C., Jinks, K.I., Marsh,
H. & Rasheed, M.A. 2018. The role of herbivory in structuring tropical
seagrass ecosystem service delivery. Frontiers
in Plant Science 9: 127.
Semedi, B. & Lutfi, O.M. 2019.
Pemanfaatan data citra satelit Sentinel-2 untuk asesmen habitat dasar perairan
pantai selatan Sempu Kabupaten Malang. JFMR-
Journal of Fisheries and Marine Research 3(2): 273-279.
Setiawan, F., Andriani, Y., Harahap,
S.A. & Hutahaean, A.A. 2012. Seagrass change detection using remote sensing
technology and its relation with carbon storage in Banten Bay. Jurnal Perikanan dan Kelautan 3(3):
275-286.
Setyawan, I.E., Siregar, V.P.,
Pramono, G.H. & Yuwono, D.M. 2014. Pemetaan profil habitat dasar perairan
dangkal berdasarkan bentuk topografi: Studi kasus Pulau Panggang, Kepulauan
Seribu Jakarta. Majalah Ilmiah Globe 16(2):
125-132.
Short, F., Carruthers, T., Dennison,
W. & Waycott, M. 2007. Global seagrass distribution and diversity: A
bioregional model. Journal of
Experimental Marine Biology and Ecology 350(1-2): 3-20.
Short, F.T., Coles, R.G. &
Pergent-Martini, C. 2001. Global seagrass distribution. In Global Seagrass Research Methods, edited by Short, F.T. &
Coles, R.G. Amsterdam: Elsevier. pp. 5-30.
Sjafrie, N.D.M., Hernawan, U.E.,
Prayudha, B., Supriyadi, I.H., Iswari, M.Y., Rahmat, Anggraini, K., Rahmawati,
S. & Suyarso. 2018. Status Padang Lamun Indonesia ver, 2. 2018.
Indonesia: Pusat Penelitian Oseanografi-LIPI. pp. 1-50.
Solana-Arellano, E.,
Echavarria-Heras, H. & Martinez, M.G. 2003. Improved leaf area index based
biomass estimations for Zostera marina L. Mathematical Medicine and
Biology: A Journal of the IMA 20(4): 367-375.
Stankovic, M., Ambo-Rappe, R.,
Carly, F., Dangan-Galon, F., Fortes, M.D., Hossain, M.S., Kiswara, W., Van
Luong, C., Minh-Thu, P., Mishra, A.K. & Noiraksar, T. 2021. Quantification
of blue carbon in seagrass ecosystems of Southeast Asia and their potential for
climate change mitigation. Science
of the Total Environment 783: 146858.
Stumpf, R.P., Holderied, K. &
Sinclair, M. 2003. Determination of water depth with high‐resolution
satellite imagery over variable bottom types. Limnology and Oceanography 48(1part2): 547-556.
Su, L. & Huang, Y. 2019.
Seagrass resource assessment using worldview-2 imagery in the redfish bay,
Texas. Journal of Marine Science and
Engineering 7(4): 1-16.
Sudo, K., Quiros, T.E., Prathep, A.,
Luong, C.V., Lin, H.J., Bujang, J.S., Ooi, J.L.S., Fortes, M.D., Zakaria, M.H.,
Yaakub, S.M. & Tan, Y.M. 2021. Distribution, temporal change, and
conservation status of tropical seagrass beds in Southeast Asia:
2000-2020. Frontiers in Marine
Science 8: 637722.
Tamondong, A.M., Blanco, A.C.,
Fortes, M.D. & Nadaoka, K. 2013. Mapping of seagrass and other benthic
habitats in Bolinao, Pangasinan using Worldview-2 satellite image. In 2013 IEEE International Geoscience and
Remote Sensing Symposium-IGARSS. IEEE. pp. 1579-1582.
Taufikurrahman, T., Selamat, M.B.
& Mashoreng, S. 2017. Karakterisasi spektral kondisi padang lamun
menggunakan citra Landsat 8 OLI. Jurnal
Ilmu Kelautan SPERMONDE 3(2): 56-62.
Thalib, M.S., Faizal, A. & La
Nafie, Y.A. 2019. Remote sensing analysis of seagrass beds in Bontosua Island,
Spermonde Archipelago. In IOP
Conference Series: Earth and Environmental Science 370(1): 012021.
Topouzelis, K., Makri, D., Stoupas,
N., Papakonstantinou, A. & Katsanevakis, S. 2018. Seagrass mapping in Greek
territorial waters using Landsat-8 satellite images. International Journal of Applied Earth Observation and Geoinformation 67:
98-113.
Traganos, D., Poursanidis, D.,
Aggarwal, B., Chrysoulakis, N. & Reinartz, P. 2018. Estimating
satellite-derived bathymetry (SDB) with the google earth engine and
sentinel-2. Remote Sensing 10(6):
1-18.
Traganos, D., Cerra, D. &
Reinartz, P. 2017. Cubesat-derived detection of seagrasses using planet imagery
following unmixing-based denoising: Is small the next big? International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives XLII-1(W1):
283-287.
Uhrin, A.V. & Townsend, P.A.
2016. Improved seagrass mapping using linear spectral unmixing of aerial
photographs. Estuarine, Coastal and
Shelf Science 171: 11-22.
Urbański, J., Mazur, A. &
Janas, U. 2009. Object-oriented classification of QuickBird data for mapping
seagrass spatial structure. Oceanological
and Hydrobiological Studies 38(1): 27-43.
Valle, M., Pala, V., Lafon, V.,
Dehouck, A., Garmendia, J.M., Borja, A. & Chust, G. 2015. Mapping estuarine
habitats using airborne hyperspectral imagery, with special focus on seagrass
meadows. Estuarine, Coastal and
Shelf Science 164: 433-442.
Vandermeulen, H. 2014. Bay-scale
assessment of eelgrass beds using sidescan and video. Helgoland Marine Research 68(4): 559-569.
Veettil, B.K. & Quang, N.X.
2018. Environmental changes near the Mekong Delta in Vietnam using remote
sensing. Rendiconti Lincei. Scienze
Fisiche e Naturali 29(3): 639-647.
Veettil, B.K., Ward, R.D., Lima,
M.D.A.C., Stankovic, M., Hoai, P.N. & Quang, N.X. 2020. Opportunities for
seagrass research derived from remote sensing: A review of current
methods. Ecological Indicators 117:
106560.
Vidyan, S. 2018. The use of remote
sensing to estimate changes of seagrass extent and biomass in Cockburn Sound,
Western Australia. In IOP Conference
Series: Earth and Environmental Science 149:
012016.
Vo, T.T., Lau, K., Liao, L.M. &
Nguyen, X.V. 2020. Satellite image analysis reveals changes in seagrass beds at
Van Phong Bay, Vietnam during the last 30 years. Aquatic Living Resources 33(4): 1-10.
Waycott, M., Duarte, C.M.,
Carruthers, T.J., Orth, R.J., Dennison, W.C., Olyarnik, S., Calladine, A.,
Fourqurean, J.W., Heck, K.L., Hughes, A.R. & Kendrick, G.A. 2009.
Accelerating loss of seagrasses across the globe threatens coastal
ecosystems. Proceedings of the
National Academy of Sciences 106(30): 12377-12381.
Wicaksono, P. 2016. Improving the
accuracy of multispectral-based benthic habitats mapping using image rotations:
The application of principle component analysis and independent component
analysis. European Journal of Remote
Sensing 49(1): 433-463.
Wicaksono, P. 2015. Mapping seagrass
leaf area index, standing crop, and above ground carbon stock using compressed
remote sensing image. The 1st
International Conference of Indonesian Society for Remote Sensing,
Surabaya.
Wicaksono, P. & Lazuardi, W.
2018. Assessment of PlanetScope images for benthic habitat and seagrass species
mapping in a complex optically shallow water environment. International Journal of Remote Sensing 39(17):
5739-5765.
Wicaksono, P. & Hafizt, M. 2013.
Mapping seagrass from space: Addressing the complexity of seagrass LAI
mapping. European Journal of Remote
Sensing 46(1): 18-39.
Wicaksono, P., Lazuardi, W. &
Munir, M. 2019. Integrating image at different spatial resolutions and field
data for seagrass percent cover mapping. ISPRS
- International Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences XLII-4(W19): 487-492.
Wicaksono, P., Kumara, I.S.W.,
Kamal, M., Fauzan, M.A., Zhafarina, Z., Nurswantoro, D.A. & Yogyantoro,
R.N. 2017. Multispectral resampling of seagrass species spectra: WorldView-2,
Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI. In IOP Conference Series: Earth and
Environmental Science 98(1): 1-12.
Winters, G., Edelist, D.,
Shem‐Tov, R., Beer, S. & Rilov, G. 2017. A low cost
field‐survey method for mapping seagrasses and their potential threats:
An example from the northern Gulf of Aqaba, Red Sea. Aquatic Conservation: Marine and Freshwater Ecosystems 27(2):
324-339.
Wulder, M.A., White, J.C., Goward,
S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R. &
Woodcock, C.E. 2008. Landsat continuity: Issues and opportunities for land
cover monitoring. Remote Sensing of
Environment 112(3): 955-969.
Yamakita, T., Sodeyama, F., Whanpetch,
N., Watanabe, K. & Nakaoka, M. 2019. Application of deep learning
techniques for determining the spatial extent and classification of seagrass
beds, Trang, Thailand. Botanica
Marina 62(4): 291-307.
Yang, C., Yang, D., Cao, W., Zhao,
J., Wang, G., Sun, Z., Xu, Z. & Ravi Kumar, M.S. 2010. Analysis of seagrass
reflectivity by using a water column correction algorithm. International Journal of Remote Sensing 31(17-18):
4595-4608.
Yusuf, D. & Indrawan. 2019. Analysis of seagrass condition using
spectral reflection of Quickbird Imagery on Saronde Island North
Gorontalo. Jambura Geoscience Review 1(1):
1-6.
Zoffoli, M.L., Gernez, P., Rosa, P.,
Le Bris, A., Brando, V.E., Barillé, A.L., Harin, N., Peters, S., Poser, K.,
Spaias, L. & Peralta, G. 2020. Sentinel-2 remote sensing of Zostera
noltei-dominated intertidal seagrass meadows. Remote Sensing of Environment 251: 112020.
Zoffoli, M.L., Frouin, R. &
Kampel, M. 2014. Water column correction for coral reef studies by remote
sensing. Sensors 14(9):
16881-16931.
* Pengarang untuk surat-menyurat; email:
wiwin.ambarwulan@big.go.id
|
|||
|
