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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
Received:
6 July 2021/Accepted: 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
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*Corresponding
author; email: wiwin.ambarwulan@big.go.id
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