scholarly journals Assessment of Sentinel-2A multispectral image for benthic habitat composition mapping

2020 ◽  
Vol 14 (2) ◽  
pp. 279-288
Author(s):  
Pramaditya Wicaksono ◽  
Muhammad Afif Fauzan ◽  
Septian Galih Widhi Asta
Keyword(s):  
Multispectral Image ◽  
Benthic Habitat ◽  
Habitat Composition ◽  
Sentinel 2A

scholarly journals KLASIFIKASI HABITAT PERAIRAN DANGKAL MENGGUNAKAN LOGIKA FUZZY DAN MAXIMUM LIKELIHOOD PADA CITRA SATELIT MULTISPEKTRAL

2018 ◽  
Vol 10 (3) ◽  
pp. 667-681
Author(s):  
Muhammad Siddiq Sangadji ◽  
Vincentius Paulus Siregar ◽  
Henry Munandar Manik
Keyword(s):  
Remote Sensing ◽  
Fuzzy Logic ◽  
Maximum Likelihood ◽  
Satellite Imagery ◽  
Data Retrieval ◽  
Classification Algorithm ◽  
Benthic Habitat ◽  
Fuzzy Logic Algorithm ◽  
Accuracy Level ◽  
Sentinel 2A

ABSTRAKLogika fuzzy memiliki aplikasi di berbagai bidang, namun memiliki arti khusus untuk penginderaan jarak jauh. Logika fuzzy memungkinkan keanggotaan parsial, bagian yang sangat penting dibidang penginderaan jarak jauh, karena keanggotaan parsial diterjemahkan secara dekat dengan masalah piksel campuran. Penelitian ini bertujuan untuk menerapkan algoritma klasifikasi logika fuzzy untuk memetakan habitat dasar Perairan dangkal pada Citra Satelit SPOT 7 dan Sentinel 2A, menguji tingkat akurasinya dan membandingkan algoritma klasifikasi logika fuzzy dengan maximum likelihood. Pengambilan data lapang berlokasi di gusung Karang Lebar dan Karang Congkak, Kepuluan Seribu pada tanggal 6 Desember sampai dengan 10 Desember 2017. Keseluruhan hasil uji akurasi menunjukan bahwa algoritma logika fuzzy masih memiliki tingkat akurasi yang baik dibandingkan dengan algoritma maximum likelihood. Perbedaan ukuran pixel (resolusi spasial) dari citra satelit juga mempengaruhi hasil akurasi, dimana citra satelit SPOT 7 memiliki tingkat akurasi yang lebih besar dibandingkan dengan Sentinel 2A.ABSTRACTFuzzy logic has applications in various fields, but has special meaning for remote sensing. Fuzzy logic allows partial membership, a very important property in the field of remote sensing, since partial membership is translated closely to the problem of mixed pixels. The aim of this research is to apply fuzzy logic classification algorithm to map benthic habitat in SPOT 7 and Sentinel 2A satellite imagery, test its accuracy level and compare fuzzy logic classification algorithm with maximum likelihood. Field data retrieval located in Karang Lebar and Karang Congkak, Kepulauan Seribu on 6 December until 10 December 2017. The overall accuracy test results show that fuzzy logic algorithm still has a good accuracy level compared to the maximum likelihood algorithm. Differences in pixel size (spatial resolution) of satellite imagery also affect accuracy results, where SPOT 7 satellite imagery has greater accuracy then Sentinel 2A. 

Linkage between fish functional groups and coral reef benthic habitat composition in the Western Indian Ocean

2016 ◽  
Vol 98 (2) ◽  
pp. 387-400 ◽  
Author(s):  
Kennedy Osuka ◽  
Marc Kochzius ◽  
Ann Vanreusel ◽  
David Obura ◽  
Melita Samoilys
Keyword(s):  
Coral Reef ◽  
Indian Ocean ◽  
Functional Groups ◽  
Habitat Type ◽  
Western Indian Ocean ◽  
Reef Fishes ◽  
Coralline Algae ◽  
Benthic Habitat ◽  
Habitat Types ◽  
Habitat Composition

Benthic habitat composition is a key factor that structures assemblages of coral reef fishes. However, natural and anthropogenic induced disturbances impact this relationship. This study investigates the link between benthic habitat composition and fish functional groups in four countries in the Western Indian Ocean (WIO). Benthic composition of 32 sites was quantified visually from percentage cover of hard and soft corals, rubble, turf, fleshy and crustose coralline algae. At each site, abundance of 12 coral-associated fish functional groups in 50 × 5 m transects was determined. Cluster analysis characterized reefs based on benthic cover and revealed five habitat types (A, B, C, D and E) typified by decreasing cover of hard corals, increasing cover of turf and/or fleshy algae and differences in benthic diversity. Habitat type A was present in all four countries. Other habitats types showed geographic affiliations: notably Comoros sites clustered in either habitats B or E, northern Madagascar had B, C and D type habitats, whereas sites in central Tanzania and northern Mozambique had habitats D and E. Fish functional groups showed significant linkages with some habitat types. The abundances of corallivores, invertivores, detritivores and grazers were higher in habitat B, whereas planktivores and small excavators showed lower abundances in the same habitat. These linkages between benthic habitat types and fish functional groups are important in informing priority reefs that require conservation and management planning.

scholarly journals Coastal Reef and Seagrass Monitoring for Coastal Ecosystem Management

2021 ◽  
Vol 16 (3) ◽  
pp. 557-568
Author(s):  
Wahyu Lazuardi ◽  
Ridwan Ardiyanto ◽  
Muh Aris Marfai ◽  
Bachtiar Wahyu Mutaqin ◽  
Denny Wijaya Kusuma
Keyword(s):  
Coral Reefs ◽  
Change Detection ◽  
Spatial Information ◽  
Support Vector ◽  
Benthic Habitat ◽  
Seagrass Beds ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Seagrass Cover ◽  
Sentinel 2A

The growth of human occupations in coastal areas and climate change impact have changed the dynamics of seagrass cover and accelerated the damage to coral reefs globally. For these reasons, coastal management measures need to be developed and renewed to preserve the state of seagrass beds and coral reefs. An example includes the improvement of spatial and multitemporal analyses. This study sought to analyze changes in seagrass cover and damages to coral reefs in Gili Sumber Kima, Buleleng Regency, Bali based on multitemporal Sentinel 2A-MSI imagery. The algorithms of a machine learning, Random Forest (RF), and a Support Vector Machine (SVM) were used to classify the benthic habitats (seagrass beds and coral reefs). Also, a change detection analysis was performed to identify the pattern and the extent to which seagrass beds had changed. The multispectral classification of, particularly, coral reefs was used to explain the condition of this benthic habitat. The results showed +-70% to +-83% accuracies of estimated seagrass cover, and the change detection analysis revealed three directions of change, namely an increase of 27.9 ha, a decrease by 86 ha, and a preserved state in 157 ha of seagrass cover. The product of coral reefs mapping had an accuracy of 42%, and the coral reefs in Gili Sumber Kima were split almost equally between the good (1505 ha) and damaged ones (1397 ha). With the spatial information on seagrass beds and coral reefs in every region, the ecological functions of the coast can be assessed more straightforwardly and appropriately incorporated as the basis for monitoring the dynamics of resources and coastal area management.

Sentinel-2A and Landsat 8 OLI to model benthic habitat biodiversity index

2020 ◽  
pp. 1-17
Author(s):  
Pramaditya Wicaksono ◽  
Ignatius Salivian Wisnu Kumara ◽  
Muhammad Afif Fauzan ◽  
Rifka Noviaris Yogyantoro ◽  
Wahyu Lazuardi
Keyword(s):  
Benthic Habitat ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Biodiversity Index ◽  
Sentinel 2A

Multispectral image observations of earthlimb clutter with the MSTI-3 medium wavelength infrared sensor

1998 ◽  
Author(s):  
James Fraser ◽  
Michael Winings ◽  
Robert Sears ◽  
Stephen Hearney
Keyword(s):  
Multispectral Image ◽  
Infrared Sensor

SHALLOW WATER HABITAT MAPPING AND REEF FISH STOCK ESTIMATION USING HIGH RESOLUTION SATELLITE DATA

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Vincentius P. Siregar ◽  
Sam Wouthuyzen ◽  
Andriani Sunuddin ◽  
Ari Anggoro ◽  
Ade Ayu Mustika
Keyword(s):  
High Resolution ◽  
Reef Fish ◽  
Satellite Imagery ◽  
Field Survey ◽  
Coral Cover ◽  
Fish Density ◽  
Fish Stock ◽  
Benthic Habitat ◽  
Live Coral ◽  
Stock Estimation

Shallow marine waters comprise diverse benthic types forming habitats for reef fish community, which important for the livelihood of coastal and small island inhabitants. Satellite imagery provide synoptic map of benthic habitat and further utilized to estimate reef fish stock. The objective of this research was to estimate reef fish stock in complex coral reef of Pulau Pari, by utilizing high resolution satellite imagery of the WorldView-2 in combination with field data such as visual census of reef fish. Field survey was conducted between May-August 2013 with 160 sampling points representing four sites (north, south, west, and east). The image was analy-zed and grouped into five classes of benthic habitats i.e., live coral (LC), dead coral (DC), sand (Sa), seagrass (Sg), and mix (Mx) (combination seagrass+coral and seagrass+sand). The overall accuracy of benthic habitat map was 78%. Field survey revealed that the highest live coral cover (58%) was found at the north site with fish density 3.69 and 1.50 ind/m2at 3 and 10 m depth, respectively. Meanwhile, the lowest live coral cover (18%) was found at the south site with fish density 2.79 and 2.18  ind/m2 at 3 and 10 m depth, respectively. Interpolation on fish density data in each habitat class resulted in standing stock reef fish estimation:  LC (5,340,698 ind), DC (56,254,356 ind), Sa (13,370,154 ind), Sg (1,776,195 ind) and Mx (14,557,680 ind). Keywords: mapping, satellite imagery, benthic habitat, reef fish, stock estimation

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