The Effect of Sunglint on Benthic Habitats Mapping in Pari Island Using Worldview-2 Imagery

Anggoro, Ari; Siregar, Vincentius P.; Agus, Syamsul Bahri

doi:10.1016/j.proenv.2016.03.101

Cited by 19 publications

(13 citation statements)

References 10 publications

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“…Moreover, the UAV surveys should be carried out during cloudy weather to reduce the sunglint effect, since it limits the quality and accuracy of remote sensing data from water bodies [61]. Anggoro et al [63] reported that the reduction of the sunglint effect increased the overall accuracy by 7%. The same accuracy degradation of was noted in the classification of the 23 mm orthophoto (Table 4.…”

Section: Precisionmentioning

confidence: 99%

A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data

2020

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Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can provide suitable data for mapping floating plastic, but most of the methods require visual interpretation and manual labeling. The main goals of this paper are to determine the suitability of deep learning algorithms for automatic floating plastic extraction from UAV orthophotos, testing the possibility of differentiating plastic types, and exploring the relationship between spatial resolution and detectable plastic size, in order to define a methodology for UAV surveys to map floating plastic. Two study areas and three datasets were used to train and validate the models. An end-to-end semantic segmentation algorithm based on U-Net architecture using the ResUNet50 provided the highest accuracy to map different plastic materials (F1-score: Oriented Polystyrene (OPS): 0.86; Nylon: 0.88; Polyethylene terephthalate (PET): 0.92; plastic (in general): 0.78), showing its ability to identify plastic types. The classification accuracy decreased with the decrease in spatial resolution, performing best on 4 mm resolution images for all kinds of plastic. The model provided reliable estimates of the area and volume of the plastics, which is crucial information for a cleaning campaign.

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Section: Precisionmentioning

confidence: 99%

A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data

2020

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“…Menurut Danoedoro, (2012) metode OBIA mampu mendefinisikan kelas-kelas objek berdasarkan aspek spasial dan aspek spektral secara bersamaan. Metode OBIA dilakukan dengan tahapan segmentasi citra (pixel level) menjadi segmen/ objek (object level) yang homogen dan sesuai dengan parameternya (Anggoro et al, 2017).…”

Section: Pendahuluanunclassified

“…Penelitian terkait pemanfaatan teknologi penginderaan jauh khususnya penyedia informasi spasial habitat bentik perairan dangkal telah banyak dilakukan (Anggoro et al, 2016(Anggoro et al, , 2017Candra et al, 2018;Hafizt et al, 2017;Prabowo et al, 2018;Putra & Nurul, 2013;Setyawan et al, 2014;Siregar, 2010;Wahiddin et al, 2015). Pemanfaatan sentinel-2A untuk pemetaan habitat bentik dilakukan oleh Mastu et al, (2018) di Pulau Wangi-wangi dengan metode OBIA (Object Based Image Analysis) serta penelitian yang dilakukan oleh (Prawoto & Hartono., 2018) menggunakan Sentinel-2A di perairan Pulau Menjangan Kecil dan Menjangan Besar dengan metode multispektral (berbasis piksel).…”

Section: Pendahuluanunclassified

“…Kombinasi untuk koreksi kolom air biasanya menggunakan band green dan blue. Formulasi Algoritma yang dikembakan oleh Lyzenga, (1981) Algoritma SVM adalah salah satu klasifikasi secara terbimbing dengan input thematic layer dari pengamatan lapang (Anggoro et al, 2017). Algoritma SVM memiliki konsep sederhana yaitu mencari hyperlane (garis) baik sebagai pemisah dua kelas dengan memaksimalkan margin antar kelas tersebut.…”

Section: Koreksi Kolom Airunclassified

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Pemetaan Habitat Bentik Pulau Salarangan Menggunakan Metode Object-Based Image Analysis

Sari

Syah

2021

Rekayasa

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Salarangan Island has supporting ecosystems that are seagrass and coral reefs with a high increase in biological competence. Basic information about the distribution map of benthic habitats on SalaranganIsland is not yet available so that technology is needed that can provide information spatially and temporally. Citra Sentinel-2A with Lyzenga transformation in mapping benthic and shallow habitats and studying the distribution of benthic habitats on Salarangan Island, Madura. The image data used is obtained free of charge from the website earthexplorer.usgs.gov and in situ data are used for image data validation. The multiscale / OBIA classification method with the SVM algorithm is used to classify objects in the navigation column. The classification results show as many as 4 benthic habitat classes with object / OBIA classification resulting in overall certification of 69.86%. Based on the classification results, the most contrasting class is the seagrass class (48.76 ha) because it has the most extensive area than the coral class (27.31%), sand (8.94 ha) and seagrass + sand (31.85 ha).

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“…Akurasi yang rendah tersebut mengindikasikan bahwa terdapat kemiripan nilai spektral pada kelas tersebut sehingga menyebabkan terjadi kesalahan klasifikasi. Dalam penelitian lainnya untuk meningkatkan akurasi pemetaan pada habitat dasar perairan dangkal dapat menerapkan beberapa koreksi pada prapemrosesan citra yaitu koreksi sunglint yang dapat meningkatkan akurasi sebesar 7% (Anggoro, et al 2016). Selain itu juga dapat menggunakan metode klasifikasi berbasis objek yang terbukti dapat meningkatkan akurasi sebesar 14% (Anggoro, et al 2018).…”

Section: Uji Akurasiunclassified

Pemetaan Habitat Perairan Dangkal Menggunakan Citra Resolusi Menengah Dengan Metode Klasifikasi Berbasis Piksel (Studi Kasus Pulau Tikus)

Anggoro¹,

Zamdial²,

Hartono³

et al. 2020

j.eng

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Pulau Tikus adalah pulau kecil yang terletak di Kota Bengkulu yang memiliki potensi terumbu karang disekitar perairan dangkal. Tujuan penelitian ini untuk memetakan kawasan habitat perairan dangkal ekosistem terumbu karang Pulau Tikus menggunakan citra satelit Landsat 8 OLI dan menguji akurasi klasifikasi peta habitat perairan dangkal di Pulau Tikus. Metode klasifikasi yang digunakan adalah klasifikasi terbimbing maximum likelihood classification. Hasil klasifikasi citra Landsat 8 OLI berdasarkan skema klasifikasi yang digunakan dari lima kelas habitat di Pulau Tikus tersebut yaitu karang hidup seluas 71,46 ha, karang campur pasir 106,9425 ha, karang mati 67,365 ha, makro alga 31,815 ha, dan pasir 40,05 ha. Uji akurasi dari perbandingan hasil klasifikasi citra dan data lapangan mendapatkan total akurasi keseluruhan yaitu sebesar 77%.SHALLOW WATER HABITATS MAPPING USING A MEDIUM RESOLUTION IMAGE WITH CLASSIFICATION METHOD PIKSEL-BASED (CASE STUDY OF THE TIKUS ISLAND). Tikus Island is a small island which located in Bengkulu City has the potential of coral reefs around the shallow water. The aims of this research were to map the area of benthic habitat in Tikus Island Bengkulu using Landsat 8 OLI satellite imagery and to test the accuracy on the benthic habitat map in Tikus Island. The method used supervised classification using maximum likelihood classification. The result of Landsat 8 OLI classification base on the five class habitats classification scheme used obtained in Tikus island showed coral reef (71,46 ha), coral mix sand (106,9425 ha), dead coral (67,365 ha), macroalgae (31,815 ha), and sand (40,05 ha). Accuracy test from the comparison of classification results and ground truth data get a total overall accuracy of 77%.

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The Effect of Sunglint on Benthic Habitats Mapping in Pari Island Using Worldview-2 Imagery

Cited by 19 publications

References 10 publications

A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data

A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data

Pemetaan Habitat Bentik Pulau Salarangan Menggunakan Metode Object-Based Image Analysis

Pemetaan Habitat Perairan Dangkal Menggunakan Citra Resolusi Menengah Dengan Metode Klasifikasi Berbasis Piksel (Studi Kasus Pulau Tikus)

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