The use of Sentinel-2 MSI data in small island's nearshore benthic habitat mapping

Mapping Intimacies ◽

10.31230/osf.io/nrup7 ◽

2018 ◽

Author(s):

Muhammad Afif Fauzan

Keyword(s):

Coastal Management ◽

Satellite Remote Sensing ◽

Reference Data ◽

Habitat Mapping ◽

Aerial Photographs ◽

Benthic Habitat ◽

Marine Habitat ◽

Mapping Techniques ◽

Management And Conservation ◽

Sentinel 2

Maps of nearshore marine habitat are vital for coastal management and conservation. While traditional field mapping techniques are still commonly used, airborne and satellite remote sensing have proven to be efficient alternatives for creating benthic habitat maps. This paper evaluates the capability of new satellite data, Sentinel-2 MSI, to map nearshore benthic habitat of Derawan Island. Available aerial photographs were used as reference data. The results show that Sentinel-2 MSI data can be used to map benthic habitat with accuracy up to 75%.

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Representative benthic habitat mapping on Lovina coral reefs in Northern Bali, Indonesia

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d221108 ◽

2021 ◽

Vol 22 (11) ◽

Author(s):

Anggita Kartikasari ◽

TODHI PRISTIANTO ◽

RIZKI HANINTYO ◽

EGHBERT ELVAN AMPOU ◽

TEJA ARIEF WIBAWA ◽

...

Keyword(s):

Coral Reef ◽

Coral Reefs ◽

Habitat Mapping ◽

Benthic Habitat ◽

Coral Rubble ◽

Spatial Coverage ◽

Coral Reef Ecosystems ◽

Optical Imagery ◽

Sentinel 2

Abstract. Kartikasari A, Pristianto T, Hanintyo R, Ampou EE, Wibawa TA, Borneo BB. 2021. Representative benthic habitat mapping on Lovina coral reefs in Northern Bali, Indonesia. Biodiversitas 22: 4766-4774. Satellite optical imagery datasets integrated with in situ measurements are widely used to derive the spatial distribution of various benthic habitats in coral reef ecosystems. In this study, an approach to estimate spatial coverage of those habitats based on observation derived from Sentinel-2 optical imagery and a field survey, is presented. This study focused on the Lovina coral reef ecosystem of Northern Bali, Indonesia to support deployment of artificial reefs within the Indonesian Coral Reef Garden (ICRG) programme. Three specific locations were explored: Temukus, Tukad Mungga, and Baktiseraga waters. Spatial benthic habitat coverages of these three waters was estimated based on supervised classification techniques using 10m bands of Sentinel-2 imagery and the medium scale approach (MSA) transect method of in situ measurement.The study indicates that total coverage of benthic habitat is 61.34 ha, 25.17 ha, and 27.88 ha for Temukus, Tukad Mungga, and Baktiseraga waters, respectively. The dominant benthic habitat of those three waters consists of sand, seagrass, coral, rubble, reef slope and intertidal zone. The coral reef coverage is 29.48 ha (48%) for Temukus covered by genus Acropora, Isopora, Porites, Montipora, Pocillopora. The coverage for Tukad Mungga is 8.69 ha (35%) covered by genus Acropora, Montipora, Favia, Psammocora, Porites, and the coverage for Baktiseraga is 11.37 ha (41%) covered by genus Montipora sp, Goniastrea, Pavona, Platygyra, Pocillopora, Porites, Acropora, Leptoseris, Acropora, Pocillopora, Fungia. The results are expected to be suitable as supporting data in restoring coral reef ecosystems in the northern part of Bali, especially in Buleleng District.

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Kajian Metode Klasifikasi Citra Landsat-8 untuk Pemetaan Habitat Bentik di Kepulauan Padaido, Papua

Oseanologi dan Limnologi di Indonesia ◽

10.14203/oldi.2017.v2i1.69 ◽

2017 ◽

Vol 2 (1) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Muhammad Hafizt ◽

Marindah Yulia Iswari ◽

Bayu Prayudha

Keyword(s):

Coral Reefs ◽

Satellite Remote Sensing ◽

Satellite Images ◽

Habitat Mapping ◽

Benthic Habitat ◽

Landsat 8 ◽

Classification Methods ◽

Landsat 8 Oli ◽

Remote Sensing Technique ◽

Object Based

<strong>Assessment of Landsat-8 Classification Method for Benthic Habitat Mapping in Padaido Islands, Papua.</strong> Indonesia is the biggest archipelagic country in the world with an area of coral reefs of 39,583 km.This area has to be managed effectively and efficiently utilizing satellite remote sensing technique capable of mapping of benthic habitat coverage, such as coral reefs, seagrasses, macroalgae, and bare substrates. The technique is supported by the availability of Landsat-8 OLI satellite images that have been recording the regions of Indonesia continuously every 16 days. This research was carried out in June 2015 in parts of Padaido Islands, Papua. This area was selected due to high coral reef damages. This study utilized Landsat-8 OLI to compare two classification methods, namely pixel based and object based methods using ‘maximum 2 likelihood’ (ML) and ‘example based feature extraction’ classifications, respectively, after water column correction (Lyzenga method). The results showed that both methods produced benthic habitat maps with 7 class covers. The pixel-based classification resulted in a better overall accuracy (47.57%) in the mapping of benthic habitats than object-based classification approach (36.17%). Thus, the ML classification is applicable for benthic habitat mapping in Padaido Islands. However, the consistency of this method must be analyzed in many diffrent locations of Indonesian waters.

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A review of the influence of marine habitat classification schemes on mapping studies: inherent assumptions, influence on end products, and suggestions for future developments

ICES Journal of Marine Science ◽

10.1093/icesjms/fsy161 ◽

2018 ◽

Vol 76 (1) ◽

pp. 10-22 ◽

Cited By ~ 10

Author(s):

James Asa Strong ◽

Annika Clements ◽

Helen Lillis ◽

Ibon Galparsoro ◽

Tim Bildstein ◽

...

Keyword(s):

Habitat Mapping ◽

Marine Habitat ◽

Classification Schemes ◽

Habitat Classification ◽

Thematic Resolution ◽

Superficial Similarity ◽

Marine Habitat Mapping ◽

Working Practices ◽

Mapping Techniques ◽

Resolution Structure

Abstract The production of marine habitat maps typically relies on the use of habitat classification schemes (HCSs). The choice of which HCS to use for a mapping study is often related to familiarity, established practice, and national desires. Despite a superficial similarity, HCSs differ greatly across six key properties, namely, purpose, environmental and ecological scope, spatial scale, thematic resolution, structure, and compatibility with mapping techniques. These properties impart specific strengths and weaknesses for each HCS, which are subsequently transferred to the habitat maps applying these schemes. This review has examined seven HCSs (that are commonly used and widely adopted for national and international mapping programmes), over the six properties, to understand their influence on marine habitat mapping. In addition, variation in how mappers interpret and apply HCSs introduces additional uncertainties and biases into the final maps. Recommendations are provided for improving HCSs for marine habitat mapping as well as for enhancing the working practices of mappers using habitat classification. It is hoped that implementation of these recommendations will lead to greater certainty and usage within mapping studies and more consistency between studies and adjoining maps.

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An evaluation of supervised and unsupervised classification techniques for marine benthic habitat mapping using multibeam echosounder data

ICES Journal of Marine Science ◽

10.1093/icesjms/fsu223 ◽

2014 ◽

Vol 72 (5) ◽

pp. 1498-1513 ◽

Cited By ~ 29

Author(s):

Jay Calvert ◽

James Asa Strong ◽

Matthew Service ◽

Chris McGonigle ◽

Rory Quinn

Keyword(s):

Selection Process ◽

Ground Truth ◽

Unsupervised Classification ◽

Horizontal Resolution ◽

Habitat Mapping ◽

Benthic Habitat ◽

Marine Habitat ◽

Ground Truth Data ◽

Multibeam Echosounder ◽

Acoustic Data

Abstract Marine habitat mapping provides information on seabed substrata and faunal community structure to users including research scientists, conservation organizations, and policy makers. Full-coverage acoustic data are frequently used for habitat mapping in combination with video ground-truth data in either a supervised or unsupervised classification. In this investigation, video ground-truth data with a camera footprint of 1 m2 were classified to level 4 of the European Nature Information System habitat classification scheme. Acoustic data with a horizontal resolution of 1 m2 were collected over an area of 130 km2 using a multibeam echosounder, and processed to provide bathymetry and backscatter data. Bathymetric derivatives including eastness, northness, slope, topographic roughness index, vector rugosity measure, and two measures of curvature were created. A feature selection process based on Kruskal–Wallis and post hoc pairwise testing was used to select environmental variables able to discriminate ground-truth classes. Subsequently, three datasets were formed: backscatter alone (BS), backscatter combined with bathymetry and derivatives (BSDER), and bathymetry and derivatives alone (DER). Two classifications were performed on each of the datasets to produce habitat maps: maximum likelihood supervised classification (MLC) and ISO Cluster unsupervised classification. Accuracy of the supervised habitat maps was assessed using total agreement, quantity disagreement, and allocation disagreement. Agreement in the unsupervised maps was assessed using the Cramer's V coefficient. Choice of input data produced large differences in the accuracy of the supervised maps, but did not have the same effect on the unsupervised maps. Accuracies were 46, 56, and 49% when calculated using the sample and 52, 65, and 51% when using an unbiased estimate of the population for the BS, BSDER, and DER maps, respectively. Cramer's V was 0.371, 0.417, and 0.366 for the BS, BSDER, and DER maps, respectively.

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Nearshore Benthic Habitat Mapping Based on Multi-Frequency, Multibeam Echosounder Data Using a Combined Object-Based Approach: A Case Study from the Rowy Site in the Southern Baltic Sea

Remote Sensing ◽

10.3390/rs10121983 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1983 ◽

Cited By ~ 17

Author(s):

Lukasz Janowski ◽

Karolina Trzcinska ◽

Jaroslaw Tegowski ◽

Aleksandra Kruss ◽

Maria Rucinska-Zjadacz ◽

...

Keyword(s):

Baltic Sea ◽

Ground Truth ◽

Habitat Mapping ◽

Acoustic Properties ◽

Benthic Habitat ◽

Marine Habitat ◽

Multibeam Echosounder ◽

Southern Baltic Sea ◽

Object Based ◽

Southern Baltic

Recently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. Based on ground-truth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. Object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.

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Nearshore substrate and marine habitat mapping to inform marine policy and coastal management

Journal of Coastal Research ◽

10.2112/si65-255.1 ◽

2013 ◽

Vol 165 ◽

pp. 1509-1514 ◽

Cited By ~ 3

Author(s):

Andrew Colenutt ◽

Travis Mason ◽

André Cocuccio ◽

Robert Kinnear ◽

David Parker

Keyword(s):

Coastal Management ◽

Habitat Mapping ◽

Marine Policy ◽

Marine Habitat ◽

Marine Habitat Mapping

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NeMO-Net – Gamifying 3D Labeling of Multi-Modal Reference Datasets to Support Automated Marine Habitat Mapping

Frontiers in Marine Science ◽

10.3389/fmars.2021.645408 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jarrett van den Bergh ◽

Ved Chirayath ◽

Alan Li ◽

Juan L. Torres-Pérez ◽

Michal Segal-Rozenhaimer

Keyword(s):

Coral Reef ◽

Citizen Science ◽

Video Game ◽

Habitat Mapping ◽

Benthic Habitat ◽

Marine Habitat ◽

Shallow Marine ◽

Learning Framework ◽

User Data ◽

Marine Habitat Mapping

NASA NeMO-Net, The Neural Multimodal Observation and Training Network for global coral reef assessment, is a convolutional neural network (CNN) that generates benthic habitat maps of coral reefs and other shallow marine ecosystems. To segment and classify imagery accurately, CNNs require curated training datasets of considerable volume and accuracy. Here, we present a citizen science approach to create these training datasets through a novel 3D classification game for mobile and desktop devices. Leveraging citizen science, the NeMO-Net video game generates high-resolution 3D benthic habitat labels at the subcentimeter to meter scales. The video game trains users to accurately identify benthic categories and semantically segment 3D scenes captured using NASA airborne fluid lensing, the first remote sensing technology capable of mitigating ocean wave distortions, as well as in situ 3D photogrammetry and 2D satellite remote sensing. An active learning framework is used in the game to allow users to rate and edit other user classifications, dynamically improving segmentation accuracy. Refined and aggregated data labels from the game are used to train NeMO-Net’s supercomputer-based CNN to autonomously map shallow marine systems and augment satellite habitat mapping accuracy in these regions. We share the NeMO-Net game approach to user training and retention, outline the 3D labeling technique developed to accurately label complex coral reef imagery, and present preliminary results from over 70,000 user classifications. To overcome the inherent variability of citizen science, we analyze criteria and metrics for evaluating and filtering user data. Finally, we examine how future citizen science and machine learning approaches might benefit from label training in 3D space using an active learning framework. Within 7 months of launch, NeMO-Net has reached over 300 million people globally and directly engaged communities in coral reef mapping and conservation through ongoing scientific field campaigns, uninhibited by geography, language, or physical ability. As more user data are fed into NeMO-Net’s CNN, it will produce the first shallow-marine habitat mapping products trained on 3D subcm-scale label data and merged with m-scale satellite data that could be applied globally when data sets are available.

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Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

Water Science & Technology ◽

10.2166/wst.1994.0659 ◽

1994 ◽

Vol 29 (1-2) ◽

pp. 135-144 ◽

Cited By ~ 21

Author(s):

C. Deguchi ◽

S. Sugio

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Satellite Imagery ◽

Satellite Remote Sensing ◽

Aerial Photographs ◽

Estimation Errors ◽

Remote Sensing Imagery ◽

Impervious Area ◽

Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

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