Object-based classification of sub-bottom profiling data for benthic habitat mapping. Comparison with sidescan and RoxAnn in a Greek shallow-water habitat

<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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Object-based Image Analysis for Coral Reef Benthic Habitat Mapping with Several Classification Algorithms

Procedia Environmental Sciences ◽

10.1016/j.proenv.2015.03.029 ◽

2015 ◽

Vol 24 ◽

pp. 222-227 ◽

Cited By ~ 27

Author(s):

Nurhalis Wahidin ◽

Vincentius P. Siregar ◽

Bisman Nababan ◽

Indra Jaya ◽

Sam Wouthuyzen

Keyword(s):

Image Analysis ◽

Coral Reef ◽

Habitat Mapping ◽

Benthic Habitat ◽

Classification Algorithms ◽

Object Based Image Analysis ◽

Object Based

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Mapping of shallow water bathymetry and reef geomorphology using Sentinel-2 satellite imagery in Genteng Besar and Genteng Kecil Island, Kepulauan Seribu

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/944/1/012048 ◽

2021 ◽

Vol 944 (1) ◽

pp. 012048

Author(s):

S B Agus ◽

V P Siregar ◽

S B Susilo ◽

M S Sangadji ◽

G F Tasirileleu ◽

...

Keyword(s):

Shallow Water ◽

Satellite Imagery ◽

Depth Estimation ◽

Benthic Habitat ◽

Sensing Technology ◽

Value Range ◽

Position Index ◽

Optimum Depth ◽

The Given

Abstract Information on seafloor characteristics is one of the essential variables in coastal management and marine ecosystems. Application methods in remote sensing technology to study about characteristics of shallow waters have continuously been done. This research consists of two parts: an estimation of depth using Sentinel 2B satellite imagery with the Lyzenga algorithm and geomorphological classification of the benthic area using the Benthic Terrain Modeler (BTM) approach. BTM is a method to analyze benthic habitat and shallow water geomorphology. Integrated Depth data were analyzed using BTM to obtain bathymetric position index (BPI), slope, and classification of reef geomorphological structures. The resulting BPI value range is directly proportional to the given spatial area (scale factor). The slope is ranged between 0.01° – 19.24°, while optimum depth estimation is applicable until 10-meter. The values of BPI and slope were used as variables to classify the geomorphology of shallow water benthic areas based on the previous classification dictionary. Six geomorphological classes resulting from this study are Back Reef, Deep Depression, Depression, Lower Bank Shelf, Mid-Slope Ridges, and Reef Crest.

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Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques

Sensors ◽

10.3390/s17112639 ◽

2017 ◽

Vol 17 (11) ◽

pp. 2639 ◽

Cited By ~ 12

Author(s):

Francisco Eugenio ◽

Javier Marcello ◽

Javier Martin ◽

Dionisio Rodríguez-Esparragón

Keyword(s):

High Resolution ◽

Shallow Water ◽

Atmospheric Correction ◽

Habitat Mapping ◽

Benthic Habitat ◽

High Resolution Imagery

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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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Post-Sandy Benthic Habitat Mapping Using New Topobathymetric Lidar Technology and Object-Based Image Classification

Journal of Coastal Research ◽

10.2112/si76-017 ◽

2016 ◽

Vol 76 ◽

pp. 200-208 ◽

Cited By ~ 5

Author(s):

Christopher E. Parrish ◽

Jennifer A. Dijkstra ◽

Jarlath P.M. O'Neil-Dunne ◽

Lindsay McKenna ◽

Shachak Pe'eri

Keyword(s):

Image Classification ◽

Habitat Mapping ◽

Benthic Habitat ◽

Object Based

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Seagrass restoration monitoring and shallow-water benthic habitat mapping through a photogrammetry-based protocol

Journal of Environmental Management ◽

10.1016/j.jenvman.2021.114262 ◽

2022 ◽

Vol 304 ◽

pp. 114262

Author(s):

Daniele Ventura ◽

Gianluca Mancini ◽

Edoardo Casoli ◽

Daniela Silvia Pace ◽

Giovanna Jona Lasinio ◽

...

Keyword(s):

Shallow Water ◽

Habitat Mapping ◽

Benthic Habitat ◽

Restoration Monitoring ◽

Seagrass Restoration

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Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses

Remote Sensing ◽

10.3390/rs13214452 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4452

Author(s):

Bisman Nababan ◽

La Ode Khairum Mastu ◽

Nurul Hazrina Idris ◽

James P. Panjaitan

Keyword(s):

Coral Reef ◽

Shallow Water ◽

Spatial Information ◽

Aerial Photographs ◽

Support Vector ◽

Benthic Habitat ◽

Benthic Habitats ◽

Object Based ◽

Svm Algorithm ◽

Level 2

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.

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