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

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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Benthic habitat classification using multiscale GEOBIA on orthophoto images of Karimunjawa waters

Communications in Science and Technology ◽

10.21924/cst.6.1.2021.332 ◽

2021 ◽

Vol 6 (1) ◽

pp. 55-59

Author(s):

Yahya Dwikarsa ◽

Abdul Basith

Keyword(s):

Learning Algorithm ◽

Support Vector ◽

Benthic Habitat ◽

Classification Algorithms ◽

K Nearest Neighbors ◽

Object Based ◽

Svm Algorithm ◽

Aerial Vehicle ◽

Orthophoto Images ◽

Selection Of

The scale value is an important part of the segmentation stage which is part of Object-Based Image Analysis (OBIA). Selection of scale value can determine the size of the object which affects the results of classification accuracy. In addition to setting the scale value (multiscale), selection of machine learning algorithm applied to classify shallow water benthic habitat objects can also determine the success of the classification. Combination of setting scale values and classification algorithms are aimed to get optimal results by examining classification accuracies. This study uses orthophoto images processed from Unmanned Aerial Vehicle (UAV) mission intended to capture benthic habitat in Karimunjawa waters. The classification algorithms used are Support Vector Machine (SVM), Bayes, and K-Nearest Neighbors (KNN). The results of the classification of combination are then tested for accuracy based on the sample and Training Test Area (TTA) masks. The result shows that SVM algorithm with scale of 300 produces the best level of accuracy. While the lowest accuracy is achieved by using SVM algorithm with scale of 100. The result shows that the optimal scale settings in segmenting objects sequentially are 300, 200, and 100

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KLASIFIKASI MULTIKSKALA UNTUK PEMETAAN ZONA GEOMORFOLOGI DAN HABITAT BENTIK MENGGUNAKAN METODE OBIA DI PULAU PARI (MULTISCALE CLASSIFICATION FOR GEOMORPHIC ZONE AND BENTHIC HABITATS MAPPING USING OBIA METHOD IN PARI ISLAND)

Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital ◽

10.30536/j.pjpdcd.1017.v14.a2622 ◽

2018 ◽

Vol 14 (2) ◽

Author(s):

Ari Anggoro ◽

Vincentius Paulus Siregar ◽

Syamsul B. Agus

Keyword(s):

Support Vector Machines ◽

Support Vector ◽

Benthic Habitat ◽

Benthic Habitats ◽

Vector Machines ◽

Optimum Scale ◽

Level 3 ◽

Geomorphic Zone ◽

Level 1 ◽

Level 2

This study used multiscale classification and applied object-based image analysis (OBIA) for geomorphic zone and benthic habitats mapping in Pari islands. An optimized segmentation was performed to get optimum classification result. Classification methods for level 1 and 2 used contextual editing classification and for level 3 used support vector machines classifier. The results showed that overall accuracy for level 1 was 97% (reef level), level 2 was 87% (geomorphic zone), and level 3 was 75% (benthic habitats). Accuracy achieved by support vector machines classification was performed only in level 3 and optimum scale value achieved was 50 in compare with other scale values, i.e. 5, 25, 50, 75, 95. OBIA methods can be used as an alternative for geomorphic zone and benthic habitats map. Abstrak Penelitian ini menggunakan klasifikasi multiskala dan penerapan analisis citra berbasis obyek (OBIA) untuk pemetaan zona geomorfologi dan habitat bentik di Pulau Pari. Analisis berbasis obyek dilakukan optimasi pada proses segmentasi untuk mendapatkan hasil klasifikasi optimal. Metode klasifikasi pada level 1 dan 2 menggunakan klasifikasi contextual editing dan pada level 3 menggunakan klasifikasi Support Vector Machines (SVM). Hasil penelitian ini menunjukkan akurasi keseluruhan pada level 1 yaitu 97% (reef level), level 2 yaitu 87% (Geomorphic level), dan level 3 yaitu 75% (benthic habitat level). Klasifikasi SVM hanya diterapkan pada level 3 dan nilai skala optimum sebesar 50 dari percobaan nilai skala yaitu 5, 25, 50, 75, 95. Metode OBIA dapat digunakan sebagai alternatif untuk pemetaan zona geomorfologi dan habitat bentik.

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Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Remote Sensing ◽

10.3390/rs13214470 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4470

Author(s):

Teo Nguyen ◽

Benoît Liquet ◽

Kerrie Mengersen ◽

Damien Sous

Keyword(s):

Machine Learning ◽

Coral Reefs ◽

Machine Learning Algorithms ◽

Aerial Photographs ◽

Support Vector ◽

Marine Biodiversity ◽

Benthic Habitat ◽

Object Based ◽

Efficient Machine ◽

Published Research

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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Object Segmentation on UAV Photo Data to Support the Provision of Rural Area Spatial Information

Forum Geografi ◽

10.23917/forgeo.v29i1.792 ◽

2015 ◽

Vol 29 (1) ◽

Cited By ~ 1

Author(s):

Nurwita Mustika Sari ◽

Dony Kushardono

Keyword(s):

Rural Area ◽

Rural Areas ◽

Spatial Information ◽

Remote Sensing Data ◽

Image Data ◽

Aerial Photographs ◽

Segmentation Method ◽

Object Based ◽

Aerial Vehicle ◽

Uav Image

The use of Unmanned Aerial Vehicle (UAV) to take aerial photographs is increasing in recent years. Photo data taken by UAV become one of reliable detailed-scale remote sensing data sources. The capability to obtain cloud-free images and the flexibility of time are some of the advantages of UAV photo data compared to satellite images with optical sensor. Displayed area at the data shows the objects clearly. Rural area has certain characteristics in its land cover namely ricefield. To delineate the area correctly there is an object-based image analysis methods (OBIA) that could be applied. In this study, proposed a novel method to execute the separation of objects that exist in the data with segmentation method. The result shows an effective segmentation method to separate different objects in rural areas recorded on UAV image data. The accuracy obtained is 90.47% after optimization process. This segmentation can be a valid basis to support the provision of spatial information in rural area.

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DEPTH ESTIMATION OF SHALLOW WATER USING MULTISPECTRAL SATELLITE IMAGERY SENTINEL-2A

Jurnal Segara ◽

10.15578/segara.v16i3.8562 ◽

2020 ◽

Vol 16 (3) ◽

Author(s):

Arip Rahman

Keyword(s):

Shallow Water ◽

Test Data ◽

Remote Sensing Data ◽

Depth Estimation ◽

Training Data ◽

Coefficient Of Determination ◽

Support Vector ◽

Data Set ◽

Svm Algorithm ◽

Sentinel 2A

Shallow water bathymetry estimation from remote sensing data has been increasing widespread, as an alternative to traditional bathymetry measurement that has disturbed by technical and logistic problem. Deriving bathymetry data from Sentinel 2A images, at visible wavelength (blue, green and red) 10 meter spatial resolution was carried out around the waters of the Kemujan Island Karimunjawa National Park Central Java. Amount of 1280 points data are used as training data sets and 854 points data as test data set produced from sounding. Dark Object Substraction (DOS) has been to correct atmospherically the Sentinel-2A images. Several algorithm has been applied to derive bathymetry data, including: linear transform, ratio transform and support vector machine (SVM). The highest correlation between depth prediction and observe resulted from SVM algorithm with a coefficient of determination (R2) 0.71 (training data) and 0.56 (test data). The assessment of the accuracy of the three methods using RMSE and MAE values, the SVM algorithm has the smallest value (< 1 m). This indicates that the SVM algorithm has a high accuracy compared to the other two methods. The bathymetry map derived from Sentinel 2A imagery cannot be used as a reference for navigation.

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Assessment of Coral Reef Life-Form Classification Scheme using Multiresolution Images on Parang Island, Indonesia

Geosfera Indonesia ◽

10.19184/geosi.v6i3.27592 ◽

2021 ◽

Vol 6 (3) ◽

pp. 377

Author(s):

Wahyu Lazuardi ◽

Pramaditya Wicaksono

Keyword(s):

Coral Reef ◽

Life Form ◽

Classification Scheme ◽

Spatial Information ◽

Satellite Image ◽

Creative Commons ◽

Central Java ◽

Object Based ◽

Efficient Data ◽

Sentinel 2A

Spatial information on the varying composition of coral reefs is beneficial for the management and preservation of natural resources in coastal areas. Its availability is inseparable from environmental management goals; however, it can also be used as a means of supporting tourism activities and predicting the emergence of certain living species. A satellite image is one of the effective and efficient data sources that provide spatial information on coral reef variations. This study aimed to evaluate the classification scheme of coral reef life-form using images with different spatial resolutions on Parang Island, Karimunjawa Islands, Central Java. These images were from PlanetScope (3m), PlanetScope resampling (6m), and Sentinel-2A MSI (10m), whose spatial resolutions functioned as the base for building the 3m, 6m, and 10m classification schemes producing 12, 11, and 9 classes, respectively. As for the classification method, it integrated both object-based and pixel-based approaches. The results showed that the highest overall accuracy (60%) was obtained using Sentinel-2A MSI image (10m), followed by PlanetScope (3m) with 48% accuracy, and PlanetScope resampling (6m) with 40% accuracy. This finding indicates that multiresolution images can be used to produce complex coral reef life-form maps with different levels of information details. Keywords: Coral reef; Life-form; Planetscope; Spatial resolution; Classification scheme Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Benthic habitat mapping using Object-Based Image Analysis (OBIA) on Tidung Island, Kepulauan Seribu, DKI Jakarta

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/944/1/012035 ◽

2021 ◽

Vol 944 (1) ◽

pp. 012035

Author(s):

M Hamidah ◽

R A Pasaribu ◽

F A Aditama

Keyword(s):

Image Analysis ◽

Image Data ◽

Water Area ◽

Coastal Areas ◽

Benthic Habitat ◽

Live Coral ◽

Benthic Habitats ◽

Object Based Image Analysis ◽

Habitat Classification ◽

Object Based

Abstract Tidung Island is one of the islands in Kepulauan Seribu, DKI Jakarta, Indonesia. This island has various benthic that live on the coastal areas, and benthic habitat has various functions both ecologically and economically. Nowadays, remote sensing technology is one way to detect benthic habitats in coastal areas. Mapping benthic habitat is essential for sustainable coastal resource management and to predict the distribution of benthic organisms. This study aims to map the benthic habitats using the object-based image analysis (OBIA) and calculate the accuracy of benthic habitat classification results in Tidung Island, Kepulauan Seribu, DKI Jakarta. The field data were collected on June 2021, and the image data used is satellite Sentinel-2 imagery acquired in June 2021. The result shows that the benthic habitat classification was produced in 4 classes: seagrass, rubble, sand, and live coral. The accuracy test result obtained an overall accuracy (OA) of 74.29% at the optimum value of the MRS segmentation scale 15;0,1;0.7 with the SVM algorithm. The results of benthic habitat classification show that the Seagrass class dominates the shallow water area at the research site with an area of 118.77 ha followed by Life Coral 104.809 ha, Sand 43.352 ha, and the smallest area is the Rubble class of 42.28 Ha.

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Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis

Remote Sensing ◽

10.3390/rs11030359 ◽

2019 ◽

Vol 11 (3) ◽

pp. 359 ◽

Cited By ~ 11

Author(s):

Kun Tan ◽

Yusha Zhang ◽

Xue Wang ◽

Yu Chen

Keyword(s):

Change Detection ◽

Uncertainty Analysis ◽

Spatial Information ◽

Gabor Filter ◽

Evidence Theory ◽

Support Vector ◽

Multi Scale ◽

Multiple Classifiers ◽

Scale Uncertainty ◽

Object Based

The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, which is of great significance in image analysis. Accordingly, an object-based approach for automatic change detection using multiple classifiers and multi-scale uncertainty analysis (OB-MMUA) in high-resolution (HR) remote sensing images is proposed in this paper. In this algorithm, the gray-level co-occurrence matrix (GLCM), morphological, and Gabor filter texture features are extracted to construct the input data, along with the spectral features, to utilize the respective advantages of the features and to compensate for the insufficient spectral information. In addition, random forest is used to select the features and determine the optimal feature vectors for the change detection. Change vector analysis (CVA) based on uncertainty analysis is then implemented to select the initial training samples. According to the diversity, support vector machine (SVM), k-nearest neighbor (KNN), and extra-trees (ExT) classifiers are then chosen as the base classifiers for Dempster-Shafer (D-S) evidence theory fusion, and unlabeled samples are selected using an active learning method with spatial information. Finally, multi-scale object-based D-S evidence theory fusion and uncertainty analysis is used to classify the difference image. To validate the proposed approach, we conducted experiments using multispectral images collected by the ZY-3 and GF-2 satellites. The experimental results confirmed the effectiveness and superiority of the proposed approach, which integrates the respective advantages of the pixel-based and object-based methods.

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