&lt;title&gt;Monitoring of coral reefs on Ishigaki Island in Japan using multitemporal remote sensing data&lt;/title&gt;

Matsunaga, Tsuneo; Hoyano, Akira; Mizukami, Yosei

doi:10.1117/12.411677

Cited by 11 publications

(7 citation statements)

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“…This included applying a water mask, cloud/fill pixel mask, and sun glint correction. The dark-pixel subtraction method was applied to correct for atmospheric contributions [77][78][79]. Finally, the preprocessing steps concluded with a calculation of the per pixel DII for water column correction.…”

Section: Methodsmentioning

confidence: 99%

Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

2019

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Despite the abundance of research on coral reef change detection, few studies have been conducted to assess the spatial generalization principles of a live coral cover classifier trained using remote sensing data from multiple locations. The aim of this study is to develop a machine learning classifier for coral dominated benthic cover-type class (CDBCTC) based on ground truth observations and Landsat images, evaluate the performance of this classifier when tested against new data, then deploy the classifier to perform CDBCTC change analysis of multiple locations. The proposed framework includes image calibration, support vector machine (SVM) training and tuning, statistical assessment of model accuracy, and temporal pixel-based image differencing. Validation of the methodology was performed by cross-validation and train/test split using ground truth observations of benthic cover from four different reefs. These four locations (Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island) as well as two additional locations (Kiritimati Island and Tabuaeran Island) were then evaluated for CDBCTC change detection. The in-situ training accuracy against ground truth observations for Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island were 87.9%, 85.7%, 69.2%, and 82.1% respectively. The classifier attained generalized accuracy scores of 78.8%, 81.0%, 65.4%, and 67.9% for the respective locations when trained using ground truth observations from neighboring reefs and tested against the local ground truth observations of each reef. The classifier was trained using the consolidated ground truth data of all four sites and attained a cross-validated accuracy of 75.3%. The CDBCTC change detection analysis showed a decrease in CDBCTC of 32% at Palmyra Atoll, 25% at Kingman Reef, 40% at Baker Island Atoll, 25% at Howland Island, 35% at Tabuaeran Island, and 43% at Kiritimati Island. This research establishes a methodology for developing a robust classifier and the associated Controlled Parameter Cross-Validation (CPCV) process for evaluating how well the model will generalize to new data. It is an important step for improving the scientific understanding of temporal change within coral reefs around the globe.

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Section: Methodsmentioning

confidence: 99%

Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

2019

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“…The water body and corals have similar spectral reflectance, which may lead to misclassification in water/coral areas. A deep-water area of interest (AOI) was selected to be used in atmospheric correction via the dark-pixel subtraction method [30,59,60]. An LDA application was then implemented and trained on the Palmyra Atoll site data.…”

Section: Methodsmentioning

confidence: 99%

“…However, it is noteworthy that there are many more satellites available that can provide remote sensing data for coral reef analysis [26][27][28]. The 400 nm-600 nm wavelength spectral radiance is known to be useful in the mapping of ocean habitats including coral [29][30][31]. This visible light portion of the spectrum can penetrate~15 to 30 m through clear waters, depending on turbidity and water quality [32].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean

2018

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This study was an evaluation of the spectral signature generalization properties of coral across four remote Pacific Ocean reefs. The sites under consideration have not been the subject of previous studies for coral classification using remote sensing data. Previous research regarding using remote sensing to identify reefs has been limited to in-situ assessment, with some researchers also performing temporal analysis of a selected area of interest. This study expanded the previous in-situ analyses by evaluating the ability of a basic predictor, Linear Discriminant Analysis (LDA), trained on Depth Invariant Indices calculated from the spectral signature of coral in one location to generalize to other locations, both within the same scene and in other scenes. Three Landsat 8 scenes were selected and masked for null, land, and obstructed pixels, and corrections for sun glint and atmospheric interference were applied. Depth Invariant Indices (DII) were then calculated according to the method of Lyzenga and an LDA classifier trained on ground truth data from a single scene. The resulting LDA classifier was then applied to other locations and the coral classification accuracy evaluated. When applied to ground truth data from the Palmyra Atoll location in scene path/row 065/056, the initial model achieved an accuracy of 80.3%. However, when applied to ground truth observations from another location within the scene, namely, Kingman Reef, it achieved an accuracy of 78.6%. The model was then applied to two additional scenes (Howland Island and Baker Island Atoll), which yielded an accuracy of 69.2% and 71.4%, respectively. Finally, the algorithm was retrained using data gathered from all four sites, which produced an overall accuracy of 74.1%.

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“…Prior to image classification, pretreatment for depth was performed on the RGB color images (Table 2). Watercolumn effects were corrected using the Lyzenga approach to work with benthic albedo as opposed to water-column reflectance + benthic albedo signals (Lyzenga 1978, 1981, Matsunaga et al 2000. For more than 20 polygons that were distributed across the whole area and contained sufficient numbers and combinations of pixel colors to create a regression model, RGB-band values for the sandy areas were extracted.…”

Section: Pretreatment Of Depth For Semi-automatic Classificationmentioning

confidence: 99%

Application of deep learning techniques for determining the spatial extent and classification of seagrass beds, Trang, Thailand

et al. 2019

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Few studies have investigated the long-term temporal dynamics of seagrass beds, especially in Southeast Asia. Remote sensing is one of the best methods for observing these dynamic patterns, and the advent of deep learning technology has led to recent advances in this method. This study examined the feasibility of applying image classification methods to supervised classification and deep learning methods for monitoring seagrass beds. The study site was a relatively natural seagrass bed in Hat Chao Mai National Park, Trang Province, Thailand, for which aerial photographs from the 1970s were available. Although we achieved low accuracy in differentiating among various densities of vegetation coverage, classification related to the presence of seagrass was possible with an accuracy of 80% or more using both classification methods. Automatic classification of benthic cover using deep learning provided similar or better accuracy than that of the other methods even when grayscale images were used. The results also demonstrate that it is possible to monitor the temporal dynamics of an entire seagrass area, as well as variations within sub-regions, located in close proximity to a river mouth.

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<title>Monitoring of coral reefs on Ishigaki Island in Japan using multitemporal remote sensing data</title>

Cited by 11 publications

References 0 publications

Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean

Application of deep learning techniques for determining the spatial extent and classification of seagrass beds, Trang, Thailand

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