Change detection of Bunaken Island coral reefs using 15years of very high resolution satellite images: A kaleidoscope of habitat trajectories

THREE-DECADE changes of reef cover in Pulau Layang-Layang, Malaysia using multitemporal Landsat images

Over the years, coral reefs in the South China Sea have degraded and faced severe threats from rapid development, coral bleaching, and Crown-of-Thorns Starfish (COTS) outbreak. There is limited knowledge relating to the effects of anthropogenic disturbances and natural events on the coral reefs of Pulau Layang-Layang. This study aims to assess reef cover changes by utilizing Landsat satellite images spanning from 1989 to 2022. Using the object-based image analysis method, this study classified the reef cover into three categories: coral, rock and rubble, and sand. The supervised classification had an overall accuracy of 86.41-87.38 % and Tau's coefficients of 0.80-0.81. The results showed island development and construction of artificial bird sanctuary have led to an increase in coral cover. Furthermore, it was illustrated that the impact of COTS outbreaks in 2010 and 2020 differed significantly, with the latter showing no signs of recovery. Our study underscores the importance of timely intervention to mitigate the spread of COTS. This study provides insights into the resilience and vulnerability of these ecosystems in the face of various stressors.

Smart Temperature Sensor Design and High-Density Water Temperature Monitoring in Estuarine and Coastal Areas

Acquiring in situ water temperature data is an indispensable and important component for analyzing thermal dynamics in estuarine and coastal areas. However, the long-term and high-density monitoring of water temperature is costly and technically challenging. In this paper, we present the design, calibration, and application of the smart temperature sensor TS-V1, a low-power yet low-cost temperature sensor for monitoring the spatial-temporal variations of surface water temperatures and air temperatures in estuarine and coastal areas. The temperature output of the TS-V1 sensor was calibrated against the Fluke-1551A sensor developed in the United States and the CTD-Diver sensor developed in the Netherlands. The results show that the accuracy of the TS-V1 sensor is 0.08 °C, while sensitivity tests suggest that the TS-V1 sensor (comprising a titanium alloy shell with a thermal conductivity of 7.6 W/(m °C)) is approximately 0.31~0.54 s/°C slower than the CTD-Diver sensor (zirconia shell with thermal conductivity of 3 W/(m °C)) in measuring water temperatures but 6.92~10.12 s/°C faster than the CTD-Diver sensor in measuring air temperatures. In addition, the price of the proposed TS-V1 sensor is only approximately 1 and 0.3 times as much as the established commercial sensors, respectively. The TS-V1 sensor was used to collect surface water temperature and air temperature in the western part of the Pearl River Estuary from July 2022 to September 2022. These data wells captured water and air temperature changes, frequency distributions, and temperature characteristics. Our sensor is, thus, particularly useful for the study of thermal dynamics in estuarine and coastal areas.

Machine-Learning for Mapping and Monitoring Shallow Coral Reef Habitats

Mapping and monitoring coral reef benthic composition using remotely sensed imagery provides a large-scale inference of spatial and temporal dynamics. These maps have become essential components in marine science and management, with their utility being dependent upon accuracy, scale, and repeatability. One of the primary factors that affects the utility of a coral reef benthic composition map is the choice of the machine-learning algorithm used to classify the coral reef benthic classes. Current machine-learning algorithms used to map coral reef benthic composition and detect changes over time achieve moderate to high overall accuracies yet have not demonstrated spatio-temporal generalisation. The inability to generalise limits their scalability to only those reefs where in situ reference data samples are present. This limitation is becoming more pronounced given the rapid increase in the availability of high temporal (daily) and high spatial resolution (<5 m) multispectral satellite imagery. Therefore, there is presently a need to identify algorithms capable of spatio-temporal generalisation in order to increase the scalability of coral reef benthic composition mapping and change detection. This review focuses on the most commonly used machine-learning algorithms applied to map coral reef benthic composition and detect benthic changes over time using multispectral satellite imagery. The review then introduces convolutional neural networks that have recently demonstrated an ability to spatially and temporally generalise in relation to coral reef benthic mapping; and recurrent neural networks that have demonstrated spatio-temporal generalisation in the field of land cover change detection. A clear conclusion of this review is that existing convolutional neural network and recurrent neural network frameworks hold the most potential in relation to increasing the spatio-temporal scalability of coral reef benthic composition mapping and change detection due to their ability to spatially and temporally generalise.

High taxonomic resolution surveys and trait-based analyses reveal multiple benthic regimes in North Sulawesi (Indonesia)

As coral reef communities change and reorganise in response to increasing disturbances, there is a growing need for understanding species regimes and their contribution to ecosystem processes. Using a case study on coral reefs at the epicentre of tropical marine biodiversity (North Sulawesi, Indonesia), we explored how application of different biodiversity approaches (i.e., use of major taxonomic categories, high taxonomic resolution categories and trait-based approaches) affects the detection of distinct fish and benthic communities. Our results show that using major categories fails to identify distinct coral reef regimes. We also show that monitoring of only scleractinian coral communities is insufficient to detect different benthic regimes, especially communities dominated by non-coral organisms, and that all types of benthic organisms need to be considered. We have implemented the use of a trait-based approach to study the functional diversity of whole coral reef benthic assemblages, which allowed us to detect five different community regimes, only one of which was dominated by scleractinian corals. Furthermore, by the parallel study of benthic and fish communities we provide new insights into key processes and functions that might dominate or be compromised in the different community regimes.

Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing

Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs.