Positive and negative effects of COVID-19 pandemic on aquatic environment: a review

In December 2019, a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak was reported for the first time in Wuhan, Hubei province, China. This coronavirus has been referred as Coronavirus Disease 2019 or COVID-19 by World Health Organization (WHO). The spread of COVID-19 has become unstoppable, infecting around 93.5 million people worldwide, with the infections and deaths still increasing. Today, the entire planet has changed due to the greatest threat on the planet since the introduction of this lethal disease. This pandemic has left the world in turmoil and various measures have been taken by many countries including movement control order or lockdown, to slow down or mitigate the infection. Since the lockdown has been implemented almost in all affected countries, there has been a significant reduction in anthropogenic activity, including a reduction in industrial operations, vehicle numbers, and marine-related activities. All of these changes have also led to some unexpected environmental consequences. As a result of this lockdown, it had a positive and negative impact on the environment including the aquatic environment. Hence this review will therefore focus on the good and bad perspectives of the lockdown toward the aquatic environment

Satellite observation of trends in sea surface temperature and coral bleaching in the Indo-Pacific region

Global warming phenomena have started to gain public attention as the associated impacts are starting to affect human livelihoods. In the marine environment, an increasing ocean temperature is threatening coral reef ecosystems. However, ocean warming across the globe is not uniform. The spatial and temporal trends of ocean temperature need to be characterised. Little is known about the past and future trends in local ocean temperatures, which may be especially important in coral rich areas like the Coral Triangle and the South China Sea. This thesis uses a combination of monthly 10 spatial resolution NOAA Optimum Interpolation (OI) Sea Surface Temperature (SST) V2 dataset (OISSTv2) and the Representative Concentration Pathways (RCP2.6) mitigation scenario of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to characterise Sea Surface Temperature (SST) trends in the Indo-Pacific region. This research revealed warming trends detected for three SST variables. In the Coral Triangle warming trends with a rate of 0.013 °C yr-1 , 0.017 °C yr-1 , and 0.019 °C yr-1 were detected over 29 years for MaxSST, MeanSST and MinSST, respectively. In the SCS, the warming rates were 0.011 °C yr-1 , (MaxSST), 0.012 °C yr-1 (MeanSST) and 0.015 °C yr-1 (MinSST) over 29 years. The CMIP5 RCP2.6 forecast suggested a future warming rate to 2100 of 0.004°C yr-1 for both areas. Using MaxSST as a proxy for coral bleaching, this thesis attempted to model their relationship using logistic regression. Coral bleaching probability maps for the past and the future were produced. Widespread coral bleaching was predicted within a few decades. Further analysis was undertaken to explore the most appropriate spatial resolution of SST data for modelling coral bleaching. This thesis demonstrated that 4 km spatial resolution MaxSST is the best resolution to model coral bleaching events in the region

Distribution of chlorophyll-a and sea surface temperature (SST) using modis data in east Kalimantan waters, Indonesia

Regular monitoring of near-shore and open-water parameters for marine management in East Kalimantan waters, Indonesia is still limited. The objective of this research is to determine and interpret the seasonal and spatial variability of sea surface temperature (SST) and Chlorophyll-a concentration (Chl-a) in East Kalimantan waters. A standard MODIS SST split-window algorithm and empirical Chlorophyll-a 0C-3M algorithm were used to generate the Level 2 MODIS SST and Chl-a images. MODIS or Moderate Resolution Imaging Spectroradiometer is a key instrument aboard the Terra and Aqua satellites. From March 2005 to August 2006, the SST and Chl-a were retrieved from the sensor data in East Kalimantan coastal and open-sea waters. In situ measurements from near-shore waters were used to validate the MODIS Level 2 data. A comparison of MODIS with in situ values for SST and Chl-a shows: RMSE=1.21°C, Bias=-3.42, n=121 and RMSE=1.01mg.m -3 , Bias=+2.45, n=75, although some anomalies were observed in the retrievals in both datasets. The analysis of seasonal variations indicates that there was low SST variability between wet and dry season. There was also low variability between SST values in near-shore and open-sea waters. However, for both seasons, open-sea SST was paradoxically found to be warmer than the near-shore waters. The Chl-a maps revealed low Chl-a variability between wet and dry season. Different value ranges in Chl-a were found between near-shore waters (1.00-56.00 mg.n -3 ) and open waters (1.00-4.00 mg.n -3 ). The Chl-a values retrieved from MODIS for both seasons were higher in near-shore water. The SST and Chl-a in near-shore waters have a low positive interrelationship in wet season. During dry season, the relationship between these two variables varies from positive to negative. This study demonstrated that MODIS Level 2 data from Malaysia Ground Receiving Station (MGRS) can successfully be used to obtain SST and Chl-a in Southeast Asian coastal and open waters

Seafloor habitat mapping using machine learning and underwater acoustic sonar

The need for detailed spatial map of marine habitats is increasingly important and demanding in managing and preserving marine biodiversity. This study integrates machine learning technique with the in-situ dataset and underwater acoustic mapping data to produce habitat classification maps. For the acoustic data, high-spatial resolution bathymetry and backscatter data were acquired using Kongsberg EM2040C multibeam sonar echosounder. A set of derivative layers were computed as follows (from bathymetry); slope, aspect, rugosity, Benthic Position Index (BPI) (broad and fine scale BPI), whilst from backscatter layers were; Hue, Saturation and Intensity (HSI), Grey Level Texture Co-Occurrence (GLCM) layers (homogeneity, entropy, correlation and mean). Layers from inversion of acoustic properties using Angular Range Analysis (ARA) were also produced such as characterization (sediment class), phi, fluid factor, gradient, index of impendence, intercept, mean far, mean near, mean outer, mean total, roughness and volume homogeneity. Habitat classification map was derived using Random Forests decision trees. Full coverage benthic habitat maps at 1 m spatial resolution were successfully constructed which explained the spatial distribution of coral, fine sand and coarse sand. Bathymetry (and its derivatives) and GLCM mean were identified as the important variables to predict these habitats. This study demonstrated the contribution of machine learning technique to be integrated with underwater sonar data for seafloor habitat mapping

Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region

The Coral Triangle (CT) and the South China Sea (SCS) are the world’s great tropical seas, located in the Indo-Pacific (IP) region. It is home to the richest marine ecosystem on Earth, with a total of 76% reef-building coral species as well as 37% coral reef fish species. Unfortunately, this sensitive area is now vulnerable to Sea Surface Temperature (SST) warming. This research explored the possible consequences of SST warming on the rich ecosystems of the IP region, specifically on bleaching of its coral reefs. Reefbase provided coral bleaching records together with the daily NOAA AVHRR Optimum Interpolation (OI) SST V2 dataset (OISSTv2)  were used to explore the relationship between coral bleaching and SST in the IP region. Three different categories of monthly mean SST were tested as potential covariates: minimum SST, mean SST and maximum SST, obtained from the OISSTv2. The fitted logistic regression (LR) model revealed a significant and large correlation between coral bleaching and annual maximum monthly mean SST in the study area using the bleaching data from an online database and the time-series of AVHRR images. Predicted maps of coral bleaching based on the LR model were highly consistent with NOAA Coral Reef Watch (CRW) Degree heating Weeks (DHW) maps. However, some important discrepancies resulted from the more specific local fitting used in the LR model. The maximum SST was forecasted from 2020 to 2100 based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset under the Representative Concentration Pathways (RCP2.6) scenario. The fitted logistic regression model was employed to transform the forecasted maximum SST values into maps of the probability of coral bleaching from 2020 to 2100. The results provide considerable cause for concern, including the likelihood of widespread coral bleaching in many places in the IP region over the next 30 years

Coral Reef Mapping of UAV: A Comparison of Sun Glint Correction Methods

Although methods were proposed for eliminating sun glint effects from airborne and satellite images over coral reef environments, a method was not proposed previously for unmanned aerial vehicle (UAV) image data. De-glinting in UAV image analysis may improve coral distribution mapping accuracy result compared with an uncorrected image classification technique. The objective of this research was to determine accuracy of coral reef habitat classification maps based on glint correction methods proposed by Lyzenga et al., Joyce, Hedley et al., and Goodman et al. The UAV imagery collected from the coral-dominated Pulau Bidong (Peninsular Malaysia) on 20 April 2016 was analyzed in this study. Images were pre-processed with the following two strategies: Strategy-1 was the glint removal technique applied to the whole image, while Strategy-2 used only the regions impacted by glint instead of the whole image. Accuracy measures for the glint corrected images showed that the method proposed by Lyzenga et al. following Strategy-2 could eliminate glints over the branching coral—Acropora (BC), tabulate coral—Acropora + Montipora (TC), patch coral (PC), coral rubble (R), and sand (S) with greater accuracy than the other four methods using Strategy-1. Tested in two different coral environments (Site-1: Pantai Pasir Cina and Site-2: Pantai Vietnam), the glint-removed UAV imagery produced reliable maps of coral habitat distribution with finer details. The proposed strategies can potentially be used to remove glint from UAV imagery and may improve usability of glint-affected imagery, for analyzing spatiotemporal changes of coral habitats from multi-temporal UAV imagery

Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data?

Remote sensing has potential in studies of the benthic habitat and extracting the reflectance from the data of multispectral sensors, but traditional image classification techniques cannot provide coral habitat maps with adequate accuracy. This study tested five traditional and three ensemble classification techniques on QuickBird for mapping the benthic composition of coral reefs on the Lang Tengah Island (Malaysia). The common techniques, minimum distance, maximum likelihood, K-nearest neighbour, Fisher and parallelepiped techniques were compared with ensemble classifiers, such as majority voting (MV), simple averaging, and mode combination. The per-class accuracy of the habitat detection improved in the ensemble classifiers; in particular, the MV classifier achieved 95%, 65%, 75% and 95% accuracies for coral, sparse coral, coral rubble and sand, respectively. Ensembles increased the accuracy of the habitat mapping classification by 28%, relative to conventional techniques. Thus, the ensemble techniques can be preferred over the traditional for benthic habitat mapping

Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers

This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping