SeeCucumbers: using deep learning and drone iagery to detect sea cucumbers on coral reef flats

Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species

Assessing the potential of remotely-sensed drone spectroscopy to determine live coral cover on Heron Reef

Coral reefs, as biologically diverse ecosystems, hold significant ecological and economic value. With increased threats imposed on them, it is increasingly important to monitor reef health by developing accessible methods to quantify coral cover. Discriminating between substrate types has previously been achieved with in situ spectroscopy but has not been tested using drones. In this study, we test the ability of using point-based drone spectroscopy to determine substrate cover through spectral unmixing on a portion of Heron Reef, Australia. A spectral mixture analysis was conducted to separate the components contributing to spectral signatures obtained across the reef. The pure spectra used to unmix measured data include live coral, algae, sand, and rock, obtained from a public spectral library. These were able to account for over 82% of the spectral mixing captured in each spectroscopy measurement, highlighting the benefits of using a public database. The unmixing results were then compared to a categorical classification on an overlapping mosaicked drone image but yielded inconclusive results due to challenges in co-registration. This study uniquely showcases the potential of using commercial-grade drones and point spectroscopy in mapping complex environments. This can pave the way for future research, by increasing access to repeatable, effective, and affordable technology

Understanding, Quantifying, and Reducing Bias in Fisheries-independent Visual Surveys

Understanding spatiotemporal changes in populations is vital for conservation managers to assess current recovery efforts, determine future conservation priorities, and forms the basis to explore complex ecological questions. In fisheries, these data have traditionally been collected using fisheries-independent surveys that rely on extractive sampling practices (e.g., longlines, gillnets, trawls). However, with the growing availability of low-cost, high-definition cameras, researchers are increasingly using visual surveys as a non-invasive alternative. Camera surveys have a number of advantages including their archivable data, and offer insights into species habitat use and behavior. However, the use of cameras has a number of inherent biases. Understanding, quantifying, and mitigating against these biases is critical if camera systems are to be used to inform management and policy. In this dissertation, potential biases were explored for two commonly used visual survey methods; baited remote underwater videos (BRUV), and unmanned aerial vehicles (UAV). Specifically, our objectives were to answer: (1) Are metrics of relative abundance derived from BRUVs linearly related to true changes in abundance for elasmobranchs, (2) Are these same metrics sensitive to changes in density-independent factors, and (3) Can UAVs be used to replace or supplement traditional diver transects for marine invertebrate species? Using a combination of standard and full-spherical camera BRUV deployments, Chapter One found that tradition BRUVs likely undercount sharks in high density environments, while also having lower probability of detection than full-spherical cameras. Using a spatially-explicit, individual-based-model, Chapter Two revealed that metrics of relative abundance derived BRUVs are also highly sensitive to factors unrelated to changes in abundance (e.g., swimming speed, current strength, and movement patterns). Lastly, using paired snorkeler-UAV transect sampling Chapter Three found counts derived from UAV transects did not significantly differ from divers, and offered a number of advantages over this traditional technique (increased percision, larger surveyed area, and automation). Furthermore, we found that UAVs could be used to improve sampling design used to quantify invertebrates, by estimating their distribution within a study region prior to initiating transect sampling. Collectively, these works improve our understanding and interpretation of video survey results that are used for management across the globe

UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring

Recent advances in unmanned aerial system (UAS) sensed imagery, sensor quality/size, and geospatial image processing can enable UASs to rapidly and continually monitor coral reefs, to determine the type of coral and signs of coral bleaching. This paper describes an unmanned aerial vehicle (UAV) remote sensing methodology to increase the efficiency and accuracy of existing surveillance practices. The methodology uses a UAV integrated with advanced digital hyperspectral, ultra HD colour (RGB) sensors, and machine learning algorithms. This paper describes the combination of airborne RGB and hyperspectral imagery with in-water survey data of several types in-water survey of coral under diverse levels of bleaching. The paper also describes the technology used, the sensors, the UAS, the flight operations, the processing workflow of the datasets, the methods for combining multiple airborne and in-water datasets, and finally presents relevant results of material classification. The development of the methodology for the collection and analysis of airborne hyperspectral and RGB imagery would provide coral reef researchers, other scientists, and UAV practitioners with reliable data collection protocols and faster processing techniques to achieve remote sensing objectives

Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users

Coral Colony-Scale Rugosity Metrics and Applications for Assessing Temporal Trends in the Structural Complexity of Coral Reefs.

Globally, coral reefs are experiencing reductions in structural complexity, primarily due to a loss of key reef building taxa. Monitoring these changes is difficult due to the time-consuming nature of in-situ measurements and lack of data concerning coral genus-specific contributions to reef structure. This research aimed to develop a new technique that uses coral colony level data to quantify reef rugosity (a 3-dimensional measure of reef structure) from three sources of coral survey data: 2D video imagery, line intercept data and UAV imagery. A database of coral colony rugosity data, comparing coral colony planar and contour length for 40 coral genera, 14 morphotypes and 9 abiotic reef substrates, was created using measurements from the Great Barrier Reef and Natural History Museum. Mean genus rugosity was identified as a key trait related to coral life history strategy. Linear regression analyses (y = mx) revealed statistically significant (p < 0.05) relationships between coral colony size and rugosity for every coral genus, morphotype and substrate. The gradient governing these relationships was unique for each coral taxa, ranging from mean = 1.23, for (encrusting) Acanthastrea, to m = 3.84, for (vase-shape) Merulina. These gradients were used as conversion factors to calculate reef rugosity from linear distances measured in video transects of both artificial reefs, used as a control test, and in-situ natural coral reefs, using Kinovea software. This calculated, ‘virtual’ rugosity had a strong, positive relationship with in-situ microscale rugosity (r2 = 0.96) measured from the control transects, but not with that measured at the meso-scale in natural, highly heterogeneous reef environments (r2 < 0.2). This showed that the technique can provide accurate rugosity information when considered at the coral colony level. The conversion factors were also applied to historic line intercept data from the Seychelles, where temporal changes in calculated rugosity were consistent with changes in coral cover between 2008 and 2017. Finally, on application to 2,283 corals digitised from UAV imagery of the Maldives, the conversion factors enabled calculation of rugosity for three 100 m2 reef areas and prediction of how this rugosity will decrease during two future scenarios of coral reef degradation and community change. The study highlights that the application of genera-specific coral rugosity data to both new and existing coral reef survey datasets could be a valuable tool for monitoring reef structural complexity over large spatial scales