Seaview Survey Photo-quadrat and Image Classification Dataset

The primary scientific dataset arising from the XL Catlin Seaview Survey project is the “Seaview Survey Photo-quadrat and Image Classification Dataset”, consisting of: (1) over one million standardised, downward-facing “photo-quadrat” images covering approximately 1m2 of the sea floor; (2) human-classified annotations that can be used to train and validate image classifiers;\ua0(3) benthic cover data arising from the application of machine learning classifiers to the photo-quadrats; and\ua0(4)\ua0the triplets of raw images (covering 360o) from which the photo-quadrats were derived.Photo-quadrats were collected between 2012 and 2018 at 860 transect locations around the world, including: the Caribbean and Bermuda, the Indian Ocean (Maldives, Chagos Archipelago), the Coral Triangle (Indonesia, Philippines, Timor-Leste, Solomon Islands), the Great Barrier Reef, Taiwan and Hawaii.For additional information regarding methodology, data structure, organization and size, please see attached document “Dataset documentation”

Caracterização da comunidade epibentônica em recifes de corais das ilhas Fiji por vídeo-imagem

TCC(graduação) - Universidade Federal de Santa Catarina. Centro de Ciências Biológicas. Biologia.A profundidade é um dos indicadores mais bem estabelecidos para o estudo da distribuição de comunidades bentônicas nos ecossistemas marinhos por estar diretamente relacionada com a zona fótica disponível. Todavia, a maioria dos estudos analisa ambientes recifais até 30 metros dado o limite do SCUBA. Consequentemente, sabemos pouco sobre estrutura de comunidades ao longo de um gradiente de profundidade entre os recifes rasos e mesofóticos. Os veículos automatizados são exemplos de tecnologias disponíveis para investigação de recifes em ambientes mais profundos. No entanto, são necessárias adequações das metodologias operacionais e amostrais para a coleta dos dados A alta quantidade de dados gerada precisa ser otimizada. Os dados podem ser integrados com resultados de outras pesquisas para ampliação do conhecimento dos processos ecológicos. Esquemas metodológicos de identificação de organismos bentônicos em imagens subaquáticas vêm sendo desenvolvidos a fim de poderem ser adaptados globalmente. O CATAMI (Collaborative and Automated Tools for Analysis of Marine Imagery) é um exemplo disso, que propõe um esquema com uma abordagem morfofuncional taxonômica hierárquica. Neste estudo utilizou-se veículos remotamente operados (ROV’s) e adotou-se a classificação hierárquica baseada no CATAMI. Modelos de distribuição de espécies foram utilizados para avaliar o efeito da profundidade na composição de comunidades bentônicas de 10 à 130 metros, em recifes de corais na área de Vatu-i-Ra, ilhas Fiji. Observou-se que a profundidade foi significantemente relacionada com a presença e abundância de três dos quatro grupos epibêntonicos investigados. A abundância de corais pétreos diminuiu com a profundidade, enquanto a abundância de corais negros, octocorais e macroalgas aumentou até os 50 metros, e então diminuiu significantemente nas profundidades subsequentes. Esponjas e ascídias foram relativamente abundantes (>30%) ao longo de toda profundidade investigada, assim como o grupo de macroalgas (>40%). Este estudo demonstra que imagens originadas de ROVs podem ser utilizadas para caracterizar a composição da comunidade epibentônica ao longo de uma ampla escala de profundidade, e assim contribui para nosso conhecimento sobre recifes de corais mesofóticos

A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats

We developed a novel integrated technology for diver-operated surveying of shallow marine ecosystems. The HyperDiver system captures rich multifaceted data in each transect: hyperspectral and color imagery, topographic profiles, incident irradiance and water chemistry at a rate of 15-30 m(2) per minute. From surveys in a coral reef following standard diver protocols, we show how the rich optical detail can be leveraged to generate photopigment abundance and benthic composition maps. We applied machine learning techniques, with a minor annotation effort (<2% of pixels), to automatically generate cm-scale benthic habitat maps of high taxonomic resolution and accuracy (93-97%). The ability to efficiently map benthic composition, photopigment densities and rugosity at reef scales is a compelling contribution to modernize reef monitoring. Seafloor-level hyperspectral images can be used for automated mapping, avoiding operator bias in the analysis and deliver the degree of detail necessary for standardized environmental monitoring. The technique can deliver fast, objective and economic reef survey results, making it a valuable tool for coastal managers and reef ecologists. Underwater hyperspectral surveying shares the vantage point of the high spatial and taxonomic resolution restricted to field surveys, with analytical techniques of remote sensing and provides targeted validation for aerial monitoring

CARACTERIZAÇÃO DE HABITATS EM ÁREAS DE PROTEÇÃO MARINHA ATRAVÉS DE IMAGEAMENTO COM VEÍCULO DE OPERAÇÃO REMOTA (ROV)

Estudos de caraterização de comunidades e habitats bentônicos são de suma importância na elaboração de planos de manejos e na preservação da biodiversidade marinha, principalmente em áreas de proteção marinha, regiões consideradas de relevante interesse ecológico. No Refúgio da Vida Silvestre (RVS) de Santa Cruz e da Área de Proteção Ambiental (APA) Costa das Algas, no estado do Espírito Santo, existem diversas atividades antrópicas potencialmente causadoras de impactos, tendo como exemplos a atividade pesqueira e a exploração e produção de petróleo e gás que ocorrem próximos a região. O objetivo desse estudo consistiu em caracterizar os principais habitats existentes na região do RVS de Santa Cruz e da APA Costa das Algas, através de técnicas imageamento com Veículo Operado Remotamente (ROV) e a sua correlação com o material sedimentológico. Os registros foram obtidos através de 50 estações de coleta distribuídas ao longo da plataforma continental, a partir da isóbata de 10 m. Foram identificados 5 habitats sendo estes descritos como: Areias Terrígenas, Cascalhos Carbonáticos, Rodolitos e Cascalhos, Recifes e Rodolitos e Lateritas (Hardground), refinando o nível de conhecimento em relação ao já existente em estudos pretéritos e de escala mais regional da área de estudo. Foi possível identificar uma maior abundância no habitat descrito como Recifes e Rodolitos, localizado na porção mais profunda da APA Costa das Algas, tendo como oposto o habitat Areias Terrígenas, compreendendo majoritariamente o RVS de Santa Cruz. O método utilizado se comprovou satisfatório para esse tipo de caracterização podendo este ser replicado em estudos de áreas mais sensíveis. Os resultados obtidos no presente estudo poderão subsidiar o órgão gestor das duas UCs na elaboração de seus respectivos planos de manejo

Leveraging Automated Image Analysis Tools to Transform Our Capacity to Assess Status and Trends of Coral Reefs

Digital photography is widely used by coral reef monitoring programs to assess benthic status and trends. In addition to creating a permanent archive, photographic surveys can be rapidly conducted, which is important in environments where bottom-time is frequently limiting. However, substantial effort is required to manually analyze benthic images; which is expensive and leads to lags before data are available. Using previously analyzed imagery from NOAA’s Pacific Reef Assessment and Monitoring Program, we assessed the capacity of a trained and widely used machine-learning image analysis tool – CoralNet coralnet.ucsd.edu – to generate fully-automated benthic cover estimates for the main Hawaiian Islands (MHI) and American Samoa. CoralNet was able to generate estimates of site-level coral cover for both regions that were highly comparable to those generated by human analysts (Pearson’s r &gt; 0.97, and with bias of 1% or less). CoralNet was generally effective at estimating cover of common coral genera (Pearson’s r &gt; 0.92 and with bias of 2% or less in 6 of 7 cases), but performance was mixed for other groups including algal categories, although generally better for American Samoa than MHI. CoralNet performance was improved by simplifying the classification scheme from genus to functional group and by training within habitat types, i.e., separately for coral-rich, pavement, boulder, or “other” habitats. The close match between human-generated and CoralNet-generated estimates of coral cover pooled to the scale of island and year demonstrates that CoralNet is capable of generating data suitable for assessing spatial and temporal patterns. The imagery we used was gathered from sites randomly located in &lt;30 m hard-bottom at multiple islands and habitat-types per region, suggesting our results are likely to be widely applicable. As image acquisition is relatively straightforward, the capacity of fully-automated image analysis tools to minimize the need for resource intensive human analysts opens possibilities for enormous increases in the quantity and consistency of coral reef benthic data that could become available to researchers and managers

Biophysical and anthropogenic influences on the status of Tonga's coral reefs and reef fish fishery

Despite increasing threats to Tonga's coral reefs from stressors that are both local (e.g. overfishing and pollution) and global (e.g. climate change), there is yet to be a systematic assessment of the status of the country's coral reef ecosystem and reef fish fishery stocks. Here, we provide a national ecological assessment of Tonga's coral reefs and reef fish fishery using ecological survey data from 375 sites throughout Tonga's three main island groups (Ha'apai, Tongatapu and Vava'u), represented by seven key metrics of reef health and fish resource status. Boosted regression tree analysis was used to assess and describe the relative importance of 11 socio-environmental variables associated with these key metrics of reef condition. Mean live coral cover across Tonga was 18%, and showed a strong increase from north to south correlated with declining sea surface temperature, as well as with increasing distance from each provincial capital. Tongatapu, the southernmost island group, had 2.5 times greater coral cover than the northernmost group, Vava'u (24.9% and 10.4% respectively). Reef fish species richness and density were comparable throughout Tongatapu and the middle island group, Ha'apai (similar to 35 species/transect and similar to 2500 fish/km(2)), but were significantly lower in Vava'u (similar to 24 species/transect and similar to 1700 fish/km(2)). Spatial patterns in the reef fish assemblage were primarily influenced by habitat-associated variables (slope, structural complexity, and hard coral cover). The biomass of target reef fish was greatest in Ha'apai (similar to 820 kg/ha) and lowest in Vava'u (similar to 340 kg/ha), and was negatively associated with higher human influence and fishing activity. Overall mean reef fish biomass values suggest that Tonga's reef fish fishery can be classified as moderately to heavily exploited, with 64% of sites having less than 500 kg/ha. This study provides critical baseline ecological information for Tonga's coral reefs that will: (1) facilitate ongoing management and research; and (2) enable accurate reporting on conservation targets locally and internationally

Reef Cover, a coral reef classification for global habitat mapping from remote sensing

Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users’ ability to interpret outputs. Here we present Reef Cover, a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes

Supplementary report to the final report of the coral reef expert group: S6. Novel technologies in coral reef monitoring

[Extract] This report summarises a review of current technological advances applicable to coral reef monitoring, with a focus on the Great Barrier Reef Marine Park (the Marine Park). The potential of novel technologies to support coral reef monitoring within the Reef 2050 Integrated Monitoring and Reporting Program (RIMReP) framework was evaluated based on their performance, operational maturity and compatibility with traditional methods. Given the complexity of this evaluation, this exercise was systematically structured to address the capabilities of technologies in terms of spatial scales and ecological indicators, using a ranking system to classify expert recommendations.An accessible copy of this report is not yet available from this repository, please contact [email protected] for more information