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

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment

PLATFORM REEF LAGOON DETECTION FROM SENTINEL-2 IN PANGGANG ISLAND AND SEMAKDAUN ISLAND

Processing of satellite image data for the detection of platform reef lagoons is intended as one of the geo-physical parameters of the reef landform. Panggang Island and Semakdaun Island were chosen to make the detection model because they are ideal for lagoon reef landforms and tapulang court reefs. This model is only valid in the continental shelf area and the back arc and small island tectonic type. Determination of this location is done to improve the accuracy of spectral-based data processing. Platform reefs are one of four classes of reef landforms. Sentinel-2A data with a spatial resolution of 10m, blue, green, red, and near infrared bands were selected to investigate their ability to detect lagoons. Processing of data by calculating the Optimum Index Factor (OIF) to produce a composite image and drawing transect lines to produce pixel values and spectral graphics of the lagoon. The results of data processing in the form of graphs, composite images and pixel values were built to realize a digital lagoon detection model. These results are used for lagoon growth stage analysis for the classification of three reef platform landforms, visually and digitally interpretation. This digital and visual detection system design is useful for monitoring coral reef ecosystems

Coral reef habitat mapping in the Red Sea (Hurghada, Egypt) based on remote sensing

Remote sensing can give information about the configuration and composition of coral reefs, about the biophysical parameters of the seas and oceans in which they occur and about the changes over time of these elements. This paper deals with the classification of a Landsat7 ETM+ data set in order to identify the different bottom types (macro-algae, coral, sea grass and sand) occurring on the reefs offshore Hurghada, Egypt. Before classification, the radiance values received at sensor are corrected for atmospheric and water column effects. ‘Depth-invariant bottom indices’ are calculated and form the basis for classification. Besides the bottom type as an ecological classification, also a geomorphological classification is made. After contextual editing of the ecological classification, both results are combined into an open-ended hierarchical classification scheme. An in-depth accuracy assessment still needs to be undertaken but a mean accuracy between 47% and 83% is to be expected

Regional High-Resolution Benthic Habitat Data From Planet Dove Imagery For Conservation Decision-Making and Marine Planning

High-resolution benthic habitat data fill an important knowledge gap for many areas of the world and are essential for strategic marine conservation planning and implementing effective resource management. Many countries lack the resources and capacity to create these products, which has hindered the development of accurate ecological baselines for assessing protection needs for coastal and marine habitats and monitoring change to guide adaptive management actions. The PlanetScope (PS) Dove Classic SmallSat constellation delivers high-resolution imagery (4 m) and near-daily global coverage that facilitates the compilation of a cloud-free and optimal water column image composite of the Caribbean’s nearshore environment. These data were used to develop a first-of-its-kind regional thirteen-class benthic habitat map to 30 m water depth using an object-based image analysis (OBIA) approach. A total of 203,676 km2 of shallow benthic habitat across the Insular Caribbean was mapped, representing 5% coral reef, 43% seagrass, 15% hardbottom, and 37% other habitats. Results from a combined major class accuracy assessment yielded an overall accuracy of 80% with a standard error of less than 1% yielding a confidence interval of 78–82%. Of the total area mapped, 15% of these habitats (31,311.7 km2) are within a marine protected or managed area. This information provides a baseline of ecological data for developing and executing more strategic conservation actions, including implementing more effective marine spatial plans, prioritizing and improving marine protected area design, monitoring condition and change for post-storm damage assessments, and providing more accurate habitat data for ecosystem service models

Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.Peer ReviewedPostprint (published version

Evaluation of large-scale unsupervised classification of New Caledonia reef ecosystems using Landsat 7 ETM+ imagery

The capacity of the Landsat 7 Enhanced Thematic Mapper Plus sensor to classify the shallow benthic ecosytems of New Caledonia (South Pacific) is tested using a novel unsupervised classification method. The classes are defined by using a set of multiple spectral decision rules based on the image spectral bands. A general model is applied to the entire Southwest lagoon (5500 km(2)) and tested on three representative sites: a section of the barrier reef, a cay reef flat rich in corals, and a cay reef flat rich in algae and seagrass beds. In the latter one, the classification results are compared with a locally optimized model, with aerial color photographs and extensive ground-truthed observations. Results show that a reconnaissance of the main benthic habitats in shallow areas (<5 m depth) is possible, at a geomorphological scale for coral reef structure and at a habitat scale for seagrass beds. However, results directly issued from the model must be cautiously interpreted according to empirical spatial rules, especially to avoid confusion between coral slopes and shallow dense seagrass.Le but de cette étude est de tester la capacité des images Landsat 7 Enhanced Thematic Mapper Plus à discriminer les principales classes d’habitats benthiques rencontrées dans les parties peu profondes du système récifal et lagonaire de Nouvelle-Calédonie (Pacifique Sud). Une méthode originale de classification non-supervisée est proposée. Les habitats benthiques correspondent à une combinaison de plusieurs règles de décision établies à partir des bandes radiométriques Landsat. Cette modélisation statistique des habitats benthiques est appliquée au lagon sud-ouest de Nouvelle-Calédonie (5500 km2). Les résultats sont testés sur trois sites témoins contrastés: un platier de récif barrière, un platier d’îlot riche en corail et un platier d’îlot riche en herbiers/algueraies. Pour ce dernier, le résultat est comparé à celui d’un modèle optimisé, construit à échelle locale et validé à partir de photographies aériennes et d’observations de terrain. Les résultats montrent qu’une reconnaissance des différentes classes benthiques est possible pour des fonds peu profonds (< 5 m de profondeur), à l’échelle géomorphologique pour les structures récifales et à l’échelle des habitats pour les herbiers. Toutefois, les résultats bruts du modèle doivent être interprétés en fonction de critères spatiaux pour corriger les confusions entre certaines classes, notamment entre les pentes coralliennes et les herbiers denses

Coastal Eye: Monitoring Coastal Environments Using Lightweight Drones

Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.Natural Environment Research Council (NERC