Evaluating eight field and remote sensing approaches for mapping the benthos of three different coral reef environments in Fiji

Monitoring of coral reef environments require accurate, timely and relevant information on their composition and condition. These environments are challenging to map due to their variation in reef type, remoteness, extent, benthic cover composition and variable water clarities. This work evaluates the accuracy, cost and relevance of eight commonly used benthic cover mapping approaches applied in three different coral reef environments in Fiji. The eight mapping techniques varied in field data source (local knowledge, point and transect surveys), image data (Quickbird 2 and Landsat 5 TM), level of image correction (none or atmospheric) and processing approaches (delineation and supervised classification). The eight mapping approaches were assessed in terms of their: map accuracy; production time and cost. Qualitative assessment was carried out by map users representing the local marine monitoring agencies. These map assessments showed that users and producers preferred mapping approaches based on: supervised classification of Quickbird imagery integrated with a basic field data. This approach produced an accurate map within a short time; with low cost that suited the user's purpose. The findings from this work demonstrate how variations in coral reef environments, and map purpose and resources management requirements affected the user's selection of a suitable mapping approach. ©2008 COPYRIGHT SPIE--The International Society for Optical Engineering

Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps

Our ability to map coral reef environments using remote sensing has increased through improved access to: satellite images and field survey data at suitable spatial scales, and software enabling the integration of data sources. These data sets can be used to provide validated maps to support science and management decisions. The objective of this paper was to compare two methods for calibrating and validating maps of coral reef benthic communities derived from satellite images captured over a variety of Coral Reefs The two methods for collecting georeferenced benthic field data were: 1), georeferenced photo transects and 2), spot checks. Quickbird imagery was acquired for three Fijian coral reef environments in: Suva, Navakavu and Solo. These environments had variable water clarity and spatial complexity of benthic cover composition. The two field data sets at each reef were each split, and half were used for training data sets for supervised classifications, and the other half for accuracy assessment. This resulted in two maps of benthic communities with associated mapping accuracies, production times and costs for each study-site. Analyses of the spatial patterns in benthic community maps and their Overall and Tau accuracies revealed that for spatially complex habitats, the maps produced from photo transect data were twice as accurate as spot check based maps. In the context of the reefs examined, our results showed that the photo- transect method was a robust procedure which could be used in a range of coral reef environments to map the benthic communities accurately. In contrast, the spot check method is a fast and low cost approach, suitable for mapping benthic communities which have lower spatial complexity. Our findings will enable scientists, technicians and managers to select appropriate methods for collecting field data to integrate with high spatial resolution multi-spectral imagery to create validated coral reef benthic community maps. © 2010 Society of Photo-Optical Instrumentation Engineer

Improving Approaches to Mapping Seagrass within the Great Barrier Reef: From Field to Spaceborne Earth Observation

Seagrass meadows are a key ecosystem of the Great Barrier Reef World Heritage Area, providing one of the natural heritage attributes underpinning the reef’s outstanding universal value. We reviewed approaches employed to date to create maps of seagrass meadows in the optically complex waters of the Great Barrier Reef and explored enhanced mapping approaches with a focus on emerging technologies, and key considerations for future mapping. Our review showed that field-based mapping of seagrass has traditionally been the most common approach in the GBR-WHA, with few attempts to adopt remote sensing approaches and emerging technologies. Using a series of case studies to harness the power of machine-and deep-learning, we mapped seagrass cover with PlanetScope and UAV-captured imagery in a variety of settings. Using a machine-learn-ing pixel-based classification coupled with a bootstrapping process, we were able to significantly improve maps of seagrass, particularly in low cover, fragmented and complex habitats. We also used deep-learning models to derive enhanced maps from UAV imagery. Combined, these lessons and emerging technologies show that more accurate and efficient seagrass mapping approaches are possible, producing maps of higher confidence for users and enabling the upscaling of seagrass mapping into the future

Bottom Reflectance in Ocean Color Satellite Remote Sensing for Coral Reef Environments

Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ hyperspectral bottom reflectance signatures and their separability for coral reef waters, when observed at the spectral resolutions of MODIS and SeaWiFS sensors. We use radiative transfer modeling to calculate the effects of bottom reflectance on the remote sensing reflectance signal, and assess detectability and discrimination of common coral reef bottom classes by clustering modeled remote sensing reflectance signals. We assess 8280 scenarios, including four IOPs, 23 depths and 45 bottom classes at MODIS and SeaWiFS bands. Our results show: (i) no significant contamination (Rrscorr 17 m for MODIS and >19 m for SeaWiFS for the brightest spectral reflectance substrate (light sand) in clear reef waters; and (ii) bottom cover classes can be combined into two distinct groups, “light” and “dark”, based on the modeled surface reflectance signals. This study establishes that it is possible to efficiently improve parameterization of bottom reflectance and water-column IOP retrievals in shallow water ocean color models for coral reef environments

Assessing tropical cyclone damage using moderate spatial resolution satellite imagery: Cyclone Sidr, Bangladesh 2007

Tropical cyclones are a common natural disaster, and are predicted to increase in intensity and frequency under future climate change scenarios in many coastal areas across the world. Satellite remote sensing can provide a capability for large area (10,000’s km2) coverage and derivation of essential map products at high to moderate spatial resolution (5 – 30 m pixels) on a regular basis weekly to monthly. These data are ideal for assessing damage produced by the cyclone and can be used to derive appropriate information for planning and directing relief efforts in a short time frames. This study developed and tested approaches for assessing multiple damages caused in 2007 by tropical Cyclone Sidr in Sarankhola upazila (151.24 km2) in Bangladesh from moderate spatial resolution satellite imagery. Object based image classification techniques were applied to map cover types in preand post-cyclone SPOT 5 satellite imagery. Post classification change detection techniques identified types of land cover changes. Our results indicate that around 60% of the study area was significantly damaged by Sidr. About 31% of croplands were flooded by storm surges and with vegetation, settlements and infrastructure of the area all being fully or partially damaged. The methods developed may be used in future to assess the damages caused by tropical cyclones in Bangladesh and other countries