Christiaan M. Roelfsema

High spatial resolution remote sensing for environmental monitoring and management preface

This special feature in the journal of Spatial Science brings together a collection of papers showing environmental monitoring and management applications of high spatial resolution remotely sensed image data. As illustrated by the papers in this special feature, and a growing number of papers in ecology, environmental management and remote sensing journals (Butler, 2006; Mumby et aL, 2001; Zanoni and Goward, 2003), there is an increasing need for spatial information derived from multi-spectral sensors at the scales of traditional aerial photography. The application of moderate spatial resolution image data has produced limited results for mapping and monitoring small features (< 10 m) within terrestrial and aquatic habitats, Such as individual tree crowns and their associated biophysical variables, coral reef structures and seagrass distribution. With nominal spatial resolutions less than 5 m x 5 m, there now exists the capacity to acquire fully radiometrically and geometrically corrected data to map and monitor complex structures and patterns of small features. High spatial resolution airborne and satellite digital image sources in various forms are now easily obtainable for agencies responsible for monitoring natural and built environments. Spatial data, including airborne and satellite images, are globally accessible thanks to Virtual Globes, such as GoogleEarth and Microsofts Virtual Earth.

Integrating field survey data with satellite image data to improve shallow water seagrass maps: the role of AUV and snorkeller surveys?

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (<5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass are often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous underwater vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here, we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (<5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photographs of the seagrass were collected along transects via snorkelling or an AUV. Photographs from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object-based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller-collected field data-sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison with snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5% of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programmes.

Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles.

Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles.

Modelling tropical cyclone hazards under climate change scenario using geospatial techniques

Tropical cyclones are a common and devastating natural disaster in many coastal areas of the world. As the intensity and frequency of cyclones will increase under the most likely future climate change scenarios, appropriate approaches at local scales (1-5 km) are essential for producing sufficiently detailed hazard models. These models are used to develop mitigation plans and strategies for reducing the impacts of cyclones. This study developed and tested a hazard modelling approach for cyclone impacts in Sarankhola upazila, a 151 km2 local government area in coastal Bangladesh. The study integrated remote sensing, spatial analysis and field data to model cyclone generated hazards under a climate change scenario at local scales covering < 1000 km2. A storm surge model integrating historical cyclone data and Digital Elevation Model (DEM) was used to generate the cyclone hazard maps for different cyclone return periods. Frequency analysis was carried out using historical cyclone data (1960 - 2015) to calculate the storm surge heights of 5, 10, 20, 50 and 100 year return periods of cyclones. Local sea level rise scenario of 0.34 m for the year 2050 was simulated with 20 and 50 years return periods. Our results showed that cyclone affected areas increased with the increase of return periods. Around 63% of study area was located in the moderate to very high hazard zones for 50 year return period, while it was 70% for 100 year return period. The climate change scenarios increased the cyclone impact area by 6-10 % in every return period. Our findings indicate this approach has potential to model the cyclone hazards for developing mitigation plans and strategies to reduce the future impacts of cyclones.

Benthic and substrate cover data derived from photo-transect surveys in Lizard Island, Great Barrier Reef conducted on October 3-7, 2012

Underwater georeferenced photo-transect surveys were conducted on October 3-7, 2012 at various sections of the reef and lagoon at Lizard Island, Great Barrier Reef. For this survey a snorkeler swam while taking photos of the benthos at a set distance from the benthos using a standard digital camera and towing a GPS in a surface float which logged the track every five seconds. A Canon G12 digital camera was placed in a Canon underwater housing and photos were taken at 1 m height above the benthos. Horizontal distance between photos was estimated by three fin kicks of the survey snorkeler, which corresponded to a surface distance of approximately 2.0 - 4.0 m. The GPS was placed in a dry bag and logged the position at the surface while being towed by the photographer (Roelfsema, 2009). A total of 1,265 benthic photos were taken. Approximation of coordinates of each benthic photo was conducted based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the GPS coordinates that were logged at a set time before and after the photo was captured. Benthic or substrate cover data was derived from each photo by randomly placing 24 points over each image using the Coral Point Count for Microsoft Excel program (Kohler and Gill, 2006). Each point was then assigned to 1 of 79 cover types, which represented the benthic feature beneath it. Benthic cover composition summary of each photo scores was generated automatically using CPCE program. The resulting benthic cover data of each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.

Seagrass habitat mapping: how do Landsat 8 OLI, Sentinel-2, ZY-3A, and Worldview-3 perform?

ABSTRACT This study assessed recently launched multispectral sensors to map seagrass properties for a ~ 150 km2 shallow bank in Moreton Bay, Australia. We utilised a previously developed semi-automated, object-based image analysis classification routine for our comparison, utilising field and image data as input. Field data were collected through georeferenced photograph transects which were analysed for species composition and percentage cover. Field data collection occurred close to image capture from Landsat 8 Operational Land Imager (30 m pixel), Sentinel-2 (10 m pixel), Ziyuan-3A (5 m pixel), and WorldView-3 (2 m pixel) sensors. The output maps had average overall accuracies of 66% for species and 57% for percentage cover maps. The study concluded that all sensors tested were suitable for mapping seagrass meadows in clear shallow waters, but the higher resolution sensors provided more detail and were considered more representative. The choice of sensor would depend upon the extent of the seagrass meadow, available funds and frequency of observation.

A new wave of marine evidence-based management: emerging challenges and solutions to transform monitoring, evaluating, and reporting

Sustainable management and conservation of the world’s oceans requires effective monitoring, evaluation and reporting. Despite the growing political and social imperative for these activities, there are some persistent and emerging challenges that marine practitioners face in undertaking these activities. In 2015, a diverse group of marine practitioners came together to discuss the emerging challenges associated with marine monitoring, evaluation and reporting, and potential solutions to address these challenges. Three emerging challenges were identified: (1) the need to incorporate environmental, social and economic dimensions in evaluation and reporting; (2) the implications of big data, creating challenges in data management and interpretation; and, (3) dealing with uncertainty throughout monitoring, evaluation and reporting activities. We point to key solutions to address these challenges across monitoring, evaluation and reporting activities: 1) integrating models into marine management systems to help understand, interpret, and manage the environmental and socio-economic dimensions of uncertain and complex marine systems; 2) utilising big data sources and new technologies to collect, process, store, and analyse data; and 3) applying approaches to evaluate, account for, and report on the multiple sources and types of uncertainty. These solutions point towards a potential for a new wave of evidence-based marine management, through more innovative monitoring, rigorous evaluation and transparent reporting. Effective collaboration and institutional support across the science–management–policy interface will be crucial to deal with emerging challenges, and implement the tools and approaches embedded within these solutions.

Spectral reflectance library of healthy corals, bleached corals and other benthic features in Fiji

Spectral reflectances of 239 samples were recorded in situ. An Ocean Optics USB2000 spectrometer was deployed in a custom made underwater housing with a 0.5 m fibre-optic probe mounted next to an artificial light source. Spectral readings were collected with the probe(bear fibre) about 5 cm from the target to ensure that the target would fill the field of view of the fibre optic (FOV diameter ~4.4 cm), as well as to reduce the attenuating effect of the intermediate water (Roelfsema et al., 2006). Spectral readings included for one target included: 1 reading of the covered spectral fibre to correct for instrument noise, 1 reading of a spectralon panel mounted on the divers wrist to measure incident ambient light, and six to eight readings of the target. Spectral reflectance was calculated for each target by first subtracting the instrument noise reading from each other reading. The corrected target readings were then divided by the corrected spectralon reading resulting in spectral reflectance of each target reading. An average target spectral reflectance was calculated by averaging six to eight individual spectral reflectances of the target. If an individual target spectral reflectance was visually considered an outlier, it was not included in the average spectral reflectance calculation. See Roelfsema at al. (2006) for additional info on the methodology of underwater spectra collection.

2003 Habitat Maps derived from Grey Nurse Shark (GNS) Project of Wolf Rock, Double Island Point, Queensland, Australia

Habitat mapping using a combination of towed GPS photo transects, aerial photography, bathymetry surveys and expert knowledge. This data provides georeferenced information regarding the major features of each of Sites mapped including Wolf Rock. Inlcudes Point features, Line features, Depth contours, Habitat features, smallpoly features.

Morphometrics of seagrasses at species level, Moreton Bay, Australia determined from core samples collected in 2012-2013

Sampling data and measurements of seagrass core samples and seagrass biomass samples, including above and below ground biomass, shoot density, leaf length, width and area. Data was collected for each species of seagrass in the samples, and was drawn from multiple sites around Moreton Bay.