A comparison of spatially explicit and classic regression modelling of live coral cover using hyperspectral remote-sensing data in the Al Wajh lagoon, Red Sea

Live coral is a key component of the Al Wajh marine reserve in the Red Sea. The management of this reserve is dependent on a sound understanding of the existing spatial distribution of live coral cover and the environmental factors influencing live coral at the landscape scale. This study uses remote-sensing techniques to develop ordinary least squares and spatially lagged autoregressive explanatory models of the distribution of live coral cover inside the Al Wajh lagoon, Saudi Arabia. Live coral was modelled as a response to environmental controls such as water depth, the concentration of suspended sediment in the water column and exposure to incident waves. Airborne hyperspectral data were used to derive information on live coral cover as a response (dependent) variable at the landscape scale using linear spectral unmixing. Environmental controls (explanatory variables) were derived from a physics-based inversion of the remote-sensing dataset and validated against field-collected data. For spatial regression, cases referred to geographical locations that were explicitly drawn on in the modelling process to make use of the spatially dependent nature of coral cover controls. The transition from the ordinary least squares model to the spatially lagged model was accompanied by a marked growth in explanatory power (R 2 = 0.26 to 0.76). The theoretical implication that follows is that neighbourhood context interactions play an important role in determining live coral cover. This provides a persuasive case for building geographical considerations into studies of coral distribution

GPS linked photos of benthic cover transect surveys in Heron Reef

Underwater photo-transect surveys were conducted on September 23-27, 2007 at different sections of the reef flat, reef crest and reef slope in Heron Reef. This survey was done by swimming along pre-defined transect sites and taking a picture of the bottom substrate parallel to the bottom at constant vertical distance (30cm) every two to three metres. A total of 3,586 benthic photos were taken. A floating GPS setup connected to the swimmer/diver by a line enabled recording of coordinates of transect surveys. Approximation of the coordinates for each benthic photo was based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software. Coordinates of each photo were interpolated by finding the the gps coordinates that were logged at a set time before and after the photo was captured. The output of this process was an ArcMap point shapefile, a Google Earth KML file and a thumbnail of each benthic photo taken. The data in the ArcMap shapefile and in the Google Earth KML file consisted of the approximated coordinate of each benthic photo taken during the survey. Using the GPS Photo Link extension within the ArcMap environment, opening the ArcMap shapefile will enable thumbnail to be displayed on the associated benthic cover photo whenever hovering with the mouse over a point on the transect. By downloading the GPSPhotoLink software from the www.geospatialexperts.com, and installing it as a trial version the ArcMap exstension will be installed in the ArcMap environment

Coral reef mapping in Vietnam's coastal waters from highspatial resolution satellite and field survey data

A process for mapping the benthic cover of coral reefs at selected sites in Vietnam’s coastal waters from high-spatial resolution, multi-spectral satellite data was developed and tested. GeoEye-1, IKONOS, QuickBird satellite images in Dam Mon, My Giang and Nha Trang respectively were used and field data was collected in 2009. Images were corrected for radiometric, atmospheric, and depth effects. Band combination of three atmospheric corrected bands, second band of PCA, and depth-invariant index of spectral band 1, 2 were used for supervised classification at fine level of description. Calibration and validation field data were acquired through georeferenced photo-transect method and photos were analysed for benthic cover. The overall accuracy of Maximum Likelihood, Minimum Distance to Means and Mahalanobis Distance classifiers were compared for each study site derived from the individual confusion matrix. Overall accuracy of the classified image using Maximum Likelihood classifier were higher than other classifiers in Dam Mon and My Giang, but Mahalanobis Distance classifier was highest in Nha Trang. Overall accuracy of classified image increased when reduced number of benthic classes. Variable environmental conditions, including water clarity and depth, along with reef structures were identified as main factors causing mis-classification and reducing the overall accuracy

Catalysing transdisciplinary synthesis in ecosystem science and management

[Extract] Ten years have elapsed since the publication of the Millennium Ecosystem Assessment (MEA, 2005), which highlighted both the plight of our planet's ecosystems and the attendant threat to the services they provide to humanity. The MEA was a call to action for citizens, governments, industry and scientists to understand why ecosystems are degrading, and to find solutions that are practical and timely in halting the degradation and restoring ecosystem function and service provision. Ecosystem science is at the forefront of developing the required understanding and finding the solutions. We need to find new ways of analysing and synthesising available information to inform policy and action on the ground

Reassessing spatial and temporal dynamics of kangaroo populations

Long-running datasets from aerial surveys of kangaroos (Macropus giganteus, Macropus [uliginosus, Macropus robustus and Macropus rufus) across Queensland, New South Wales and South Australia have been analysed, seeking better predictors of rates of increase which would allow aerial surveys to be undertaken less frequently than annually. Early models of changes in kangaroo numbers in response to rainfall had shown great promise, but much variability. We used normalised difference vegetation index (NDVI) instead, reasoning that changes in pasture condition would provide a better predictor than rainfall. However, except at a fine scale, NDVI proved no better; although two linked periods of rainfall proved useful predictors of rates of increase, this was only in some areas for some species. The good correlations reported in earlier studies were a consequence of data dominated by large droughtinduced adult mortality, whereas over a longer time frame and where changes between years are less dramatic, juvenile survival has the strongest influence on dynamics. Further, harvesting, density dependence and competition with domestic stock are additional and important influences and it is now clear that kangaroo movement has a greater influence on population dynamics than had been assumed. Accordingly, previous conclusions about kangaroo populations as simple systems driven by rainfall need to be reassessed. Examination of this large dataset has permitted descriptions of shifts in distribution of three species across eastern Australia, changes in dispersion in response to rainfall, and an evaluation of using harvest statistics as an index of density and harvest rate. These results have been combined into a risk assessment and decision theory framework to identify optimal monitoring strategies

Geometric correction and accuracy assessment of Landsat-7 ETM+ and Landsat-5 TM imagery used for vegetation cover monitoring in Queensland, Australia from 1988 to 2007

A range of programs exist globally that use satellite imagery to derive estimates of vegetation-cover for developing vegetation-management policy, monitoring policy compliance and making natural-resource assessments. Consequently, the satellite imagery must have a high degree of geometric accuracy. It is common for the accuracy assessment to be performed using the root mean square error (RMSE) only. However the RMSE is a non-spatial measure and more rigorous accuracy assessment methods are required. Currently there is a lack of spatially explicit accuracy assessment methods reported in the literature that have been demonstrated to work within operational monitoring programs. This paper reports on the method used by the Statewide Landcover and Trees Study (SLATS) to georegister and assess the registration accuracy of Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) imagery in Queensland, Australia. A geometric baseline with high accuracy (a statewide mean RMSE of 4.53 m) was derived by registering Landsat-7 ETM+ panchromatic imagery acquired in 2002 to a database of over 1600 control points, collected on the ground using a differential global positioning system. Landsat-5 TM and Landsat-7 ETM+ imagery for 12 selected years from 1988 to 2007 was registered to the baseline in an automated procedure that used linear geometric correction models. The reliability of the geometric correction for each image was determined using the RMSE, calculated using independent check points, as an indicator of model fit; by analysing the spatial trends in the model residuals; and through visual assessment of the corrected imagery. The mean RMSE of the statewide coverage of images for all years was less than 12.5 m (0.5 pixels). Less than 1 percent of images had non-linear spatial trends in the model residuals and some image misregistration after applying a linear correction-model; in those cases a quadratic model was deemed necessary for correction. Further research in the development of automated spatially explicit accuracy assessment methods is required

Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100m2) scales. However, landscape scale (>100km2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142km2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management