The coastal wetlands of north-eastern New South Wales (NSW) Australia are increasingly being affected by anthropogenic factors such as urbanisation, residential development and agricultural development. However, little is known about their vulnerability to sea level rise as a result of climate change. The aim of this research is to predict the potential impact of sea level rise (SLR) on the coastal wetland communities. Sea Level Affecting Marshes Model (SLAMM) was used to predict the potential impacts of sea level rise. Geographic Information System (GIS) was used for mapping and analysis. It was found that a meter rise in sea level could decrease coastal wetlands such as Inland fresh marshes from about 225.67 km 2 in February 2009 to about 168.04 km 2 by the end of the century in north-eastern NSW, Australia. The outcomes from this research can contribute to enhancing wetland conservation and management in NSW.

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Natural wetlands constitute a major source of methane emission to the atmosphere, accounting for approximately 32 ± 9.4% of the total methane emission. Estimation of methane emission from wetlands at both local and national scale using process-based models would improve our understanding of their contribution to global methane emission. The aim of the study is to estimate the amount of methane emission from the coastal wetlands in north-eastern New South Wales (NSW), Australia, using Landsat ETM+ and to estimate emission with a temperature increase. Supervised wetland classification was performed using the Maximum Likelihood Standard algorithm. The temperature dependent factor was obtained through land surface temperature (LST) estimation algorithms. Measurements of methane fluxes from the wetlands were performed using static chamber techniques and gas chromatography. A process-based methane emission model, which included productivity factor, wetland area, methane flux, precipitation and evaporation ratio, was used to estimate the amount of methane emission from the wetlands. Geographic information system (GIS) provided the framework for analysis. The variability of methane emission from the wetlands was high, with forested wetlands found to produce the highest amount of methane, i.e., 0.0016 ± 0.00009 teragrams (Tg) in the month of June, 2001. This would increase to 0.0022 ± 0.0001 Tg in the month of June with a 1 °C rise in mean annual temperature by the year 2030 in north-eastern NSW, Australia. OPEN ACCESSRemote Sensing 2010, 2 1379

The coastal wetland communities of north-eastern New South Wales (NSW) Australia exist in a subtropical climate with high biodiversity and are affected by anthropogenic and natural stressors such as urbanization and climate change.

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Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.