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Abstract:The reflected radiance in topographically complex areas is severely affected by variations in topography; thus, topographic correction is considered a necessary pre-processing step when retrieving biophysical variables from these images. We assessed the performance of five topographic corrections: (i) C correction (C), (ii) Minnaert, (iii) Sun Canopy Sensor (SCS), (iv) SCS + C and (v) the Processing Scheme for Standardised Surface Reflectance (PSSSR) on the Landsat-5 Thematic Mapper (TM) reflectance in the context of prediction of Foliage Projective Cover (FPC) in hilly landscapes in north-eastern Australia. The performance of topographic corrections on the TM reflectance was assessed by (i) visual comparison and (ii) statistically comparing TM predicted FPC with ground measured FPC and LiDAR (Light Detection and Ranging)-derived FPC estimates. In the majority of cases, the PSSSR method performed best in terms of eliminating topographic effects, providing the best relationship and lowest residual error when comparing ground measured FPC and LiDAR FPC with TM predicted FPC. The Minnaert, C and SCS + C showed the poorest performance. Finally, the use of TM surface reflectance, which OPEN ACCESS Remote Sens. 2013, 5 6768 includes atmospheric correction and broad Bidirectional Reflectance Distribution Function (BRDF) effects, seemed to account for most topographic variation when predicting biophysical variables, such as FPC.

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