Sumith Pathirana

Modeling methane emission from wetlands in North-Eastern New South Wales, Australia using Landsat ETM+.

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.

Monitoring coastal wetland communities in north-eastern NSW using ASTER and Landsat satellite data

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. The aim of the research is to map and monitor the coastal wetland communities in north eastern NSW using satellite data. Advanced Spaceborne Thermal Emission and Reflectance Radiometer, Landsat ETM+ and Landsat TM satellite imagery of November 2003, June 2001 and September 1989 respectively were used to identify and monitor the wetland communities. Supervised classification was performed using the maximum likelihood standard algorithm. Normalized Difference Vegetation Index was produced and the health of the wetland vegetation was evaluated. The wetland maps present significant changes in the coastal wetland communities in the months of September 1989, June 2001 and November 2003. This information could be used by coastal wetland managers in order to enhance the management of these ecosystems.

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass (AGB) by fusion of LiDAR and Landsat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carried out separately for LiDAR and Landsat derived variables individually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation (3% for eucalypt forest and 2% combined sites) of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, and fusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical rainforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.

Evaluation of Different Topographic Corrections for Landsat TM Data by Prediction of Foliage Projective Cover (FPC) in Topographically Complex Landscapes

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 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.

Distribution of Errors in a Classified Map of Satellite Data

Abstract The output from any spatial data processing method may contain some uncertainty. With the increasing use of satellite data products as a source of data for Geographical Information Systems (GIS), there have been some major concerns about the accuracy of the satellite‐based information. Due to the nature of spatial data and remotely sensed data acquisition technology, and conventional classification, any single classified image can contain a number of mis‐classified pixels. Conventional accuracy evaluation procedures can report only the number of pixels that are mis‐classified based on some sampling observation. This study investigates the spatial distribution and the amount of these pixels associated with each cover type in a product of satellite data. The study uses Thematic Mapper (TM) and SPOT multispectral data sets obtained for a study area selected in North East New South Wales, Australia. The Fuzzy c‐Means algorithm is used to identify the classified pixels that contained some uncertainty....

Stresses over surface water sources in a human dominated environment: A case study in Hamilton canal, Sri Lanka

Purpose The Hamilton canal in the western province of Sri Lanka is a man-made canal situated in an area with immense anthropogenic pressures. The purpose of this study is to identify the quality variations of the water in Hamilton canal and human perception about the present status of the water of the canal. Design/methodology/approach Sampling has been carried out in seven locations in the canal during dry and wet periods for water quality analysis. In situ field-testing and laboratory analysis have been conducted for physicochemical, heavy metal, oil and grease analysis of water. Only Pb, Cd, oil and grease were tested in the canal sediments. The samples were analyzed as per the standard methods of the American Public Health Association (APHA) Manual: 20th edition. A semi-structured questionnaire survey has been carried out to assess the human perception on the water of the canal. Findings The results revealed that average EC, Turbidity, Total Hardness, TDS, F−, Fe2+, Cl−, SO42− and PO43− of the canal water remained above the threshold limits of inland water standards. Concentrations of Pb and Cd were also above the standards in some locations. Oil and grease were in a very high level in water and sediments. Originality/value The water of the canal has been affected by nutrient, heavy metal and oil and grease pollution at present. Discharge of domestic, industrial, municipal wastes and sewage are the prominent reasons which have encouraged the deterioration of the quality of water in the canal.