Negombo Lagoon is located in the vicinity of a highly industrial and urbanized area. Thus, analysis of heavy metals, oil and grease in the lagoon is of importance at present. The present study has been carried out to assess the contamination levels of heavy metals (Cd and Pb), oil and grease of water and sediments in the Negombo lagoon. Sampling has been carried out in 8 locations. Atomic Absorption Spectrophotometer and standard method of Microwave Digestion Detection by Atomic Absorption Spectrophotometry were used for the heavy metal and oil analysis. Spatial interpolation technique in Arc GIS was used to analyse the data. The Cadmium ion (Cd++) in Negombo lagoon varied between 2.1 and 4.9 ppb and Lead (Pb++) between 17.6 and 48.6 ppb.The concentration of Cd++ and Pb++ of lagoon water in most locations were close to the upper limit of the inland water standards. High Cd and Pb concentrations were observed in Eastern half of the lagoon. The concentration of oil and grease of the water was between 200 and 5600 µg/l which was extremely high when compared to the minimum quality of the inland water standards. Cd levels of sediments varied between 1002 and 1280 µg/kg while the Pb levels varied between 12,300 and 18,300 µg/kg. Oil and grease concentrations of sediments varied between 30,000 and 3,720,000 µg/kg. Concentration of Cd in sediment when compared to that of water was 477 times higher in its lower limit and 2526.2 times in its upper limit. Likewise, Pb in sediment is was 699 times higher than lower limit and 376 times higher in upper limit. Oil and grease in sediments were 15times and 66.4 times higher than lower limit and upper limit of the water respectively. The probable reasons for the contamination is lead to be the manmade activities linked to unplanned development with less attention on environment concern.

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.

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 ACCESS Remote Sensing 2010, 2 1379

Aim of study: The study aimed to characterise variation in structural attributes of vegetation in relation to variations in topographic position using LIDAR data over landscapes.Area of study: The study was conducted in open canopy eucalypt-dominated forest (Richmond Range National Park-RRNP) and closed canopy subtropical rainforest (Border Ranges National Park-BRNP) in north-eastern New South Wales, Australia.Material and Methods: one metre resolution digital canopy height model (CHM) was extracted from the LIDAR data and used to estimate maximum overstorey height and crown area. LIDAR fractional cover representing the photosynthetic and non-photosynthetic component of canopy was calculated using LIDAR points aggregated into 50 m spatial bins. Potential solar insolation, Topographic Wetness Index (TWI), slope and the elevation were processed using LIDAR derived digital elevation models.Main results: No relationship was found between maximum overstorey height and insolation gradient in the BRNP. Maximum overstorey height decreased with increasing insolation in the RRNP (R2 0.45). Maximum overstorey height increased with increasing TWI in the RRNP. Average crown area decreased with increasing insolation in both study areas. LIDAR fractional cover decreased with increasing insolation (R2 0.54), and increased with increasing TWI (R2 0.57) in the RRNP.Research highlights: The characterization of structural parameters of vegetation in relation to the variation of the topography was possible in eucalyptus dominated open canopy forest. No reportable difference in variation of structural elements of vegetation was detected with topographic variation of subtropical rainforest.

The study was conducted in the Negombo estuarine lagoon locates in the Gampaha District of Sri Lanka. In this research, an investigation was carried out to identify and assess the distribution pattern of mangrove diversity across salinity gradient in the lagoon. A field survey was performed to collect primary data and vegetation sampling was carried out in two transects along the periphery of the lagoon. Located sample size is 5 m × 10 m. Fifteen samples were selected maintaining distribution of species heterogeneity. Only mangrove species were enumerated. In situ field-testing of salinity was carried out at monthly intervals during October 2012 to September 2013 for 15 samples. Shannon-Wiener diversity index was calculated to compare about the diversity of mangrove species. The Inverse Distance Weighted (IDW) interpolation technique in ArcGIS was performed to prepare spatial distribution maps. There are 18 mangrove species identified belonging to 14 genera and 12 families. Among them 14 species are “True” and 4 species are “Mangrove Associates”. Rhizophora mucronata, Rhizophora apiculata, Avicennia officinalis, Excoecaria agallocha and Acrostichum aureum are the most common species and Aegiceras corniculata, Aegiceras corniculata, Bruguiera sexangula and Xylocarpus granatom are the least common true mangrove species types found in the Negombo lagoon. Spatially the highest mangrove species diversity could be identified in the southern quarter and a small patch around the outlet of the lagoon. Relatively low species diversity is identified at the middle periphery of the lagoon. Spatial differences of the floristic composition and the diversity reflect the salinity tolerance ability of the different mangrove species.

Mangroves are a unique vegetation community that can adapt to harsh climatic conditions, including in areas of high temperature and high salinity levels. It is an important coastal wetland community in many countries that provide a multitude of ecosystem services. Qatar has a small mangrove community covering about 21 km2 and it is probably the only natural vegetation type found in Qatar. They are important because of their aesthetic value, as a buffer zone protecting the lowland coastal area as well as its role in storing carbons. Therefore, it is important to understand mangroves response to global climatic variability. This is particularly important as Avicennia, which is the only mangrove species found in Qatar has limited elevation range and less able to resist extreme physical and environmental changes. Species distribution models combined with GIS and Remote Sensing are some of the tools that can be used to project the potential change of mangrove vegetation communities. These spatial information technologies can be used to extract and map current distribution of mangrove vegetation while species distribution model can be used to predict the potential geographical distribution of suitable habitats and species occurrence. In the current research, MaxEnt, GIS and high resolution World View 3 satellite data were used to classify, map and predict mangrove vegetation. The preliminary findings show the potential habitats in the east and the northwest part of Qatar. This research is important as there are no current studies examining the spatial distribution of mangroves or assessing the potential impact of climate variability on mangrove communities in Qatar.

Groundwater is an essential and finite resource in the world. Numerous knowledge gaps remain in the understanding of groundwater resources in Sri Lanka, mainly due to the lack of accurate data. The purpose of this study is to assess the water quality in groundwater and its spatial distribution in Negombo-Muthurajawela area in Sri Lanka. The data collection was conducted at the beginning and at the end of south west monsoon (May and September, respectively) in 2013. A pilot survey was carried out using 116 dug wells. A total of thirty-one dug wells were selected for physiochemical analysis. In situ field testing of electrical conductivity (EC), salinity, and pH were carried out and laboratory tests were performed for HCO3−, Na+, K+, Ca2+, Cl−, SO42−, Mg2−, PO43−, NO3−, and total hardness (TH). Principal component analysis (PCA) was performed to assess water quality and interpolation technique in ArcGIS was performed to analyze and prepare spatial distribution maps of water. Sri Lankan standards for drinking water were used to determine the threshold levels of physiochemical parameters. The results of the PCA reveal that thirty-one observation wells can be classified under three main components: the first based on the impact on EC, HCO3−, Na−, K+, Cl−, Mg2−, and TH; the second component considering the pH, HCO3, Ca2+, SO42−, PO43−, and TH; and the third component based on NO3−. These three components evidence the role of salt water intrusion, the influence of Muthurajawela wetlands, and the anthropogenic discharges on groundwater quality in the Negombo-Muthurajawela area.

LiDAR remote sensing can be considered a key instrument for studies related to quantifyingthe vegetation structure. We utilised LiDAR metrics to estimate plot-scale structuralparameters of subtropical rainforest and eucalyptus dominated open forest in topographicallydissected landscape, in North-eastern Australia. This study is considered an extremeapplication of LiDAR technology for structurally complex subtropical forests in complexterrain. Thirty-one LiDAR metrics of vegetation functional parameters were examined.Multiple linear regression models were able to explain 62% of the variability associated withbasal area, 66% for mean dbh, 61% for dominant height and 60% for foliage projective coverin subtropical rainforest. In contrast, mean height (adjusted R2 = 0.90) and dominant height(adjusted R2 = 0.81) were predicted with highest accuracy in the eucalyptus dominated opencanopy forest. Nevertheless, the magnitude of error for predicting structural parameters ofvegetation was much higher in subtropical rainforest than those documented in the literature.Our findings reinforced that obtaining accurate LiDAR estimates of vegetation structure is afunction of the complexity of horizontal and vertical structural diversity of vegetation.

EDIRIWEERA S, PATHIRANA S, DANAHER T 8c NICHOLS D. 2014. LiDAR remote sensing of structural properties of subtropical rainforest and eucalypt forest in complex terrain in north-eastern Australia. LiDAR remote sensing can be considered a key instrument for studies related to quantifying the vegetation structure. We utilised LiDAR metrics to estimate plot-scale structural parameters of subtropical rainforest and eucalypt-dominated open forest in topographically dissected landscape in north-eastern Australia. This study is considered an extreme application of LiDAR technology for structurally complex subtropical forests in complex terrain. A total of 31 LiDAR metrics of vegetation functional parameters were examined. Multiple linear regression models were able to explain 62% of the variability associated with basal area, 66% for mean diameter at breast height, 61% for dominant height and 60% for foliage projective cover in subtropical rainforest. In contrast, mean height (adjusted r2 = 0.90) and dominant height (adjusted r2 = 0.81) were predicted with highest accuracy in the eucalypt-dominated open canopy forest. Nevertheless, the magnitude of error for predicting structural parameters of vegetation was much higher in subtropical rainforest than those documented in the literature. Our findings reinforce that obtaining accurate LiDAR estimates of vegetation structure is a function of the complexity of horizontal and vertical structural diversity of vegetation.

Water quality in natural lagoons that are located within close proximity to human settlements is generally at contamination risk due to increasing anthropogenic activities. The Negombo lagoon situated in the Gampaha District in Sri Lanka is a lagoonal estuary. It receives surface water runoff mainly from Dandugamoya, Ja-ela, Hamilton and Dutch canals. During the recent past, it has been noted by several researches that there is increasing evidence in anthropogenic activities in Negombo lagoon and surrounding areas. The present study was carried out to assess the contamination levels of heavy metals of water in the Negombo lagoon and interconnected water sources. Sampling was carried out in 19 locations; 6 in the Negombo lagoon and 13 from the interconnected sources (5 samples from Hamilton canal, 2 samples each from Dutch canal, Dandugamoya and Ja-Ela and one sample each from Kelani estuary and Ocean-Negombo). The data collection was conducted during relatively wet (May) and relatively dry (September) months in 2013. Water samples were analysed in the laboratory as per the standards methods of American Public Health Association (APHA manual) by using the Atomic Absorption Spectrophotometer. The tests were carried out to detect heavy metals: cadmium (Cd), chromium (Cr), copper (Cu), Lead (Pb), manganese (Mn), and zinc (Zn) in water. Data analysis was accomplished using ArcGIS (version 9.3) software package along with Microsoft Excel. Standards for inland water and drinking water of Sri Lanka were used to determine the threshold levels of heavy metals. The results show that concentrations of Cr, Cu, Mn and Zn of all water bodies were below the threshold level of human consumption and quality standards for inland waters in Sri Lanka. The Cd and Pb levels of water in Negombo lagoon and Hamilton canal were comparatively high. Furthermore the Cd and Pb levels of Dandugamoya, Ja-ela and Dutch canals were below the maximum permissible levels in both relatively wet and relatively dry periods. Concentration of Cd and Pb in Negombo lagoon and Hamilton canal showed seasonal oscillation with the rainfall. Both the parameters demonstrate a negative relationship with precipitation. Comparatively a high Cd and Pb concentrations was observed during the dry period. In conclusion, the Cd and Pb levels were high in the lagoon and Hamilton canal while the concentration of Cd and Pb were below the threshold level in Dandugamoya, Ja-ela and Dutch canal waters. The findings were important as the study indicates the spatial and seasonal variations of presence of heavy metals in the lagoonal water and which probably links to anthropogenic activities.

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.

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.

Quantitative retrieval of land surface biological parameters (e.g. foliage projective cover [FPC] and Leaf Area Index) is crucial for forest management, ecosystem modelling, and global change monitoring applications. Currently, remote sensing is a widely adopted method for rapid estimation of surface biological parameters in a landscape scale. Topographic correction is a necessary pre-processing step in the remote sensing application for topographically complex terrain. Selection of a suitable topographic correction method on remotely sensed spectral information is still an unresolved problem. The purpose of this study is to assess the impact of topographic corrections on the prediction of FPC in hilly terrain using an established regression model. Five established topographic corrections [C, Minnaert, SCS, SCS+C and processing scheme for standardised surface reflectance (PSSSR)] were evaluated on Landsat TM5 acquired under low and high sun angles in closed canopied subtropical rainforest and eucalyptus dominated open canopied forest, north-eastern Australia. The effectiveness of methods at normalizing topographic influence, preserving biophysical spectral information, and internal data variability were assessed by statistical analysis and by comparing field collected FPC data. The results of statistical analyses show that SCS+C and PSSSR perform significantly better than other corrections, which were on less overcorrected areas of faintly illuminated slopes. However, the best relationship between FPC and Landsat spectral responses was obtained with the PSSSR by producing the least residual error. The SCS correction method was poor for correction of topographic effect in predicting FPC in topographically complex terrain.

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.

Climate change will have a profound impact on coastal ecosystems, particularly, wetland cover types. It is therefore important that such changes are predicted so that appropriate adaptations can be suggested. This study investigated the changes of spatial distribution of four coastal wetland plant species in response to potential climate change in northeastern NSW, Australia. The study used BIOCLIM, a bioclimatic analysis and prediction system modeling package to generate the climate profiles of the species. Multispectral Landsat TM images were used to delineate wetland classes and to extract the wetland species. Bioclim climate parameters generated using Digital Elevation Model (DEM) and species climate profile were then used in BIOMAP to generate the predicted locations of the wetland species in response to mean annual temperature increase. Predicted locations were imported into GIS for mapping the potential spatial distribution of the wetland species. The study found that an increasing mean annual temperature would likely redistribute some of the wetland species such as Avicennia marina and Melaleuca quinquenervia southwards, in northeastern NSW. This information could be used to enhance wetland conservation.