Biomass burning influenced particles characteristics in Northern Territory Australia based on airborne measurements

Airborne measurements of particle number concentrations from biomass burning were conducted in the Northern Territory, Australia, during June and September campaigns in 2003, which is the early and the late dry season in that region. The airborne measurements were performed along horizontal flight tracks, at several heights in order to gain insight into the particle concentration levels and their variation with height within the lower boundary layer (LBL), upper boundary layer (UBL), and also in the free troposphere (FT). The measurements found that the concentration of particles during the early dry season was lower than that for the late dry season. For the June campaign, the concentration of particles in LBL, UBL, and FT were (685 ± 245) particles/cm3, (365 ± 183) particles/cm3, and (495 ± 45) particle/cm3 respectively. For the September campaign, the concentration of particles were found to be (1233 ± 274) particles/cm3 in the LBL, (651 ± 68) particles/cm3 in the UBL, and (568 ± 70) particles/cm3 in the FT. The particle size distribution measurements indicate that during the late dry season there was no change in the particle size distribution below (LBL) and above the boundary layer (UBL). This indicates that there was possibly some penetration of biomass burning particles into the upper boundary layer. In the free troposphere the particle concentration and size measured during both campaigns were approximately the same.

Oxidative potential of logwood and pellet burning particles assessed by a novel profluorescent nitroxide probe

This study reports the potential toxicological impact of particles produced during biomass combustion by an automatic pellet boiler and a traditional logwood stove under various combustion conditions using a novel profluorescent nitroxide probe BPEAnit. This probe is weakly fluorescent, but yields strong fluorescence emission upon radical trapping or redox activity. Samples were collected by bubbling aerosol through an impinger containing BPEAnit solution, followed by fluorescence measurement. The fluorescence of BPEAnit was measured for particles produced during various combustion phases, at the beginning of burning (cold start), stable combustion after refilling with the fuel (warm start) and poor burning conditions. For particles produced by the logwood stove under cold-start conditions significantly higher amounts of reactive species per unit of particulate mass were observed compared to emissions produced during a warm start. In addition, sampling of logwood burning emissions after passing through a thermodenuder at 250oC resulted in an 80-100% reduction of the fluorescence signal of BPEAnit probe, indicating that the majority of reactive species were semivolatile. Moreover, the amount of reactive species showed a strong correlation with the amount of particulate organic material. This indicates the importance of semivolatile organics in particle-related toxicity. Particle emissions from the pellet boiler, although of similar mass concentration, were not observed to lead to an increase in fluorescence signal during any of the combustion phases.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

The role of vegetation in enhancing radon concentration and ion production in the atmosphere

The role of ions in the production of atmospheric particles has gained wide interest due to their profound impact on climate. Away from anthropogenic sources, molecules are ionized by alpha radiation from radon exhaled from the ground and cosmic gamma radiation from space. These molecular ions quickly form into ‘cluster ions’, typically smaller than about 1.5 nm. Using our measurements and the published literature, we present evidence to show that cluster ion concentrations in forest areas are consistently higher than outside. Since alpha radiation cannot penetrate more than a few centimetres of soil, radon present deep in the ground cannot directly contribute to the measured cluster ion concentrations. We propose an additional mechanism whereby radon, which is water soluble, is brought up by trees and plants through the uptake of groundwater and released into the atmosphere by transpiration. We estimate that, in a forest comprising eucalyptus trees spaced 4m apart, approximately 28% of the radon in the air may be released by transpiration. Considering that 24% of the earth’s land area is still covered in forests; these findings have potentially important implications for atmospheric aerosol formation and climate.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Effect of geometry on the melting rates of iron rods burning in high pressure oxygen

The effect of sample geometry on the melting rates of burning iron rods was assessed. Promoted-ignition tests were conducted with rods having cylindrical, rectangular, and triangular cross-sectional shapes over a range of cross-sectional areas. The regression rate of the melting interface (RRMI) was assessed using a statistical approach which enabled the quantification of confidence levels for the observed differences in RRMI. Statistically significant differences in RRMI were observed for rods with the same cross-sectional area but different cross-sectional shape. The magnitude of the proportional difference in RRMI increased with the cross-sectional area. Triangular rods had the highest RRMI, followed by rectangular rods, and then cylindrical rods. The dependence of RRMI on rod shape is shown to relate to the action of molten metal at corners. The corners of the rectangular and triangular rods melted faster than the faces due to their locally higher surface area to volume ratios. This phenomenon altered the attachment geometry between liquid and solid phases, increasing the surface area available for heat transfer, causing faster melting. Findings relating to the application of standard flammability test results in industrial situations are also presented.

A burning issue : fashion and sun protection

The 'dick' tog, a briefs-style male swimsuit as it is colloquially referred to, is linked to Australia's national identity with overtly masculine bronzed 'Aussie' bodies clothed in this iconic apparel. However, the reality is, our hunger for worshiping the sun and the addiction to a beach culture is tempered by the pragmatic need to cover up and wear neck-to-knee, or more apt, head-to-toe sun protective clothing. Australia, in particular the state of Queensland, has one of the highest rates of skin cancer in the world; nevertheless, even after wide-ranging public programs for sun safety awareness many people still continue to wear designs that provide minimal sun protection. This paper will examine issues surrounding fashion and sun safe clothing. It will be proposed that in order to have effective community adoption of sun safe practices it is critical to understand the important role that fashion plays in determining sun protective behaviour.

Importance of soil ecology in vegetation rehabilitation on a North Stradbroke Island sand mine

Sibelco Australia Limited (SAL), a mineral sand mining operation on North Stradbroke Island, undertakes progressive rehabilitation of mined areas. Initial investigations have found that some areas at SAL’s Yarraman Mine have failed to redevelop towards approved criteria. This study, undertaken in 2010, examined ground cover rehabilitation of different aged plots at the Yarraman Mine to determine if there was a relationship between key soil and vegetation attributes. Vegetation and soil data were collected from five plots rehabilitated in 2003, 2006, 2008, 2009 and 2010, and one unmined plot. Cluster (PATN) analysis revealed that vegetation species composition, species richness and ground cover differed between plots. Principal component analysis (PCA) extracted ten soil attributes that were then correlated with vegetation data. The attributes extracted by PCA, in order of most common variance, were: water content, pH, terrolas depth, elevation, slope angle, leaf litter depth, total organic carbon, and counts of macrofauna, fungi and bacteria. All extracted attributes differed between plots, and all except bacteria correlated with at least one vegetation attribute. Water content and pH correlated most strongly with vegetation cover suggesting an increase in soil moisture and a reduction in pH are required in order to improve vegetation rehabilitation at Yarraman Mine. Further study is recommended to confirm these results using controlled experiments and to test potential solutions, such as organic amendments.

The influence of vegetation and soil type on the speciation of iron in soil water

Soluble organic matter derived from exotic Pinus species has been shown to form stronger complexes with iron (Fe) than that derived from most native Australian species. It has also been proposed that the establishment of exotic Pinus plantations in coastal southeast Queensland may have enhanced the solubility of Fe in soils by increasing the amount of organically complexed Fe, but this remains inconclusive. In this study we test whether the concentration and speciation of Fe in soil water from Pinus plantations differs significantly from soil water from native vegetation areas. Both Fe redox speciation and the interaction between Fe and dissolved organic matter (DOM) were considered; Fe - DOM interaction was assessed using the Stockholm Humic Model. Iron concentrations (mainly Fe 2+) were greatest in the soil waters with the greatest DOM content collected from sandy podosols (Podzols), where they are largely controlled by redox potential. Iron concentrations were small in soil waters from clay and iron oxide-rich soils, in spite of similar redox potentials. This condition is related to stronger sorption on to the reactive clay and iron oxide mineral surfaces in these soils, which reduces the amount of DOM available for electron shuttling and microbial metabolism, restricting reductive dissolution of Fe. Vegetation type had no significant influence on the concentration and speciation of iron in soil waters, although DOM from Pinus sites had greater acidic functional group site densities than DOM from native vegetation sites. This is because Fe is mainly in the ferrous form, even in samples from the relatively well-drained podosols. However, modelling suggests that Pinus DOM can significantly increase the amount of truly dissolved ferric iron remaining in solution in oxic conditions. Therefore, the input of ferrous iron together with Pinus DOM to surface waters may reduce precipitation of hydrous ferric oxides (ferrihydrite) and increase the flux of dissolved Fe out of the catchment. Such inputs of iron are most probably derived from podosols planted with Pinus.

Vertical vegetation design decisions and their impact on energy consumption in subtropical cities

Vertical vegetation is vegetation growing on, or adjacent to, the unused sunlit exterior surfaces of buildings in cities. Vertical vegetation can improve the energy efficiency of the building on which it is installed mainly by insulating, shading and transpiring moisture from foliage and substrate. Several design parameters may affect the extent of the vertical vegetation's improvement of energy performance. Examples are choice of vegetation, growing medium geometry, north/south aspect and others. The purpose of this study is to quantitatively map out the contribution of several parameters to energy savings in a subtropical setting. The method is thermal simulation based on EnergyPlus configured to reflect the special characteristics of vertical vegetation. Thermal simulation results show that yearly cooling energy savings can reach 25% with realistic design choices in subtropical environments. Heating energy savings are negligible. The most important parameter is the aspect of walls covered by vegetation. Vertical vegetation covering walls facing north (south for the northern hemisphere) will result in the highest energy savings. In making plant selections, the most significant parameter is Leaf Area Index (LAI). Plants with larger LAI, preferably LAI>4, contribute to greater savings whereas vertical vegetation with LAI<2 can actually consume energy. The choice of growing media and its thickness influence both heating and cooling energy consumption. Change of growing medium thickness from 6cm to 8cm causes dramatic increase in energy savings from 2% to 18%. For cooling, it is best to use a growing material with high water retention, due to the importance of evapotranspiration for cooling. Similarly, for increased savings in cooling energy, sufficient irrigation is required. Insufficient irrigation results in the vertical vegetation requiring more energy to cool the building. To conclude, the choice of design parameters for vertical vegetation is crucial in making sure that it contributes to energy savings rather than energy consumption. Optimal design decisions can create a dramatic sustainability enhancement for the built environment in subtropical climates.