An approach for adaptive and integrated environmental planning to deal with uncertainty in a Great Barrier Reef Catchment

This paper presents the Mossman Mill District Practices Framework. It was developed in the Wet Tropics region within the Great Barrier Reef in north-eastern Australia to describe the environmental benefits of agricultural management practices for the sugar cane industry. The framework translates complex, unclear and overlapping environmental plans, policy and legal arrangements into a simple framework of management practices that landholders can use to improve their management actions. Practices range from those that are old or outdated through to aspirational practices that have the potential to achieve desired resource condition targets. The framework has been applied by stakeholders at multiple scales to better coordinate and integrate a range of policy arrangements to improve natural resource management. It has been used to structure monitoring and evaluation in order to underpin a more adaptive approach to planning at mill district and property scale. Potentially, the framework and approach can be applied across fields of planning where adaptive management is needed. It has the potential to overcome many of the criticisms of property-scale and regional Natural Resource Management.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Analysis of the needs of the East Coast cluster regional natural resource management bodies in relation to planning for climate change adaptation

The Climate Change Adaptation for Natural Resource Management (NRM) in East Coast Australia Project aims to foster and support an effective “community of practice” for climate change adaptation within the East Coast Cluster NRM regions that will increase the capacity for adaptation to climate change through enhancements in knowledge and skills and through the establishment of long‐term collaborations. It is being delivered by six consortium research partners: * The University of Queensland (project lead) * Griffith University * University of the Sunshine Coast * CSIRO * New South Wales Office of Environment and Heritage * Queensland Department of Science, IT, Innovation and the Arts (Queensland Herbarium). The project relates to the East Coast Cluster, comprising the six coastal NRM regions and regional bodies between Rockhampton and Sydney: * Fitzroy Basin Association (FBA) * Burnett‐Mary Regional Group (BMRG) * SEQ Catchments (SEQC) * Northern Rivers Catchment Management Authority (CMA) (NRCMA) * Hunter‐Central Rivers CMA (HCRCMA) * Hawkesbury Nepean CMA (HNCMA). The aims of this report are to summarise the needs of the regional bodies in relation to NRM planning for climate change adaptation, and provide a basis for developing the detailed work plan for the research consortium. Two primary methods were used to identify the needs of the regional bodies: (1) document analysis of the existing NRM/ Catchment Action Plans (CAPs) and applications by the regional bodies for funding under Stream 1 of the Regional NRM Planning for Climate Change Fund, and; (2) a needs analysis workshop, held in May 2013 involving representatives from the research consortium partners and the regional bodies. The East Coast Cluster includes five of the ten largest significant urban areas in Australia, world heritage listed natural environments, significant agriculture, mining and extensive grazing. The three NSW CMAs have recently completed strategic level CAPs, with implementation plans to be finalised in 2014/2015. SEQC and FBA are beginning a review of their existing NRM Plans, to be completed in 2014 and 2015 respectively; while BMRG is aiming to produce a NRM and Climate Variability Action Strategy. The regional bodies will receive funding from the Australian Government through the Regional NRM Planning for Climate Change Fund (NRM Fund) to improve regional planning for climate change and help guide the location of carbon and biodiversity activities, including wildlife corridors. The bulk of the funding will be available for activities in 2013/2014, with smaller amounts available in subsequent years. Most regional bodies aim to have a large proportion of the planning work complete by the end of 2014. In addition, NSW CMAs are undergoing major structural change and will be incorporated into semi‐autonomous statutory Local Land Services bodies from 2014. Boundaries will align with local government boundaries and there will be significant change in staff and structures. The regional bodies in the cluster have a varying degree of climate knowledge. All plans recognise climate change as a key driver of change, but there are few specific actions or targets addressing climate change. Regional bodies also have varying capacity to analyse large volumes of spatial or modelling data. Due to the complex nature of natural resource management, all regional bodies work with key stakeholders (e.g. local government, industry groups, and community groups) to deliver NRM outcomes. Regional bodies therefore require project outputs that can be used directly in stakeholder engagement activities, and are likely to require some form of capacity building associated with each of the outputs to maximise uptake. Some of the immediate needs of the regional bodies are a summary of information or tools that are able to be used immediately; and a summary of the key outputs and milestone dates for the project, to facilitate alignment of planning activities with research outputs. A project framework is useful to show the linkages between research elements and the relevance of the research to the adaptive management cycle for NRM planning in which the regional bodies are engaged. A draft framework is proposed to stimulate and promote discussion on research elements and linkages; this will be refined during and following the development of the detailed project work plan. The regional bodies strongly emphasised the need to incorporate a shift to a systems based resilience approach to NRM planning, and that approach is included in the framework. The regional bodies identified that information on climate projections would be most useful at regional and subregional scale, to feed into scenario planning and impact analysis. Outputs should be ‘engagement ready’ and there is a need for capacity building to enable regional bodies to understand and use the projections in stakeholder engagement. There was interest in understanding the impacts of climate change projections on ecosystems (e.g. ecosystem shift), and the consequent impacts on the production of ecosystem services. It was emphasised that any modelling should be able to be used by the regional bodies with their stakeholders to allow for community input (i.e. no black box models). The online regrowth benefits tool was of great interest to the regional bodies, as spatial mapping of carbon farming opportunities would be relevant to their funding requirements. The NSW CMAs identified an interest in development of the tool for NSW vegetation types. Needs relating to socio‐economic information included understanding the socio‐economic determinants of carbon farming uptake and managing community expectations. A need was also identified to understand the vulnerability of industry groups as well as community to climate change impacts, and in particular understanding how changes in the flow of ecosystem services would interact with the vulnerability of these groups to impact on the linked ecologicalsocio‐economic system. Responses to disasters (particularly flooding and storm surge) and recovery responses were also identified as being of interest. An ecosystem services framework was highlighted as a useful approach to synthesising biophysical and socioeconomic information in the context of a systems based, resilience approach to NRM planning. A need was identified to develop processes to move towards such an approach to NRM planning from the current asset management approach. Examples of best practice in incorporating climate science into planning, using scenarios for stakeholder engagement in planning and processes for institutionalising learning were also identified as cross‐cutting needs. The over‐arching theme identified was the need for capacity building for the NRM bodies to best use the information available at any point in time. To this end a planners working group has been established to support the building of a network of informed and articulate NRM agents with knowledge of current climate science and capacity to use current tools to engage stakeholders in NRM planning for climate change adaptation. The planners working group would form the core group of the community of practice, with the broader group of stakeholders participating when activities aligned with their interests. In this way, it is anticipated that the Project will contribute to building capacity within the wider community to effectively plan for climate change adaptation.

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metrictopological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.

Adaptive estimation of HMM transition probabilities

This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMM's) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the ...

Not learning from the past: Adaptive governance challenges for Australian natural resource management

Australia’s governance arrangements for NRM have evolved considerably over the last thirty years. The impact of changes in governance on NRM planning and delivery requires assessment. We undertake a multi-method program evaluation using adaptive governance principles as an analytical frame and apply this to Queensland to assess the impacts of governance change on NRM planning and governance outcomes. Data to inform our analysis includes: 1) a systematic review of sixteen audits/evaluations of Australian NRM over a fifteen-year period; 2) a review of Queensland’s first generation NRM Plans; and 3) outputs from a Queensland workshop on NRM planning. NRM has progressed from a bottom-up grassroots movement into a collaborative regional NRM model that has been centralised by the Australian Government. We found that while some adaptive governance challenges have been addressed, others remained unresolved. Results show that collaboration and elements of multi-level governance under the regional model were positive moves, but also that NRM arrangements contained structural deficiencies across multiple governance levels in relation to public involvement in decision-making and knowledge production for problem responsiveness. These problems for adaptive governance have been exacerbated since 2008. We conclude that the adaptive governance framework for NRM needs urgent attention so that important environmental management problems can be addressed.

Can social resilience inform SA/SIA for adaptive planning for climate change in vulnerable regions?

This book represents a landmark effort to probe and analyze the theory and empirics of designing water disaster management policies. It consists of seven chapters that examine, in-depth and comprehensively, issues that are central to crafting effective policies for water disaster management. The authors use historical surveys, institutional analysis, econometric investigations, empirical case studies, and conceptual-theoretical discussions to clarify and illuminate the complex policy process. The specific topics studied in this book include a review and analysis of key policy areas and research priority areas associated with water disaster management, community participation in disaster risk reduction, the economics and politics of ‘green’ flood control, probabilistic flood forecasting for flood risk management, polycentric governance and flood risk management, drought management with the aid of dynamic inter-generational preferences, and how social resilience can inform SA/SIA for adaptive planning for climate change in vulnerable areas. A unique feature of this book is its analysis of the causes and consequences of water disasters and efforts to address them successfully through policy-rich, cross-disciplinary and transnational papers. This book is designed to help enrich the sparse discourse on water disaster management policies and galvanize water professionals to craft creative solutions to tackle water disasters efficiently, equitably, and sustainably. This book should also be of considerable use to disaster management professionals, in general, and natural resource policy analysts.

Predicting the need for adaptive radiotherapy in head and neck cancer

Background and purpose Adaptive radiotherapy (ART) can account for the dosimetric impact of anatomical change in head and neck cancer patients; however it can be resource intensive. Consequently, it is imperative that patients likely to require ART are identified. The purpose of this study was to find predictive factors that identify oropharyngeal squamous cell carcinoma (OPC) and nasopharyngeal carcinoma (NPC) patients more likely to need ART. Materials and methods One hundred and ten patients with OPC or NPC were analysed. Patient demographics and tumour characteristics were compared between patients who were replanned and those that were not. Factors found to be significant were included in logistic regression models. Risk profiles were developed from these models. A dosimetric analysis was performed. Results Nodal disease stage, pre-treatment largest involved node size, diagnosis and initial weight (categorised in 2 groups) were identified as significant for inclusion in the model. Two models were found to be significant (p = 0.001), correctly classifying 98.2% and 96.1% of patients respectively. Three ART risk profiles were developed. Conclusion Predictive factors identifying OPC or NPC patients more likely to require ART were reported. A risk profile approach could facilitate the effective implementation of ART into radiotherapy departments through forward planning and appropriate resource allocation.

Adaptive immunity to rhinoviruses: Sex and age matter

Background: Rhinoviruses (RV) are key triggers in acute asthma exacerbations. Previous studies suggest that men suffer from infectious diseases more frequently and with greater severity than women. Additionally, the immune response to most infections and vaccinations decreases with age. Most immune function studies do not account for such differences, therefore the aim of this study was to determine if the immune response to rhinovirus varies with sex or age. Methods: Blood mononuclear cells were isolated from 63 healthy individuals and grouped by sex and age (≤50 years old and ≥52 years old). Cells were cultured with rhinovirus 16 at a multiplicity of infection of 1. The chemokine IP-10 was measured at 24 h as an index of innate immunity while IFNγ and IL-13 were measured at 5 days as an index of adaptive immunity. Results: Rhinovirus induced IFNγ and IL-13 was significantly higher in ≤50 year old women than in age matched men (p < 0.02 and p < 0.05) and ≥52 year old women (p < 0.02 and p > 0.005). There was no sex or age based difference in rhinovirus induced IP-10 expression. Both IFNγ and IL-13 were negatively correlated with age in women but not in men. Conclusions: This study suggests that pre-menopausal women have a stronger adaptive immune response to rhinovirus infection than men and older people, though the mechanisms responsible for these differences remain to be determined. Our findings highlight the importance of gender and age balance in clinical studies and in the development of new treatments and vaccines.

Impact of a new mandatory reporting law on reporting and identification of child sexual abuse: A seven year time trend analysis

Child sexual abuse is widespread and difficult to detect. To enhance case identification, many societies have enacted mandatory reporting laws requiring designated professionals, most often police, teachers, doctors and nurses, to report suspected cases to government child welfare agencies. Little research has explored the effects of introducing a reporting law on the number of reports made, and the outcomes of those reports. This study explored the impact of a new legislative mandatory reporting duty for child sexual abuse in the State of Western Australia over seven years. We analysed data about numbers and outcomes of reports by mandated reporters, for periods before the law (2006-08) and after the law (2009-12). Results indicate that the number of reports by mandated reporters of suspected child sexual abuse increased by a factor of 3.7, from an annual mean of 662 in the three year pre-law period to 2448 in the four year post-law period. The increase in the first two post-law years was contextually and statistically significant. Report numbers stabilised in 2010-12, at one report per 210 children. The number of investigated reports increased threefold, from an annual mean of 451 in the pre-law period to 1363 in the post-law period. Significant decline in the proportion of mandated reports that were investigated in the first two post-law years suggested the new level of reporting and investigative need exceeded what was anticipated. However, a subsequent significant increase restored the pre-law proportion, suggesting systemic adaptive capacity. The number of substantiated investigations doubled, from an annual mean of 160 in the pre-law period to 327 in the post-law period, indicating twice as many sexually abused children were being identified.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

Mixed pixel analysis and assessment for flood mapping

The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered by water. This can be referred to as a typical problem of the presence of mixed pixels in the images. A number of automatic information extracting image classification algorithms have been developed over the years for flood mapping using optical remote sensing images, with most labelling a pixel as a particular class. However, they often fail to generate reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve this problem, spectral unmixing methods have been developed. In this thesis, methods for selecting endmembers and the method to model the primary classes for unmixing, the two most important issues in spectral unmixing, are investigated. We conduct comparative studies of three typical spectral unmixing algorithms, Partial Constrained Linear Spectral unmixing, Multiple Endmember Selection Mixture Analysis and spectral unmixing using the Extended Support Vector Machine method. They are analysed and assessed by error analysis in flood mapping using MODIS, Landsat and World View-2 images. The Conventional Root Mean Square Error Assessment is applied to obtain errors for estimated fractions of each primary class. Moreover, a newly developed Fuzzy Error Matrix is used to obtain a clear picture of error distributions at the pixel level. This thesis shows that the Extended Support Vector Machine method is able to provide a more reliable estimation of fractional abundances and allows the use of a complete set of training samples to model a defined pure class. Furthermore, it can be applied to analysis of both pure and mixed pixels to provide integrated hard-soft classification results. Our research also identifies and explores a serious drawback in relation to endmember selections in current spectral unmixing methods which apply fixed sets of endmember classes or pure classes for mixture analysis of every pixel in an entire image. However, as it is not accurate to assume that every pixel in an image must contain all endmember classes, these methods usually cause an over-estimation of the fractional abundances in a particular pixel. In this thesis, a subset of adaptive endmembers in every pixel is derived using the proposed methods to form an endmember index matrix. The experimental results show that using the pixel-dependent endmembers in unmixing significantly improves performance.

Raman microscopy of formamide-intercalated kaolinites treated by controlled-rate thermal analysis technology

Raman spectroscopy of formamide-intercalated kaolinites treated using controlled-rate thermal analysis technology (CRTA), allowing the separation of adsorbed formamide from intercalated formamide in formamide-intercalated kaolinites, is reported. The Raman spectra of the CRTA-treated formamide-intercalated kaolinites are significantly different from those of the intercalated kaolinites, which display a combination of both intercalated and adsorbed formamide. An intense band is observed at 3629 cm-1, attributed to the inner surface hydroxyls hydrogen bonded to the formamide. Broad bands are observed at 3600 and 3639 cm-1, assigned to the inner surface hydroxyls, which are hydrogen bonded to the adsorbed water molecules. The hydroxyl-stretching band of the inner hydroxyl is observed at 3621 cm-1 in the Raman spectra of the CRTA-treated formamide-intercalated kaolinites. The results of thermal analysis show that the amount of intercalated formamide between the kaolinite layers is independent of the presence of water. Significant differences are observed in the CO stretching region between the adsorbed and intercalated formamide.

Detailed analysis of a conservative difference approximation for the time fractional diffusion equation

Diffusion equations that use time fractional derivatives are attractive because they describe a wealth of problems involving non-Markovian Random walks. The time fractional diffusion equation (TFDE) is obtained from the standard diffusion equation by replacing the first-order time derivative with a fractional derivative of order α ∈ (0, 1). Developing numerical methods for solving fractional partial differential equations is a new research field and the theoretical analysis of the numerical methods associated with them is not fully developed. In this paper an explicit conservative difference approximation (ECDA) for TFDE is proposed. We give a detailed analysis for this ECDA and generate discrete models of random walk suitable for simulating random variables whose spatial probability density evolves in time according to this fractional diffusion equation. The stability and convergence of the ECDA for TFDE in a bounded domain are discussed. Finally, some numerical examples are presented to show the application of the present technique.