AUSLEM (AUStralian Land Erodibility Model): A tool for identifying wind erosion hazard in Australia

We present AUSLEM (AUStralian Land Erodibility Model), a land erodibility modelling system that utilizes a rule-set of surficial and climatic thresholds applied through a Geographic Information System (GIs) modelling framework to predict landscape susceptibility to wind erosion. AUSLEM is distinctive in that it quantitatively assesses landscape susceptibility to wind erosion at a 5 x 5 km. spatial resolution on a monthly time-step across Australia. The system was implemented for representative wet (1984), dry (1994), and average rainfall (1997) years with corresponding low, high and moderate dust storm day frequencies. Results demonstrate that AUSLEM can identify landscape erodibility, and provide an interpretation of the physical nature and distribution of erodible landscapes in Australia. Further, results offer an assessment of the dynamic tendencies of erodibility in space and time in response to the El Nino Southern Oscillation (ENSO) and seasonal synoptic scale climate variability. A comparative analysis of AUSLEM output with independent national and international wind erosion, atmospheric aerosol and dust event records indicates a high level of model competency. (c) 2006 Elsevier B.V. All rights reserved.

Modelling sediment delivery ratio over the Murray Darling Basin

This paper presents a scientific and technical description of the modelling framework and the main results of modelling the long-term average sediment delivery at hillslope to medium-scale catchments over the entire Murray Darling Basin (MDB). A theoretical development that relates long-term averaged sediment delivery to the statistics of rainfall and catchment parameters is presented. The derived flood frequency approach was adapted to investigate the problem of regionalization of the sediment delivery ratio (SDR) across the Basin. SDR, a measure of catchment response to the upland erosion rate, was modeled by two lumped linear stores arranged in series: hillslope transport to the nearest streams and flow routing in the channel network. The theory shows that the ratio of catchment sediment residence time (SRT) to average effective rainfall duration is the most important control in the sediment delivery processes. In this study, catchment SRTs were estimated using travel time for overland flow multiplied by an enlargement factor which is a function of particle size. Rainfall intensity and effective duration statistics were regionalized by using long-term measurements from 195 pluviograph sites within and around the Basin. Finally, the model was implemented across the MDB by using spatially distributed soil, vegetation, topographical and land use properties under Geographic Information System (GIs) environment. The results predict strong variations in SDR from close to 0 in floodplains to 70% in the eastern uplands of the Basin. (c) 2005 Elsevier Ltd. All rights reserved.

Towards rope shovel automation operation information system

The road to electric rope shovel automation is marked with technological innovations that include an increase in operational information available to mining operations. The CRCMining Shovel Operator Information System not only collects machine operational data but also provides the operator with knowledge-of-performance and influences his/her performance to achieve higher productivity with reduced machine duty. The operator’s behaviour is one of the most important aspects of the man-machine interaction to be considered before semi- or fully-automated shovel systems can be realised. This paper presents the results of the rope shovel studies conducted by CRCMining between 2002 and 2004, provides information on current research to improve shovel performance and briefly discusses the implications of human-system interactions on future designs of autonomous machines.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

A vulnerability analysis of the temperate forests of south central Chile A vulnerability analysis of the temperate forests of south central Chile. Biological Conservation, 122 2005: 9-21.

Areas of the landscape that are priorities for conservation should be those that are both vulnerable to threatening processes and that if lost or degraded, will result in conservation targets being compromised. While much attention is directed towards understanding the patterns of biodiversity, much less is given to determining the areas of the landscape most vulnerable to threats. We assessed the relative vulnerability of remaining areas of native forest to conversion to plantations in the ecologically significant temperate rainforest region of south central Chile. The area of the study region is 4.2 million ha and the extent of plantations is approximately 200000 ha. First, the spatial distribution of native forest conversion to plantations was determined. The variables related to the spatial distribution of this threatening process were identified through the development of a classification tree and the generation of a multivariate. spatially explicit, statistical model. The model of native forest conversion explained 43% of the deviance and the discrimination ability of the model was high. Predictions were made of where native forest conversion is likely to occur in the future. Due to patterns of climate, topography, soils and proximity to infrastructure and towns, remaining forest areas differ in their relative risk of being converted to plantations. Another factor that may increase the vulnerability of remaining native forest in a subset of the study region is the proposed construction of a highway. We found that 90% of the area of existing plantations within this region is within 2.5 km of roads. When the predictions of native forest conversion were recalculated accounting for the construction of this highway, it was found that: approximately 27000 ha of native forest had an increased probability of conversion. The areas of native forest identified to be vulnerable to conversion are outside of the existing reserve network. (C) 2004 Elsevier Ltd. All tights reserved.

Residents' perceptions of walkability attributes in objectively different neighbourhoods: a pilot study

Physical attributes of local environments may influence walking. We used a modified version of the Neighbourhood Environment Walkability Scale to compare residents' perceptions of the attributes of two neighbourhoods that differed on measures derived from Geographic Information System databases. Residents of the high-walkable neighbourhood rated relevant attributes of residential density, land-use mix (access and diversity) and street connectivity, consistently higher than did residents of the low-walkable neighbourhood. Traffic safety and safety from crime attributes did not differ. Perceived neighbourhood environment characteristics had moderate to high test retest reliabilities. Neighbourhood environment attribute ratings may be used in population surveys and other studies. (c) 2004 Elsevier Ltd. All rights reserved.

Ross River virus disease clusters and spatial relationship with mosquito biting exposure in Redland Shire, southern Queensland, Australia

The spatial heterogeneity in the risk of Ross River virus (family Togaviridae, genus Alphavirus, RRV) disease, the most common mosquito-borne disease in Australia, was examined in Redland Shire in southern Queensland, Australia. Disease cases, complaints from residents of intense mosquito biting exposure, and human population data were mapped using a geographic information system. Surface maps of RRV disease age-sex standardized morbidity ratios and mosquito biting complaint morbidity ratios were created. To determine whether there was significant spatial variation in disease and complaint patterns, a spatial scan analysis method was used to test whether the number of cases and complaints was distributed according to underlying population at risk. Several noncontiguous areas in proximity to productive saline water habitats of Aedes vigilax (Skuse), a recognized vector of RRV, had higher than expected numbers of RRV disease cases and complaints. Disease rates in human populations in areas which had high numbers of adult Ae. vigilax in carbon dioxide- and octenol-baited light traps were up to 2.9 times those in areas that rarely had high numbers of mosquitoes. It was estimated that targeted control of adult Ae. vigilax in these high-risk areas could potentially reduce the RRV disease incidence by an average of 13.6%. Spatial correlation was found between RRV disease risk and complaints from residents of mosquito biting. Based on historical patterns of RRV transmission throughout Redland Shire and estimated future human population growth in areas with higher than average RRV disease incidence, it was estimated that RRV incidence rates will increase by 8% between 2001 and 2021. The use of arbitrary administrative areas that ranged in size from 4.6 to 318.3 km2, has the potential to mask any small scale heterogeneity in disease patterns. With the availability of georeferenced data sets and high-resolution imagery, it is becoming more feasible to undertake spatial analyses at relatively small scales.

Incorporating level set methods in Geographical Information Systems (GIS) for land-surface process modelling

Land-surface processes include a broad class of models that operate at a landscape scale. Current modelling approaches tend to be specialised towards one type of process, yet it is the interaction of processes that is increasing seen as important to obtain a more integrated approach to land management. This paper presents a technique and a tool that may be applied generically to landscape processes. The technique tracks moving interfaces across landscapes for processes such as water flow, biochemical diffusion, and plant dispersal. Its theoretical development applies a Lagrangian approach to motion over a Eulerian grid space by tracking quantities across a landscape as an evolving front. An algorithm for this technique, called level set method, is implemented in a geographical information system (GIS). It fits with a field data model in GIS and is implemented as operators in map algebra. The paper describes an implementation of the level set methods in a map algebra programming language, called MapScript, and gives example program scripts for applications in ecology and hydrology.