Electronic case management in New South Wales, Australia

In the last five years there has been a concerted movement towards electronic case management (“ECM”) to enhance the efficiency of legal proceedings in New South Wales. This paper provides a detailed overview of the relevant developments, from legislative reform by the NSW Government, to the making of appropriate instruments, rules and practice notes.     

Cover Wildfire monitoring in New South Wales,Australia

The first flight model of ScaRaB (Scanner for Earth Radiation Budget) on board the Russian Meteor-3/07 weather satellite (altitude 1200 km; inclination, 82.56 ) transmitted data over a period of 1 year (24 February 1994-5 March 1995). We use solar channel (shortwave radiation, 0.2 mu m-4.0 mu m) data over desert regions (clear-sky scenes) to study the stability of the radiometer signal. We consider 1.25 1.25 areas in desert regions and we compute bi-directional reflectance for these areas. These values, depending on the three angles defined by the Sun-target-satellite geometry, are grouped by a similar angular configuration for each area. In this way it is possible to compare reflectance, with the same illumination and observation geometry, for different months, for each area. The results show great stability of the reflectance values, demonstrating the stability of the radiometer signal. For computing variability of the reflectance for each area (the relative standard deviation over an area) we study uniform desert zones. The low-variability zones agree very well with uniform zones selected by Cosnefro' for CNES (Centre National d'Etudes Spatiales, France) in the Saharan and Arabian deserts, from Meteosat-4 data. This demonstrates the high quality of the ScaRaB shortwave radiometer which scans with a pixel size 540 times greater than Meteostat-4.     

Multitemporal MERIS images for land-cover mapping at a national scale: a case study of Portugal

Medium-spatial-resolution satellite images have already proved to be successful in automatic production of global land-cover maps. However, their operational use for land-cover mapping at a national scale has not yet been well established. We find that the reasons for this are not data-source dependent, but are due to the land-cover nomenclatures properties adopted, regional landscape specificities and the methodological approaches used. The aim of this paper is to evaluate the suitability for national applications of land-cover maps derived from automatic classification of medium-spatial-resolution satellite images. To tackle this issue, we produce a land-cover map of Continental Portugal from multitemporal MEdium Resolution Imaging Spectrometer (MERIS) full-resolution satellite images of 2005 and evaluate its accuracy. For the accuracy assessment of the final map, we compute unbiased estimates of overall, user and producer accuracies using an independent testing sample collected through a stratified random sampling design. The overall accuracy of the final map is 80%, with an absolute precision of 2% at the 95% confidence level. High independent accuracy assessment results demonstrate that medium-spatial-resolution satellite images can be used on an operational basis for annual production of land-cover maps suitable for national applications.     

Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected. Several supervised classification approaches were tested: spectral classification of single Landsat data, temporal classification of normalized difference vegetation index time series from Landsat images, and two-step classification integrating both these approaches. The accuracy of the land-cover maps obtained ranges between 80% and 90% overall for the two-step classification approach. Comparison of the maps between the two years indicates a stable semi-arid region, where some change in hot spots exists despite a generally constant level of rainfall in the area during this period. In particular, the Dallol Bosso fossil valley highlights an increase in cultivated land, while a decrease in herbaceous vegetation was observed outside the valley where rangeland is the predominant natural landscape.     

Classification of seasonal images for monitoring irrigated crops in a salinity-affected area of Australia

An attempt was made to observe the impact of salinity management on agricultural crops in a highly salinity-affected area of Australia. A classification strategy based on crop phenology and irrigation practices, was developed to identify and monitor the three major irrigated crops. For this, Normalized Difference Vegetation Indices (NDVIs) were derived from Landsat Thematic Mapper (TM) data for two consecutive seasons (summer and autumn) each of 1989-1990 and 1995-1996. Using the 1995-1996 field survey the results of parametric and nonparametric classifiers were compared. Higher accuracies were achieved with the nonparametric classifier. Substantial changes were found in pasture culture from 1989-1990 to 1995-1996.     

A remote sensing approach to determine environmental flows for wetlands of the Lower Darling River,New South Wales,Australia

A remote sensing approach was adopted to determine environmental flow requirements for wetlands in the Lower Darling River. The regulation of river flows by the Menindee Lakes Storage has altered the natural flow regime in the river and is likely to be affecting the health of both the river and its wetlands. Landsat Thematic Mapper (TM) images corresponding to four flood events (1989, 1990 and two in 1996) were analysed and a relationship between river flow heights and wetland inundation was established. Wetlands were classified according to broad commence-to-flow river discharges. The study concluded that 13 000 Ml d^-1 (megalitres per day) at Weir 32 would inundate approximately 50% of the wetlands along the Lower Darling River, this figure being 10 000 Ml d^-1 less than estimated in a previous study.     

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Rapid global economic development has resulted in a corresponding intensification of urbanization, which has in turn impacted the ecology of vast regions of the world. A series of problems have thus been introduced, such as changes in land-use/land-cover (LULC) and changes in local climate. The process of urbanization predominantly represents changes in land-use, and is deemed by researchers to be the chief cause of climate change and ecological change. One of the principal purposes of the research in this field is to find ways to mitigate the influence of land-use change on local or global environments. In the study presented in this article, satellite images were utilized to extract information regarding land-use in Beijing City, and to develop maps of land surface temperature (LST) during two different periods of time: 2 August 1999 and 8 August 2010. A supervised classification scheme, a support vector machine, was used to derive the land-use change map for the above periods. Maps of surface temperature are derived from the thermal band of Landsat images using the mono-window algorithm. Results from post-classification comparison indicated that an increase in impervious surface areas was found to be dramatic, while the area of farmland decreased rapidly. The changes in LULC were found to have led to a variation in surface temperature, as well as a spatial distribution pattern of the urban heat island phenomenon. This research revealed that the hotspots were mainly located in areas dominated by three kinds of material: bare soil, rooftops, and marble surfaces. Results from the local Moran's I index indicated that the use of lower surface temperature materials will help to mitigate the influence of the urban heat island phenomenon. The results of this research study provide a reference for government departments involved in the process of designing residential regions. Such a reference should enable the development of areas sympathetic to environmental changes and hence mitigate the effects of the growing intensity of urbanization.     

Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia

The objectives of this study are to quantify, based on remote sensing data, processes of land-cover change and to test a Markov-based model to generate short-term land-cover change projections in a region characterised by exceptionally high rates of change. The region of Lusitu, in the Southern Province of Zambia, has been a land-cover change 'hot spot' since the resettlement of 6000 people in the Lusitu area and the succession of several droughts. Land-cover changes were analysed on the basis of a temporal series of three multispectral SPOT images in three steps: (i) land-cover change detection was performed by combining the postclassification and image differencing techniques; (ii) the change detection results were examined in terms of proportion of land-cover classes, change trajectories and spatio-temporal patterns of change; (iii) the process of land-cover change was modelled by a Markov chain to predict land-cover distributions in the near future. The remote sensing approach allowed: (i) to quantify land-cover changes in terms of percentage of area affected and rates of change; (ii) to qualify the nature of changes in terms of impact on natural vegetation; (iii) to map the spatial pattern of land-cover change. 44% of the area has been affected by at least one change in land cover during the period 1986 to 1997. The average annual rate of land-cover change was 4.0%. Agricultural expansion was the dominant change process. Land-cover change trajectories highlighted the dynamic character of changes. The results obtained by applying a Markov chain for projecting future evolutions showed the continuing upward trend of bare soils and cultivated land, and the rapid downward trend of forests and other natural vegetation covers.     

Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests

Detection of land-cover changes through time can be complicated because of sensor-specific differences in spatial and spectral resolutions; classified land-cover changes can be due to either real changes on the ground or a switch in sensors used to collect data. This study focused on two objectives: (1) selecting the best predictor variables for the classification of semi-arid Zagros forests given the characteristics of the study area and available data sets and (2) evaluating the application of the random forest (RF) algorithm as a unified technique for the classification of data sets acquired from different sensors. Three images of the same study area were acquired from the Landsat-5 Thematic Mapper (TM) sensor in 2009, the Landsat-7 Enhanced Thematic Mapper (ETM+) sensor with Scan Line Corrector (SLC) in 1999 and the Landsat-2 Multispectral Scanner (MSS) sensor in 1975. Following image preprocessing, the RF algorithm was applied for variable selection and classification. A test of equivalence was used to compare the overall accuracy of the classified maps from the three sensors. Slope, normalized difference vegetation index (NDVI) and elevation were determined to be the most important predictor variables for all three images. High overall classification accuracies were achieved for all three images (97.90% for MSS, 95.43% for TM and 95.29% for ETM). The ETM- and TM-derived maps had equivalent overall accuracy and even significantly higher overall accuracy was obtained for the MSS-derived map. The post-classification comparison showed an increase in agriculture and a decrease in forest cover. The selected predictor variables were consistent with ecological reality and showed more details on the changes of the land-cover classes across biophysical variables of the study area through time.     

Field-based remote-sensing for experimental intertidal ecology: Case studies using hyperspatial and hyperspectral data for New South Wales (Australia)

Many ecological processes in intertidal areas act at very small (mm to cm) spatial scales so manipulative ecological experiments are done at scales at which these processes are proposed to occur. Remote-sensing using field-based sensors enable ecologists to capture variability that must be measured to test hypotheses about small-scale processes.We present two case studies where remote-sensing from field-based platforms has been used to acquire data for manipulative ecological experiments in intertidal areas. The first study, from a rock platform, investigates the spatial scales of variability in chlorophyll (as an index of algal biomass) in experimental plots prior to and after the removal of encrusting macro-algae. Digital colour-infrared (CIR) imagery of the rock platform was obtained before the removal of the encrusting macro-algae and on 1, 44, 75, 117 and 160 days after removal. Amount of chlorophyll was derived from the CIR data using a simple ratio of reflectances at near-infrared (NIR) and red wavelengths. Block Mean Square (BMS) analysis showed that there was little variability in amounts of chlorophyll at very small (≤ 2.26 cm²) scales, prior to and after removal of encrusting macro-algae. The greatest variability occurred at the largest block-size analysed (36.19 cm²). This identifies the spatial scale at which micro-algal food resources are variably scattered on the shore. Results discriminate variability at about 60–80 mm distances. Distributions of micro-algae are caused by a complicated interplay of scale-dependent ecological processes, including grazing. These findings provide a basis for the design of manipulative experiments at appropriate scales to unravel these processes.The second study used field reflectance spectra (350–1050 nm) to: i) determine whether the amount of algae in soft sediments could be changed by manipulating the amounts of light reaching the sediment surface and ii) test the hypothesis that the strength of in vivo absorption features, as indices of the amount of chlorophyll and other pigments, was correlated with measures of sediment-stability (erosion threshold and erosion rate). Several different measures of the strength of absorption features were investigated to determine the best predictor of stability of the sediment. Reflectance spectra showed that amounts of algae had been successfully manipulated. Derivative reflectance at individual wavelengths, indicative of absorption by chlorophyll-a and chlorophyll-c, were the best predictors of sediment-stability.These case studies demonstrate some novel uses of field-based remote-sensing to provide information at the spatial scales relevant to many intertidal ecologists. Such techniques should have an increasingly important role in ecological field experimentation where non-destructive sampling of photosynthetic pigments is desired.     

Multivariate detection and attribution of land-cover changes in the Central Pilbara,Western Australia

The Multivariate Alteration Detection (MAD) method was applied to locate areas where land-cover changes occurred between 2003 and 2009 in the Central Pilbara, Western Australia. It was demonstrated that each of the six MAD variates contained information of land-cover changes at various spatial scales. This allowed attribution of the identified changes to particular stressors such as climate variability, fire events, and mining activity in the area. The results were analysed and interpreted using time series of multispectral normalized difference vegetation index, normalized difference wetness index, and normalized burn ratio grids derived from Landsat data observed over the study period. In addition, various ground truth data such as fire maps, historical climate data, and the available information about mine operations and water management, which could lead to alteration of natural water regime, were utilized.     

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

On the ensemble of multiscale object-based classifiers for aerial images: a comparative study

Remote sensing images (RSIs) are increasingly used as data source to produce maps used in several applications. Modern sensors launched into space from the end of the 1990s have been producing high spatial resolution RSIs. The use of classification methods based on regions, called as Geographic Object-Based Image Analysis (GEOBIA), has been demonstrated to be more appropriate to deal with this kind of image. However, finding the appropriate segmentation scale, which is not a trivial task, is crucial for the success of a GEOBIA method. In this paper, we perform a comparative study involving seven methods for RSI multiclass classification that combine different features extracted from different scales: M1-OvA, M2-OvO, M3-AdaMH, M4-Samme, M5-MV, M5-WMV, and M6-Cascade. The first four methods are boosting-based techniques and the last three are based on the majority vote approach. The effectiveness of the proposed methods was evaluated by analyzing the results of experiments conducted in three RSIs datasets. The methods were compared with the baseline SVM with Kernel RBF by measuring the overall accuracy, the Kappa Index, and the accuracy per class. The results show that all the proposed methods are effective for RSI classification.     

Analytical assessment of intelligent segmentation techniques for cortical tissues of MR brain images: a comparative study

Medical image segmentation is one of the difficult tasks in image processing since the accuracy of segmentation determines the eventual success or failure of proper diagnosis. In medical imaging identification of each pixel in a region has vital importance since it can increase the standard of evaluation criteria. In this respect segmentation of brain MR images has become more significant in research and medical applications related to diagnosis of abnormality and diseases appearing in human brain. Segmentation initiates the process of extraction of various cortical tissues which is a key issue in neuroscience, to detect early neural disorders. The aim of present study is to comprehensively evaluate intensity based fuzzy C-means and Markov random field approaches, both stochastic and deterministic, for the segmentation of brain MR images into three different cortical tissues—gray matter, white matter and cerebrospinal fluid. Along with the analytical assessment of the segmentation techniques including efficiency and user interaction, this work is concentrated on empirical evaluation based on area based matrix. The results illustrate that in all respects, Markov Random field based approaches are showing better performance as compared to fuzzy C-means. Further, the Markov random field approaches are compared to find out which segmentation technique will suit which initial conditions.     

Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa Province in Italy

This article explores the simultaneous use of pre-classification and post-classification change-detection techniques to map and monitor land-cover and land-use change using multi-temporal Landsat Multi-spectral Scanner and Enhanced Thematic Mapper plus data over one of the most important tourism centres of Italy (e.g. Pisa Province) for 1972, 2000 and 2006.

Pre-classification approaches of principal-component analysis and band combination are potentially tailored to reduce data redundancy of the satellite imagery in order to highlight different objects of significance for change-detection analysis across time-series data. In this work, the application of pre-classification techniques could contribute to produce land-cover and land-use maps with higher quality of classification. At this point, the average value of overall classification accuracies for the three classification outputs was an estimated 90%.

Then, ‘from–to’ change information, as well as the area and the type of landscape transformations, are provided through the post-classification technique. The findings of this study show that the province of Pisa has significantly experienced a high rate of deforestation and urban development over past decades. It is revealed that artificial structures (e.g. urban and industrial zones) in Pisa Province increased at a change rate of around 265% and forested land decreased from approximately 45% to 32% of the total area of the province between 1972 and 2006. Likewise, the perceptible growth of built-up structures from about 4% to 10.6% in Pisa City during this 34-year period has imposed a heavy pressure on the landscape of Pisa.     

Automatic identification of cardiac health using modeling techniques: A comparative study

Heart rate variability (HRV), a widely adopted quantitative marker of the autonomic nervous system can be used as a predictor of risk of cardiovascular diseases. Moreover, decreased heart rate variability (HRV) has been associated with an increased risk of cardiovascular diseases. Hence in this work HRV signal is used as the base signal for predicting the risk of cardiovascular diseases. The present study concerns nine cardiac classes that include normal sinus rhythm (NSR), congestive heart failure (CHF), atrial fibrillation (AF), ventricular fibrillation (VF), preventricular contraction (PVC), left bundle branch block (LBBB), complete heart block (CHB), ischemic/dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). A total of 352 cardiac subjects belonging to the nine classes were analyzed in the frequency domain. The fast Fourier transforms (FFT) and three other modeling techniques namely, autoregressive (AR) model, moving average (MA) model and the autoregressive moving average (ARMA) model are used to estimate the power spectral densities of the RR interval variability. The spectral parameters obtained from the spectral analysis of the HRV signals are used as the input parameters to the artificial neural network (ANN) for classification of the different cardiac classes. Our findings reveal that the ARMA modeling technique seems to give better resolution and would be more promising for clinical diagnosis.     

Remote sensing of land-cover change and landscape context of the National Parks: A case study of the Northeast Temperate Network

National park units and protected areas face critical management challenges because of changing land-cover types and variability of landscape contexts within and adjacent the park boundaries. In this study we developed and implemented a multi-scale protocol for detecting and monitoring land-cover change in and adjacent to National Parks and ten segments of the Appalachian National Scenic Trail (AT) in the northeastern United States. We used Landsat imagery from 1970 to 2002 and recent ground-based photography to evaluate changes within park boundaries and within 0.5, 1, and 5 km buffers. The study concluded that all of the studied park units, except one segment of AT in Maine, experienced increases of urban land and declines of forest cover in the immediately adjacent areas and extended buffer zones. Over 30 years and across all parks and trail segments, urban land increased 172% and 181% within 0.5 and 1 km, respectively, of the park boundary or trail centerline. Over the same time period, forested area decreased by 5% and 6% within 0.5 and 1 km, respectively, of the park boundary or trail centerline, with more loss of forest near the parks (18%) than the trail segments (2%). This study provided baseline data demonstrating land-cover alteration over the past three decades and a foundation for a land-cover change and landscape context protocol suitable for monitoring future changes of National Parks and protected areas.     

Major trends in the land surface phenology (LSP) of Africa,controlling for land-cover change

ABSTRACT

Monitoring land surface phenology (LSP) trends is important in understanding how both climatic and non-climatic factors influence vegetation growth and dynamics. Controlling for land-cover changes in these analyses has been undertaken only rarely, especially in poorly studied regions like Africa. Using regression models and controlling for land-cover changes, this study estimated LSP trends for Africa from the enhanced vegetation index (EVI) derived from 500 m surface reflectance Moderate-Resolution Imaging Spectroradiometer (MOD09A1), for the period from 2001 to 2015. Overall end of season showed slightly more pixels with significant trends (12.9% of pixels) than start of season (11.56% of pixels) and length of season (LOS) (5.72% of pixels), leading generally to more ‘longer season’ LOS trends. Importantly, LSP trends that were not affected by land-cover changes were distinguished from those that were influenced by land-cover changes such as to map LSP changes that have occurred within stable land-cover classes and which might, therefore, be reasonably associated with climate changes through time. As expected, greater slope magnitudes were observed more frequently for pixels with land-cover changes compared to those without, indicating the importance of controlling for land cover. Consequently, we suggest that future analyses of LSP trends should control for land-cover changes such as to isolate LSP trends that are solely climate-driven and/or those influenced by other anthropogenic activities or a combination of both.