Application of advanced techniques for the remote detection, modelling and spatial analysis of mesquite (prosopis spp.) invasion in Western Australia

Invasive plants pose serious threats to economic, social and environmental interests throughout the world. Developing strategies for their management requires a range of information that is often impractical to collect from ground based surveys. In other cases, such as retrospective analyses of historical invasion rates and patterns, data is rarely, if ever, available from such surveys. Instead, historical archives of remotely sensed imagery provides one of the only existing records, and are used in this research to determine invasion rates and reconstruct invasion patterns of a ca 70 year old exotic mesquite population (Leguminoseae: Prosopis spp.) in the Pilbara Region of Western Australia, thereby helping to identify ways to reduce spread and infill. A model was then developed using this, and other, information to predict which parts of the Pilbara are most a risk. This information can assist in identifying areas requiring the most vigilant intervention and pre-emptive measures. Precise information of the location and areal extent of an invasive species is also crucial for land managers and policy makers for crafting management strategies aimed at control, confinement or eradication of some or all of the population. Therefore, the third component of this research was to develop and test high spectral and spatial resolution airborne imagery as a potential monitoring tool for tracking changes at various intervals and quantifying the effectiveness of management strategies adopted. To this end, high spatial resolution digital multispectral imagery (4 channels, 1 m spatial resolution) and hyperspectral imagery (126 channels, 3 m spatial resolution) was acquired and compared for its potential for distinguishing mesquite from coexisting species and land covers.These three modules of research are summarised hereafter. To examine the rates and patterns of mesquite invasion through space and time, canopies were extracted from a temporal series of panchromatic aerial photography over an area of 450 ha using unsupervised classification. Non-mesquite trees and shrubs were not discernible from mesquite using this imagery (or technique) and so were masked out using an image acquired prior to invasion. The accuracy of the mesquite extractions were corroborated in the field and found to be high (R2 = 0.98, P36 m2 (66-94%) with both approaches and image types. However, both approaches used on the hyperspectral imagery were more reliable at capturing patches >36 m2 than the DMSI using either approach. The lowest omission and commission rates were obtained using pairwise separation on the hyperspectral imagery, which was significantly more accurate than DMSI using an overall separation approach (Z=2.78, P36 m2. However, hyperspectral imagery processed using pairwise separation appears to be superior, even though not statistically different to hyperspectral imagery processed using overall separation or DMSI processed using pairwise separation at the 95% confidence level. Mapping smaller patches may require the use of very high spatial resolution imagery, such as that achievable from unmanned airborne vehicles, coupled with a hyperspectral instrument. Alternatively, management may continue to rely on visual airborne surveys flown at low altitude and speed, which have proven to be capable at mapping small and isolated mesquite shrubs in the study area used in this research

Modeling Eurasian watermilfoil (Myriophyllum spicatum) habitat with geographic information systems

Eurasian watermilfoil (Myriophyllum spicatum) habitat was predicted at multiple scales, including a lake, regional, and national level. This dissertation illustrates how habitat can be predicted for M. spicatum using publically-available data for both presence and environmental variables. Models were generated using statistical procedures and quantative methods to determine where the greatest likelihood of presence was located. For the single lake, presence and absence data were available, but the larger-scale models used presence-only methods of prediction. These models were paired with a Geographic Information System so that data could be visualized on a map. For the selected lake, Pend Oreille (Idaho), spatial analysis using general linear mixed models was used to show that depth and fetch could be used to predict habitat, although differences were seen in their importance between the littoral and pelagic zones. For the states of Minnesota and Wisconsin, Mahalanobis distance and maximum entropy methods were used to demonstrate that available habitat will not always mean presence of M. spicatum. The differing approaches to management in these states illustrated how an aggressive public education campaign can limit spread of M. spicatum, even when habitat is available. Bass habitat appeared to be the largest predictor of M. spicatum in Minnesota, although this was due to the similar environmental preferences by these species. Using maximum entropy, on a national level, presence of M. spicatum appeared to be best predicted by annual precipitation. Again, results showed that habitat is colonized as time permits, and not necessarily as conditions permit

Predictive modelling of species' potential geographical distributions

Models that are used for predicting species' potential distributions are important tools that have found applications in a number of areas of applied ecology. The majority of these models can be classified as correlative, as they rely on strong, often indirect, links between species distribution records and environmental predictor variables to make predictions. Correlative models are an alternative to more complex mechanistic models that attempt to simulate the mechanisms considered to underlie the observed correlations with environmental attributes. This study explores the influence of the type and quality of the data used to calibrate correlative models. In terms of data type, the most popular techniques in use are group discrimination techniques, those that use both presence and absence locality data to make predictions. However, for many organisms absence data are either not available or are considered to be unreliable. As the available range of profile techniques (those using presence only data) appeared to be limited, new profile techniques were investigated and evaluated. A new profile modelling technique based on fuzzy classification (the Fuzzy Envelope Model) was developed and implemented. A second profile technique based on Principal Components Analysis was implemented and evaluated. Based on quantitative model evaluation tests, both of these techniques performed well and show considerable promise. In terms of data quality, the effects on model performance of false absence records, the number of locality records (sample size) and the proportion of localities representing species presence (prevalence) in samples were investigated for logistic regression distribution models. Sample size and prevalence both had a significant effect on model performance. False absence records had a significant influence on model performance, which was affected by sample size. A quantitative comparison of the performance of selected profile models and group discrimination modelling techniques suggests that different techniques may be more successful for predicting distributions for particular species or types of organism than others. The results also suggest that several different model design! sample size combinations are capable of making predictions that will on average not differ significantly in performance for a particular species. A further quantitative comparison among modelling techniques suggests that correlative techniques can perform as well as simple mechanistic techniques for predicting potential distributions

Quantifying the spatio-temporal dynamics of woody plant encroachment using an integrative remote sensing, GIS, and spatial modeling approach.

Despite a longstanding universal concern about and intensive research into woody plant encroachment (WPE)---the replacement of grasslands by shrub- and woodlands---our accumulated understanding of the process has either not been translated into sustainable rangeland management strategies or with only limited success. In order to increase our scientific insights into WPE, move us one step closer toward the sustainable management of rangelands affected by or vulnerable to the process, and identify needs for a future global research agenda, this dissertation presents an unprecedented critical, qualitative and quantitative assessment of the existing literature on the topic and evaluates the utility of an integrative remote sensing, GIS, and spatial modeling approach for quantifying the spatio-temporal dynamics of WPE.In sum, this dissertation demonstrates that integrative remote sensing, GIS, and spatial modeling approaches have enormous potential for addressing questions relevant to both rangelands research and management. However, it also suggests that much work remains to be done before we can translate our understanding of WPE into sustainable rangeland management strategies. In particular, we need to more fully explore the limitations and potentials of currently available data and techniques for quantifying WPE; build structures for data sharing and integration; develop a set of relevant standards; more actively engage in collaborative research efforts; and foster cross-cutting dialogues among researchers, managers, and communities.Specifically, this research demonstrates that the application of cutting-edge remote sensing techniques (Multiple Endmember Spectral Mixture Analysis, fuzzy logic-based change detection) to conventional medium spatial and spectral resolution imagery (Landsat Thematic Mapper, Landsat Enhanced Thematic Mapper Plus, ASTER) can be used to generate spatially explicit estimates of temporal changes in the abundance of woody plants and other surface materials. The research also shows that spatial models (Geographically Weighted Regression, Weights of Evidence, Weighted Logistic Regression) integrating this timely remotely sensed information with readily available GIS data can yield reasonably accurate estimates of an area's relative vulnerability to WPE and of the importance of anthropogenic and geoecological variables influencing the process. Such models may also be used for the testing of existing and generation of new scientific hypotheses about WPE, for evaluating the impact of natural or human-induced modifications of a landscape on the landscape's vulnerability to WPE, and for identifying target areas for conservation, restoration, or other management objectives.Findings from this research suggest that gaps in our current understanding of WPE and difficulties in devising sustainable rangeland management strategies are in part due to the complex spatio-temporal web of interactions between geoecological and anthropogenic variables involved in the process as well as limitations of presently available data and techniques. However, an in-depth analysis of the published literature also reveals that aforementioned problems are caused by two further crucial factors: the absence of information acquisition and reporting standards and the relative lack of long-term, large-scale, multi-disciplinary research efforts. The methodological framework proposed in this dissertation yields data that are easily standardized according to various criteria and facilitates the integration of spatially explicit data generated by a variety of studies. This framework may thus provide one common ground for scientists from a diversity of fields. Also, it has utility for both research and management

Intelligent model-based control of complex multi-link mechanisms

Complex under-actuated multilink mechanism involves a system whose number of control inputs is smaller than the dimension of the configuration space. The ability to control such a system through the manipulation of its natural dynamics would allow for the design of more energy-efficient machines with the ability to achieve smooth motions similar to those found in the natural world. This research aims to understand the complex nature of the Robogymnast, a triple link underactuated pendulum built at Cardiff University with the purpose of studying the behaviour of non-linear systems and understanding the challenges in developing its control system. A mathematical model of the robot was derived from the Euler-Lagrange equations. The design of the control system was based on the discrete-time linear model around the downward position and a sampling time of 2.5 milliseconds. Firstly, Invasive Weed Optimization (IWO) was used to optimize the swing-up motion of the robot by determining the optimum values of parameters that control the input signals of the Robogymnast’s two motors. The values obtained from IWO were then applied to both simulation and experiment. The results showed that the swing-up motion of the Robogymnast from the stable downward position to the inverted configuration to be successfully achieved. Secondly, due to the complex nature and nonlinearity of the Robogymnast, a novel approach of modelling the Robogymnast using a multi-layered Elman neural ii network (ENN) was proposed. The ENN model was then tested with various inputs and its output were analysed. The results showed that the ENN model to be capable of providing a better representation of the actual system compared to the mathematical model. Thirdly, IWO is used to investigate the optimum Q values of the Linear Quadratic Regulator (LQR) for inverted balance control of the Robogymnast. IWO was used to obtain the optimal Q values required by the LQR to maintain the Robogymnast in an upright configuration. Two fitness criteria were investigated: cost function J and settling time T. A controller was developed using values obtained from each fitness criteria. The results showed that LQRT performed faster but LQRJ was capable of stabilizing the Robogymnast from larger deflection angles. Finally, fitness criteria J and T were used simultaneously to obtain the optimal Q values for the LQR. For this purpose, two multi-objective optimization methods based on the IWO, namely the Weighted Criteria Method IWO (WCMIWO) and the Fuzzy Logic IWO Hybrid (FLIWOH) were developed. Two LQR controllers were first developed using the parameters obtained from the two optimization methods. The same process was then repeated with disturbance applied to the Robogymnast states to develop another two LQR controllers. The response of the controllers was then tested in different scenarios using simulation and their performance was evaluated. The results showed that all four controllers were able to balance the Robogymnast with the fastest settling time achieved by WMCIWO with disturbance followed by in the ascending order: FLIWOH with disturbance, FLIWOH, and WCMIWO

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Adaptive bio-inspired firefly and invasive weed algorithms for global optimisation with application to engineering problems

The focus of the research is to investigate and develop enhanced version of swarm intelligence firefly algorithm and ecology-based invasive weed algorithm to solve global optimisation problems and apply to practical engineering problems. The work presents two adaptive variants of firefly algorithm by introducing spread factor mechanism that exploits the fitness intensity during the search process. The spread factor mechanism is proposed to enhance the adaptive parameter terms of the firefly algorithm. The adaptive algorithms are formulated to avoid premature convergence and better optimum solution value. Two new adaptive variants of invasive weed algorithm are also developed seed spread factor mechanism introduced in the dispersal process of the algorithm. The working principles and structure of the adaptive firefly and invasive weed algorithms are described and discussed. Hybrid invasive weed-firefly algorithm and hybrid invasive weed-firefly algorithm with spread factor mechanism are also proposed. The new hybridization algorithms are developed by retaining their individual advantages to help overcome the shortcomings of the original algorithms. The performances of the proposed algorithms are investigated and assessed in single-objective, constrained and multi-objective optimisation problems. Well known benchmark functions as well as current CEC 2006 and CEC 2014 test functions are used in this research. A selection of performance measurement tools is also used to evaluate performances of the algorithms. The algorithms are further tested with practical engineering design problems and in modelling and control of dynamic systems. The systems considered comprise a twin rotor system, a single-link flexible manipulator system and assistive exoskeletons for upper and lower extremities. The performance results are evaluated in comparison to the original firefly and invasive weed algorithms. It is demonstrated that the proposed approaches are superior over the individual algorithms in terms of efficiency, convergence speed and quality of the optimal solution achieved

Recognition of Ziziphus lotus through Aerial Imaging and Deep Transfer Learning Approach

Abstract: Please refer to full text to view abstrac

Exploring ecological and biogeographic questions using biological databases derived from natural history collections and surveys

Biogeographic research has benefited from the digitizing of large databases derived from natural history collections and biological surveys. These resources made available via the Internet can be accessed by biogeographers around the world to address a multitude of ecological and geographic questions. Utilizing this data taps into hundreds of years of study and countless hours of research conducted by biologists across the globe. This dissertation could not have been completed without the availability of data collected by legions of researchers from museums, herbaria, and government agencies. By taking advantage of data collected by others, I was able to work at a geographic scale that would have been impossible had I gathered all my own data.In chapter one, I use herbarium data to describe the temporal and spatial patterns of invasive and expansive species for the entire state of Oklahoma. Because of the inherent bias in collections of natural history specimens. I test techniques for eliminating temporal collecting bias: regression models and proportion curves. I found that patterns of species invasion and expansion in Oklahoma could be detected using these techniques which were developed for regions with longer collecting plant histories. The proportion curve analysis eliminated some biases inherent in herbarium data by reducing the effect of collecting effort. Both the regression model and proportion curve analyses illustrate the temporal invasion patterns of alien, invasive species. However, the native species did not show a clear expansion pattern. The information found in recently established herbaria may not be sensitive enough to detect the increase of abundance of native species.Currently species distribution modelling is one of the most popular methods of utilizing large, georeferenced, biological databases. Chapter two is a brief review of the overabundant literature on species distribution modelling. Topics covered are the theoretical basis for distribution modelling, species and predictor data, modelling techniques, model evaluation, and uses for predictive maps created by modelling.Using survey data collected for the U.S. Fish and Wildlife Service, I apply species distribution modelling techniques to predict suitable habitat for the endangered American burying beetle (Nicrophorus americanus). Using a suite of predictor variable thought to influence a burrowing insect, I built several models using a variety of modelling techniques. The Maxent modelling algorithm performed the best. However, being a generalist species, the suitable habitat for N. americanus was not well modelled. Model performance could be improved by incorporating information on the cause of N. americanus's endangered status and its population shrinkage. To improve the models and consequently the recovery effort for the species, I need to take into account interactions including congener and vertebrate competition and a reduction in optimally sized prey. Creating an accurate spatial layer of this data will be a future challenge. My hope was to produce a map of potentially suitable habitat for N. americanus that would guide conservation efforts within the state of Oklahoma. Although the model was not highly accurate, the map of suitable habitat can help to inform conservation biologists of areas that have suitable habitat for the N. americanus.In chapter four, I return to the invasive species theme by addressing the question of whether the introduced distribution of invasive species can be predicted from its native range. I modelled the potential distribution within the United States of three alien invasive species native to Europe using the Maxent modelling technique. Using occurrence data from both the native (Europe) and introduced (US) ranges, I used reciprocal modelling to evaluate habitat discrepancies between the introduced and native ranges. This modelling approach can help to determine which environmental factors within the introduced range are different from the native range and which habitats within the native range are not represented in the introduced range. Further, reciprocal modelling can reveal potential problems with occurrence data and predictor variables in both native and introduced ranges, but it also has also been used to investigate ecological phenomena, such as niche shifts of invasive species in their introduced range. The native occurrences in Europe accurately predicted the distribution within Europe; and introduced occurrences in the US accurately predicted the US distribution. However, the reciprocal models did not perform well. The explanations for the dissociated ranges of each species in Europe and US can possibly be related to the hypotheses postulated for invasive species success. The characteristics that make a species invasive may be the cause of the species' environmental range to be different in the native and introduced regions. My aim was to see if we could use easily obtained data to model the potential areas of invasion within our state and use this information to assist conservation efforts such as early detection and rapid response. My model results indicate that the occupied niches are too inconsistent between the native and introduced ranges to make models useful at the scale we are interested in. Further modeling attempts will utilize more introduced occurrence data from areas within our region of the United States. This will entail a more concerted effort to locate available data in the areas where the species may be expanding

New hybrid invasive weed optimization and machine learning approach for fault detection

Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions