Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.

The combined effect of biological control with plant competition on the management of parthenium weed (Parthenium hysterophorus l.)

Parthenium hysterophorus L., (Asteraceae) commonly known as parthenium weed, is a highly invasive plant that has become a problematic weed of pasture lands in Australia and many other countries around the world. For the management of this weed, an integrated approach comprising biological control and plant competition strategies was tested in southern central Queensland. Two competitive pasture plant species (butterfly pea and buffel grass), selected for their high competitive ability, worked successfully with the biological control agent (Epiblema strenuana Walker) to synergistically reduce the biomass of parthenium weed, by between 62 and 69%. In the presence of biological control agent, the corresponding biomass of competitive plants, butterfly pea and buffel grass increased in comparison to when the biological control agent had been excluded, by 15 and 35%, respectively. This suggests that biological control and competitive plants can complement one another to bring about improved management of parthenium weed in Australia. Further, this approach may be adopted in countries where some of the biological control agents are already present including South Africa, Ethiopia, India, Pakistan and Nepal.

Fine-tuning of unmanned aerial surveillance for ecological systems

Much of our understanding and management of ecological processes requires knowledge of the distribution and abundance of species. Reliable abundance or density estimates are essential for managing both threatened and invasive populations, yet are often challenging to obtain. Recent and emerging technological advances, particularly in unmanned aerial vehicles (UAVs), provide exciting opportunities to overcome these challenges in ecological surveillance. UAVs can provide automated, cost-effective surveillance and offer repeat surveys for pest incursions at an invasion front. They can capitalise on manoeuvrability and advanced imagery options to detect species that are cryptic due to behaviour, life-history or inaccessible habitat. UAVs may also cause less disturbance, in magnitude and duration, for sensitive fauna than other survey methods such as transect counting by humans or sniffer dogs. The surveillance approach depends upon the particular ecological context and the objective. For example, animal, plant and microbial target species differ in their movement, spread and observability. Lag-times may exist between a pest species presence at a site and its detectability, prompting a need for repeat surveys. Operationally, however, the frequency and coverage of UAV surveys may be limited by financial and other constraints, leading to errors in estimating species occurrence or density. We use simulation modelling to investigate how movement ecology should influence fine-scale decisions regarding ecological surveillance using UAVs. Movement and dispersal parameter choices allow contrasts between locally mobile but slow-dispersing populations, and species that are locally more static but invasive at the landscape scale. We find that low and slow UAV flights may offer the best monitoring strategy to predict local population densities in transects, but that the consequent reduction in overall area sampled may sacrifice the ability to reliably predict regional population density. Alternative flight plans may perform better, but this is also dependent on movement ecology and the magnitude of relative detection errors for different flight choices. Simulated investigations such as this will become increasingly useful to reveal how spatio-temporal extent and resolution of UAV monitoring should be adjusted to reduce observation errors and thus provide better population estimates, maximising the efficacy and efficiency of unmanned aerial surveys.

Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: Species differentiation and prediction for fisheries monitoring and assessment

Four species of large mackerels (Scomberomorus spp.) co-occur in the waters off northern Australia and are important to fisheries in the region. State fisheries agencies monitor these species for fisheries assessment; however, data inaccuracies may exist due to difficulties with identification of these closely related species, particularly when specimens are incomplete from fish processing. This study examined the efficacy of using otolith morphometrics to differentiate and predict among the four mackerel species off northeastern Australia. Seven otolith measurements and five shape indices were recorded from 555 mackerel specimens. Multivariate modelling including linear discriminant analysis (LDA) and support vector machines, successfully differentiated among the four species based on otolith morphometrics. Cross validation determined a predictive accuracy of at least 96% for both models. An optimum predictive model for the four mackerel species was an LDA model that included fork length, feret length, feret width, perimeter, area, roundness, form factor and rectangularity as explanatory variables. This analysis may improve the accuracy of fisheries monitoring, the estimates based on this monitoring (i.e. mortality rate) and the overall management of mackerel species in Australia.

The host specificity and climatic suitability of the gall flyCecidochares connexa(Diptera: Tephritidae), a potential biological control agent for Chromolaena odorata(Asteraceae) in Australia

The gall fly Cecidochares connexa (Diptera: Tephritidae) is a potential biological control agent for Chromolaena odorata in Australia. Its host specificity was determined against 18 species in the tribe Eupatorieae (Family Asteraceae) in which C. odorata belongs, in quarantine in Brisbane, Australia. Oviposition occurred and flies developed on only C. odorata and Praxelis clematidea, both of which are in the subtribe Praxelinae. P. clematidea is considered a weed outside tropical America. In both multiple-species-minus-C. odorata choice tests and single-species no-choice tests, the mean number of galls/plant was significantly greater on C. odorata (48 and 41, respectively) than on P. clematidea (2 and 9, respectively). There were also significantly more adults emerging from C. odorata (mean 129 and 169, respectively) in the two types of tests than from P. clematidea (1 and 8, respectively). Paired choice, multiple generation (continuation) and time dependent tests further clarified the extent that C. connexa could develop on P. clematidea. In these tests, the mean number of galls formed and the mean number of emerging adults were consistently less for P. clematidea than C. odorata and populations of C. connexa could not be maintained on P. clematidea. Galls were not seen on any other plant species tested. This study supports the results of host specificity testing conducted in seven other countries and confirms that C. connexa poses little risk to other plant species in Australia. C. connexa has been released in 10 countries and an application seeking approval to release in Australia has been submitted to the Australian Government.

The host range of four new biotypes of Dactylopius tomentosus (Hemiptera: Dactylopiidae) from southern USA and their potential as biological control agents of Cylindropuntiaspp.(Cactaceae) in Australia: Part II.

Eight Cylindropuntia species have naturalised in Australia and pose serious economic, environmental and social impacts. Two biotypes of Dactylopius tomentosus have been used as bio-control agents to control different Cylindropuntia species. The host range of four additional biotypes of Dactylopius tomentosus from southern USA was investigated. Feeding and development were restricted to the genus Cylindropuntia. However, they showed differences in specificity within this genus and some biotypes discriminated between the provenances of C. rosea and C. tunicata. Efficacy trials were conducted to determine whether populations of each biotype could be sustained on the naturalised Cylindropuntia species and if these populations could retard the growth or kill these plants. The acanthocarpa biotype offers potential control of C. rosea (Lorne Station), while the cylindropuntia sp. biotype shows great potential to control C. rosea (Grawin). The cylindropuntia sp. biotype also had a high impact on C. kleiniae and C. imbricata, and a moderate impact on C. leptocaulis and C. prolifera. The acanthocarpa X echinocarpa biotype had its greatest impact on C. tunicata (Grawin), killing this plant in 18 weeks. A fourth biotype, leptocaulis, was damaging to some species, but was less effective than the other biotypes. Cylindropuntia spinosior is the only naturalised species in Australia where no effective biocontrol agent has been found.

Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Symposium: Food pricing and affordability of ‘healthy’ versus ‘less healthy’ diets: An international approach to monitoring and surveillance

Symposium co-ordinated by The International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS) Purpose Global monitoring of the price and affordability of foods, meals and diets is urgently needed. There are major methodological challenges in developing robust, cost-effective, standardized, and policy relevant tools, pertinent to nutrition, obesity, and diet-related non-communicable diseases and their inequalities. There is increasing pressure to take into account environmental sustainability. Changes in price differentials and affordability need to be comparable between and within countries and over time. Robust tools could provide baseline data for monitoring and evaluating structural, economic and social policies at the country/regional and household levels. INFORMAS offers one framework for consideration.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.