ORCID Profile
0000-0002-5284-4155
Current Organisations
University of Technology Sydney
,
Aalborg Universitet
,
Australian Bureau of Agricultural and Resource Economics and Sciences
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Publisher: Elsevier BV
Date: 08-2014
Publisher: Springer Science and Business Media LLC
Date: 11-12-2009
Publisher: Informa UK Limited
Date: 04-2006
Publisher: IEEE
Date: 07-2015
Publisher: Elsevier BV
Date: 10-2012
Publisher: Elsevier BV
Date: 02-2015
Publisher: Copernicus GmbH
Date: 31-07-2012
DOI: 10.5194/ISPRSARCHIVES-XXXIX-B7-185-2012
Abstract: Abstract. Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM). For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010–2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95%) was observed in the object-based SVM compared with that of traditional pixel-based classification (89%) using maximum likelihood classifier (MLC). Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of different shape, textural and spectral variables, and their weights on crop-mapping accuracy, was also examined. Temporal change in the spectral characteristics, specifically through vegetation indices derived from multi-temporal Landsat data, was found to be the most critical information that affects the accuracy of classification. However, use of these variables was constrained by the data availability and cloud cover.
Publisher: Springer Science and Business Media LLC
Date: 22-03-2015
Publisher: American Chemical Society (ACS)
Date: 20-11-2014
DOI: 10.1021/NP500436R
Abstract: The closely related species Fusarium graminearum and Fusarium pseudograminearum differ in that each contains a gene cluster with a polyketide synthase (PKS) and a nonribosomal peptide synthetase (NRPS) that is not present in the other species. To identify their products, we deleted PKS6 and NRPS7 in F. graminearum and NRPS32 in F. pseudograminearum. By comparing the secondary metabolite profiles of the strains we identified the resulting product in F. graminearum as fusaristatin A, and as W493 A and B in F. pseudograminearum. These lipopeptides have previously been isolated from unidentified Fusarium species. On the basis of genes in the putative gene clusters we propose a model for biosynthesis where the polyketide product is shuttled to the NPRS via a CoA ligase and a thioesterase in F. pseudograminearum. In F. graminearum the polyketide is proposed to be directly assimilated by the NRPS.
Publisher: Copernicus GmbH
Date: 29-09-2014
Abstract: Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing. To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continent-wide investigation and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition from the El Niño-induced decadal droughts to flooding caused by La Niña.
Publisher: Informa UK Limited
Date: 03-2006
Location: United Kingdom of Great Britain and Northern Ireland
Location: Australia
Location: Australia
Location: Australia
No related grants have been discovered for Henriette Giese.