The spatial dynamics of invasive para grass on a monsoonal floodplain, Kakadu National Park, northern Australia

Abstract: African para grass (Urochloa mutica) is an invasive weed that has become prevalent across many important freshwater wetlands of the world. In northern Australia, including the World Heritage landscape of Kakadu National Park (KNP), its dense cover can displace ecologically, genetically and culturally significant species, such as the Australian native rice (Oryza spp.). In regions under management for biodiversity conservation para grass is often beyond eradication. However, its targeted control is also necessary to manage and preserve site-specific wetland values. This requires an understanding of para grass spread-patterns and its potential impacts on valuable native vegetation. We apply a multi-scale approach to examine the spatial dynamics and impact of para grass cover across a 181 km2 floodplain of KNP. First, we measure the overall displacement of different native vegetation communities across the floodplain from 1986 to 2006. Using high spatial resolution satellite imagery in conjunction with historical aerial-photo mapping, we then measure finer-scale, inter-annual, changes between successive dry seasons from 1990 to 2010 (for a 48 km2 focus area); Para grass presence-absence maps from satellite imagery (2002 to 2010) were produced with an object-based machine-learning approach (stochastic gradient boosting). Changes, over time, in mapped para grass areas were then related to maps of depth-habitat and inter-annual fire histories. Para grass invasion and establishment patterns varied greatly in time and space. Wild rice communities were the most frequently invaded, but the establishment and persistence of para grass fluctuated greatly between years, even within previously invaded communities. However, these different patterns were also shown to vary with different depth-habitat and recent fire history. These dynamics have not been previously documented and this understanding presents opportunities for intensive para grass management in areas of high conservation value, such as those occupied by wild rice

Deployment of Open Data Kit for Information Management for Various Engineering Projects In Rural, Indonesia

Abstract—Open data kit was used in an Engineering context as a data collection, storage and management tool for diverse data types from irrigation channels, weirs, saturated rice paddies and ephemeral stream catchments in the Eastern Indonesian province of Nusa Tenggara Timur. In the islands of West Timor and Flores, information was primarily used to report maintenance issues in the weir, primary, secondary, tertiary and quaternary channels. In Sumba, data was recorded for flow velocity and elevation in saturated zones and sedimentation in an ephemeral stream. Information regarding physical characteristics from groundwater wells was also collected to assess drinking water quality. Smart phone data input forms were developed progressively using Open data kit Build, according to the specific needs of each research project. It was found to be a very user-friendly and effective tool to use in the Engineering context. The features of Open data kit Build allowed us to modify the various forms in remote locations. Developing multiple choice input focused forms made data collection easier for farmers and local government workers. It virtually eliminated the need for hardcopy data, and allowed for versatility with respect to the nature of the multidisciplinary research

Getting a grip on sensorimotor effects in lexical-semantic processing

One of the strategies that researchers have used to investigate the role of sensorimotor information in lexical-semantic processing is to examine effects of words’ rated body-object interaction (BOI; the ease with which the human body can interact with a word’s referent). Processing tends to be facilitated for words with high BOI compared to words with low BOI, across a wide variety of tasks. Such effects have been referenced in debates over the nature of semantic representations, but their theoretical import has been limited by the fact that BOI is a fairly coarse measure of sensorimotor experience with words’ referents. In the present study we collected ratings for 621 words on seven semantic dimensions (graspability, ease of pantomime, number of actions, animacy, size, danger, and usefulness) in order to investigate which attributes are most strongly related to BOI ratings, and to lexical-semantic processing. BOI ratings were obtained from previous norming studies (Bennett, Burnett, Siakaluk, & Pexman, 2011; Tillotson, Siakaluk, & Pexman, 2008) and measures of lexical-semantic processing were obtained from previous behavioural megastudies involving the semantic categorization task (concrete/abstract decision; Pexman, Heard, Lloyd, & Yap, 2017) and the lexical decision task (Balota et al., 2007). Results showed that the motor dimension of graspability, ease of pantomime, and number of actions were all related to BOI and that these dimensions together explained more variance in semantic processing than did BOI ratings alone. These ratings will be useful for researchers who wish to study how different kinds of bodily interactions influence lexical-semantic processing and cognition

Object-based mapping of native vegetation and para grass (Urochloa mutica) on a monsoonal wetland of Kakadu NP using a Landsat 5 TM Dry-season time series

This paper evaluates the use of multi-temporal Landsat 5 TM for object-based classification of native wetland vegetation and the perennial aquatic weed para grass within Kakadu National Park, Northern Territory, Australia. Using identical training data and segmentation, a nearest-neighbour classification produced from a four-image (dry season) time-series was compared with four 'single-date' classifications produced from the individual images of the same series. A 15-class vegetation map generated from the multi-date classification produced an overall accuracy of 82 percent (kappa = 0.80). This was an average increase in accuracy of 25 percent (kappa = 0.28) compared to single-date classifications. The multi-date image composite also improved segmentation quality and spectral separability of vegetation classes. Reliable maps of wetland vegetation, potentially useful for strategic conservation, can be produced by integrated, object-based, analysis of multi-temporal Landsat