Comparing proportional compositions of geospatial technology-related programs at three universities

Today, graduates seek employment in a global marketplace, regardless of the country in which they studied. Comparing academic programs helps students, academics and employers to make informed decisions about study options, program offerings and the employment of recent graduates. In this study, we juxtapose geospatial technologyrelated programs at three universities located in Europe, Africa and America. Initially, the authors contributed information about these programs through a questionnaire comprising several open-ended questions about the origins and development of the respective programs. Subsequently, the proportional thematic compositions of programs at the three universities were compared. As expected, this study was not without challenges. From the outset, we struggled with agreeing on terminology and semantics. Results of the study indicate that there is not a one-size-fits-all strategy for establishing, shaping and sustaining such programs. Program composition is guided by many factors, including staff expertise, university politics, legislation, attractiveness to students,  technological developments, demands in the job market and requirements set by a professional body. Some of these factors are strongly influenced by the local (university) environment (e.g. staff expertise), others are of national relevance (e.g. legislation and a national professional body), while some apply globally (e.g. technological developments). The study illustrated how a comparison of proportional program composition can reveal significant differences and similarities that are not obvious when only content is compared. The compositional differences naturally result in graduates with different knowledge and skills that allow different career paths and meet different needs of the job market

Oral literature in South Africa: 20 years on

I offer a retrospective on the field of orality and performance studies in South Africa from the perspective of 2016, assessing what has been achieved, what may have happened inadvertently or worryingly, what some of the significant implications have been, what remain challenges, and how we may think of, or rethink, orality and performance studies in a present and future that are changing at almost inconceivable pace.DHE

Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation

Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for maintaining global ecosystem services. Monitoring vegetation in these ecosystems is required to assess rangeland health, to gauge habitat suitability for wildlife and domestic livestock, to combat invasive weeds, and to elucidate temporal environmental changes. Although rangeland ecosystems cover vast areas, traditional monitoring techniques are often time-consuming and cost-inefficient, subject to high observer bias, and often lack adequate spatial information. Image-based vegetation monitoring is faster, produces permanent records (i.e., images), may result in reduced observer bias, and inherently includes adequate spatial information. Spatially balanced sampling designs are beneficial in monitoring natural resources. A protocol is presented for implementing a spatially balanced sampling design known as balanced acceptance sampling (BAS), with imagery acquired from ground-level cameras and unmanned aerial systems (UAS). A route optimization algorithm is used in addition to solve the ‘travelling salesperson problem’ (TSP) to increase time and cost efficiency. While UAS images can be acquired 2–3x faster than handheld images, both types of images are similar to each other in terms of accuracy and precision. Lastly, the pros and cons of each method are discussed and examples of potential applications for these methods in other ecosystems are provided

Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model

The scope of wildfires over the previous decade has brought these natural hazards to the forefront of risk management. Wildfires threaten human health, safety, and property, and there is a need for comprehensive and readily usable wildfire simulation platforms that can be applied effectively by wildfire experts to help preserve physical infrastructure, biodiversity, and landscape integrity. Evaluating such platforms is important, particularly in determining the platforms’ reliability in forecasting the spatiotemporal trajectories of wildfire events. This study evaluated the predictive performance of a wildfire simulation platform that implements a Monte Carlo-based wildfire model called WyoFire. WyoFire was used to predict the growth of 10 wildfires that occurred in Wyoming, USA, in 2017 and 2019. The predictive quality of this model was determined by comparing disagreement and agreement areas between the observed and simulated wildfire boundaries. Overestimation–underestimation was greatest in grassland fires (>32) and lowest in mixed-forest, woodland, and shrub-steppe fires (<−2.5). Spatial and statistical analyses of observed and predicted fire perimeters were conducted to measure the accuracy of the predicated outputs. The results indicate that simulations of wildfires that occurred in shrubland- and grassland-dominated environments had the tendency to over-predict, while simulations of fires that took place within forested and woodland-dominated environments displayed the tendency to under-predict