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The COVID-19 pandemic has caused severe disturbances in the work of hundreds of millions of people around the world. One of the groups affected is the academic staff at higher education institutions, whose original business model, i.e., presence teaching, suddenly changed to online learning. This has, in turn, exacerbated pre-existing problems such as shortage of time, busy schedules, and challenges to a work-life balance. Since academic staff plays a key role in respect of teaching and research, often acting as leaders in their fields, it is important to reflect on the influences of the lockdowns on their work routines. In order to address this research need, this paper reports on a study that examined the impacts of the lockdowns on the work of academic staff at universities. Using a bibliometric analysis and investigation of a set of case studies, the study sheds light on the difficulties encountered and the means deployed to address them. Our study did not identify a one-size-fits-all response to manage the manifold changes brought on HEIs by the COVID-19 pandemic. Selected arising priorities include creating a culture of educational resilience through a container of complementary measures., peer-reviewed

The past few decades have seen an increasing demand for environmental information on which to make more informed decisions, strengthened by several international calls for improving available environmental information. In a democratic South Africa, the involvement of people in policy and decision-making processes has led to an even greater need for accessible information. In response to this demand for information, State of the Environment (SOE) reporting has gained momentum and become highly popular in South Africa. This popularity has grown despite the absence of any legal mandate for reporting in South Africa, as well as the lack of formalised institutional arrangements for reporting. The current situation has resulted in a number of challenges for SOE reporting which need to be addressed. These challenges, and appropriate solutions, are discussed. The paper aims to stimulate discussion in the field of SOE reporting, both within South Africa and internationally.

In support of the urgent call for climate change mitigation and pursuing the vision of sustainable, circular economy, increasing number of universities have been developing courses to prepare students in meeting the growing demand for green and sustainability related jobs, which require systems perspective training in Industrial Ecology (IE) and Life Cycle Assessment (LCA). LCA analyses emissions and resource extractions from mining and processing of resources (cradle) to disposal of wastes and residuals (grave) and even back to cradle. This paper plans to contribute in developing an online-based information system which provides an interdisciplinary approach to teach IE and LCA for sustainable industrial development. Specifically, the aims of this e-learning project are: (1) To develop innovative teaching and learning materials in line with flipped classroom strategies and (2) To create a one-stop Blackboard in support of learning the principles and concepts of IE and LCA for Sustainable Production and Consumption. Though the developed materials are used first to support faceto- face learning, this is being leveraged to meet demand for distance/online LCA education in the near future..

T he accuracy of classified results is often measured in comparison with reference or “ground truth” information. However, in inaccessible or remote natural areas, sufficient ground truth data may not be cost-effectively acquirable. In such cases investigative measures towards the optimisation of the classification process may be required. The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as well as band combinations and the use of vegetation indices. It was found that adding probability thresholds to the classification may provide a measure of suitability regarding training area characteristics and band combinations. The analysis illustrated that finding a balance between training area size and heterogeneity may be fundamental to achieving an optimum classified result. Furthermore, results indicated that the addition of vegetation index values introduced as additional image bands could potentially improve classified products and that threshold outcomes could be used to illustrate confidence levels when mapping classified results.