Production of semi-real time media-GIS contents of natural disasters using MODIS satellite data

In the event of a natural disaster, the information provided to the public can play an important role in its mitigation and management. Use of media-GIS content has been shown to provide information that is visual and accessible to the public. This report focuses on the information provided to the public through the media and develops rigorous production methods and quality practices to encourage increased strategic use of media-GIS content. The report utilizes three natural disaster case studies to evaluate the production method and presents recommendations and conclusions based on the information these provide. Previous studies identified five aspects that are important to media-GIS contents. These are accuracy, high aesthetic quality, speed, low cost and reusability. A review of MODIS imagery has shown it to sufficiently satisfy all five aspects. The report identifies an ideal source of MODIS data and a production method based on the information available to be obtained. By applying this methodology to the three case studies, it was found that the process could be more streamlined than previously identified methods. Further observations identified both positive and negative aspects of the method allowing improvements to be made were possible. Whilst limitations of MODIS were identified, the properties of MODIS data make it evident that it is the most effective source of satellite data for the production of media-GIS content where time and cost need to be minimised. Completion of the case studies led to the production of a guidebook, presented in Appendix F, which is intended to be issued to media outlets as an instruction manual for producing media-GIS contents. It is hoped that this will encourage an increase in the use of GIS within the media industry and provide thorough production method and quality practices information

Increasing resilience to natural hazards through crowd-sourcing in St. Vincent and the Grenadines

In this project we aim to demonstrate how volcanic environments exposed to multiple hazards tend to be characterised by a lack of relevant data available both in real time and over the longer term (e.g. months to years). This can be at least partially addressed by actively involving citizens, communities, scientists and other key stakeholders in the collection, analysis and sharing of observations, samples and measurements of changes in the environment. Such community monitoring and co-production of knowledge over time can also build trusting relationships and resilience (Stone et al. 2014). There are more than 100 institutions worldwide that monitor volcanoes and other natural hazards, contribute to early warning systems and are embedded in communities. They have a key role in building resilience alongside civil protection/emergency management agencies. In this report, we propose that such institutions are involved in big data initiatives and related research projects. In particular, we suggest that tools for crowd-sourcing may be of particular value. Citizen science, community monitoring and analysis of social media can build resilience by supporting: a) coordination and collaboration between scientists, authorities and citizens, b) decision-making by institutions and individuals, c) anticipation of natural hazards by monitoring institutions, authorities and citizens, d) capacity building of institutions and communities, and e) knowledge co-production. We propose a mobile phone app with a supporting website as an appropriate crowd-sourcing tool for St Vincent and the Grenadines. The monitoring institution is the key contact for users and leads on the required specifications based on local knowledge and experience. Remote support is provided from the UK on technical issues, research integration, data management, validation and evaluation. It is intended that the app facilitates building of long-term relationships between scientists, communities and authorities. Real-time contributions and analysis of social media support early warning, real-time awareness and real-time feedback enhancing the response of scientists and authorities. The app has potential to facilitate, for example, discussions on new or revised hazards maps, multiple hazard analysis and could contribute to real-time risk monitoring. Such an approach can be scaled up to facilitate regional use – and is transferable to other countries. Challenges of such an approach include data validation and quality assurance, redundancy in the system, motivating volunteers, managing expectations and ensuring safety. A combination of recruiting a core group of known and reliable users, training workshops, a code of conduct for users, identifying information influx thresholds beyond which external support might be needed, and continuing evaluation of both the data and the process will help to address these issues. The app is duplicated on the website in case mobile phone networks are down. Development of such approaches would fit well within research programmes on building resilience. Ideally such research should be interdisciplinary in acknowledgement of the diversity and complexity of topics that this embraces. There may be funding inequality between national monitoring institutions and international research institutions but these and other in-country institutions can help drive innovation and research if they are fully involved in problem-definition and research design. New innovations arising from increasing resolution (temporal and spatial) of EO products should lead to useful near-real time products from research and operational services. The app and website can ensure such diverse products from multiple sources are accessible to communities, scientists and authorities (as appropriate). Other innovations such as machine learning and data mining of time-series data collected by monitoring institutions may lead to new insights into physical processes which can support timely decision-making by scientists in particular (e.g. increasing alert levels)

Spatio-temporal analysis of coastal sediment erosion in Cape Town through remote sensing and geoinformation science

Coastal erosion can be described as the landward or seaward propagation of coastlines. Coastal processes occur over various space and time scales, limiting in-situ approaches of monitoring change. As such it is imperative to take advantage of multisensory, multi-scale and multi-temporal modern spatial technologies for multi-dimensional coastline change monitoring. The research presented here intends to showcase the synergy amongst remote sensing techniques by showcasing the use of coastal indicators towards shoreline assessment over the Kommetjie and Milnerton areas along the Cape Town coastline. There has been little progress in coastal studies in the Western Cape that encompass the diverse and dynamic aspects of coastal environments and in particular, sediment movement. Cape Town, in particular; is socioeconomically diverse and spatially segregated, with heavy dependence on its 240km of coastline. It faces sea level rise intensified by real-estate development close to the high-water mark and on reclaimed land. Spectral indices and classification techniques are explored to accommodate the complex bio-optical properties of coastal zones. This allows for the segmentation of land and ocean components to extract shorelines from multispectral Landsat imagery for a long term (1991-2021) shoreline assessment. The DSAS tool used these extracted shorelines to quantify shoreline change and was able to determine an overall averaged erosional rate of 2.56m/yr. for Kommetjie and 2.35m/yr. for Milnerton. Beach elevation modelling was also included to evaluate short term (2016-2021) sediment volumetric changes by applying Differential Interferometry to Sentinel-1 SLC data and the Waterline method through a combination of Sentinel -1 GRD and tide gauge data. The accuracy, validation and correction of these elevation models was conducted at the pixel level by comparison to an in-field RTK GPS survey used to capture the current state of the beaches. The results depict a sediment deficit in Kommetjie whilst accretion is prevalent along the Milnerton coastline. Shoreline propagation and coastal erosion quantification leads to a better understanding of geomorphology, hydrodynamic and land use influences on coastlines. This further informs climate adaptation strategies, urban planning and can support further development of interactive coastal information systems