Best Researcher Award
Irvin Shandu
University of KwaZulu-Natal
Irvin Shandu is affiliated with the University of KwaZulu-Natal and has contributed to flood mapping research through geospatial analysis, remote sensing applications, and environmental data interpretation. His published work emphasizes practical approaches for disaster risk reduction, hydrological assessment, and spatial decision support. The following profile summarizes his scholarly background and suitability for recognition through the Research Data Analysis Awards.[1]
Abstract
Irvin Shandu’s scholarly activities focus on flood mapping, spatial analytics, and environmental risk assessment using geospatial datasets. His publications demonstrate the application of data-driven methodologies for improving hazard identification and supporting evidence-based planning. Although the publication record is modest, the work contributes to understanding flood vulnerability and sustainable environmental management.[2]
Keywords
Flood Mapping, Remote Sensing, GIS, Environmental Analysis, Hydrology, Spatial Data, Disaster Management, Geospatial Analytics.
Introduction
Modern flood mapping combines satellite observations, geographic information systems, and statistical modelling to improve disaster preparedness. Research in this field supports infrastructure planning, environmental monitoring, and climate resilience by transforming complex spatial datasets into actionable information.[3]
Research Profile
According to available bibliographic indicators, the researcher has published three indexed documents with nineteen citations and an h-index of two. The research portfolio reflects an emerging contribution to flood mapping and geospatial environmental analysis within South Africa.[1]
Research Contributions
Developed research supporting accurate flood extent identification using spatial datasets and remote sensing technologies for environmental assessment and disaster planning. Applied GIS techniques to interpret environmental patterns, producing location-based insights useful for hazard monitoring and sustainable resource management. Combined multiple sources of geographic and observational information to improve analytical reliability and support evidence-based environmental decision making.
Publications
The publication record includes peer-reviewed studies related to flood mapping and geospatial applications. These articles employ recognized scientific methodologies and contribute to regional environmental research through reproducible analytical approaches and data interpretation.[4]
Research Impact
Bibliometric indicators show early scholarly influence through citations and indexed publications. The research contributes to practical environmental management while providing a foundation for future interdisciplinary collaborations and expanded scientific impact.[1]
Award Suitability
The research portfolio aligns with the objectives of the Research Data Analysis Awards by demonstrating scientific integrity, practical environmental relevance, and effective application of analytical methods. Recognition would acknowledge continued development in flood mapping and geospatial research.[5]
Conclusion
Irvin Shandu represents an emerging researcher whose work integrates environmental science with spatial data analysis. His contributions to flood mapping illustrate the value of geospatial technologies in supporting disaster resilience and informed environmental decision making, making this profile appropriate for academic recognition.
External Links
References
- Elsevier. (n.d.). Scopus Author Details: Irvin Shandu, Author ID 58643854300.
https://www.scopus.com/authid/detail.uri?authorId=58643854300
- ORCID. (n.d.). Irvin Shandu ORCID Record.
https://orcid.org/0000-0002-0127-5714
- Shandu, I. D., Xulu, S., & Gebreslasie, M. (2026). Exploring Landsat-8 imagery for flood inundation mapping using thresholding, machine learning and hybrid techniques: A case study of the April 2022 flood event in the East Coast of South Africa. Remote Sensing Applications: Society and Environment. Advance online publication.
- Shandu, I. D., Xulu, S., Gebreslasie, M., & Atif, I. (2025). Comparative analysis of groundwater potential zones using machine learning and hybrid method in Nelson Mandela Bay Metropolitan Municipality, South Africa. Next Research. Advance online publication.
- Mapuru, M., Xulu, S., Shandu, I. D., Daemane, E., & Munyai, L. (2025). Potential drivers of fire severity in Golden Gate Highlands National Park: A case study of the November 2022 big fire event. Fire Ecology,