Back to
Dr Abhirup Dikshit
Research Associate

Dr Abhirup Dikshit

Science
Climate Change Research Centre

Abhirup Dikshit is a geospatial ecohydrologist using advanced remote sensing tools and machine learning models to monitor vegetation health and function in the face of climate change, land use, and other major disturbance events.
Abhirup’s Ph.D. was on examining large-scale soil-vegetation-climate interactions and processes with remotely sensed measurements from satellites under the mentorship of Prof. Biswajeet Pradhan & Prof. Alfredo Huete. Here, he received valuable professional mentoring in ecology, machine learning applications, and remote sensing to conduct research on ecological resilience, geospatial modeling, and environmental monitoring. He specializes in the use of next-generation geostationary satellites to examine extreme dry events, vegetation dynamics, and flash droughts to better understand Australia's ecosystems.
E-mail
a.dikshit@unsw.edu.au

  • ​​​​​​2021 - Recipient of the Cross-Faculty Project funded by the School of Information Systems and Modelling, UTS.
  • 2020 - Recipient of the ISM Research Incentivisation by the School of Information Systems and Modelling, UTS.
  • 2019 - Recipient of the International Research Training Program Scholarship (IRTP) funded by the Australian Government under the Department of Education and Training.
  • 2019 - Co-CI - Impact of climate change, Land use land cover, and socio-economic dynamics on landslides in South and East Asia. (Granted by International Science Council (ICSU).

  • 2022 - HDR Excellence Awards  - UTS Faculty of Engineering & IT (Commendation)
  • 2021 - Best Paper Award in Gondwana Research, Elsevier for the article titled ’Pathways and challenges of the application of artificial intelligence to geohazards modelling’. This article has been recognized as a 'Highly Cited Paper' by Clarivate Analytics, Web of Science. Link: https://doi.org/10.1016/j.gr.2022.11.009
  • 2020 - Best Paper Award in Atmosphere, MDPI for the article titled ’Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches’. Link: https://www.mdpi.com/journal/atmosphere/awards/1600 
  • 2020 - Won 2nd prize in the IEEE NSW Chapter computational Challenge Competition, 2020, presenting work on Drought Forecasting.
  • 2020 - Won 2nd prize in the Urban MAXAR Challenge, 2020 a national challenge competition addressing solutions to Australia's biggest challenges.

  • Understanding vegetation response during extreme dry events.
  • Analysing ecosystems function by tracking important sub-daily and daily processes.
  • Improve our understanding and modelling of unusual bushfire behaviour.
  • Analyzing the land-atmosphere feedback mechanism post bushfires.
  • Empirical modeling of of rainfall-induced landslides.