Guest Researcher

Muhammad Imran is a geospatial scientist visiting IRI THESys for a year. His research interests center on understanding how climate extremes propagate through agro-ecosystems – from hazard drivers (precipitation deficits, heat stress, flood inundation) to vegetation responses (phenological shifts, biomass loss), and ultimately to impacts on production and livelihoods.

His PhD research at the University of Twente, the Netherlands (under the supervision of Prof. Alfred Stein and Prof. Raul Zurita-Milla), developed a spatial-statistical, uncertainty-aware upscaling framework within a Spatial Data Infrastructure (SDI) to integrate Earth observation (EO) and socio-environmental data for farm-scale agricultural impact assessment. This work linked agricultural outputs to livelihood and poverty outcomes in Burkina Faso, West Africa. He has supervised and successfully graduated two PhD scholars in Pakistan:

  • Ansar Ali (PhD awarded: 2022). Title: Quantifying biophysical and biochemical traits of citrus (Kinnow) fruit crops using multispectral and hyperspectral remote sensing.
  • Asmat Ali (PhD awarded: 2022). Title: Spatial Data Infrastructure as a means to assemble geographic information necessary for effective agricultural policies in Pakistan.

At IRI THESys, he is engaged in multidisciplinary research to develop the causal backbone of farm/field level digital twins that connect (i) climate-induced hydro-meteorological hazards (heatwaves and droughts) studied at IRI THESys, (ii) GeoAI/ML and EO advances from the Earth Observation Lab, and (iii) application of crop-yield process models from ZALF. In doing so, he emphasizes that these agricultural twins should be scientifically rigorous and undergo robust validation, uncertainty quantification, and risk analysis aligned with EU regulations on the responsible use of GeoAI/ML models and data (e.g., the EU AI Act and GDPR). In this context, he is also investigating how EU data-access regimes shape the viability of GeoAI and agricultural digital twins, particularly their ability to operate in real time and remain adaptable using data streams ranging from citizen sensors to regional and local SDIs (e.g., INSPIRE and Germany’s GDI-DE).

Research Interests

  • Impact of climate extremes on agriculture and livelihood
  • GeoAI/ML, EO and digital twining
  • Spatial data quality & governance

Publications

Imran, M., Haider, F. (2024). Forest ecosystem services of water-related filtration and regulation, a multi-source assessment and economic valuation in Mangla watershed. Water Supply, 24(11), 3680-3696. https://doi.org/10.2166/ws.2024.235

Shah, M., Imran, M., Yasin, M. (2024). Transforming cropland to forests in Pakistan, reducing net carbon footprints and contributing carbon credits. GeoJournal, 89(5), 199. https://doi.org/10.1007/s10708-024-11177-5

Ali, A., Imran, M. (2023). National Spatial Data Infrastructure vs. Cadastre System for Economic Development: Evidence from Pakistan. Land, 10, 188, 1-17. https://doi.org/10.3390/land10020188

Ali, A., Imran, M. (2020). Evaluating the potential of red edge position (REP) of Hyperspectral remote sensing data for real time estimation of LAI & Chlorophyll content of Kinnow fruit orchards. Scientia Horticulturae. 267 (109326). https://doi.org/10.1016/j.scienta.2020.109326

Imran M. & Mehmood, A. (2020). Analysis and mapping of present and future drivers of local urban climate using remote sensing: a case of Lahore, Pakistan. Arabian Journal of Geosciences. 278(13). https://doi.org/10.1007/s12517-020-5214-2

Imran, M., Sumra, K., Mahmood, A.. (2019). Mapping flood vulnerability from socioeconomic classes and GI data: Linking socially resilient policies to geographically sustainable neighborhoods using PLS-SEM. International Journal of Disaster Risk Reduction. 41. https://doi.org/10.1016/j.ijdrr.2019.101288

Imran, M., Sumra, K., Abbas, N. (2019). Spatial Distribution and Opportunity Mapping: applicability of evidence-based policy implications in Punjab using remote sensing and global products. Sustainable Cities and Society. 50.

Imran, M. (2019, March). Enabling Crowdsourcing in the Framework of User-centred SDIs for Information Management of Geographical Volunteer Content. In 2019 5th International Conference on Information Management (ICIM) (pp. 7-12). IEEE Xplore. https://doi.org/10.1109/INFOMAN.2019.8714670

Imran, M., Stein, A., Zurita-Milla, R. (2015). Using Geographical Weighted Kriging for crop yield mapping in West Africa. International Journal of Geographic Information Science. 29(2):1-24 https://doi.org/10.1080/13658816.2014.959522

Imran, M., Stein, A., Zurita-Milla, R. (2014). Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products. International Journal of Applied Earth Observation and Geoinformation: 26, Pages 322–334. https://doi.org/10.1016/j.jag.2013.08.012

 

Complete List of publications:

Google Scholar: https://scholar.google.com/citations?user=5TkjqGoAAAAJ&hl=en&authuser=3

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