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Curriculum Vitae of Chima Iheaturu—Remote Sensing and Geodata Scientist with expertise in tropical forest monitoring and land systems analysis.
Basics
| Name | Chima Jude Iheaturu, Ph.D. (cand.) |
| Label | Remote Sensing & Geodata Scientist |
| chima.iheaturu@unibe.ch | |
| Url | https://linkedin.com/in/chimaiheaturu |
| Summary | Ph.D. researcher specializing in remote sensing, UAV-based forest mapping, LiDAR analysis, and spatio-temporal modelling of forest dynamics. Experienced in machine learning for land-cover mapping and dedicated to advancing ecological knowledge through open science, co-production, and interdisciplinary approaches. |
Work
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2020.10 - 2021.10 GIS & Database Manager
Wildlife Conservation Society (WCS)
Managed spatial data for conservation areas in Nigeria, performing advanced forest change detection and stakeholder reporting.
- Developed semi-automated pipelines for deforestation monitoring.
- Integrated socio-economic data with spatial analytics for conservation planning.
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2019.01 - 2020.10 GIS Analyst & UAV Pilot
ENVI Geospatial Ltd.
Led UAV operations and GIS analysis for environmental and engineering mapping projects across Nigeria.
- Introduced drone LiDAR capabilities for 3D forestry mapping.
- Mentored interns and junior analysts in GIS, photogrammetry, and Python scripting.
Education
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2021.11 - 2025.10 Bern, Switzerland
Ph.D. (in progress)
University of Bern
Geography & Sustainable Development
- Forest remote sensing
- UAV-LiDAR integration
- Machine learning for ecology
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2017.09 - 2018.11 Glasgow, UK
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2009.10 - 2014.09 Owerri, Nigeria
Awards
- 2018.11.01
Best M.Sc. Project Award
University of Glasgow
Recognized for outstanding thesis using low-cost photogrammetry to document GNSS sites.
- 2018.11.01
- 2014.09.01
Governor’s Prize – Best Graduating Student
Imo State University
Best student in Surveying and the Faculty of Environmental Science.
Publications
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2025.03.01 An integrated object-based sampling approach for validating non-contiguous forest cover maps
International Journal of Applied Earth Observation
Proposes object-based stratification for validation in fragmented tropical landscapes.
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2024.08.01 Integrating UAV LiDAR and multispectral data to assess forest disturbance
Ecological Informatics
Multi-sensor fusion approach for mapping disturbance in West African forests.
Skills
| Remote Sensing & GIS | |
| LiDAR processing | |
| UAV photogrammetry | |
| QGIS, ArcGIS, ENVI, GEE |
| Programming | |
| Python | |
| R | |
| Machine learning (scikit-learn, TensorFlow) | |
| Google Earth Engine |
Languages
| English | |
| Fluent |
| Igbo | |
| Native speaker |
| Yoruba | |
| Professional working proficiency |
| French | |
| Basic |
| German | |
| Basic (learning) |
Interests
| Forest Conservation & Ecology | |
| Landscape dynamics | |
| Forest patch connectivity | |
| Mangrove monitoring |
Projects
- 2021.11 - Present
SUSTAINFORESTS Project
Transdisciplinary research on tropical forest persistence using satellite data, UAV-LiDAR, and local knowledge integration.
- Co-designed methods for integrating narratives and spatial data
- Mapped forest patch dynamics across West Africa