Associate Professor, at UKZN, South Africa.
Dr. Kabir Peerbhay is a distinguished environmental scientist specializing in remote sensing and precision forestry. Currently serving as Principal Research Officer at SAPPI Forests Southern Africa and Honorary Associate Professor at the University of KwaZulu‑Natal (UKZN), he combines academic rigour with industry leadership. With a PhD in Environmental Science (Remote Sensing) earned in 2014, and a Cum Laude MSc obtained in 2011, Dr. Peerbhay’s career is marked by research excellence, supervision of numerous graduate students, and multiple awarded grants. His work has earned him an NRF C2 rating and recognition among UKZN’s top young publishers. Passionate about leveraging machine learning and space-based imagery for sustainable forest management, he drives initiatives ranging from forest health mapping to carbon monitoring. Dr. Peerbhay is also active in community development, serving as the chairman of the VULA Youth Development board since 2021, reflecting his commitment to social and environmental impact.
Professional Profile
Scopus
ORCID
🎓 Education
Dr. Peerbhay’s academic journey began with a Bachelor of Social Science in Geography & Environmental Management (UKZN) in 2008, followed by an Honours degree in the same field in 2009. He pursued a Cum Laude MSc in Applied Environmental Science (Remote Sensing) in 2011, under supervisors Prof. Onisimo Mutanga and Dr. Riyad Ismail. His doctoral research, completed in 2014, culminated in a PhD in Environmental Science (Remote Sensing) at UKZN with the same supervisory team. In 2016, he augmented his expertise by completing an NQF Level 5 Project Management course. His education reflects a continuous and focused investment in remote sensing technologies, geospatial analysis, and environmental management, laying the foundation for his later contributions to forest monitoring, land‑use change, and ecosystem health.
💼 Experience
Dr. Peerbhay’s professional experience spans academia, applied research, industry, and consulting. Since 2018, he has served as Principal Research Officer at SAPPI Forests Southern Africa, leading the Precision Forestry Department. In parallel, he’s been an Honorary Associate Professor at UKZN (SAEES) since 2016, with an NRF C2 rating. Previously, he worked as a Senior Research Scientist in Spatial Technologies at the Institute for Commercial Forestry Research (2015–2018), and as a Geospatial Consultant at the Institute of Natural Resources (2013–2015). He has also lectured undergraduates at UKZN and moderated courses at Durban University of Technology. His early career included GIS technician work and even mechanical/electrical support for Nestlé SA. This diverse background highlights his interdisciplinary skills—from field data collection, remote sensing, spatial analysis to project leadership and supervisory roles.
🔬 Research Interest
Dr. Peerbhay’s research focuses on remote sensing, machine learning, and spatial analysis for ecosystem monitoring and sustainable forest management. He is interested in:
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Forest health & disturbance detection, including stress, nutrient deficiency, and damage mapping using multispectral/hyperspectral and SAR imagery.
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Carbon estimation and sequestration, leveraging machine learning for above‑ground biomass quantification.
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Land‑use change & suitability modeling, focusing on urban and rural landscape transitions, restoration, and rehabilitation.
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Advanced classification techniques, integrating LiDAR, textural metrics, and ensemble learning (RF, CNN, ANN).
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Precision forestry, optimizing plantation management through high‑resolution monitoring tools.
He also explores ecosystem services mapping in urban and commercial forestry contexts. This interdisciplinary blend aims to innovate climate‑resilient forestry and spatially‑informed environmental decision‑making.
🏅 Awards
Dr. Peerbhay’s achievements have been recognized through several honors. In 2012, he entered the Golden Key International Honour Society, awarded to the top 15% of UKZN graduates. Since 2016, he’s held an Honorary Research Fellow appointment at UKZN. In 2018, he gained nationwide recognition as an NRF Y‑rated scientist, later advancing to an NRF C2 rating in 2024. In 2020, he was listed among UKZN’s Top 10 Young Publishers. Beyond academia, since 2021 he’s chaired the VULA Youth Development board, contributing to youth empowerment. Awards also include Supplemental Instruction supervision, peer‑review roles (e.g., ISPRS), and participation in professional bodies like the South African Institute of Foresters. Together, these accolades acknowledge his contributions to education, scientific research, and community leadership.
📃 Top Noted Publications
Here’s a selection of recent peer-reviewed publications by Dr. Peerbhay (with citation counts and journal links):
1. Assessing the extent of land degradation in the eThekwini municipality using land cover change and soil organic carbon
Journal: International Journal of Remote Sensing (2024)
Citations: 15
Highlights:
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Focuses on quantifying land degradation using multi-temporal land cover data and soil organic carbon (SOC) as key indicators.
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Utilizes remote sensing techniques (likely Sentinel-2 or Landsat) to monitor spatial and temporal changes in land use/cover.
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Employs GIS and statistical models to link land cover change with declines in SOC levels.
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Application centered on eThekwini municipality, making the findings regionally significant.
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Significance: Offers a replicable framework for local governments to monitor degradation and inform land management policies.
2. The use of synthetic aperture radar technology for crop biomass monitoring: A systematic review
Journal: Remote Sensing Applications: Society and Environment (2024)
Citations: 10
Highlights:
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Provides a comprehensive review of SAR (e.g., Sentinel-1, RADARSAT, ALOS) for estimating crop biomass.
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Assesses backscatter characteristics, polarimetric variables, and their correlation with biomass metrics.
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Identifies advantages over optical systems (e.g., cloud penetration, all-weather capabilities).
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Discusses methodological challenges, such as speckle noise, and future research directions.
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Significance: Establishes SAR as a robust tool for agricultural biomass monitoring, especially in cloud-prone regions.
3. A machine learning approach to mapping suitable areas for forest vegetation in the eThekwini municipality
Journal: Remote Sensing Applications: Society and Environment (2024)
Citations: 8
Highlights:
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Applies machine learning models (possibly Random Forest, SVM, or XGBoost) to predict suitable zones for afforestation.
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Inputs include topography, soil data, rainfall, land cover, and anthropogenic influences.
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Produces predictive habitat suitability maps to guide urban greening and forest restoration efforts.
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Localized to eThekwini, aligning with environmental planning goals.
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Significance: Supports sustainable land use and climate mitigation through informed reforestation strategies.
4. Comparing the utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel‑2 MSI to estimate aboveground grass biomass
Journal: Sustainability (2024)
Citations: 5
Highlights:
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Compares ANN and CNN architectures for predicting grassland biomass from Sentinel-2 imagery.
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Evaluates performance metrics such as RMSE, R², and training efficiency.
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CNNs likely found to better capture spatial patterns due to their hierarchical feature extraction.
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Incorporates vegetation indices (NDVI, EVI), texture features, and raw spectral bands.
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Significance: Advances precision agriculture and rangeland management by identifying the best deep learning approach.
5. Assessing above‑ground biomass in reforested urban landscapes using machine learning and remotely sensed data
Journal: Journal of Spatial Science (2024)
Citations: 7
Highlights:
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Targets urban forestry by estimating AGB (above-ground biomass) in restored urban sites.
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Leverages remote sensing inputs and machine learning algorithms (e.g., Gradient Boosting, Random Forest).
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Integrates LiDAR, optical imagery, and GIS data for improved accuracy.
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Emphasizes urban sustainability, carbon sequestration, and ecological benefits of reforestation.
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Significance: Contributes to carbon accounting and urban ecological modeling using advanced analytics.
Conclusion
Dr. Kabir Peerbhay is exceptionally well-qualified for the Best Researcher Award. He demonstrates an impressive blend of scholarly excellence, practical forestry innovation, research leadership, and student mentorship. His work has significant impact on both academic knowledge and applied environmental management, particularly in the forestry and remote sensing sectors.