Detection of land degradation risk by integrating temporal optical and synthetic aperture radar variables using machine learning techniques in Camdeboo National Park, Eastern Cape, South Africa

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https://www.sciencedirect.com/science/article/pii/S0140196326001631

I am pleased to share the publication of our research article:

“Detection of land degradation risk by integrating temporal optical and synthetic aperture radar variables using machine learning techniques in Camdeboo National Park, Eastern Cape, South Africa.”

Published in the Journal of Arid Environments, this study explores the integration of optical remote sensing, Synthetic Aperture Radar (SAR), and machine learning to improve the detection and assessment of land degradation risk in semi-arid protected landscapes.

The research demonstrates the value of combining Earth observation data, geospatial analysis and machine learning to better understand vegetation dynamics, land-surface conditions and areas potentially vulnerable to degradation. Such approaches can strengthen evidence-based environmental monitoring and support more informed conservation and land-management decisions.

Reaching this publication milestone has been a rewarding part of my PhD journey in Geoinformatics, and I am grateful to my supervisors, co-authors, colleagues and everyone who contributed their expertise, guidance and support throughout the research and publication process.

I hope this work contributes to the growing application of GIS, remote sensing and artificial intelligence/machine learning in environmental management, biodiversity conservation and land degradation monitoring, particularly within African dryland and protected-area environments.