about me

Shiva Pariyar was born and grown up in Nepal, a country marked by striking ecological diversity from the subtropical Terai to the alpine Himalayas. Growing up close to forests and rural landscapes, he developed an early appreciation of the deep connections between people, land, and natural resources. Witnessing forest degradation, land-use change, and climate-related pressures fostered a strong curiosity about how forests function, respond to stress, and can be managed sustainably. This motivation led him to pursue undergradate degree in Forestry at Tribhuvan University, where extensive fieldwork across diverse forest types grounded his belief that effective forest management must be based on sound ecological understanding and reliable data.

Seeking broader scientific exposure, Shiva moved to Australia to undertake a Master of Forest Ecosystem Science at the University of Melbourne. There, he studied a more quantitative and data-driven approach to forest science, focusing on ecosystem processes, forest health, and climate-vegetation interactions. This period marked his first deep engagement with remote sensing and geospatial technologies, where he developed skills in satellite imagery analysis, geospatial modelling, and ecosystem assessment. Exposure to Australia’s plantation forestry sector and applied research challenges further shaped his interest in technology-enabled forest monitoring.

Shiva’s academic journey then brought him to New Zealand, where he is currently a PhD candidate in Forest Observation with Remote Sensing Technologies (FOReST) Group at the University of Canterbury, within the New Zealand School of Forestry. His doctoral research focuses on assessing forest health and vitality in plantation forests using UAVs, LiDAR, and satellite imagery, with a particular emphasis on foliar insect herbivory as an indicator of stress. By combining fine‑scale structural data with spectral information across multiple spatial scales, his work advances high‑resolution, objective, repeatable, and scalable methods for the early detection, mapping, and monitoring of forest stress to support sustainable forest management.