William Lidberg

My research bridges cutting-edge technology with practical applications, supporting sustainable, multi-use forestry through advanced spatial analysis, machine learning, and environmental data science.

I work across hydrology, ecology, archaeology, and forestry, developing AI-driven methods to improve forest and water management decision-making. A significant focus of my research is predicting and mapping surface water dynamics in forested landscapes, which has led to successful collaborations with governmental agencies, industry partners, and international research teams. These efforts have contributed to implementing national soil moisture and wet area maps and improving land management strategies across multiple countries.

My research spans hydrology, forestry, and ecology, developing AI-driven tools for mapping and predicting environmental and cultural features on a national scale. I have pioneered the use of machine learning and deep learning to enhance hydrological modeling, soil moisture mapping, and biodiversity assessments, resulting in widely adopted industry tools such as the SLU Soil Moisture Map and AI-detected drainage networks. My work bridges technology and policy, investigating how AI impacts decision-making and stakeholder trust in forestry. Through collaborations with governmental agencies, forestry companies, and international partners, I ensure that my research delivers practical, high-impact solutions. By integrating social science perspectives, I strive to develop ethical and transparent AI applications that balance environmental conservation with economic and societal needs.

I have developed, am course responsible for, and currently teach a master’s-level course, Analysis of Environmental Data 2, which integrates machine learning, deep learning, and geospatial data (e.g., LiDAR and satellite data) to address environmental challenges.

Main supervisor of one PhD student and co-supervisor of four others and a postdoc, all scheduled to finish between 2026 and 2027. 

Supervision of Postdoc 

Francesco Zignol, Co-supervisor, dynamic wet area mapping

Supervision of students (PhD level)

Mariana Dos Santos Toledo Busarello, Main-supervisor, Preliminary PhD Thesis title, “Challenges and social consequences of artificial intelligence in Swedish forests”. Forest Ecology & Management, SLU, Sweden, Expected defense Spring 2026.

Joakim wising, Co-supervisor, Preliminary PhD Thesis title, “Challenges and social consequences of artificial intelligence in Swedish forests”. political science, Umeå University, Sweden, Expected defense Spring 2026.

Yiqi Lin, Co-supervisor, Preliminary PhD Thesis title, “Potential Application of Digital Soil Mapping to Accelerate Quaternary Deposit Mapping in Sweden”. Forest Ecology & Management, SLU, Sweden, Expected defense Spring 2026.

Olivia Anderson, Co-supervisor, Preliminary PhD Thesis title, “Interdisciplinary decision support for drained wetlands”. Forest Ecology & Management, SLU, Sweden, Expected defense Fall 2026.

Alejandro Gandara, Co-supervisor, Preliminary PhD Thesis title, “Trade-offs, upscaling, & connectivity of a blue-green network”. Forest Ecology & Management, SLU, Sweden, Expected defense fall 2027.