Daniel Doktor

Dr. Daniel Doktor is the Head of the 'Land-Cover & Dynamics' research group at the Helmholtz Centre for Environmental Research (UFZ), Department of Remote Sensing. He holds a PhD from Imperial College London and has been a Postdoc at UFZ since 2008, leading groundbreaking research in remote sensing applications for environmental monitoring. His work focuses on analyzing climate and land-use impacts on vegetation through satellite data, radiative transfer modeling, and machine learning.

Education:

• Abitur, Martin-Luther-Schule, Marburg (1994)
• Diplom in Geography, Westfälische Wilhelms Universität Münster (2002)
• PhD in Biology from Imperial College London (2007), focusing on satellite imagery analysis of phenology and climate interactions

Research Interests: Dr. Doktor's research integrates remote sensing with environmental science to understand land-use changes, plant traits, and ecosystem services. Key areas include:

• Optical time-series analysis for vegetation condition monitoring
• Hyperspectral data acquisition and trait measurements
• Machine learning for land-use classification and biodiversity mapping
• Linking land-use intensity to biodiversity and ecosystem health
• Radiative transfer modeling and sensor fusion techniques

His work emphasizes innovative methods like the APiC algorithm for land-cover mapping and the Forest Condition Monitor for Germany.

Grants & Collaborations: Key projects include:

• Helmholtz-funded 'Impacts of hydroclimatic extremes on forest condition' (2025-2029)
• BMEL 'Waldresilienz' project (2024-2025)
• iDiv-funded biodiversity detection studies (2022-2024)

He collaborates with Leipzig University through the RSC4Earth initiative and coordinates the MoDEV PhD program on environmental remote sensing.

Labs & Teams: Leads the Land-Cover & Dynamics group, collaborating with experts in radiative transfer modeling, ecological data science, and Earth observation. The team operates the UFZ satellite validation test site and develops processing chains for remote sensing data.