Daniel Doktor, Dr.

-

Head of working group

Land-Cover & Dynamics

Helmholtz Centre for Environmental Research - UFZ
Department Remote Sensing
Permoserstrasse 15, 04318 Leipzig - Germany

Tel.: +49 (0) 341 / 6025 1943
Daniel Doktor

Full member of German Centre for Integrative Biodiversity Research (iDiv)


Profile

  • Within the context of analysing the impacts of climate and global change on vegetation, my research focus is on the land-use, land-use intensity, plant traits and ecosystem services.
  • Acquistion and leading of third party funded projects (BMWI, BfN, BMEL, Helmholtz Association, EU)
  • Collaboration in UFZ's program oriented research (POF) and strategic processes, e.g. establishment of the Centre for Remote Sensing (RSC4Earth) in cooperation with Leipzig University

My research focuses on the detection and evaluation of climate (extreme) effects and land-use / land-cover change on terrestrial vegetation. This concerns the following components:

1. Analysing optical-reflective time-series of satellite data to derive forest condition, plant traits (phenology, pigments, leaf area) and ecosystem services (biodiversity, productivity). This is done by inverse parameterisation of radiative transfer models, classical empirical approaches as well as data science methods.

2. Acquisition of remotely sensed hyperspectral (airborne and field) data accompanied by respective trait measurements and in-situ vegetation records

3. Derivation of land-use (crop types, tree species), land-use intensity (mowing events, grazing intensity, fertilisation) and habitats from optical-reflective time-series of satellite data using data science methods

4. Establish / evaluate links between land-use intensity / biodiversity and plant traits / vegetation condition

5. Design von processing chains of remotely sensed raw data

Education

  • 1994            Abitur, Martin-Luther-Schule, Marburg
  • 1996-2002   Studies of Geography at the Westfälische Wilhelms Universität Münster, Diplom
  • 1998-1999   Studies of Geography at the Universität Rouen, France
  • 2003-2007   PhD at Imperial College, London


Non-academic

  • 1994-1995   Civil Service, Universitätsklinikum Marburg


Academic posts

  • since 2008    Postdoc at the Helmholtz Centre for Environmental Research - UFZ, Leader of the group 'Land-Cover & Dynamics' (LACY)'
  • 2007-2008    Research Associate, Imperial College London, Falklands Group
  • 2003-2007    PhD, Imperial College (London, U.K.), Department of Biology: Using satellite imagery and ground observations to quantify the effect of intra-annually changing temperature patterns on spring time phenology. Project: 'Time Geographical approaches to Emergence and Sustainable Societies' (TiGrESS)
  • 2002-2003    Research Associate, Potsdam Institute of Climate Impact Research (PIK); Projects: 'Sensitivity and Adaptation of Forests under Global Change' (SAFE) and 'Climate Change Adaptility of    Wine' (CLAWINE) 

New developments strengthen the link between modelling and remote sensing and sensor fusion, e.g. Helmholtz Alliance ‘Earth System Dynamics’ (http://hgf-eda.de/). Furthermore, large-scale validation sites for remote sensing products including spectral sensor networks are being establishment (Sentinel Missions, ACROSS, GCEF). Data are gathered at different spatial scales covering also micro-meteorological, biological and hydrological aspects (EnMAP project) to facilitate up-scaling (s. Figures below).

flyer upscaling
Depiction of the satellite validation test site at UFZ with 1) Eddy flux measurements, 2) spectral ground measurements and 3) airborne hyperspectral flight campaigns
licor-eddy-spectra
Different measurement techniques for assessing plant productivity: gas-exchange a leaf level and ecosystem level as well as hyperspectral data acquisitions (airborne and staitionary).

Extracting & simulating phenological metrics
The focus within vegetation phenology is on analysing the response of spring time phenology to climate change using ground and satellite observations. We also assessed the influence of heterogeneous landscapes on computed green-up dates and analysed trends of computed green-up dates on a European scale. A variety of methods to extract phenological metrics has been implemented in R package ’phenex’ to be of public use.

mean green up-dates
Mean green-up date from 1989-2007 from 1 km daily NOAA AVHRR observations

Physical-based phenological modelling
Coupling remote sensing with ecosystem modelling faclitates a better understanding of how bio-physical processes on the earth's surface translate into an electromagnetic signal received by e.g. a satellite sensor. Here, we employ a model driven by temperature and day length (PIM) to simulate phenological growth stages of tree species.
simulated budburst
Dependency of simulated budburst occurrence on temperature sums for different tree species based on climate scenarios (2000-2100).
In: Lange, M., Schaber, J., Marx, A., Jäckel, G., Badeck, F.W., Seppelt, R., Doktor, D. (2016). Simulation of forest tree species' phenological phases for different climate scenarios: chilling requirements and photo-period may limit bud burst advancement". International Journal of Biometeorology. DOI 10.1007/s00484-016-1161-8

Extraction of biopyhsical vegetation variables
The inversion of radiative transfer models is at the heart of determining e.g. Chlorophyll or water content of vegetation. The figure below shows simulated reflection profiles of vegetation based on 4 parameters sets with increasing complexity (+ noise) using PROSAIL.

wavelength nanometers
Radiative Transfer Modelling

The working group is also simulating at-sensor radiances by combining two models: the vegetation radiative transfer model SLC and the atmosphere radiative transfer model MODTRAN. This allows to work with signals received directly at the sensor, which makes it easier to identify vegetation parameters. Furthermore, this procedure reduces the number of variables for inversion and the overall computational effort required. 

Radiance
Measured at-sensor radiances (AISA dual) and simulated radiances of vegetation (wheat). red=at-sensor radiances, green=mean at-sensor radiances, black=simulated radiances. Preidl, S. A new framework for radiative transfer model inversion (in prep.) 

Land-use classification, habitat & biodiversity mapping
This is actually an old (remote sensing) topic which has seen a renaissance in the light of new satellite missions. This allows for example to discriminate tree species or to map crop types at field level (as shown below).

land use classification

The link between pollination types at the community level and optical traits allows us to map spatial patterns of pollination types with remotely sensed hyperspectral data. 

habitat-map
Distribution of the pollination types across the study site (a) and Shannon’s entropy H of the three pollination types (b) as mapped from airborne imaging spectroscopy data. Forested and agricultural areas were not covered by the sampling and thus masked. Feilhauer, H., Doktor, D., Schmidtlein, S., Skidmore, A. (2016). Mapping pollination types with remote sensing, Journal of Vegetation Science 27. pp. 999-1011
  • Radiative transfer modelling (PROSAIL, SLC, DART)
  • Data Sciene methods such as machine learning (PCA, randomForest, PLSR, SVR, Gaussian processes), geostatistics
  • Radiometric, geometric and atmospheric correction of hyperspectral data
  • High performance cluster computation

Ongoing

  • Helmholtz Association: "Impacts of hydroclimatic extremes on long- term forest condition anomalies" (PI) in PhD cohort "Societal and Environmental impacts of complex ExtremeS in a chAnging World" (2025-2029)
  • Federal Ministry for Food and Agriculture (BMEL): "Waldresilienz" (2024-2025)
  • "KI-basierte Integration von Fernerkundungs- und Citizen-Science- Daten zur Ableitung der Biodiversität in Wäldern" (iForest). Federal Ministry of Education and Research (BMBF).
     (2023-2024)
  • Flexpool funding iDiv: "Towards robust detection of plant diversity & management in grasslands’ spectral signal", PI (2022-2024)
  • UFZ PhD colleg MoDEV, project "Assessment of species and trait diversity in temperate vegetation from optical remote sensing data". With Leipzig University, Prof. Feilhauer (2021-2024)
  • Helmholtz Knowledge Transfer Project "Waldzustandsmonitor" (2021-2024)

Finished

  • EU 'Ecopotential' ('Horizon 2020'). 'Derivation of bio-physical variables from remotely sensed imagery' (2015-2018)
  • BMWi program ‚Vorbereitung der wissenschaftlichen und kommerziellen Nutzung der Sentinel-Missionen und nationalen Missionen‘: ‚PhenoS - Phänologische Strukturierung von zeitlich hochauflösenden Sentinel 2- Datensätzen zur Optimierung von Landnutzungsklassifikationen‘ (2013- 2017)
  • Helmholtz-Alliance ‘Remote Sensing and Earth System Dynamics’ (Biosphere): 'Fusion of radar (L-band) and hyperspectral data to derive biomass, leaf area index and vegetation disturbance' (2012-2017)
  • BfN assignement on ‚Characterisation of forest types using remotely sensed imagery‘ (2016)
  • BMWi program ‚Vorbereitung der wissenschaftlichen und kommerziellen Nutzung der Sentinel-Missionen und nationalen Missionen‘: ‘Validierung von Sentinel-Produkten auf Basis kontinuierlicher spektraler und Eddy- Flux-Messungen’ (2012-2015)
  • BMWi program ‚Development of methods and algorithms for data analysis in preparation of the EnMAP mission': ‘Methoden zur Ableitung des funktionellen Zusammenhanges ökosystemarer Prozesse in hyperspektralen Daten unterschiedlicher räumlicher Auflösung’ (2010- 2013)
  • Project within ‚Ecosystem Services under Changing Land-use and Climate‘ (ESCALATE, http://www.ufz.de/escalate/): Ecosystem services assessment in a Central European floodplain forest: an ecosystem approach using reflective and thermal remote sensing data (2013-2016)

Integrated projects (IP, UFZ funded):

Within the topic 'From local scale processes to regional predictions' (T53) =>

  • "Estimation of terrestrial gross primary production (GPP) from remote sensing data“ (2013-2017)
  • "Modelling and measuring fluorescence of a deciduous forest" (2016-2019)

Index:

You could use our publication index for further requests.

2024 (5)

to index

2022 (3)

to index

2021 (1)

to index

2020 (3)

to index

2018 (1)

to index

2017 (5)

to index

2016 (6)

to index

2015 (2)

to index

2014 (3)

to index

2013 (5)

to index

2012 (1)

to index

2011 (1)

to index

2009 (1)

to index