Dr. Daniel Doktor

Head of working group

Remote Sensing of Ecosystems (ROSE)

Helmholtz Centre for Environmental Research - UFZ
Department Computational Landscape Ecology
Permoserstrasse 15, 04318 Leipzig - Germany

Tel.: +49 (0) 341 / 235-1943
Daniel Doktor


  • Within the context of analysing the impacts of climate and global change on vegetation my research focus is on the derivation vegetation dynamics and land-use / land-cover
  • Leading of several third party funded projects (BMWI, BfN, EU)
  • Collaboration in UFZ strategic processes, e.g. establishment of a Centre for Remote Sensing in cooperation with the University Leipzig

Remotely sensed temporal changes of terrestrial ecosystems are induced by numerous factors. These can be changing temperature and precipitation patterns which potentially affect bio-physical plant composition and subsequently also productivity: feedbacks between ecosystems and biotic factors. However, similar changes can also be introduced by anthropogenic factors such as a modified land-use intensity or effects of land degradation and land-use change. Therefore, the core of my research is at identifying and discriminating the above factors using remotely sensed data. New satellite generation also facilitate the derivation of land-use intensity.

Another aspect of my research is the integration of remotely sensed data into ecosystem models and combining ecosystem modelling with remote sensing studies.

Consequently my research focusses on the following aspects:
  1. Analysis and modelling of vegetation dynamics/phenology at a regional to continental level
  2. Derivation of bio-physical vegetation parameters => Essential Climate Variables and Essential  Biodiversity  Variables, e.g. plant productivity
  3. Land-use classification at plot level / land-use change / land-use intensity (forest, agriculture), regional to national scale
  4. Methods: Radiative Transfer Modelling + Machine learning method for inverse parameterisation and the    analysis of highly dimensional and auto-correlated data
  5. Prosessing remotely sensed raw data


  • 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


  • 1994-1995   Civil Service, Universitätsklinikum Marburg

Academic posts

  • since 2008    Postdoc at the Helmholtz Centre for Environmental Research - UFZ, Leader of the group 'Remote Sensing of Ecosystems (ROSE)'
  • 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
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. 

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. 

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)
  • Machine learning (PCA, randomForest, PLSR, SVR, Gaussian processes), geostatistics
  • Radiometric, geometric and atmospheric correction of hyperspectral data
  • High performance cluster computation


  • 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)


Doktor, D., Koslowsky, D., Lange, M., Seppelt, R., Badeck, F.W. (2017) ‚Disparate applicability of methods to extract phenological metrics and broad spatial satellite resolution affect computed trends of European spring phenology‘. Remote Sensing of Environment (submitted)

Lange, M., Dechant, B., Rebmann, C., Vohland, M., Cuntz, M., Doktor, D. (2017) Establishment and Configuration of an Experimental Site for the Validation of Earth Observation Satellite Products - a Case Study on Phenology. Sensors (accepted)

Xu, X., Conrad, C., Doktor, D. (2017) Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS). Remote Sensing , 9, 254.

Dechant, B., Cuntz, M., Vohland, M., Schulz, E., Doktor, D. (2017). Estimation of leaf photosynthesis traits from reflectance spectra: correlation to nitrogen content as dominating mechanism. Remote Sensing of Environment.

Gerstmann, H., Doktor, D., Gläßer C., Möller M. (2016). PHASE: A geostatistical model for the Kriging-based spatial prediction of crop phenology using public phenological and climatological observations. Computers and Electronics in Agriculture 127 (2016) pp. 726–738

Richter, R., Reu, B., Wirth, C., Doktor, D., Vohland, M. (2016). The use of airborne hyperspectral data for tree species classification ina species-rich Central European forest area. International Journal of Applied Earth Observation and Geoinformation 52 (2016). pp. 464–474

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

Feilhauer, H., Doktor, D., Schmidtlein, S., Skidmore, A. (2016). Mapping pollination types with remote sensing, Journal of Vegetation Science 27. pp. 999-1011

Carl G, Doktor D, Schweiger O, Kühn I (2016): Assessing relative variable importance across different spatial scales: a two-dimensional wavelet analysis. Journal of Biogeography (43) 2502–2512.

Luft, L., Neumann, C., Itzerott, S., Lausch, A., Doktor, D., Freude, M., Blaum, N., Jeltsch, F. (2016). Digital and real-habitat modeling of Hipparchia statilinus based on hyperspectral remote sensing data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY. 13 (1). pp. 187-200

Neumann, C., Weiss, G., Schmidtlein, S., Itzerott, S., Lausch, A., Doktor, D. and Brell, M. (2015), Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring, Remote Sensing 7(3), 2871-2898

Doktor, D., Lausch, A., Spengler, D., Thurner, M. (2014): Extraction of plant physiological status from hyperspectral signatures using machine learning methods. Remote Sensing 6 (12), 12247-12274.

Feilhauer, H., Dahlke, C., Doktor, D., Lausch, A., Schmidtlein, S., Schulz, G., Stenzel, S. Mapping the local variability of Natura 2000 habitats with remote sensing. Applied Vegetation Science 17 (4). 765–779

Brosinsky, A., Lausch, A., Doktor, D. et al. (2014) Analysis of Spectral Vegetation Signal Characteristics as a Function of Soil Moisture Conditions Using Hyperspectral Remote Sensing, JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 42 (2), 311-324

Lausch, A., Pause, M., Merbach, I., Zacharias, S., Doktor, D., Volk, M., Seppelt, R (2013): A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape. ENVIRONMENTAL MONITORING AND ASSESSMENT 185 (2). 1215-1235

Carl, G., Doktor, D. and Kühn, I. (2012): Phase difference analysis of temperature and vegetation phenology for beech forest: a wavelet approach. Stochastic Environmental Research and Risk Assessment

Rogass, C., D. Spengler, M. Bochow, K. Segl, A. Lausch, D. Doktor, S. Roessner, R. Behling, H. U. Wetzel and H. Kaufmann (2011). Reduction of Radiometric Miscalibration-Applications to Pushbroom Sensors. Sensors 11(6): 6370-6395.

Doktor, D., Bondeau A., Koslowski D., Badeck F.W. (2009): Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations. Remote Sensing of Environment 113(12), p. 2618-2632

Badeck, F.-W., Böttcher, K, Bondeau, A., Doktor, D., Lucht, W., Schaber, J., Sitch, S. (2004). Responses of spring time phenology to climate change. New Phytologist 162, p. 295-309.