Working group

Remote Sensing of Ecosystems (ROSE)

Our research and activities are lined up in order to assess the impact of climate change and land-use change on vegetation:

  • Deriving Essential Climate and Essential Biodiversity Variables (EBV & ECV) using optical-reflective imagery, e.g. phenological metrics, plant productivity or Chlorophyll content
  • Land-use classification, habitat and biodiversity mapping
  • Gathering remotely sensed hyperspectral data (airborne and on the ground, s. Figure below) incl. setting-up satellite product validation test sites
  • Design of processing chains (of remotely sensed raw data)

New developments strengthen the link between modelling and remote sensing and sensor fusion, e.g. Helmholtz Alliance ‘Earth System Dynamics’ ( 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
Different measurement techniques for assessing plant productivity: gas-exchange a leaf level and ecosystem level as well as hyperspectral data acquisitions (airborne and staitionary).

Due to UFZ's long tradition in ecophysiological modelling the generation of new remote sensing products is done in close collaboration with experts in the respective field. These products shall be fed directly into ecophysiological models which are also developed and employed within the group (s. Figure below). Here phenological phases of forest tree species (temperature and day-length driven) are simulated with the R-package ‘phenmod’ analysing e.g. the impact of future climate scenarios on forest phenology.

simulated budburst
Dependency of simulated budburst occurrence on temperature sums for different tree species based on climate scenarios (2000-2100)

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

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

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


AISA Dual, Hyspex, OceanOptics, ASD, mini MCA, Skye, TDR, PAM, Spad, LAI


GFZ, HU Berlin, FH Dessau, University of Zurich, Uni Erlangen,


  • EU 'Ecopotential' ('Horizon 2020'). 'Derivation of bio-physical variables from remotely sensed imagery' (2015-2018)
  • 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‘: ‚PhenoS - Phänologische Strukturierung von zeitlich hochauflösenden Sentinel 2- Datensätzen zur Optimierung von Landnutzungsklassifikationen‘ (2013- 2016)
  • 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)


  • 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, Ecosystem services assessment in a Central European floodplain forest: an ecosystem approach using reflective and thermal remote sensing data (2013-2016)
  • WP 3 within the BMBF Projekt ‚Sustainable Land and Water Management of Reservoir Catchments‘ (SaLMaR, http://salmar.uni- &L=2): ‚Remote sensing of land use land cover (LULC) and point sources‘ (2012-2015)

 Integrated projects (IP, UFZ fundet):

1) Within the topic 'From local scale processes to regional predictions' (T53) => „Estimation of terrestrial gross primary production (GPP) from remote sensing data“ (2013-2016 and 2016-2019)

2) Within ‚Catchment Dynamics‘ (T31) =>“Estimating organic carbon and plant properties from remotely sensed data“ (2013-2016)


ECV and EBV extraction

Lausch, A., Salbach, C., Doktor, D., Schmidt, A., Merbach, I., Pause, M., 2015. Estimation of phenological stages of barley from time series measurements of high imaging spatial-temporal hyperspectral data. Ecological Modelling. 295, 123-135,

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.

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

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

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.

Land-use classification and habitat mapping

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

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

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 hyper spectral 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

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

Ecological modelling

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

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


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. Doi:10.1111/jbi.12781

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

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


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.

Reviews and concepts

Lausch, A., Bannehr, L., Beckmann, M., Boehm, C., Feilhauer, H., Hacker, J.M., Heurich, M., Jung, A., Klenke, R., Neumann, C., Pause, M., Rocchini, D., Schaepman, M.E.; Schmidtlein, S., Schulz, K., Selsam, P., Settele, J., Skidmore, A.K., Cord, A.F., 2016. Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecological Indicators 70., 317-339., doi: 10.1016/j.ecolind.2016.06.022.

Lausch, A., Blaschke, T., Haase, D., Herzog, F., Syrbe, R.-U., Tischendorf, L., Walz, U., 2015. Understanding and quantifying landscape structure – A review on relevant process characteristics, data models and landscape metrics. Ecological Modelling 295, 31-41,