Remote Sensing and Ecological Modelling

Canopy photo and LiDAR scene
Photograph of a flight campaign and a LiDAR scene across the Hainich National Park. Photo: Stephan Getzin

The target of this research topic is the development of new methods for the calculation of vegetation attributes by using remote sensing systems. The outcome should contribute to continuous coverage mapping of aboveground carbon stocks and other attributes in vegetation and their dynamics. The most important goal is gaining a better understanding of the following scientific questions:

  • How can we obtain important forest attributes (biomass, leaf area, productivity) from remote sensing?
  • How can we use forest parameters derived from remote sensing to quantify disturbance type and disturbance intensity in forests?
  • Is it possible to derive relationships which relate forest structure parameters as derived by remote sensing to local tree species richness?
  • How can we couple local dynamic forest models and global vegetation models with satellite data and how can we use satellite data to parameterise forest models?

Forest growth simulations serve as an important tool to explore these questions. With forest models like FORMIND ( large datasets of virtual forest inventories can be generated, which thereafter can be systematically screened for the above mentioned research questions.

LiDAR model
Visualization of the remote sensing simulation model (here LiDAR) developed in the department for ecological modelling. a) Visualization of a simulated forest stand by the forest model FORMIND b) voxel representation of the same forest stand with colors indicating the probability to contain a LiDAR return, depending on the count of tree voxels above each voxel c) simulated LiDAR point cloud with colors indicating height above ground.

We work with different types of remote sensing data (such as opitcal data, LiDAR and Radar) in different projects:

  • The large Helmholtz Alliance “Remote Sensing and Earth System Dynamics” (19 research institutes) aims at the development and evaluation of novel products derived from data acquired by a new generation of remote sensing satellites; and their integration in Earth system models for improving understanding and modelling ability of global environmental processes and ecosystem change. Especially we aim to integrate and use forest structure parameters derived from radar remote sensing techniques in monitoring the state and the properties of global forests with a particular focus on biological properties.
    Scanning grasslands and forests with an unmanned aerial vehicle.
  • Another project investigates the applicability of unmanned aerial vehicles in the field of ecology. The unmanned aerial vehicle of the OESA department is a quadcopter. This drone has four different sensors: a 24-megapixel RGB camera, a multispectral and a thermal camera, as well as a LiDAR that can be used for 3D-laserscanning of vegetation or other objects. With these sensors, the drone can be used for e.g. acquiring high-resolution images of forests, measuring tree heights based on LiDAR or extracting the NDVI index for the forest canopy.
Brazilian Forest Fragments
Forest fragments of the Brazilian Atlantic Forest in the North-East of Brazil, surround by sugar cane plantations. Photo: Mateus de Dantas de Paula

We also use satellite images to analyze how tropical rainforests in the Amazon region and coastal tropical forests are spatially distributed – affected by forest fragmentation. Because of deforestation of tropical rainforests in Brazil, significantly more carbon has been lost than was previously assumed. To estimate additional carbon emissions at the forest edges, we developed a new approach that integrates results from remote sensing, ecology and forest modelling. The effect of degradation has been underestimated in fragmented forest areas, since it was not possible before to calculate the loss of biomass at forest edges and the higher emission of carbon dioxide. In a nutshell, ecological modelling can play an important role for remote sensing in various ways.

Selected Publications

  • Knapp, N., Fischer, R., Huth, A., (2018):
    Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states
    Remote Sens. Environ. 205 , 199 - 209
    full text (url)
  • Taubert, F., Fischer, R., Groeneveld, J., Lehmann, S., Müller, M.S., Rödig, E., Wiegand, T., Huth, A., (2018):
    Global patterns of tropical forest fragmentation
    Nature 554 (7693), 519 - 522
    full text (url)
  • Brinck, K., Fischer, R., Groeneveld, J., Lehmann, S., Dantas de Paula, M., Pütz, S., Sexton, J.O., Song, D., Huth, A., (2017):
    High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle
    Nat. Commun. 8 , art. 14855
    full text (url)
  • Drusch, M., Moreno, J., Del Bello, U., Franco, R., Goulas, Y., Huth, A., Kraft, S., Middleton, E.M., Miglietta, F., Mohammed, G., Nedbal, L., Rascher, U., Schüttemeyer, D., Verhoef, W., (2017):
    The FLuorescence EXplorer mission concept—ESA’s Earth Explorer 8
    IEEE Trans. Geosci. Remote Sensing 55 (3), 1273 - 1284
    full text (url)
  • Getzin, S., Fischer, R., Knapp, N., Huth, A., (2017):
    Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro
    Landsc. Ecol. 32 (9), 1881 - 1894
    full text (url)
  • Dantas de Paula, M., Groeneveld, J., Huth, A., (2016):
    The extent of edge effects in fragmented landscapes: Insights from satellite measurements of tree cover
    Ecol. Indic. 69 , 196 - 204
    full text (url)
  • Shugart, H.H., Asner, G.P., Fischer, R., Huth, A., Knapp, N., Le Toan, T., Shuman, J.K., (2015):
    Computer and remote-sensing infrastructure to enhance large-scale testing of individual-based forest models
    Front. Ecol. Environ. 13 (9), 503 - 511
    full text (url)
  • Köhler, P., Huth, A., (2010):
    Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests
    Biogeosciences 7 (8), 2531 - 2543
    Volltext (PDF)
  • Pütz, S., Groeneveld, J., Henle, K., Knogge, C., Martensen, A.C., Metz, M., Metzger, J.P., Ribeiro, M.C., Dantas de Paula, M., Huth, A., (2014):
    Long-term carbon loss in fragmented Neotropical forests
    Nat. Commun. 5 , art. 5037
    Volltext (PDF)
  • Shugart, H.H., Asner, G.P., Fischer, R., Huth, A., Knapp, N., Le Toan, T., Shuman, J.K., (2015):
    Computer and remote-sensing infrastructure to enhance large-scale testing of individual-based forest models
    Front. Ecol. Environ. 13 (9), 503 - 511
    Volltext (PDF)