Remote Sensing (A1)

Remote Sensing (A1)

A1) Remote sensing based methods for the assessment of forest structures

Barbara Koch, Holger Weinacker & Julian Frey

University of Freiburg, Faculty of Environment & Natural Resources, Institute of Earth & Environmental Sciences, Chair of Remote Sensing and Landscape Information Systems


Assessing and monitoring biodiversity are important for effective forest management and they require measurements that are quantifiable both spatially and temporally. Dynamic treatment of biodiversity information would provide a novel foundation for developing scenarios that highlight interactions between retention measures and other forest related ecosystem services.  Structural diversity has been promoted as an indicator of biodiversity (1,2). Abundance, heterogeneity, and spatial distribution of structural elements, such as habitat trees and degree of decaying wood, are assessed as predictors of biodiversity in all ConFoBi Module B projects. Currently, methods to assess structural elements across temporal and spatial scales are rare.

Study questions and hypotheses

Multiple and remote sensing may allow adaptive measuring, and modeling, of structural elements – both temporally and spatially. Advanced remote sensing techniques, e.g. LiDAR and digital stereo photogrammetry, in conjunction with platforms such as Unmanned Aerial Vehicles UAV and terrestrial laser are useful for testing remote sensing techniques.  New algorithms for extraction and unification of data are required to provide structural information at scales appropriate for ConFoBi projects.

Combined sensor techniques need to be applied in order to quantitatively assess full 3-D information for complex vegetation structures. Terrestrial laser systems provide the best foundation from which to derive tree and plot spatial structures. UAV or other airplane mounted systems, such as digital aerial photographs and discrete laser systems, provide 3-D structural information for entire forest stands.  Analysis and comparison of various crown structures can be used as indicators for the structural classification of entire trees. This information can can also be extrapolated to provide useful, level specific, indicators from the crown down to terrestrial sites. Furthermore, crown structure parameters and information from aerial photography or satellite imaging can be analyzed together to assess forest structures ranging from single trees to large study plots. Accuracy decreases as sites increase in size.  Combines sensor techniques are one possibility to quantify and assess 3-D and spectral information for complex vegetation structures.

Terrestrial laser systems together with measurements from Octocopter platforms (one form of UAV) provide the best basis to derive structures in a spatially explicit way for single trees and forest plots, while measurements from Remotely Piloted Aircraft Systems (REPAS another form of UAVs which can cover larger areas) or airplane mounted systems like digital aerial photographs and discrete laser systems provide 3-D structural information for forest stands or local forest areas.  This can be complemented by very high resolution (between 50 cm to 5 m) satellite data to link the information to larger areas. Information from the different systems will be nested into each other. Data such as a digital terrain model (dtm) and a digital surface model (dsm) for Baden-Württemberg, as well as instruments such as a terrestrial laser scanner are available at FeLis.

Major study questions are:

  • What is the most adequate, multi-scale remote sensing constellation to provide useful parameters representing abundance, quality, and distribution of structural retention elements?
  • Which algorithms and new processing approaches are needed to achieve high quality information for modelling structural elements from the different remote sensing constellations?
  • How can the remote sensing based information support a set of rules (classification system) that indicates the degree of biodiversity?

Approach and methods

Sensitivity of the modelled structural parameters are validated for their use in B projects. The aim is to weight the input parameters according to their sensitivities in order to find an optimal use. Virtual reality visualization are applied to selected plots and trees. This allows a visual representation of typecast examples. Modelled parameters are provided to all ConFoBi projects. A1 provides multi-scale structural data for all 135 study plots.


For RTG phase II, A1 will consider an advanced monitoring concept of forest structures. Monitoring concepts require multi-temporal data sets and special alignment of different data sets and appropriate change detection algorithms to avoid artefacts. For the development of the change detection method laser data from 2002 to 2004 are available at FeLis for the whole project region.

Further reading

  • Ferries & Humphrey 1999. A review on potential biodiversity indicators for application in British forests. Forestry 72: 313-328.
  • White et al.2010. Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data. 2010. Remote Sensing of Environment 114: 1576-1589.