Call for papers: Special Issue: Remote Sensing-assisted inventory of forest attributes

Guest editors:

Dr. Hooman Latifi
Department of Remote Sensing
Institute of Geography and Geology
University of Wuerzburg
Campus Hubland Nord.86
D-97074 Wuerzburg

Phone: +49-(0)931-31-89638

Prof. Dr. Barbara Koch
Chair of Remote Sensing and Landscape Information Systems
University of Freiburg
Tennenbacherstr. 4
D-79106 Freiburg

Phone: +49-(0)761-2033694

Deadline for manuscript submissions: 31 October 2016

Dear colleagues

Forest management include a diverse range of planning stages which are highly variable according to the goals and strategies. Descriptors of forestry attributes should essentially be able to express the necessary information required for the specific management purpose. However, forests are often described by complex characteristics in terms of their composition, function, and structure. In case they are measurable, conventional field-based inventory methods have been in use for long time. Since these methods which involve field samplings of various intensities succeed in capturing only a limited portion of the available heterogeneity, remote sensing data have opened new horizons for area-wide mapping from tree to landscape, regional and even global scales. Moreover, remote sensing methods are more cost effective and can be applied in remote areas with limited access, as is typical for majority of protected areas. Remote sensing has been previously proven to be functional to support ground based inventory systems. Different tools have shown potentials for the derivation of forest attributes via the application of methods involving active and passive remotely sensed data. The available published research in this field embraces numerous reports and studies. However, many aspects concerning remote sensing-assisted inventory of forest attributes have been underrepresented, in particular in view of the fact that the availability of remote sensing data sources is constantly and rapidly under progress.

The special issue Remote Sensing-assisted inventory of forest attributes aims at covering a portion of state-of-the-art research in forest inventory by using multi-source remote sensing data and methods. The results of ongoing research work published by this issue can possibly support the practitioners and decision makers towards an advanced and extended use of remote sensing in design and implementation of forest inventories on various scales. To this aim, both fundamental and practice-oriented research works are welcome that accommodate one of the following scopes:

  • Remote sensing for National Forest Inventories (NFI): possibilities and challenges
  • Data assimilation, fusion and integration from multiple remote sensing platforms
  • Statistical issues: Bias/Variance trade-off, model setup, distributional properties and dimension reduction techniques
  • Multitemporal mapping and monitoring of forest disturbances caused by biotic and abiotic agents
  • New spectral, textural and structural indices to support forest inventory
  • Species-specific information for forest inventory: data- , tree type- and process-driven influential factors
  • Integration of forest phenology in remote sensing-assisted forest inventory
  • Remote sensing o forest successional stages
  • Modeling forest structural attributes which metrics to derive? Which data to use? Which performance to achieve?
  • Forest as a bioenergy pool: How well we are at deriving forest biomass by remote sensing?
  • Global models for estimation of Gross Primary Production (GPP) and carbon binding

In addition to original research articles, well-funded review articles are also welcome.


After a careful reading of the Instructions to authors on the PFG website (, the intended full manuscripts should be sent via email to Manuscripts can only be submitted until the announced deadline. All manuscripts will be sent to competent scientists in the field for review. The accepted manuscripts will be published at once in the issue 3/2017 of Photogrammetrie, Fernerkundung, Geoinformation (PFG).