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The Relevance of Forest Structure for Biomass and Productivity in Temperate Forests: New Perspectives for Remote Sensing

  • Rico FischerEmail author
  • Nikolai Knapp
  • Friedrich Bohn
  • Herman H. Shugart
  • Andreas Huth
Article
  • 122 Downloads

Abstract

Forests provide important ecosystem services such as carbon sequestration. Forest landscapes are intrinsically heterogeneous—a problem for biomass and productivity assessment using remote sensing. Forest structure constitutes valuable additional information for the improved estimation of these variables. However, survey of forest structure by remote sensing remains a challenge which results mainly from the differences in forest structure metrics derived by using remote sensing compared to classical structural metrics from field data. To understand these differences, remote sensing measurements were linked with an individual-based forest model. Forest structure was analyzed by lidar remote sensing using metrics for the horizontal and vertical structures. To investigate the role of forest structure for biomass and productivity estimations in temperate forests, 25 lidar metrics of 375,000 simulated forest stands were analyzed. For the lidar-based metrics, top-of-canopy height arose as the best predictor for describing horizontal forest structure. The standard deviation of the vertical foliage profile was the best predictor for the vertical heterogeneity of a forest. Forest structure was also an important factor for the determination of forest biomass and aboveground wood productivity. In particular, horizontal structure was essential for forest biomass estimation. Predicting aboveground wood productivity must take into account both horizontal and vertical structures. In a case study based on these findings, forest structure, biomass and aboveground wood productivity are mapped for whole of Germany. The dominant type of forest in Germany is dense but less vertically structured forest stands. The total biomass of all German forests is 2.3 Gt, and the total aboveground woody productivity is 43 Mt/year. Future remote sensing missions will have the capability to provide information on forest structure (e.g., from lidar or radar). This will lead to more accurate assessments of forest biomass and productivity. These estimations can be used to evaluate forest ecosystems related to climate regulation and biodiversity protection.

Keywords

Forest structure Biomass Aboveground wood productivity Remote sensing Lidar Forest model 

Notes

Acknowledgements

This study originates from the workshop “Space-based Measurement of Forest Properties for Carbon Cycle Research” at the International Space Science Institute in Bern during November 2017. We thank the Thünen Institute for providing the German national forest inventory data. We also want to thank Hans Pretzsch, Peter Biber and Michael Heym (TUM) for their input on forest structure and structure metrics. Kostas Papathanassiou, Victor Cazcarra-Bes, Matteo Pardini and Marivi Tello Alonso (DLR) gave useful insights into linking forest structure and remote sensing. We also thank the anonymous reviewers for their insightful comments and suggestions. This study was part of the HGF-Helmholtz-Alliance “Remote Sensing and Earth System Dynamics” HA-310 under the funding reference RA37012. NK was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under the funding reference 50EE1416. FB was funded by the Deutsche Forschungsgemeinschaft (DFG) within the research unit FOR1246 (Kilimanjaro ecosystems under global change: linking biodiversity, biotic interactions and biogeochemical ecosystem processes). HHS was funded by NASA grants 14-TE14-0085 and 16-ESUSPI-16-0015.

Supplementary material

10712_2019_9519_MOESM1_ESM.rds (70.1 mb)
Supplementary material 1 (RDS 71812 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Ecological ModelingHelmholtz Centre for Environmental Research GmbH – UFZLeipzigGermany
  2. 2.Institute for Meteorology and Climate Research, Atmospheric Environmental ResearchKarlsruhe Institute of TechnologyGarmisch-PartenkirchenGermany
  3. 3.Department of Environmental SciencesUniversity of VirginiaCharlottesvilleUSA
  4. 4.German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-LeipzigLeipzigGermany
  5. 5.Institute of Environmental Systems ResearchUniversity of OsnabrückOsnabrückGermany

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