Remote Sensing Measurements of Forest Structure Types for Ecosystem Service Mapping
Forests represent an important pool in the global carbon cycle. However, biomass stocks and carbon fluxes are variable due to the fact that forest dynamics are driven by processes that act on different spatial and temporal scales. Estimating forest biomass and productivity for larger regions is therefore a major challenge. In this study, horizontal and vertical forest structure is used to improve forest ecosystem service mapping by remote sensing. By linking remote sensing techniques with vegetation modelling (here FORMIND) and forest inventories, forest structure maps were derived for Germany (resolution 4 km). Using these maps, the role of forest structure for selected ecosystem services of forests has been investigated. For forest state estimations (like biomass) horizontal forest structure plays a key role while for productivity estimations both horizontal and vertical structures are relevant. This concept of forest structure classification in combination with forest modelling and remote sensing has high potential for applications at continental scales as future remote sensing missions will provide information on forest structure.
KeywordsForest structure Biomass Productivity Remote sensing Forest model Germany
We thank the Thünen Institute for providing the German national forest inventory (BWI) 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. This study was part of the Helmholtz-Alliance Remote Sensing and Earth System Dynamics. NK was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under the funding reference 50EE1416.
- 7.Reifsnyder WE. The role of forests in the global and regional water and energy balances. In: CAgM, editor. CAgM report, no 8. Geneva: World Meteorological Organization; 1982.Google Scholar
- 8.Intergovernmental Panel on Climate Change. Climate change 2014: impacts, adaptation, and vulnerability. Cambridge: Cambridge University Press; 2015.Google Scholar
- 9.Reineke LH. Perfecting a stand-density index for even-aged forests. J Agric Res. 1933;46:627–38.Google Scholar
- 10.Pretzsch H. Forest dynamics, growth and yield. Berlin: Springer Verlag; 2009. p. 281.Google Scholar
- 11.Dritte Bundeswaldinventur. Thuenen-Institut – Basisdaten (Stand 20.03.2015). 2015. https://bwi.info/Download/de/BWI-Basisdaten/ACCESS2003/. Accessed 7 Oct 2017.