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Open AccessArticle

Satellite-Based Derivation of High-Resolution Forest Information Layers for Operational Forest Management

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Environmental Remote Sensing and Geoinformatics Department, University of Trier, Behringstraße 21, 54286 Trier, Germany
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State Forest Service Rhineland-Palatinate, Office for Forest Planning, Südallee 15–19, 56068 Koblenz, Germany
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Ministry for Environment, Agriculture, Food, Viticulture and Forestry, Kaiser-Friedrich-Straße 1, 55116 Mainz, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Joanne C. White and Eric J. Jokela
Forests 2015, 6(6), 1982-2013; https://doi.org/10.3390/f6061982
Received: 15 April 2015 / Revised: 23 May 2015 / Accepted: 29 May 2015 / Published: 3 June 2015
A key factor for operational forest management and forest monitoring is the availability of up-to-date spatial information on the state of forest resources. Earth observation can provide valuable contributions to these information needs. The German federal state of Rhineland-Palatinate transferred its inherited forest information system to a new architecture that is better able to serve the needs of centralized inventory and planning services, down to the level of forest districts. During this process, a spatially adaptive classification approach was developed to derive high-resolution forest information layers (e.g., forest type, tree species distribution, development stages) based on multi-temporal satellite data. This study covers the application of the developed approach to a regional scale (federal state level) and the further adaptation of the design to meet the information needs of the state forest service. The results confirm that the operational requirements for mapping accuracy can, in principle, be fulfilled. However, the state-wide mapping experiment also revealed that the ability to meet the required level of accuracy is largely dependent on the availability of satellite observations within the optimum phenological time-windows. View Full-Text
Keywords: remote sensing; forest information layers; tree species mapping; spatially adaptive classification; Central Europe; operational forest management remote sensing; forest information layers; tree species mapping; spatially adaptive classification; Central Europe; operational forest management
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Stoffels, J.; Hill, J.; Sachtleber, T.; Mader, S.; Buddenbaum, H.; Stern, O.; Langshausen, J.; Dietz, J.; Ontrup, G. Satellite-Based Derivation of High-Resolution Forest Information Layers for Operational Forest Management. Forests 2015, 6, 1982-2013.

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