Abstract
From global monitoring to regional forest management there is an increasing demand for information about forest ecosystems. For border regions that are closely connected ecologically and economically, a key factor is the cross-border availability and consistency of up-to-date information such as the forest type. The combination of existing forest information with Earth observation data is a rational method and can provide valuable contribution to serve the increased information demand on a transnational level. We present an approach for the remote sensing-based generation of a transnational and temporally consistent forest type information layer for the German federal states of Rhineland-Palatinate and Saarland, and the Grand Duchy of Luxembourg. Existing forest information data from different countries were merged and combined with suitable vegetation indices derived from Landsat 8 and Sentinel-2 imagery acquired in early spring. An automated bootstrap-based approximation of the optimum threshold for the distinction of “broadleaved” and “coniferous” forest was applied. The spatially explicit forest type information layer is updated annually depending on image availability. Overall accuracies between 79 and 96 percent were obtained. Every spot in the region will be updated successively within a period of expectably three years. The presented approach can be integrated in fully automated processing chains to generate basic forest type information layers on a regular basis.
Highlights
Forests are one of the most important ecosystems containing the largest reserve of carbon biomass on earth with an annual uptake of about one-third of the global fossil fuel emissions [1]
We present an approach for the remote sensing-based generation of a transnational and temporally consistent forest type information layer for the German federal states of Rhineland-Palatinate and Saarland, and the Grand Duchy of Luxembourg
Existing forest information data from different countries were merged and combined with suitable vegetation indices derived from Landsat 8 and Sentinel-2 imagery acquired in early spring
Summary
Forests are one of the most important ecosystems containing the largest reserve of carbon biomass on earth with an annual uptake of about one-third of the global fossil fuel emissions [1]. Forests provide important basic provisional, ecosystem and social-economic services, which are essential for global life [2,3,4]. National laws such as the federal and federal state forest laws [5,6] as well as many international agreements on forest protection, such as the Kyoto protocol [7], the United Nations Forum on Forests [8], the New York Declaration on Forests [9] or European Forests 2020 [10] lead to an increased request on up-to-date forest information. Terrestrial data alone are not capable of fulfilling the information demands
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