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Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region
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Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region
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Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region
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Journal Article
Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region
2019
Overview
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.
Publisher
MDPI AG
Subject
