Potential of remote sensing-based forest attribute models for harmonising large-scale forest inventories on regional level: a case study in Southwest Germany
Image-based 3D information can provide metrics for forest attribute modelling that are robust within the study region. This enables transferability of predictive models to other data sets of the same region without loss of accuracy. As a result, aerial images can potentially provide auxiliary data for supporting different large-scale forest inventories within a geographic region.
Context Due to their high spatial coverage and acquisition frequencies, aerial imagery can provide auxiliary data to support large-scale forest inventories. If the methods are applicable to different data sets and forest inventory protocols, they will also facilitate harmonisation of forest inventory data.
Aims This study aims to investigate the level of transferability of such image-based methods. This is crucial for their applicability across different large-scale forest inventories. The investigation focusses on one geographic region.
Methods Three blocks of aerial images were used to generate models of forest canopies and extract 3D metrics. These were utilised for building timber volume models separately for each image block. The models were applied to the respective other blocks and the achieved accuracy of timber volume prediction was assessed. Additionally, 3D metric changes between blocks were also assessed.
Results Some metrics were found more robust than others. Transferring models based on robust metrics achieved RMSE% between 38 and 45%, which is similar to the model accuracy.
Conclusion This indicates transferability of models within the study region without loss of accuracy, and there is potential for further improvement of model accuracy. Therefore, forest attribute models based on remote sensing have potential to support harmonisation of large-scale forest inventories within the study region.
Keywords
Aerial images, Canopy height model, Forest inventory, Timber volume, Model transferability
Publication
Kirchhoefer, M., Schumacher, J. & Adler, P. Annals of Forest Science (2019) 76: 33. https://doi.org/10.1007/s13595-019-0804-4
For the read-only version of the full text: https://rdcu.be/buDZY
Data availability
The data that support the findings of this study are available from the land surveying authority of Baden-Württemberg (LGL) and Thünen-Institut, respectively, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of LGL and Thünen-Institut, respectively.
