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Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock

Immitzer, Markus, Stepper, Christoph, Böck, Sebastian, Straub, Christoph, Atzberger, Clement
Forest ecology and management 2016 v.359 pp. 232-246
algorithms, conifers, forest industries, forest inventory, forest management, forest mensuration, forest stands, mixed forests, models, prediction, satellites, Germany
Angle-count sampling (ACS) is an established method in forest mensuration and is implemented in different National Forest Inventories (NFI). However, due to the lack of fixed reference areas of the inventory plots, these ACS-based field data are seldom used as training data for wall-to-wall mapping applications at forest enterprise level. In this paper, we demonstrate an approach to overcome this shortcoming. For a study area in northern Bavaria, Germany, we used ACS-based NFI data for model training to generate wall-to-wall maps of growing stock for broadleaf, conifer and mixed forest stands. Both spectral and height information from the very high resolution WorldView-2 (WV2) satellite were used as auxiliary information and the non-parametric Random Forests (RF) algorithm was chosen as modeling approach. The growing stock predictions were validated using out-of-bag (OOB) samples and further verified at the plot and stand level using additional data. For validation, field plots from a Management Forest Inventory (MFI) and delineated forest stands were used. Compared to stand-level aggregations based on field plots from the MFI, our approach explained 56% of the variability in the growing stock (R2) with a relative RMSE of 15% at the stand level (n=252). As expected, the scatter was higher at the plot-level (n=3973). Nonetheless, the models still achieved acceptable performance measures (R2=0.44; RMSE=34%).