Abstract
Determining the exact position of a forest inventory plot—and hence the position of the sampled trees—is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011–2012) within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.
Highlights
Modeling and characterizing forest stands on small scales using high-resolution remote sensing data requires spatially explicit linking of inventory information and remote sensing data [1,2,3].The exact sampling positions of field inventory plots are often determined using non-differential [4]or differential Global Navigation Satellite System (GNSS), which gives rise to location errors of up to several meters
We performed a co-registration of 133 BWI sampling plots to airborne laser scanning (ALS)-derived individual tree detections of a study area in Rhineland-Palatinate, Germany as a preparatory step for a forest characterization on the individual tree level
As erroneous tree pair assignments result in a reduction of model quality, we searched for a method for co-registration and tree matching which rates the reliability of a match
Summary
Modeling and characterizing forest stands on small scales using high-resolution remote sensing data requires spatially explicit linking of inventory information and remote sensing data [1,2,3].The exact sampling positions of field inventory plots are often determined using non-differential [4]or differential Global Navigation Satellite System (GNSS), which gives rise to location errors of up to several meters. The effect of positional displacements between terrestrial reference data and ALS data has been investigated by Gobakken and Næsset [6] and Frazer et al [7] at the sample plot level. They found that with increasing positional displacements the performance of biophysical models decreases in relation to the variable and stand characteristics. This problem exacerbates on individual tree level, since survey trees might get incorrectly linked to aerial detected trees (e.g., using ALS), which results in erroneous output data for further analyses
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