Abstract
Deadwood mapping is of high relevance for studies on forest biodiversity, forest disturbance, and dynamics. As deadwood predominantly occurs in forests characterized by a high structural complexity and rugged terrain, the use of remote sensing offers numerous advantages over terrestrial inventory. However, deadwood misclassifications can occur in the presence of bare ground, displaying a similar spectral signature. In this study, we tested the potential to detect standing deadwood (h > 5 m) using orthophotos (0.5 m resolution) and digital surface models (DSM) (1 m resolution), both derived from stereo aerial image matching (0.2 m resolution and 60%/30% overlap (end/side lap)). Models were calibrated in a 600 ha mountain forest area that was rich in deadwood in various stages of decay. We employed random forest (RF) classification, followed by two approaches for addressing the deadwood-bare ground misclassification issue: (1) post-processing, with a mean neighborhood filter for “deadwood”-pixels and filtering out isolated pixels and (2) a “deadwood-uncertainty” filter, quantifying the probability of a “deadwood”-pixel to be correctly classified as a function of the environmental and spectral conditions in its neighborhood. RF model validation based on data partitioning delivered high user’s (UA) and producer’s (PA) accuracies (both > 0.9). Independent validation, however, revealed a high commission error for deadwood, mainly in areas with bare ground (UA = 0.60, PA = 0.87). Post-processing (1) and the application of the uncertainty filter (2) improved the distinction between deadwood and bare ground and led to a more balanced relation between UA and PA (UA of 0.69 and 0.74, PA of 0.79 and 0.80, under (1) and (2), respectively). Deadwood-pixels showed 90% location agreement with manually delineated reference to deadwood objects. With both alternative solutions, deadwood mapping achieved reliable results and the highest accuracies were obtained with deadwood-uncertainty filter. Since the information on surface heights was crucial for correct classification, enhancing DSM quality could substantially improve the results.
Highlights
Deadwood is an important resource for more than 30% of all forest species and of great relevance for forest biodiversity [1,2,3,4]
The reduced Random forest (RF) model based on seven variables (DDLG) performed similar compared to the initial, full model including 18 variables, with a Cohen’s Kappa of 0.92 and 0.93 and an overall accuracy of 0.95 and 0.94, respectively (Table 4)
We presented two alternative methods for enhancing pixel-based standing deadwood detection based on RF classification, using either post-processing or a deadwood-uncertainty filter
Summary
Deadwood is an important resource for more than 30% of all forest species and of great relevance for forest biodiversity [1,2,3,4]. Fresh or in later stages of decay, deadwood offers a variety of microhabitats for various species [5,6,7,8], in addition to providing substrate for lichens, mosses, and fungi [9,10,11], and nutrients for a new generation of trees. A differentiation between standing and lying deadwood in various stages of decay, or even between different tree species, may be required [16,17]
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