Paper Info
Title
Same Viewpoint Different Perspectives - A Comparison of Expert Ratings with a TLS Derived Forest Stand Structural Complexity Index.
Abstract
Forests are one of the most important terrestrial ecosystems for the protection of biodiversity, but at the same time they are under heavy production pressures. In many cases, management optimized for timber production leads to a simplification of forest structures, which is associated with species loss. In recent decades, the concept of retention forestry has been implemented in many parts of the world to mitigate this loss, by increasing structure in managed stands. Although this concept is widely adapted, our understanding what forest structure is and how to reliably measure and quantify it is still lacking. Thus, more insights into the assessment of biodiversity-relevant structures are needed, when aiming to implement retention practices in forest management to reach ambitious conservation goals. In this study we compare expert ratings on forest structural richness with a modern light detection and ranging (LiDAR) -based index, based on 52 research sites, where terrestrial laser scanning (TLS) data and 360 degrees photos have been taken. Using an online survey (n = 444) with interactive 360 degrees panoramic image viewers, we sought to investigate expert opinions on forest structure and learn to what degree measures of structure from terrestrial laser scans mirror experts' estimates. We found that the experts' ratings have large standard deviance and therefore little agreement. Nevertheless, when averaging the large number of participants, they distinguish stands according to their structural richness significantly. The stand structural complexity index (SSCI) was computed for each site from the LiDAR scan data, and this was shown to reflect some of the variation of expert ratings (p = 0.02). Together with covariates describing participants' personal background, image properties and terrain variables, we reached a conditional R-2 of 0.44 using a linear mixed effect model. The education of the participants had no influence on their ratings, but practical experience showed a clear effect. Because the SSCI and expert opinion align to a significant degree, we conclude that the SSCI is a valuable tool to support forest managers in the selection of retention patches.
Year
DOI
Venue
2019
10.3390/rs11091137
REMOTE SENSING
Keywords
Field
DocType
terrestrial laser scanning,stand structural complexity index,forest structures,retention forestry,guideline implementation,photo-based expert survey,mixed methods
Computer vision,Structural complexity,Artificial intelligence,Geology,Machine learning
Journal
Volume
Issue
ISSN
11
9
2072-4292
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Julian Frey110.70
Bettina Joa200.34
Ulrich Schraml300.34
Barbara Koch4878.38