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Same Viewpoint Different Perspectives—A Comparison of Expert Ratings with a TLS Derived Forest Stand Structural Complexity Index

1
Chair of Remote Sensing and Landscape Information Systems FeLIS, University of Freiburg, 79106 Freiburg, Germany
2
Faculty of Environment and Natural Resources, University of Freiburg, 79106 Freiburg, Germany
3
Forest Research Institute Baden-Wuerttemberg, 79100 Freiburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1137; https://doi.org/10.3390/rs11091137
Received: 11 April 2019 / Revised: 3 May 2019 / Accepted: 11 May 2019 / Published: 13 May 2019
(This article belongs to the Special Issue Virtual Forest)
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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° photos have been taken. Using an online survey (n = 444) with interactive 360° 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 R2 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. View Full-Text
Keywords: terrestrial laser scanning; stand structural complexity index; forest structures; retention forestry; guideline implementation; photo-based expert survey; mixed methods terrestrial laser scanning; stand structural complexity index; forest structures; retention forestry; guideline implementation; photo-based expert survey; mixed methods
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Frey, J.; Joa, B.; Schraml, U.; Koch, B. Same Viewpoint Different Perspectives—A Comparison of Expert Ratings with a TLS Derived Forest Stand Structural Complexity Index. Remote Sens. 2019, 11, 1137.

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