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Special Issue "Virtual Forest"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 30 June 2019

Special Issue Editor

Guest Editor
Prof. Dr. Juha Hyyppä

Finish Geospatial Research Institute, Masala, Finland
Website | E-Mail
Interests: laser scanning (airborne, mobile and terrestrial); 3D remote sensing; individual tree detection; virtual forests

Special Issue Information

Dear Colleagues,

A virtual world is - according some definition [1] - a computer-based replica of the environment which can be populated by users having a personal avatar, and simultaneously and independently explore the virtual world, participate in its activities and communicate with others. In a same way, Virtual Forest is a computer-based replica of the real forest which is assumed to be of interest for professional and non-professional forest users. For example, forest owners, buyers, and decision makers can walk about the wanted forests virtually from any place. In this special issue we are especially looking for multidisciplinary papers describing

  • Creation of Virtual Forests, Point Cloud Processing and Texturing Algorithms and Computer Science related to creation of the Virtual Forest
  • High-quality visualization techniques of Virtual Forests
  • Novel Application, Virtual Services, Serious Games and future visions related to using Virtual Forests
  • Use of Virtual Forest for the benefit of Forestry, especially Precision Forestry
  • Use of Novel concepts

The contributions are not limited to previous topics, but in general innovative, non-published, un-conventional and multidisciplinary approaches are especially welcomed. Several article types, such as Articles, Reviews and Technical Notes/Letters can be included in the special issue (see https://www.mdpi.com/journal/remotesensing/instructions).

Reference

  1. Virtual world. Available online: https://en.wikipedia.org/wiki/Virtual_world

Prof. Juha Hyyppä
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Virtual Forest, Virtual Replica, Digital Twin, Augmented Reality
  • Point Cloud, Texturing, 3D forest
  • Game Engine, Serious Games, Gamification
  • Visualization, Computer Graphics, Computer Science
  • Business applications, Decision Making
  • Precision Forestry, Individual Tree Modelling

Published Papers (1 paper)

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Research

Open AccessArticle
Same Viewpoint Different Perspectives—A Comparison of Expert Ratings with a TLS Derived Forest Stand Structural Complexity Index
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
PDF Full-text (6253 KB) | HTML Full-text | XML Full-text | Supplementary Files
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Virtual Forest)
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