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: 31 December 2020.

Special Issue Editor

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 2200 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 (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Analysis of Parameters for the Accurate and Fast Estimation of Tree Diameter at Breast Height Based on Simulated Point Cloud
Remote Sens. 2019, 11(22), 2707; https://doi.org/10.3390/rs11222707 - 19 Nov 2019
Abstract
Terrestrial laser scanning (TLS) is a high-potential technology in forest surveys. Estimating the diameters at breast height (DBH) accurately and quickly has been considered a key step in estimating forest structural parameters by using TLS technology. However, the accuracy and speed of DBH [...] Read more.
Terrestrial laser scanning (TLS) is a high-potential technology in forest surveys. Estimating the diameters at breast height (DBH) accurately and quickly has been considered a key step in estimating forest structural parameters by using TLS technology. However, the accuracy and speed of DBH estimation are affected by many factors, which are classified into three groups in this study. We adopt an additive error model and propose a simple and common simulation method to evaluate the impacts of three groups of parameters, which include the range error, angular errors in the vertical and horizontal directions, angular step width, trunk distance, slice thickness, and real DBH. The parameters were evaluated statistically by using many simulated point cloud datasets that were under strict control. Two typical circle fitting methods were used to estimate DBH, and their accuracy and speed were compared. The results showed that the range error and the angular error in horizontal direction played major roles in the accuracy of DBH estimation, the angular step widths had a slight effect in the case of high range accuracy, the distance showed no relationship with the accuracy of the DBH estimation, increasing the scanning angular width was relatively beneficial to the DBH estimation, and the algebraic circle fitting method was relatively fast while performing DBH estimation, as is the geometrical method, in the case of high range accuracy. Possible methods that could help to obtain accurate and fast DBH estimation results were proposed and discussed to optimize the design of forest inventory experiments. Full article
(This article belongs to the Special Issue Virtual Forest)
Show Figures

Graphical abstract

Open AccessArticle
AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees
Remote Sens. 2019, 11(18), 2074; https://doi.org/10.3390/rs11182074 - 04 Sep 2019
Cited by 6
Abstract
Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. However, some works cannot guarantee sufficient modelling accuracy, while [...] Read more.
Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. However, some works cannot guarantee sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct detailed 3D tree models from laser scans. We first extract an initial tree skeleton from the input point cloud by establishing a minimum spanning tree using the Dijkstra shortest-path algorithm. Then, the initial tree skeleton is pruned by iteratively removing redundant components. After that, an optimization-based approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The overall fitting error (i.e., the distance between the input points and the output model) is less than 10 cm. The reconstructed tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc. The source code of this work is freely available at https://github.com/tudelft3d/adtree. Full article
(This article belongs to the Special Issue Virtual Forest)
Show Figures

Figure 1

Open AccessArticle
A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner
Remote Sens. 2019, 11(15), 1791; https://doi.org/10.3390/rs11151791 - 31 Jul 2019
Cited by 2
Abstract
Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead [...] Read more.
Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS. Full article
(This article belongs to the Special Issue Virtual Forest)
Show Figures

Graphical abstract

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 - 13 May 2019
Cited by 1
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)
Show Figures

Graphical abstract

Back to TopTop