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Special Issue "Predicting Vegetation Size Maps"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Quantitative Methods and Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Timo Tokola

School of Forest Science, University of Eastern Finland, Joensuu 80101, Finland
Website | E-Mail
Interests: Natural resource inventory; GIS; Information system planning; Developing methods for using remote sensing in natural resource inventory and computer applications for supporting regional decision making; Forest management planning including aerial photography, photogrammetry, satellite remote sensing, terrestial and airborne laser scanning, GPS based mapping, GIS database design, analysis of GIS data and implementation of desktop GIS systems

Special Issue Information

Dear Colleagues,

The size distribution of vegetation provides useful information for assessing the economic value, growth and yield. Tree and shrub diameter and height distribution are correlated with species diversity and ecological values. It can also provide useful information on past disturbance events and the structure and successional status of a forest. The characterization of woody vegetation and quality of ecosystem depends highly on spatial size distribution of trees in forest stand.

Since it is inefficient to measure the size of various type of vegetation structures, the diameter distribution is normally assessed through the tree stem frequency distribution. Airborne laser scanning data and other GIS information has recently comprised a revolution in technological advancements with an enormous possibility for increasing the accuracy of large-scale forest inventories and reducing their costs.

Methodologically, prediction of tree diameter distribution is reliant on different aspects, such as:

  • the choice of a suitable distribution function i.e. parametric and the choice of statistical method
  • the choice of the dependent variable and independent predictor (e.g., partly field information and utilizing big data fusion methods, remote sensing aspects);
  • a multi-modal or irregular distribution of diameters due to unmanaged, uneven-aged forest;
  • the needed number of sample plots and structure of target population; and
  • the applicability of sub-distribution as well as new approaches and applications

Prof. Dr. Timo Tokola
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. Forests is an international peer-reviewed open access monthly 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

  • diameter distribution
  • height distribution
  • timber sortiments
  • stand structure
  • big data fusion

Published Papers (1 paper)

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Research

Open AccessArticle Predicting Tree Diameter Distributions from Airborne Laser Scanning, SPOT 5 Satellite, and Field Sample Data in the Perm Region, Russia
Forests 2018, 9(10), 639; https://doi.org/10.3390/f9100639
Received: 13 September 2018 / Revised: 9 October 2018 / Accepted: 11 October 2018 / Published: 13 October 2018
PDF Full-text (2412 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A tree list is a list of trees in the area of interest containing, for example, the species, diameter, height, and stem volume of each tree. Tree lists can be used to derive various characteristics of the growing stock, and are therefore versatile [...] Read more.
A tree list is a list of trees in the area of interest containing, for example, the species, diameter, height, and stem volume of each tree. Tree lists can be used to derive various characteristics of the growing stock, and are therefore versatile and informative sources of data for several forest management purposes. Especially in heterogonous and unmanaged forest structures with multiple species, tree list estimates imputed from local reference field data can provide an alternative to mean value estimates of growing stock (e.g., basal area, total stem volume, mean tree diameter, mean tree height, and number of trees). In this study, reference field plots, airborne laser scanning (ALS) data, and SPOT 5 satellite (Satellite Pour l’Observation de la Terre) imagery were used for tree list imputation applying the k most similar neighbors (k-MSN) estimation method in the West Ural taiga region of the Russian Federation for diameter distribution estimation. In k-MSN, weighted average of k field reference plots with highest similarity between field reference plot and target (forest grid cell, or field plot) based on ALS and SPOT 5 features were used to predict the mean values of growing stock and tree lists for the target object simultaneously. Diameter distributions were then constructed from the predicted tree lists. The prediction of mean values and diameter distributions was tested in 18 independent validation plots of 0.25–0.5 ha in size, whose species specific diameter distributions were measured in the field and grouped into three functional groups (Pines, Spruce/Fir, Broadleaf Group), each containing several species. In terms of root mean squared error relative to mean of validation plots, the accuracy of estimation was 0.14 and 0.17 for basal area and total stem volume, respectively. Reynolds error index values and visual inspection showed encouraging results in evaluating the goodness-of-fit statistics of the estimated diameter distributions. Although estimation accuracy was worse for functional group mean values and diameter distributions, the results indicate that it is possible to predict diameter distributions in forests of the test area with the tested methodology and materials. Full article
(This article belongs to the Special Issue Predicting Vegetation Size Maps)
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