E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Forest Landscape Management: From Data to Decision"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: 15 March 2019

Special Issue Editors

Guest Editor
Dr. Jan Kašpar

Department of Forest Management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Prague, Czech Republic
Website | E-Mail
Interests: sustainable forest management; decision support systems; optimization; spatial harvest scheduling
Guest Editor
Prof. Dr. Róbert Marušák

Department of Forest Management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Prague, Czech Republic
Website | E-Mail
Interests: optimization techniques and decision support systems for forest management; forestry planning; forest mensuration; forest monitoring and inventories; forest yield in changing climate and ecological conditions
Guest Editor
Prof. Dr. Harald Vacik

Institute of Silviculture, University of Natural Resources and Applied Life Sciences, Vienna, Austria
Website | E-Mail
Interests: sustainable forest management; decision support systems; biodiversity; silviculture
Guest Editor
Prof. Dr. Pete Bettinger

Warnell School of Forestry and Natural Resources, University of Georgia, 180 East Green Street, Athens, GA 30602, USA
Website | E-Mail
Interests: forest management and planning; combinatorial optimization; spatial harvest scheduling; landscape planning; geographic information systems; global positioning systems; urban forests

Special Issue Information

Dear Colleagues,

Forest management practices and their impacts on ecosystems have been mostly studied at the stand level or other smaller local spatial scales to support multifunctionality and sustainability of forests. Translation to higher scales cannot be achieved by simple multiplying of the data from the stand or local scale by the larger area of the landscape. At the same time, it is evident that the forest alone influences the landscape and its other elements and vice versa. Thus, proper forest practices have a significant impact on economic, as well as environmental and social aspects at the landscape level. Forest resources management impacts on the larger landscape scale is insufficiently studied and understood. To achieve a sustainable landscape it requires precise data, reliable models and relevant decision systems. This Special Issue consists of a presentation of high quality scientific papers on (i) data, (ii) risk and uncertainty, (iii) modelling and assessment, and (iv) decision. New, progressive and innovative approaches and scientific achievements in terrestrial data gathering, remote sensing and data processing as a base of the landscape management are all addressed within the first thematic area of the papers. It was recognised that climate change brings many associated effects, which include increasing occurrence of extreme natural events and disturbances, such as storms, floods, fires, heat waves and droughts, soil erosion, desertification, and damage caused by pests and diseases. These factors are significantly affecting the landscape on the scale of forest and non-forest ecosystems. Risk and uncertainty analysis and modelling approaches for predicting landscape components, disturbances and landscape development are also covered by this special issue. To build a bridge between science and practice is the prerequisite for development of an effective decision support system based on temporal and spatial optimization of landscape management supporting bio-economy and ecosystem services.

Dr. Jan Kašpar
Prof. Dr. Róbert Marušák
Prof. Dr. Harald Vacik
Prof. Dr. Pete Bettinger
Guest Editors

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

  • landscape
  • sustainability
  • management
  • decision
  • ecosystem services

Published Papers (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Open AccessArticle Spatial Analysis of Temperate Forest Structure: A Geostatistical Approach to Natural Forest Potential
Forests 2019, 10(2), 168; https://doi.org/10.3390/f10020168 (registering DOI)
Received: 18 January 2019 / Revised: 13 February 2019 / Accepted: 14 February 2019 / Published: 16 February 2019
PDF Full-text (6314 KB) | HTML Full-text | XML Full-text
Abstract
Forest ecosystems represent an important means of ecosystem services; they are key as carbon sinks, water collectors, soil stabilizers, suppliers of great biological diversity, among other benefits. In addition, regionalization based on forest conditions provides a valuable approach to understanding and analyzing spatial [...] Read more.
Forest ecosystems represent an important means of ecosystem services; they are key as carbon sinks, water collectors, soil stabilizers, suppliers of great biological diversity, among other benefits. In addition, regionalization based on forest conditions provides a valuable approach to understanding and analyzing spatial patterns, which is useful as a tool for the implementation of forest ecosystem protection and conservation programs. In this research, the structure of a temperate forest in the western Sierra Madre region of Mexico was analyzed and characterized. The study unit was the watershed and the analysis used a geospatial approach combined with multivariate techniques such as: Principal Component Analysis, Cluster Analysis (CA), Discriminant Analysis (DA) and Multivariate Analysis of Variance. We evaluated the relationships among spectral satellite data, thematic maps and structural forest variables. A total of 345 watersheds were grouped based on these variables. The grouping of watersheds under low, medium and high production conditions was carried out with CA, defining 3 groups. The validation of the grouping was performed through DA, estimating errors with the restitution method, as well as with the cross-validation method. Significant differences were found among the groups. The grouping of watersheds provides observable evidence of the variability of the forest condition throughout the area. This study allows identifying forest areas with different levels of productivity and can help to detect levels of vulnerability and ecological fragility in natural forests in temperate zones. Full article
(This article belongs to the Special Issue Forest Landscape Management: From Data to Decision)
Figures

Figure 1

Open AccessArticle High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry
Forests 2018, 9(11), 696; https://doi.org/10.3390/f9110696
Received: 11 October 2018 / Revised: 7 November 2018 / Accepted: 8 November 2018 / Published: 10 November 2018
Cited by 1 | PDF Full-text (3789 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In [...] Read more.
Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In our study, we focused on the reconstruction of trees separately within the forest stand. We investigated the influence of camera lens, tree species, and height of diameter on the accuracy of the tree perimeter and diameter estimation. Furthermore, we investigated the variance of the perimeter and diameter reference measurements. We chose four tree species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst. and Abies alba Mill.). The perimeters and diameters were measured at three height levels (0.8 m, 1.3 m, and 1.8 m) and two types of lenses were used. The data acquisition followed a circle around the tree at a 3 m radius. The highest accuracy of the perimeter estimation was achieved when a fisheye lens was used at a height of 1.3 m for Fagus sylvatica (root mean square error of 0.25 cm). Alternatively, the worst accuracy was achieved when a non-fisheye lens was used at 1.3 m for Quercus petraea (root mean square error of 1.27 cm). The tree species affected the estimation accuracy for both diameters and perimeters. Full article
(This article belongs to the Special Issue Forest Landscape Management: From Data to Decision)
Figures

Figure 1

Open AccessArticle Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data
Forests 2018, 9(7), 371; https://doi.org/10.3390/f9070371
Received: 23 May 2018 / Revised: 15 June 2018 / Accepted: 19 June 2018 / Published: 21 June 2018
PDF Full-text (3384 KB) | HTML Full-text | XML Full-text
Abstract
Forest management planning is increasingly relying on airborne laser scanning (ALS) in forest inventory. The area-based method to interpret ALS data requires sample plots measured in the field. The aim of this study was to assess and trace the impacts of the positioning [...] Read more.
Forest management planning is increasingly relying on airborne laser scanning (ALS) in forest inventory. The area-based method to interpret ALS data requires sample plots measured in the field. The aim of this study was to assess and trace the impacts of the positioning errors of field plots along the entire forest management planning process, from their effect on forest inventory results to the outcome of forest management planning. This research links plot positioning errors with the spatio-temporal allocation of forest treatments and calculates the inoptimality losses arising from plot positioning errors in ALS-based forest inventory. The study area was a forest management unit in Central Spain. Growing stock attributes were predicted for a grid of square-shaped cells. Alternative management schedules were simulated for the grid cells by using growth and yield models. Then, a spatial forest planning problem aiming at maximizing timber production with even-flow cuttings was formulated. Spatial objective variables were used to cluster management prescriptions into dynamic treatment units. We used simulated annealing to conduct spatial optimization. First, the true plot locations were used and then the whole process was repeated with normally distributed random errors with standard deviation equal to 2.5, 5 and 10 m, resulting in an average error of 1.47, 3.06 and 8.34 m, respectively. Increasing the level of positioning errors resulted in higher variability in the estimated growing stock attributes and in the achieved values of management goals. Sub-optimal prescriptions caused by the tested plot positioning errors caused up to 4.62% losses in timber production and up to 3.35% losses in utility. The losses increased with increasing plot positioning error. Full article
(This article belongs to the Special Issue Forest Landscape Management: From Data to Decision)
Figures

Figure 1

Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top