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Forests
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Geographic Information Systems and Their Applications in Forests
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Geographic Information Systems and Their Applications in Forests

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

Deadline for manuscript submissions: closed (15 October 2019) | Viewed by 21716

Special Issue Editor

Dr. Francois Girard

E-Mail Website
Guest Editor
Department of Geography, Université de Montréal, Montréal, QC, Canada
Interests: plant ecology; forest biogeography; geographic information systems and their applications; modelling and statistics; dendro-ecology and dendro-climatology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic Information Systems (or GIS) are now implemented in most research labs around the world. They allow to create, manage, analyze or map spatial data such as points, lines, polygons, air photos, satellite images or else. From the simple map in a journal article to very complex spatial analysis, researchers are now using and see GIS every day. Applications in forest research are extensive and varied. Historically, computer power had limited GIS analysis over large areas. Nowadays, machine learning can achieve a lot of processes simultaneously to provide researchers exceptional computer power for GIS analysis. Global understanding of forest dynamics, processes and, fluxes at the landscape level is now trending. Forest managers are quite interested in the big picture when planning sylviculture. Forest GIS datasets such as national inventories are based on field observations that can be interpolated in non-sampled areas. Thus, GIS analysis is non-expensive tool that forest managers use on a daily basis.

This Special Issue of Forests is focused on “Geographic Information Systems (GIS) and Their Applications in Forests”, and how the different GIS techniques can improve the global understanding of forest dynamics. Research articles may focus on any aspect GIS sciences, such as mapping, modelling, remote sensing or spatial statistics.

Dr. François Girard
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 submissions that pass pre-check are 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 2600 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

  • Geographic Information Systems
  • remote sensing
  • machine learning
  • forest management
  • landscape ecology

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Published Papers (4 papers)

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Research

14 pages, 3872 KiB  
Article
Mount Taishan Forest Ecosystem Health Assessment Based on Forest Inventory Data
by Yan Meng, Banghua Cao, Chao Dong and Xiaofeng Dong
Forests 2019, 10(8), 657; https://doi.org/10.3390/f10080657 - 5 Aug 2019
Cited by 24 | Viewed by 4653
Abstract
Forest health is an important aspect of sustainable forest management. The practical significance of health assessments of forest ecosystems is becoming more and more prominent because good knowledge about the health level of forests and the causes of unhealthy forests enables the identification [...] Read more.
Forest health is an important aspect of sustainable forest management. The practical significance of health assessments of forest ecosystems is becoming more and more prominent because good knowledge about the health level of forests and the causes of unhealthy forests enables the identification of proper actions for enhancing sustainable development of forest ecosystems. This paper evaluated the health status of the forest ecosystem of Mount Taishan using the spatial analysis technique of GIS (Geographic Information System) and local forest inventory data. A comprehensive indicator system that reflects the health status of forestsin the study areawas established. Based on this indicator system, the health level of each sub-compartment of the forests in the study area was assessed. The results show that the high-quality grade forest (80.4 ha) and healthy grade forest (2671 ha) accounted for only 23.5% of the total forest area of Mount Taishan. About 60.5% of Mount Taishan forest was in a sub-health status. The area of unhealthy forests was 1865 ha (accounting for 16% of the total forest area), of which about 98 ha was inextremely unhealthy conditions.Asmore than two-thirds of the forests in Mount Taishan are in a sub-health or unhealthy state, effective measures for improving forest health are in urgent need in the study area. Full article
(This article belongs to the Special Issue Geographic Information Systems and Their Applications in Forests)
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19 pages, 5622 KiB  
Article
Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys
by Guangpeng Fan, Feixiang Chen, Yan Li, Binbin Liu and Xu Fan
Forests 2019, 10(8), 643; https://doi.org/10.3390/f10080643 - 29 Jul 2019
Cited by 16 | Viewed by 3852
Abstract
In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic [...] Read more.
In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic information system (GIS) survey. To obtain a better solution, this paper presents the design and implementation of a new ground measurement tool in which mobile devices play a very important role. Based on terrestrial photogrammetry, location-based services (LBS), and computer vision, the tool assists forest GIS surveys in obtaining important forest structure factors such as tree position, diameter at breast height (DBH), tree height, and tree species. This paper selected two plots to verify the accuracy of the ground measurement tool. Experiments show that the root mean square error (RMSE) of the position coordinates of the trees was 0.222 m and 0.229 m, respectively, and the relative root mean square error (rRMSE) was close to 0. The rRMSE of the DBH measurement was 10.17% and 13.38%, and the relative Bias (rBias) of the DBH measurement was −0.88% and −2.41%. The rRMSE of tree height measurement was 6.74% and 6.69%, and the rBias of tree height measurement was −1.69% and −1.27%, which conforms to the forest investigation requirements. In addition, workers usually make visual observations of trees and then combine their personal knowledge or experience to identify tree species, which may lead to the situations when they cannot distinguish tree species due to insufficient knowledge or experience. Based on MobileNets, a lightweight convolutional neural network designed for mobile phone, a model was trained to assist workers in identifying tree species. The dataset was collected from some forest parks in Beijing. The accuracy of the tree species recognition model was 94.02% on a test dataset and 93.21% on a test dataset in the mobile phone. This provides an effective reference for workers to identify tree species and can assist in artificial identification of tree species. Experiments show that this solution using the ground measurement tool saves time and cost for forest resources GIS surveys. Full article
(This article belongs to the Special Issue Geographic Information Systems and Their Applications in Forests)
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11 pages, 2645 KiB  
Article
Global Assessment of Climate-Driven Susceptibility to South American Leaf Blight of Rubber Using Emerging Hot Spot Analysis and Gridded Historical Daily Data
by Reza Golbon, Marc Cotter, Mehdi Mahbod and Joachim Sauerborn
Forests 2019, 10(3), 203; https://doi.org/10.3390/f10030203 - 26 Feb 2019
Cited by 8 | Viewed by 5134
Abstract
South American leaf blight (SALB) of Para rubber trees (Hevea brasiliensis Muell. Arg.) is a serious fungal disease that hinders rubber production in the Americas and raises concerns over the future of rubber cultivation in Asia and Africa. The existing evidence of [...] Read more.
South American leaf blight (SALB) of Para rubber trees (Hevea brasiliensis Muell. Arg.) is a serious fungal disease that hinders rubber production in the Americas and raises concerns over the future of rubber cultivation in Asia and Africa. The existing evidence of the influence of weather conditions on SALB outbreaks in Brazil has motivated a number of assessment studies seeking to produce risk maps that illustrate this relationship. Subjects with dynamic and cyclical spatiotemporal features need to embody sufficiently fine spatial resolution and temporal granulation for both input data and outputs in order to be able to reveal the desired patterns. Here, we apply emerging hot spot analysis to three decades of gridded daily precipitation and surface relative humidity data to depict their temporal and geographical patterns in relation to the occurrence of weather conditions that may lead to the emergence of SALB. Inferential improvements through improved handling of the uncertainties and fine-scaled temporal breakdown of the analysis have been achieved in this study. We have overlaid maps of the potential distribution of rubber plantations with the resulting dynamic and static maps of the SALB hot spot analysis to highlight regions of distinctly high and low climatic susceptibility for the emergence of SALB. Our findings highlight the extent of low-risk areas that exist within the rubber growing areas outside of the 10° equatorial belt. Full article
(This article belongs to the Special Issue Geographic Information Systems and Their Applications in Forests)
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14 pages, 3624 KiB  
Article
Predicting the Potential Distribution of Paeonia veitchii (Paeoniaceae) in China by Incorporating Climate Change into a Maxent Model
by Keliang Zhang, Yin Zhang and Jun Tao
Forests 2019, 10(2), 190; https://doi.org/10.3390/f10020190 - 20 Feb 2019
Cited by 69 | Viewed by 6812
Abstract
A detailed understanding of species distribution is usually a prerequisite for the rehabilitation and utilization of species in an ecosystem. Paeonia veitchii (Paeoniaceae), which is an endemic species of China, is an ornamental and medicinal plant that features high economic and ecological values. [...] Read more.
A detailed understanding of species distribution is usually a prerequisite for the rehabilitation and utilization of species in an ecosystem. Paeonia veitchii (Paeoniaceae), which is an endemic species of China, is an ornamental and medicinal plant that features high economic and ecological values. With the decrease of its population in recent decades, it has become a locally endangered species. In present study, we modeled the potential distribution of P. veitchii under current and future conditions, and evaluated the importance of the factors that shape its distribution. The results revealed a highly and moderately suitable habitat for P. veitchii that encompassed ca. 605,114 km2. The central area lies in northwest Sichuan Province. Elevation, temperature seasonality, annual mean precipitation, and precipitation seasonality were identified as the most important factors shaping the distribution of P. veitchii. Under the scenario with a low concentration of greenhouse gas emissions (RCP 2.6), we predicted an overall expansion of the potential distribution by 2050, followed by a slight contraction in 2070. However, with the scenario featuring intense greenhouse gas emissions (RCP 8.5), the range of suitable habitat should increase with the increasing intensity of global warming. The information that was obtained in the present study can provide background information related to the long-term conservation of this species. Full article
(This article belongs to the Special Issue Geographic Information Systems and Their Applications in Forests)
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