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Forests
Special Issues
Spatial Decision Support for Forest Management
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Spatial Decision Support for Forest Management

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 7698

Special Issue Editor

Prof. Dr. Michael P. Strager

E-Mail Website
Guest Editor
Division of Resource Economics and Management, School of Natural Resources, Davis College of Agriculture, Natural Resources and Design, West Virginia University, Morgantown, WV 26508-6108, USA
Interests: spatial data analysis; natural resources; multi-criteria decision analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The management of forest resources is a complex undertaking involving many issues, data, people, and processes. Rarely is just one tool or model effective in management work. Integrated approaches provide more flexibility to address problems. Spatial decision support systems can provide the framework for integrating and analyzing spatial, economic, technical, or other non-commensurate data to aid in the planning process. With the many advances now emerging in computing, programming, big data analytics, and machine learning, spatial decision support systems have grown in applications to better manage forest resources. We encourage interdisciplinary submissions, including experimental studies, model development, and computer system models, to contribute to this Special Issue in order to promote knowledge and innovative strategies to expand this field of study.

Prof. Dr. Michael P. Strager
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

  • Spatial decision support
  • Trade-off analysis
  • Optimization
  • Alternative futures
  • Water resources planning
  • Ecosystem services
  • Forest management
  • Multi-use planning
  • Spatial data science
  • Data analytics
  • Machine learning

Published Papers (3 papers)

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Research

17 pages, 14757 KiB  
Article
Spatiotemporal Variation of NDVI in Anhui Province from 2001 to 2019 and Its Response to Climatic Factors
by Weijie Han, Donghua Chen, Hu Li, Zhu Chang, Jian Chen, Lizao Ye, Saisai Liu and Zuo Wang
Forests 2022, 13(10), 1643; https://doi.org/10.3390/f13101643 - 7 Oct 2022
Cited by 11 | Viewed by 1857
Abstract
This paper intends to clarify that the spatial and temporal evolutionary patterns of regional vegetation and their relationship with climate form a premise of ecological conservation and environmental governance, and play an important role in maintaining regional ecosystem balance and promoting sustainable development. [...] Read more.
This paper intends to clarify that the spatial and temporal evolutionary patterns of regional vegetation and their relationship with climate form a premise of ecological conservation and environmental governance, and play an important role in maintaining regional ecosystem balance and promoting sustainable development. Based on measured data collected from NDVI remote sensing products and meteorological stations, NDVI variation in Anhui Province from 2001 to 2019 was determined through trend analysis and measurement methods involving coefficient of variation and Hurst index; in addition, the response to climatic factors was also explored. It was concluded that, firstly, in terms of spatiotemporal analysis, the interannual variation of NDVI in Anhui Province showed an increasing trend with a rate of 0.024/10 a, while the monthly variation showed a weak bimodal pattern, with the highest value in August and the lowest value in January. Furthermore, NDVI in Anhui Province showed significant spatial heterogeneity, with high values concentrated in mountainous regions in southern Anhui and Dabie Mountain region, and low values concentrated in the hilly areas of Jianghuai and areas along the Yangtze River. At the same time, the overall spatial variation of NDVI showed an increasing trend, and the areas with extremely significant and significant improvement in vegetation coverage accounted for 54.69% of the total area of Anhui Province. Secondly, in terms of the analysis on variation characteristics, the variation of NDVI in Anhui Province was generally stable, with an average CV coefficient of variation of 0.089, which, however, was quite different in different regions; meanwhile, the future trend of NDVI variation in the study areas was mostly in a random manner. Thirdly, the response of NDVI in Anhui Province to climatic factors showed significant spatial heterogeneity. NDVI was found to be positively correlated with precipitation and negatively correlated with temperature; in general, the impact of precipitation on NDVI was greater than that of temperature. In the 19 years studied, NDVI in Anhui Province showed an increasing trend; and climate, topography and human activities led to heterogeneous spatial distribution of vegetation. Therefore, in the future, the evolutionary trend of vegetation will be relatively random, and NDVI will be more greatly affected by temperature, than by precipitation. Full article
(This article belongs to the Special Issue Spatial Decision Support for Forest Management)
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12 pages, 3384 KiB  
Article
A 25-Year Study of the Population Dynamics of a Harvested Population of Sika Deer on Kyushu Island, Japan
by Kei K. Suzuki, Yasumitsu Kuwano, Yuki Kanamori, Yohei Kawauchi, Yoshihiko Uchimura, Masatoshi Yasuda, Hiroshi Kondoh and Teruki Oka
Forests 2022, 13(5), 760; https://doi.org/10.3390/f13050760 - 16 May 2022
Cited by 2 | Viewed by 2037
Abstract
Sika deer (Cervus nippon) populations have damaged habitats, agricultural crops, and commercial forests in many parts of the world, including Asia, Europe, northern America, and New Zealand. Population management of sika deer is an important task in those areas. To better [...] Read more.
Sika deer (Cervus nippon) populations have damaged habitats, agricultural crops, and commercial forests in many parts of the world, including Asia, Europe, northern America, and New Zealand. Population management of sika deer is an important task in those areas. To better understand large-scale management and improve management efficiency, the authors estimated spatio-temporal changes of density distribution and population dynamics of a managed population of sika deer on Kyushu Island (approximately 36,750 km2), Japan. The authors estimated these changes by using fecal pellet count surveys conducted from 1995 to 2019 and results from a vector autoregressive spatio-temporal model. No decreasing trend of populations were observed at the island and prefectural scales, even though the management goal has been to reduce the population by half, and harvesting on the island increased annually until it reached about 110,000 sika deer in 2014. A possible explanation for the stable population dynamics is that the population used to determine the harvest number under the prefectural management plan was originally underestimated. This study highlights not only the difficulties of wide-area management of sika deer but also three important factors for successful management: reducing the risk of management failure, using an adaptive management approach, and appropriate management scale. Full article
(This article belongs to the Special Issue Spatial Decision Support for Forest Management)
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16 pages, 3679 KiB  
Article
Predictive Models to Estimate Carbon Stocks in Agroforestry Systems
by Maria Fernanda Magioni Marçal, Zigomar Menezes de Souza, Rose Luiza Moraes Tavares, Camila Viana Vieira Farhate, Stanley Robson Medeiros Oliveira and Fernando Shintate Galindo
Forests 2021, 12(9), 1240; https://doi.org/10.3390/f12091240 - 14 Sep 2021
Cited by 4 | Viewed by 2833
Abstract
This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural [...] Read more.
This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems. Full article
(This article belongs to the Special Issue Spatial Decision Support for Forest Management)
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