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Forests
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Advanced Statistical Modeling in Forests Climate Change and Natural Hazards
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Advanced Statistical Modeling in Forests Climate Change and Natural Hazards

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3484

Special Issue Editors

Dr. Kourosh Ahmadi

E-Mail Website
Guest Editor
Forest Department, Fars Agricultural and Natural Resources Research and Education Center (AREEO), Shiraz 71555-617, Iran
Interests: climate change; natural hazards; forest fire; machine learning; optimization algorithmes; statistical modeling
Dr. Shirin Mahmoodi

E-Mail Website
Guest Editor
National Center for Genetic Resources (NCGR), Agricultural and Natural Resources Research and Education Center (AREEO), Tehran 15614, Iran
Interests: climate change; plant diseases epidemics; machine learning; optimization algorithmes; statistical modeling; environmental science; species distribution model
Dr. Quoc Bao Pham

E-Mail Website
Guest Editor
Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland
Interests: hydrology; groundwater; machine learning; water resources management; climate change; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is focused on the application of advanced statistical methods in the investigation of the impacts of natural hazards and climate change on forest ecosystems. Despite some studies having been conducted in the field of designing, developing, or testing statistical models for predicting, assessing, or monitoring the consequences of such events in forests, there is a need for further improvement and refinement of these methods. Natural hazards such as landslides, floods, droughts, erosion, pest and pathogen outbreaks, and fires can significantly impact forests globally. Thus, the primary aim of this Special Issue is to showcase the use of advanced statistical techniques, such as Bayesian statistics and machine learning algorithm optimization, in addressing the impacts of these natural hazards and other relevant events on forests in the current and future climates. We welcome submissions from researchers who are developing and optimizing statistical models and artificial intelligence approaches to address the challenges posed by climate change and natural hazards in forest ecosystems.

Potential topics include, but are not limited to:

  • Advanced statistical modeling;
  • Optimization algorithms;
  • Machine learning;
  • Bayesian statistics;
  • Bayesian data analysis and computation;
  • Natural disasters;
  • Climate change;
  • Pest/diseases;
  • Species distribution model;
  • Risk assessment of climate change and natural hazards;
  • Identification of areas at high risk of natural hazards and climate change using advanced models.

Dr. Kourosh Ahmadi
Dr. Shirin Mahmoodi
Dr. Quoc Bao Pham
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 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

  • forest fires
  • landslides
  • floods
  • droughts
  • soil erosion
  • climate change
  • pest and pathogen outbreaks
  • other hazards
  • species distribution models and climate change

Published Papers (3 papers)

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Research

18 pages, 3531 KiB  
Article
Characteristics of Forest Windthrow Produced in Eastern Carpathians in February 2020
by Mihai Ciocirlan and Vasile Răzvan Câmpu
Forests 2024, 15(1), 176; https://doi.org/10.3390/f15010176 - 15 Jan 2024
Viewed by 686
Abstract
Windthrow is a phenomenon that causes major changes to tree stand evolution by blowing down or breaking either isolated trees or entire tree stands, with a strong ecological, social and economic impact. Both scattered and large-scale windthrow occurred in spruce (Picea abies [...] Read more.
Windthrow is a phenomenon that causes major changes to tree stand evolution by blowing down or breaking either isolated trees or entire tree stands, with a strong ecological, social and economic impact. Both scattered and large-scale windthrow occurred in spruce (Picea abies (L.) Karst.) tree stands of Romania. They affected surfaces of various dimensions from harvestable forests. Such a phenomenon took place in the Curvature Carpathians in February 2020. Large-scale windthrow occurred in this area in 1995 as well, in the upper watershed of Bâsca river. Using the climate data from February 2020, this paper aims to identify the manner in which factors such as climate and site conditions together with tree stand characteristic and the anthropogenic factor impacted and influenced the occurrence of windthrow. The results showed that the intensity of this phenomenon had maximum effects when the wind coming from north/northeast reached the maximum speed of 32 m·s−1. Pure spruce tree stands situated on slopes with an inclination between 16 and 30° were mainly affected. Their position was counter to the wind direction, at an altitude between 1300 and 1500 m, on cambisols and spodosols. The analysis and statistical interpretation of data in the case of scattered and large-scale windthrow from the two management units showed that the same factors studied influence the variation of windthrow intensity in a different manner, or sometimes they do not influence it at all or they can only account for a small part of this variation. Full article
(This article belongs to the Special Issue Advanced Statistical Modeling in Forests Climate Change and Natural Hazards)
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15 pages, 5548 KiB  
Article
Spatio-Temporal Evolution of Forest Landscape in China’s Giant Panda National Park: A Case Study of Jiudingshan Nature Reserve
by Juan Wang, Dan Zhao, Xian’an Liu, Qiufang Shao, Danli Yang, Fanru Zeng, Yu Feng, Shiqi Zhang, Peihao Peng and Jinping Liu
Forests 2023, 14(8), 1606; https://doi.org/10.3390/f14081606 - 09 Aug 2023
Viewed by 968
Abstract
The continuous prohibition of commercial logging and intensifying conservation endeavors have encompassed the implementation of the Natural Forest Conservation Program (NFCP) and the Grain-to-Green Program (GTGP) by the Chinese government since 1999. Nevertheless, the efficacy of the commercial logging ban and its effectiveness [...] Read more.
The continuous prohibition of commercial logging and intensifying conservation endeavors have encompassed the implementation of the Natural Forest Conservation Program (NFCP) and the Grain-to-Green Program (GTGP) by the Chinese government since 1999. Nevertheless, the efficacy of the commercial logging ban and its effectiveness in halting deforestation remain uncertain. Likewise, the destructive aftermath of the 7.9 magnitude Wenchuan earthquake in 2008 continues to be under scrutiny, necessitating ongoing study and analysis. Thus, there exists a pressing need to comprehensively monitor the spatio-temporal evolution of the forest habitat and assess the ecological status over the past two decades. The Jiudingshan Nature Reserve (JNR) is situated in the upper reaches of the Tuojiang River basin in Sichuan province, China, constituting an integral part of the Giant Panda National Park (GPNP). In this study, we classified land cover types and conducted a meticulous monitoring of forest habitat alterations within JNR, by a multilayer perceptron model (MLP) with a highly learning-sensitive algorithm. To quantify these changes, the Simple Ratio Index (SRI) and the Normalized Difference Vegetation Index (NDVI) were computed from Landsat TM/OLI images of four years (i.e., 1997, 2007, 2008, and 2018). Additionally, elevation, slope, aspect, and other topographic data were acquired from the Digital Elevation Model (DEM). The findings of our study unveil a notable expansion in both the scope and proportion of mixed conifer and broadleaf forest from 1997 to 2004. The growth of coniferous forest and the augmented areas of mixed conifer and broadleaf forest signify a substantial improvement in panda habitat. However, the seismic event of 2008 exhibited a pronounced adverse impact on vegetation, particularly within forested regions. Although there is evidence of forest recovery spanning 21 years, concerns regarding fragmentation linger. It is pivotal to acknowledge the potential long-term adverse implications arising from widespread socio-economic development and a multitude of geohazards. Hence, sustained long-term monitoring coupled with effective management strategies remain pivotal for the preservation and rehabilitation of the Giant Panda National Park (GPNP) and giant panda habitat in the future. Full article
(This article belongs to the Special Issue Advanced Statistical Modeling in Forests Climate Change and Natural Hazards)
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18 pages, 2909 KiB  
Article
Improving Sustainable Forest Management of Pinus halepensis Mill. Mid-Aged Stands in a Context of Rural Abandonment, Climate Change, and Wildfires
by Eduardo Rojas-Briales, Jose-Vicente Oliver-Villanueva, Victoria Lerma-Arce, David Fuente and Edgar Lorenzo-Sáez
Forests 2023, 14(3), 527; https://doi.org/10.3390/f14030527 - 07 Mar 2023
Viewed by 1334
Abstract
Pinus halepensis Mill. covers most lowland forests on limestone soils and semiarid to sub-humid climates in the Mediterranean basin. It is considered a key species in climate change due to its pioneer nature, versatility, and flexibility. Moreover, its industrial potential is an additional [...] Read more.
Pinus halepensis Mill. covers most lowland forests on limestone soils and semiarid to sub-humid climates in the Mediterranean basin. It is considered a key species in climate change due to its pioneer nature, versatility, and flexibility. Moreover, its industrial potential is an additional incentive to promote forest management to increase its quality and productivity while contributing to other environmental and social objectives. However, there is a considerable gap in science-based knowledge on the effects of different silvicultural treatments on Pinus halepensis stands. Thus, this research compares the impact of four different treatments (light thinnings, strong thinnings, transformation to uneven-aged, and diameter-driven uneven-aged) on even-aged mid-rotation stands of Pinus halepensis in terms of growth, vulnerability, and resilience to extreme weather events, regeneration, and shrub cover. The effects of four treatments are evaluated in 12 research plots of 0.49 ha each (three per treatment) and contrasted with the other three non-managed control plots. Light and strong thinning treatments show better growth—at least in the short term—and stock results than those reported in the reference yield tables. Transformation to uneven-aged treatment shows advantages in maintaining periodic growth, regeneration, and stability. In addition, it offers an alternative for steep slope stands, smallholders, and extended narrow-aged estates to speed up the desirable balanced age class distribution. Diameter-driven uneven-aged treatment implies greater vulnerability to extreme weather events during the first years and considerable stock reduction while offering faster and taller tree regeneration. A dual regeneration pattern of Ulex parviflorus Pourr is observed in addition to post-fire regeneration in the case of sudden and well-distributed tree cover reduction around 40% of the canopy due to the transformation into the uneven-aged stand. An observation period longer than a decade of the research plots will confirm these first conclusions. Full article
(This article belongs to the Special Issue Advanced Statistical Modeling in Forests Climate Change and Natural Hazards)
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