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Special Issue "Forests as a Key Climate Solution"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Use of the Environment and Resources".

Deadline for manuscript submissions: 30 June 2018

Special Issue Editors

Guest Editor
Prof. Dr. Miguel A. Zavala

Departmento de Ciencias de la Vida, Edificio Ciencias, Universidad de Alcalá, Campus Universitario, 28805 Alcalá de Henares (Madrid), Spain
Website | E-Mail
Interests: forest ecology; climate change adaptation; sustainability; biodiversity; mathematical ecology
Guest Editor
Dr. Paloma Ruiz-Benito

Departamento de Ciencias de la Vida, Edificio Ciencias, Universidad de Alcalá, Campus Universitario, 28805 Alcalá de Henares (Madrid), Spain
Website | E-Mail
Interests: climate change; diversity; ecosystem services; forest ecology; forest functioning; functional biogeography; land use change; mathematical ecology

Special Issue Information

Dear Colleagues,

Forests provide a wide range of ecosystem services critical to human well-being. Forests have a key role on the global carbon cycle and constitute a critical ecosystem for mitigation. Forests are threatened by human impacts chiefly land use changes and the increase of extreme events, such as droughts and fires, yet it is estimated that forest carbon could provide up to one third of climate solution that we need over the next two decades.

This Special Issue welcomes contributions about:

  • Key examples of forests as a potential solution to climate change issues.
  • Forests as drivers of the global carbon cycle and the role of forests in climate change mitigation.
  • Theoretical and empirical approximations to estimate carbon stored by forests including valuation of carbon storage and other ecosystem services.
  • Effects of climate change on forests, including extreme climatic events and disturbances.
  • Management solutions to adapt forests to climate change and to maintain ecosystem services.
  • Interdisciplinary issues related with forest adaptation and mitigation, especially with a social or policy component.

We invite scientist involved in theoretical, methodological and practical studies of forest ecology, mitigation and adaptation to contribute original research papers that will illustrate the continuing effort to understand the role of forests in climate regulation and the develop of novel approximations to mitigation, adaptation and sustainable forest management.

Prof. Dr. Miguel A. Zavala
Dr. Paloma Ruiz-Benito
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. Sustainability 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 1400 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

  • adaptation
  • carbon
  • climate change
  • drivers
  • ecosystem services
  • forests
  • mitigation

Published Papers (1 paper)

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Research

Open AccessArticle Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements
Sustainability 2018, 10(1), 203; doi:10.3390/su10010203
Received: 30 October 2017 / Revised: 8 January 2018 / Accepted: 10 January 2018 / Published: 17 January 2018
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Abstract
Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the
[...] Read more.
Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements. Full article
(This article belongs to the Special Issue Forests as a Key Climate Solution)
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