Special Issue "Optical Data for Assessing Carbon Dynamics and Biodiversity of Forests"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 March 2020).

Special Issue Editor

Dr. Gherardo Chirici
Website
Guest Editor
Dipartimento di Gestione dei Sistemi Agrari, Alimentari e Forestali, Universita degli Studi di Firenze, Via S. Bonaventura, 13 - 50145 Firenze, Italy
Interests: forest remote sensing; geomatics; forest inventory; biodiversity; decision support systems;
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Special Issue Information

Dear Colleagues,

Forests play a crucial role in sustainable development, ensuring human well-being, a healthy environment, and economic development. Forests produce a large set of ecosystem services which potentially support a green economy, climate change mitigation, biodiversity conservation, and enhancing water quality and combating desertification.

Remote sensing technologies have evolved rapidly in the last few decades, contributing with new sensors and new methods for producing updated and highly detailed information for supporting forest management and planning.

This Special Issue of Remote Sensing is intended to examine the state-of-art in more recent advancements in optical remote sensing (alone or in combination with other sensors) for assessing spatial and temporal dynamics of carbon stocks and sequestration, as well as biodiversity trends in forest ecosystems. We are focused on contributions based on the integration between remotely sensed and field data for estimating forest variables or for feeding ecosystem modeling, as well as for advancements in forest mapping issues. Applications must be based on innovative approaches and rigorous statistical methods and should be based, as far as possible, on large datasets. A theme of special interest is the analysis of temporal dynamics.

Prof. Gherardo Chirici
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 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2200 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 monitoring
  • Optical remote sensing
  • Carbon sequestration
  • Biodiversity

Published Papers (1 paper)

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Research

Open AccessArticle
Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
Remote Sens. 2020, 12(9), 1356; https://doi.org/10.3390/rs12091356 - 25 Apr 2020
Abstract
This study simulates annual net primary production (NPP) of forests over peninsular Spain during the years 2005–2012. The modeling strategy consists of a linked production efficiency model based on the Monteith approach and the bio-geochemical model Biome-BGC. Recently produced databases and data layers [...] Read more.
This study simulates annual net primary production (NPP) of forests over peninsular Spain during the years 2005–2012. The modeling strategy consists of a linked production efficiency model based on the Monteith approach and the bio-geochemical model Biome-BGC. Recently produced databases and data layers over the study area including meteorological daily series, ecophysiological parameters, and maps containing information about forest type, rooting depth, and growing stock volume (GSV), were employed. The models, which simulate forest processes assuming equilibrium conditions, were previously optimized for the study area. The production efficiency model was used to estimate daily gross primary production (GPP), while Biome-BGC was used to simulate growth (RG) and maintenance (RM) respirations. To account for actual forest conditions, GPP, RG, and RM were corrected using the ratio of the remotely-sensed derived actual to potential GSV as an indicator of the actual state of forests. The obtained results were evaluated against current annual increment observations from the Third Spanish Forest Inventory. Coefficients of determination ranged from 0.46 to 0.74 depending on the forest type. A simplified dataset was produced by applying regular increments in air temperature and reductions in precipitation to the original 2005–2012 daily series with the goal of covering the range of variation of the climate projections corresponding to the different climate change scenarios reported in the literature. The modified meteorological series were used to simulate new GPP, RG, and RM through Biome-BGC and corrected using GSV. Precipitation was confirmed as the main limiting factor in the study area. In the regions where precipitation was already a limiting factor during 2005–2012, both the increment in air temperature and the reduction in precipitation contributed to a reduction of NPP. In the regions where precipitation was not a limiting factor during 2005–2012, the increment in air temperature led to an increment of NPP. This study is therefore relevant to characterize the growth of Spanish forests both in current and expected climate conditions. Full article
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