Climatic Regulation of Leaf, Cambial Phenology, Physiology, and Metabolism in Wood- and Semi-wood Plants

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

Deadline for manuscript submissions: closed (28 January 2023) | Viewed by 1969

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Faculty of Agronomy, University of Córdoba, Montería, Córboda 230002, Colombia
Interests: plant stresses; metabolome; plant anatomy; seed germination; leaf area estimation; bioenergy
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Special Issue Information

Dear Colleagues,

We have reached a pivotal moment in time, and action is urgently needed if we want to save our planet. Greenhouse gas emissions (GGE) are continuing to rise, raising the global temperature, collecting, retaining, and dumping more water, changing weather patterns, and making wetlands wetter and dry areas drier. The intertropical zone of the planet is anticipated to be the most affected by GGE, with large forest fires expected. Every year, scientists say that an area of about 4 million square kilometers of forest is burned. Most of these fires in Africa are a natural process that has been going on for thousands of years according to Niels Andela (Cardiff University), while Brazilian, Californian, and European forests are being burned due to the drier and hotter summers, a consequence of GGE. Regardless, every forest fire introduces huge volumes of CO2 into the atmosphere, which only exacerbates the environmental climate problem. Many scientists are warning that adaptation to climate in vulnerable countries is inadequate to deal with forest fires, whose tendency is to worsen in frequency and intensity. In these terms, studies that promote the sustainable use of forests, the implementation of exclusive afforestation for the sustainable extraction of cellulose, latex, and pigments, as well as projects and studies of environmental impacts of reforestation policies are welcome in this Special Issue.

Potential topics include but are not limited to:

  • Effect of fires on the regeneration of native forests;
  • Catastrophic effect of fires on global warming;
  • Climate studies: how to predict or accelerate the fight against large fires;
  • Catastrophic effects of fires in the Cerrado and Brazilian Amazon Forest;
  • Carbon credits and sustainable forests;
  • Agroforestry systems;
  • How green islands can mitigate atmospheric pollution in large urban centers.

Dr. Marcelo Francisco Pompelli
Guest Editor

Manuscript Submission Information

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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.

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Keywords

  • carbon credits
  • forest fires
  • agroforestry systems
  • reforestation
  • CO2 and global warming
  • green island
  • plant physiology and metabolism

Published Papers (1 paper)

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Research

20 pages, 14599 KiB  
Article
Mapping and Monitoring Forest Plantations in São Paulo State, Southeast Brazil, Using Fraction Images Derived from Multiannual Landsat Sensor Images
by Yosio E. Shimabukuro, Egidio Arai, Gabriel M. da Silva, Andeise C. Dutra, Guilherme Mataveli, Valdete Duarte, Paulo R. Martini, Henrique L. G. Cassol, Danilo S. Ferreira and Luís R. Junqueira
Forests 2022, 13(10), 1716; https://doi.org/10.3390/f13101716 - 18 Oct 2022
Cited by 6 | Viewed by 1632
Abstract
This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. [...] Read more.
This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas’ rotation, especially the eucalypt’s short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations’ characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales. Full article
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