Observation and Research on Ecological Restoration of Degraded Forests

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 2024) | Viewed by 1490

Special Issue Editors

Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Interests: restoration ecology; species association; structure and function of pioneer community; new technologies and methods for ecological restoration

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Guest Editor
Laboratory of Silviculture, Department of Forestry and Natural Environment, Faculty of Geotechnical Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: carbon sequestration in forests; climate value of urban tree; forest restoration; climate-adaptive reforestation; restoration of degraded Mediterranean ecosystems
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Special Issue Information

Dear Colleagues,

According to the Global Forest Resources Assessment (FRA) 2020, forest degradation has become a global environmental issue due to human activities, severe climatic events, fire, pests, diseases and other environmental disturbances. Forest degradation is defined as the long-term reduction in the levels of goods and services including carbon loss, greenhouse gas emissions, wood production, biodiversity conservation, ecosystem functions, human health and, even worse, the decline of forest resilience—the ability of a forest ecosystem to re-organize and recover following disturbance. Forest degradation is one of the more challenging types of disturbances to measure and monitor. With the development of observation technology, the processes of forest degradation and restoration can be monitored. However, how to assess these degraded forests in a timely and accurate manner is still a considerable question. Nonetheless, it is society's obligation both to protect forests from any degradation, restoring and transforming them to be healthier, with better structure and function. Tackling forest degradation can be achieved through the implementation of silvicultural measures to restore and transform degraded forests to high-level forests (e.g. mitigation of degradation process, forest stand tending, natural regeneration, reforestation, afforestation), control of forest fires, conservation of marginal farmland to forest lands, development of protective shelterbelts in the environmental fragile sites, implementation of policies and measures for biodiversity and natural reserves conservation. This Special Issue aims to provide observation technology, driving forces and restoration methods to address degraded forests in terms of improving forest sustainability.

Potential topics include, but are not limited to:

  • Observation technology of degraded forests;
  • Structural and functional degradation of forests;
  • Degradation mechanisms of natural forests or plantation forests;
  • Plant-soil feedback of degraded forests;
  • Driving forces of degradation forests at larger scale;
  • Restoration or transformation methods of degraded forests;
  • Environmental effects of forest degradation or restoration;
  • New technologies and methods for forest ecological restoration;
  • Climate adapted reforestation for ecological restoration of degraded forests.

Dr. Long Yang
Dr. Marianthi Tsakaldimi
Guest Editors

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.

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

  • degraded forest
  • ecological forest restoration
  • observation technology
  • plant–soil feedback
  • sustainable forestry
  • silvicultural measures
  • optimization of forest structure and function

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Published Papers (1 paper)

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Research

30 pages, 11909 KiB  
Article
Estimation of Picea Schrenkiana Canopy Density at Sub-Compartment Scale by Integration of Optical and Radar Satellite Images
by Yibo Wang, Xusheng Li, Xiankun Yang, Wenchao Qi, Donghui Zhang and Jinnian Wang
Forests 2024, 15(7), 1145; https://doi.org/10.3390/f15071145 - 1 Jul 2024
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Abstract
This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development [...] Read more.
This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments. Full article
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