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Urban and Peri-Urban Forest Role in a Sustainable Ecosystem

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Forestry".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 4688

Special Issue Editor

National Council for Scientific Research, 11-8281 Beirut, Lebanon
Interests: remote sensing; image segmentation; forest monitoring and mapping; crop mapping and yield estimation; machine learning

Special Issue Information

Dear Colleagues,

Urban and peri-urban forests comprise all the trees and associated vegetation found in and around cities. They occur in a range of settings, including in managed parks, natural areas (e.g., protected areas), residential areas, and informal green spaces; along streets; and around wetlands and water bodies. Urbanization places pressure on forests in and around cities due to construction, which demands more space, and from pollution caused by daily economic activities.

Forests’ importance in the urban environment is obvious in creating biodiversity, providing economic and environmental services, such as elevating property values, and in reducing green gases and aerosol. Today, research is focusing on the importance of monitoring urban and peri-urban forest stress for sustainable benefits. Monitoring efforts are focused on direct and indirect stressors, such as forest utilization or land use changes, climate change, air pollution, and globalization, which in turn may bring about an additional impact from invasive species.

Moreover, decision makers rely heavily on advanced tools to improve their management of forests in a very complex texture, such as an urban or peri-urban environment. These tools vary from standalone tools such as remote sensing for monitoring land cover change to combinations of different technologies, such as decision support systems, including scenarios and modeling.

This Special Issue calls for original outcomes from research activities and aims to publish innovative approaches that promote sustainable forest covers in urban and peri-urban forests. The following are the topics that can be covered by the Special Issue on urban and peri-urban forest environments:

1–Advanced technology use in monitoring urban and peri-urban forests;

2–Machine learning role in sustainable urban and peri-urban forests;

3–Carbon sequestration and urban and peri-urban forestry;

4–Edible urban forests for sustainable cities;

5–Green infrastructure and urban landscapes;

6–Forests and sustainable water resources in cities;

7–Urban and peri-urban forests sustainability and climate;

8–Urban and peri-urban forest sustainability and energy conservation.

Prof. Dr. Mohamad Awad
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 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. Sustainability 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 2400 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

  • urban
  • peri-urban
  • forests
  • sustainability
  • geospatial technologies
  • deep learning
  • carbon dioxide
  • pollution
  • food source
  • mitigation

Published Papers (2 papers)

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Research

18 pages, 2694 KiB  
Article
Effects of Agricultural Land and Urban Expansion on Peri-Urban Forest Degradation and Implications on Sustainable Environmental Management in Southern Ethiopia
by Deneke Tilahun, Kassahun Gashu and Getnet Tarko Shiferaw
Sustainability 2022, 14(24), 16527; https://0-doi-org.brum.beds.ac.uk/10.3390/su142416527 - 09 Dec 2022
Cited by 5 | Viewed by 1515
Abstract
Policy failure in controlling horizontal urban expansion coupled with agricultural/cultivated land expansion typically leads to forest degradation mostly in developing countries. Peri-urban areas are havens and vulnerable and dispute areas of uncontrolled urban expansion and forest degradation. This study was aimed to assess [...] Read more.
Policy failure in controlling horizontal urban expansion coupled with agricultural/cultivated land expansion typically leads to forest degradation mostly in developing countries. Peri-urban areas are havens and vulnerable and dispute areas of uncontrolled urban expansion and forest degradation. This study was aimed to assess the effect of cultivated land and urban expansion land use/land cover change (LULCC) dynamics rate on peri-urban forest degradation and implications on sustainable environment management there by identifying the derivers of LULCC. The study used Landsat images of 2002, 2010 and 2018 and examines the underlying factors. The results revealed significant conversion from forest and grass land to built-up and cultivated land. The proportion of built-up area and cultivated land increased to 75 ha yr−1 and 85 ha yr−1 of the study area from 2002 to 2018, respectively. The identified drivers were generally grouped as proximate and underlying drivers. The effect of driving factors in shaping LULCC tends to remain stable over time, and the gradual enforcement of spatial planning policies appears to be important factors in dynamics of LULCC. Hence, it was suggested that integrated land-use planning and management has a paramount importance of reducing peri-urban forest degradation and maintaining sustainable environmental management. Full article
(This article belongs to the Special Issue Urban and Peri-Urban Forest Role in a Sustainable Ecosystem)
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15 pages, 4492 KiB  
Article
Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests
by Mohamad M. Awad and Marco Lauteri
Sustainability 2021, 13(10), 5548; https://0-doi-org.brum.beds.ac.uk/10.3390/su13105548 - 16 May 2021
Cited by 13 | Viewed by 2349
Abstract
Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy [...] Read more.
Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy of forest-type classification. Several classification methods are used to map urban and peri-urban forests and to identify healthy and non-healthy ones. Some of these methods have shown success in the classification of forests where others failed. The successful methods used specific remote sensing data technology, such as hyper-spectral and very high spatial resolution (VHR) images. However, both VHR and hyper-spectral sensors are very expensive, and hyper-spectral sensors are not widely available on satellite platforms, unlike multi-spectral sensors. Moreover, aerial images are limited in use, very expensive, and hard to arrange and manage. To solve the aforementioned problems, an advanced method, self-organizing–deep learning (SO-UNet), was created to classify forests in the urban and peri-urban environment using multi-spectral, multi-temporal, and medium spatial resolution Sentinel-2 images. SO-UNet is a combination of two different machine learning technologies: artificial neural network unsupervised self-organizing maps and deep learning UNet. Many experiments have been conducted, and the results showed that SO-UNet overwhelms UNet significantly. The experiments encompassed different settings for the parameters that control the algorithms. Full article
(This article belongs to the Special Issue Urban and Peri-Urban Forest Role in a Sustainable Ecosystem)
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