Special Issue "Remote Sensing of Post-disturbance Vegetation Recovery Monitoring"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Emanuele Lingua
E-Mail Website
Guest Editor
Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Viale dell’Università 16, 35020 Legnaro (PD), Italy
Interests: forest ecology and management; natural disturbances; RS for forestry; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Matteo Garbarino
E-Mail Website
Co-Guest Editor
Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Grugliasco, TO, Italy
Interests: forest ecology; landscape ecology; land use change; historical ecology; disturbance ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural disturbances are important drivers of forest dynamics, shaping their composition and structure, and determining succession trajectories. According to recent findings, disturbance regimes are shifting due to climate and land-use change drivers, mostly due to anthropogenic activities. Large disturbances with increasing severity are affecting forest stands with unprecedented patterns, both spatial and temporal. Enhancing the knowledge on how forest ecosystems are able to recover from these major disturbances is of vital relevance for a sustainable forest and land management.

In order to understand the resistance and resilience of forest ecosystems, and trying to enhance these characteristics, remote sensing techniques can therefore provide a fundamental set of information. Analyzing the post-disturbance dynamics from remote allow us to capture the large heterogeneity and variability of the patterns and processes involved. In this special issue, we invite contributions from all fields of remote sensing in order to promote knowledge on disturbance ecology with multi-scale, multi-temporal, and multidisciplinary studies. We encourage the submission to this Special Issue of review papers, technical notes, and original research contributions dealing with different sensors and platforms in order to promote knowledge on vegetation recovery in ecosystems affected by natural disturbances.

Prof. Dr. Emanuele Lingua
Prof. Dr. Matteo Garbarino
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. 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 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.


  • Natural disturbances
  • Wildfire
  • Insect outbreaks
  • Windthrows
  • Post-disturbance restoration
  • Forest resilience
  • LiDAR
  • UAV
  • Satellite imagery

Published Papers (1 paper)

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Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019
Remote Sens. 2020, 12(19), 3191; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193191 - 29 Sep 2020
Cited by 3 | Viewed by 1286
Long-term surface mining and subsequent vegetation recovery greatly alter land cover types, reshape landscape patterns and impose several impacts on local ecosystem services. However, studies on the history of forest changes in mining areas from the 1960s to the present have not been [...] Read more.
Long-term surface mining and subsequent vegetation recovery greatly alter land cover types, reshape landscape patterns and impose several impacts on local ecosystem services. However, studies on the history of forest changes in mining areas from the 1960s to the present have not been reported. This study developed a new idea to investigate the spatial and temporal dynamics of forest cover in a mining area of Mufu Mountain (Mt. Mufu) from 1967 to 2019 by integrating Landsat and Corona data, and to explore the relationships among the forest changes, landscape structures and ecosystem functions. Firstly, we applied the vegetation change tracker (VCT) algorithm and visual interpretation to create annual forest change datasets. Subsequently, the forest loss process was divided into subdivision, shrinkage, perforation and attrition components. An improved forest restoration model in this study extended the recovery process to bridge, branch, infilling and increment components. Finally, remote sensing variables and crown density were coupled to assess the forest aboveground biomass (AGB) to reflect the ecosystem function in the restoration area. Results showed that the combined use of Corona and the dense time series of Landsat can provide more detailed information on forest changes. Forest cover sharply decreased from 343.89 in 1967 to 298.44 ha in 1990, and after 2003, the forest area substantially increased and finally reached a maximum of 434.16 ha in 2019. Subdivision and bridge not only occupied the larger areas in the process of forest loss and restoration, but also they had strong correlations with forest changes and the Pearson correlation coefficients (r) were respectively 0.96 and 0.91. These all revealed that forest changes mainly affected landscape structure connectivity. The total forest AGB of Mt. Mufu increased from 20,173.35 in 2006 to 31,035.77 t in 2017, but the increases in AGB were only 30-40 t/ha in most recovery areas with high structure connectivity (bridge regions), indicating there is room for improving restoration projects in the future. The obtained findings can provide mining site restoration managers with clear, long-term forest change information and mine restoration assessment methods. Full article
(This article belongs to the Special Issue Remote Sensing of Post-disturbance Vegetation Recovery Monitoring)
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