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Remote Sensing
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Forest Resilience to Extreme Events
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Forest Resilience to Extreme Events

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

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 6636

Special Issue Editors

Prof. Dr. Chuixiang Yi

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Guest Editor
Queens College, City University of New York, Flushing, NY 11367, USA
Interests: forest resilience; tipping point; extreme climates; nonlinear system theory; dendroclimatology; carbon cycle; atmosphere–biosphere interactions; abrupt climate transition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuli Niu

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Guest Editor
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China
Interests: global change ecology; ecosystem ecology; biogeoscience; plant ecology
Dr. Jingfeng Xiao

E-Mail Website
Guest Editor
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Interests: carbon cycle; remote sensing; climate-vegetation interactions; upscaling; terrestrial biosphere modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The magnitude and frequency of climate-related extreme events are increasing as CO2 levels continue to rise and the climate continues to warm. Climate warming is a fundamental cause of increasing extreme climate events, as predicted by the first and second laws of thermodynamics, as a consequence of warmer air being able to hold more water molecules. Forest resilience in the face of increasing extreme events has become a global concern. In this Special Issue, we invite the submission of the latest research related to measuring forest resilience, resistance, recovery, vulnerability, sustainability, and the study of forest stability in extreme events, particularly including climate-induced forest mortality from drought stress, insect attachment, forest fires, and other related climate changes by using various approaches, e.g., sap flow measurements, tree-ring data, manipulative experiments, remote sensing images, physiological modeling, and ecosystem–climate modeling.

Prof. Dr. Chuixiang Yi
Prof. Dr. Shuli Niu
Dr. Jingfeng Xiao
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 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. 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 2700 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 resilience
  • forest recovery
  • forest resistance
  • forest fire
  • drought
  • insect attachment and disease
  • tipping point
  • remote sensing
  • vulnerability
  • forest stability
  • modeling

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Published Papers (2 papers)

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Research

24 pages, 9487 KiB  
Article
Plant Ontogeny Strongly Influences SO2 Stress Resistance in Landscape Tree Species Leaf Functional Traits
by Aru Han, Yongbin Bao, Xingpeng Liu, Zhijun Tong, Song Qing, Yuhai Bao and Jiquan Zhang
Remote Sens. 2022, 14(8), 1857; https://doi.org/10.3390/rs14081857 - 12 Apr 2022
Cited by 1 | Viewed by 2176
Abstract
Sulfur dioxide (SO2) is a major atmospheric pollutant and abiotic stressor. Although physiological studies on abiotic stressors have focused on fully expanded leaves, the resistance of leaf functional traits to SO2 during individual leaf development has not been studied. Thus, [...] Read more.
Sulfur dioxide (SO2) is a major atmospheric pollutant and abiotic stressor. Although physiological studies on abiotic stressors have focused on fully expanded leaves, the resistance of leaf functional traits to SO2 during individual leaf development has not been studied. Thus, this study aimed to conduct SO2 static artificial fumigation experiments to evaluate changes in leaf functional traits and resistance to SO2 for three common landscape tree species (Syringa oblata Lindl. (S. oblata), Prunus cerasifera var. atropurpurea Jack. (P. cerasifera), and Ulmus pumila ‘Jinye’ (U. pumila)) in Changchun City and ontogeny under SO2 stress. Samples were collected on three days in autumn (1 September, 9 September, and 19 September 2019) for two different leaf stages (10 days and 40 days). In addition, remote sensing data were combined to explore the resistance mechanisms of broadleaf forests to different SO2 concentration classes during different seasons on a large scale. The results showed that the chlorophyll content, leaf temperature, green-peak reflectance, and Fv/Fm (maximal photochemical efficiency) at 10 days were significantly lower than that at 40 days, regardless of sampling date or SO2 concentration. Additionally, in general the SO2 resistance for 10 days leaves was consistently smaller than those for 40 days leaves in 3 tree species. On 9 September, 10 days leaves of the three tree species showed different leaf resistance performances under different SO2 concentrations in the order: P. cerasifera > S. oblata > U. pumila. Lastly, the extent of resistance decreased with increasing ρ(SO2) classes in different seasons, and the SO2 resistance was affected by season. We conclude that mature leaves are more resistant to SO2 stress than young leaves are. These results will provide scientific guidance on artificial plant community construction and prevention of future vegetation degradation. Full article
(This article belongs to the Special Issue Forest Resilience to Extreme Events)
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19 pages, 3865 KiB  
Article
Monitoring Forest Resilience Dynamics from Very High-Resolution Satellite Images in Case of Multi-Hazard Disaster
by Reza Rezaei and Saman Ghaffarian
Remote Sens. 2021, 13(20), 4176; https://doi.org/10.3390/rs13204176 - 19 Oct 2021
Cited by 8 | Viewed by 3060
Abstract
Typhoons strongly impact the structure and functioning of the forests, especially in the coastal areas in which typhoon-induced flooding imposes additional stress on the ecosystem via physical destruction and rising soil salinity. The impact of typhoons on forest ecosystems is becoming even more [...] Read more.
Typhoons strongly impact the structure and functioning of the forests, especially in the coastal areas in which typhoon-induced flooding imposes additional stress on the ecosystem via physical destruction and rising soil salinity. The impact of typhoons on forest ecosystems is becoming even more significant in the changing climate, which triggers atmospheric mechanisms that increase their frequency and intensity. This study investigates the resiliency of the Philippines’ forest areas (i.e., two selected forestry areas in Tacloban and Guiuan) in the aftermath of Super Typhoon Haiyan, which was followed by coastal flooding, as well as changes in ecosystem and biomass content using remote sensing. For this, we first evaluated the sensitivity of the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and enhanced vegetation index (EVI) in detecting temporal changes in biomass content using very high-resolution satellite images. Then, employing three resilience concepts: amplitude, malleability, and elasticity, the most sensitive biomass index (i.e., NDVI) and digital elevation model (DEM) data were used to measure the resiliency of the Guiuan and Tacloban sites. We also applied a mean-variance analysis to extract and illustrate the shifts in the ecosystem status. The results show that despite a considerable biomass loss (57% in Guiuan and 46% in Tacloban), the Guiuan and Tacloban sites regained 80% and 70% of their initial biomass content within a year after the typhoon, respectively. However, the presence of canopy gaps in the Tacloban site makes it vulnerable to external stressors. Furthermore, the findings demonstrate that the study areas return to their initial states within two years. This indicates the high resiliency of those areas according to elasticity results. Moreover, the evaluation of typhoon impacts according to the elevation demonstrates that the elevation had a substantial impact on both damage severity and biomass recovery. Full article
(This article belongs to the Special Issue Forest Resilience to Extreme Events)
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