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Forest and Agro-Ecosystem Monitoring under Climate Change Based on Remote...
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Forest and Agro-Ecosystem Monitoring under Climate Change Based on Remote Sensing

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2687

Special Issue Editors

Dr. Yuan Zhang

E-Mail Website
Guest Editor
Key Laboratory of Geographical Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
Interests: land use and land cover changes (LULCC) using optical/microwave remotely sensed data; quantitative biophysical estimation of rice with canopy radiative transfer model; simulation of carbon/nitrogen/water cycles in agro-ecosystem using process-based biogeochemical model
Dr. Mauro Maesano

E-Mail Website
Guest Editor
Department for Innovation in Biological, Agro-Food and Forest Systems—DIBAF—University of Tuscia, 01100 Viterbo, Italy
Interests: sustainable forest management; remote sensing; unmanned aerial vehicle; LiDAR; RGB sensor; forest certification; forest management; urban forests; vegetation index; mulltispectral sensor
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest and agroecosystems are critical for sustaining life on earth, and their proper management is essential to mitigate climate change effects. However, these systems are under constant threat from climate change, which is causing changes in vegetation patterns and affecting ecosystem services. Remote sensing technologies can provide valuable information on vegetation patterns, growth rates, and the overall health of forests and agroecosystems. The use of remote sensing has enabled researchers to analyze the patterns and dynamics of land cover changes, vegetation growth, and productivity. Policymakers and researchers can identify areas that require immediate attention and take necessary actions to mitigate the impacts of climate change. The data obtained through remote sensing can aid in decision making for managing forest- and agro-ecosystems. The proper management of these ecosystems can help to reduce greenhouse gas emissions and enhance carbon sequestration, thereby mitigating climate change.

This Special Issue aims to explore the latest advancements in remote sensing technologies for forest monitoring and management for improving our understanding of forest ecosystems. The subject matter of this Special Issue aligns perfectly with the scope of Remote Sensing, which focuses on publishing research on the acquisition, processing, analysis, and interpretation of remote sensing data. This Special Issue provides a platform for researchers to showcase their work, exchange ideas, and collaborate on future research endeavours related to forest remote sensing.

Therefore, multisource remote sensing data integration (e.g., multispectral, hyperspectral, and thermal) and multiscale approaches and models focused on forest ecosystem monitoring are welcome. This Issue covers a range of topics, such as:

  • Forest health assessment;
  • Forest biomass estimation;
  • Forest structure mapping;
  • Forest change detection;
  • Forest restoration and conservation.

Dr. Yuan Zhang
Dr. Mauro Maesano
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

  • remote sensing
  • vegetation indices
  • forest productivity
  • climate change
  • carbon sequestration
  • forest types and species mapping
  • forest disturbance detection
  • biomass estimation
  • deforestation monitoring
  • forest health assessment

Published Papers (2 papers)

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Research

20 pages, 7295 KiB  
Article
Cutting the Greenness Index into 12 Monthly Slices: How Intra-Annual NDVI Dynamics Help Decipher Drought Responses in Mixed Forest Tree Species
by Andrea Cecilia Acosta-Hernández, Marín Pompa-García, José Alexis Martínez-Rivas and Eduardo Daniel Vivar-Vivar
Remote Sens. 2024, 16(2), 389; https://doi.org/10.3390/rs16020389 - 18 Jan 2024
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Abstract
We studied the correspondence between historical series of tree-ring width (TRW) and the normalized difference vegetation index (NDVI, i.e., greenness index) values acquired monthly over an entire year by unmanned aerial vehicles. Dendrochronological techniques revealed differentiated responses between species and seasonality. Pinus engelmannii [...] Read more.
We studied the correspondence between historical series of tree-ring width (TRW) and the normalized difference vegetation index (NDVI, i.e., greenness index) values acquired monthly over an entire year by unmanned aerial vehicles. Dendrochronological techniques revealed differentiated responses between species and seasonality. Pinus engelmannii Carrière and Juniperus deppeana Steudel were affected by warm temperatures (TMAX) during the winter prior to growth and benefited from precipitation (PP) during the seasons prior to the spring period. The standardized precipitation–evapotranspiration index (SPEI) confirmed the high sensitivity of P. engelmannii to drought (r = 0.7 SPEI). Quercus grisea Liebm. presented a positive association with PP at the beginning and end of its growth season. Monthly NDVI data at the individual tree level in the three species (NDVI ~0.37–0.48) statistically confirmed the temporal differences. Q. grisea showed a drastic decrease during the dry season (NDVI = 0.1) that had no impact on drought sensitivity in the same period, according to the climate-TRW relationship. We conclude that a relationship is plausible between the crown greenness index and radial growth, although more extended temporal windows of the NDVI should be explored. Differences in susceptibility to drought found among the species would presumably have implications for the composition of these forests under drought scenarios. Full article
(This article belongs to the Special Issue Forest and Agro-Ecosystem Monitoring under Climate Change Based on Remote Sensing)
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16 pages, 10374 KiB  
Article
Climate Sensitivity and Drought Legacy of Tree Growth in Plantation Forests in Northeast China Are Species- and Age-Dependent
by Ting Li, Qiaoqi Sun, Hongfei Zou and Petra Marschner
Remote Sens. 2024, 16(2), 281; https://doi.org/10.3390/rs16020281 - 10 Jan 2024
Viewed by 1050
Abstract
The occurrence, frequency, and severity of drought are accelerating due to global warming. Understanding the vulnerability of plantation forests to climate change, particularly to drought events, is critical to revealing the underlying mechanisms of tree resilience, recovery, and acclimation, which are important for [...] Read more.
The occurrence, frequency, and severity of drought are accelerating due to global warming. Understanding the vulnerability of plantation forests to climate change, particularly to drought events, is critical to revealing the underlying mechanisms of tree resilience, recovery, and acclimation, which are important for plantation management. How the stand age affects the climate sensitivity of tree growth, as well as the direction, magnitude, and duration of the drought legacy, in plantation forests in northeast China is still unclear. In this study, we used MODIS-derived NDVI time series with gridded climate data from 2000 to 2020 to fill this knowledge gap. The selected plantation forests were dominated by four coniferous species: Korean pine (Pinus koraiensis), Scots pine (Pinus sylvestris), Japanese larch (Larix kaempferi), and Dahurian larch (Larix gmelinii). The results show that the climate sensitivity of tree growth differed among species and age groups. The growth of Korean pine and Scots pine was mostly dependent upon precipitation, while the growth of Japanese larch and Dahurian larch was determined primarily by temperature. Old Japanese larch (21–40 years) and Dahurian larch trees (31–60 years) were more sensitive to temperature and precipitation than young conspecifics, whereas old Korean pine (41–60 years) and Scots pine (31–60 years) were less sensitive to precipitation and temperature than young conspecifics. Furthermore, the legacy of drought lasted one year for Korean pine, Japanese larch, and Dahurian larch and over three years for Scots pine. Old trees were more severely affected by drought, particularly Scots pine and Dahurian larch. The findings of the study can help improve plantation forest management for better adaptation to future climate change. Full article
(This article belongs to the Special Issue Forest and Agro-Ecosystem Monitoring under Climate Change Based on Remote Sensing)
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