Exploring Emerging Climatic Changes and Responses in Plant Sciences Using Remote Sensing

A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 727

Special Issue Editors


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Guest Editor
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: land use changes; climate modeling; climate change and variability

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Guest Editor
School of Global Policy and Strategy, University of California, San Diego, CA 92093, USA
Interests: land‒atmosphere‒human interactions; climate dynamics; tropical rainforest; plant physiology; water cycle; remote sensing; machine learning; natural hazard; food security

Special Issue Information

Dear Colleagues,

Because of climate change, plants’ structures, compositions, and functions may be significantly impacted and altered. Increased risks of extreme weather events and long-term climatic disruptions pose direct threats to plant phenology, productivity, biogeochemical cycles, and biome conversation, which could endanger critical ecosystems, crop production, and economic activity. Remote sensing, which involves the use of satellite technology to measure and monitor Earth’s surface structure and dynamics, has emerged as a powerful tool for detecting landscape patterns and shifts. With satellites such as NASA MODIS, VIIRS, USGS Landsat, ESA Sentinel, and others, more than 50 petabytes of public information of Earth’s landscape have been collected over the past 30 years. These high-resolution aerial images provide tremendous opportunities to identify and evaluate the challenges and consequences of climate change on plants.

This Special Issue aims to incorporate applications of remote sensing to analyze variations in plant properties that respond to climate change. By using visible and near-infrared (VNIR) images, it is possible to measure canopy cover, loss, and dynamics. Thermal infrared images and active remote sensing techniques such as LiDAR and radar can provide additional insights into photosynthesis and plant mortality. Long-term remote sensing records offer continuous measurements to monitor changes in plant systems and biomes at both regional and global scales. Therefore, we invite papers on the following related topics:

  • The utilization of high-resolution images to estimate changes in plant traits and functions.
  • The use of remote sensing to evaluate alterations in the composition, phenology, and structure of critical plant ecosystems under extreme weather events or water and temperature stresses.
  • Advanced application of remote sensing in crop monitoring and urban plant management regarding climatic impacts.

In addition, we welcome review articles discussing the progress of using remote sensing in plant sciences concerning climate change. We also invite works that apply advanced hyperspectral images, the Google Earth Engine, and machine learning algorithms to improve the acquisition of remotely sensed information about plant stress.

Prof. Dr. Wenjian Hua
Dr. Yan Jiang
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. Plants 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
  • climate change
  • optical, near-infrared, thermal, and microwave images
  • plant function, structure, and composition
  • sensing for crops, urban plants
  • water stress
  • heat stress
  • hyperspectral imaging
  • Google Earth Engine
  • machine learning for plants monitoring and pattern recognition

Published Papers (1 paper)

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Review

31 pages, 864 KiB  
Review
RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review
by Anastasiia Kior, Lyubov Yudina, Yuriy Zolin, Vladimir Sukhov and Ekaterina Sukhova
Plants 2024, 13(9), 1262; https://doi.org/10.3390/plants13091262 - 30 Apr 2024
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Abstract
Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be [...] Read more.
Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary. Full article
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