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Plants
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Remote Sensing-Based Monitoring of Vegetation Phenology in a Changing Environment
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Remote Sensing-Based Monitoring of Vegetation Phenology in a Changing Environment

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Ecology".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 3027

Special Issue Editors

Dr. Jing Xie

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Interests: remote sensing; ecology; land use/land cover; ecosystem; vegetation; phenology
Dr. Zhi Ding

E-Mail Website
Guest Editor
Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Interests: land use/land cover; ecosystem restoration projects; remote sensing; coastal zone; vegetation

Special Issue Information

Dear Colleagues,

In the context of climate and environmental change, the study of vegetation phenology in ecosystems has assumed increasing significance, particularly in the pursuit of the 2030 Agenda for Sustainable Development Goals (SDGs). Currently, the monitoring of vegetation phenology in a changing environment relies heavily on field recordings and limited remote sensing data. This reliance leads to inefficiencies in data analysis, a lack of coherence in modeling, diminished reliability, and unsustainability in diverse environmental conditions.

Emerging as a key information technology science in recent years, remote sensing (RS) big data offers a promising avenue for addressing these challenges. Leveraging database platforms, information services, and reanalysis modeling, RS big data enables more scientific and quantitative approaches to vegetation phenology analysis, spanning from landscape to crown levels. This Special Issue seeks to bridge the gap between remote sensing big data and field observations/recording. We intend to employ cutting-edge technologies such as satellite imagery, airborne remote sensing, and big data analytics to construct robust vegetation phenology analysis models. Ultimately, our goal is to furnish decision-making tools for scientists engaged in the study of vegetation ecosystems.

We invite researchers to contribute their original research papers, technical reports, or review articles to this Special Issue, with a particular emphasis on the applications and prospects of remote sensing in the field of vegetation phenology, such as:

  • Vegetation phenology detection and modeling;
  • Remote sensing / big data for vegetation phenology monitor;
  • Vegetation phenology change;
  • Vegetation cover and green spaces change;
  • Response of vegetation phenology to climate change and human activity;
  • Effect of vegetation phenology on ecosystem environment.

We look forward to receiving your original research articles and reviews.

Dr. Jing Xie
Dr. Zhi Ding
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.

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Published Papers (3 papers)

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Research

19 pages, 5390 KiB  
Article
The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology
by Mengxi Shi, Shuhan Xing, He Bai, Dawei Xu and Lei Shi
Plants 2024, 13(22), 3164; https://doi.org/10.3390/plants13223164 - 11 Nov 2024
Viewed by 526
Abstract
This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use [...] Read more.
This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring. Full article
(This article belongs to the Special Issue Remote Sensing-Based Monitoring of Vegetation Phenology in a Changing Environment)
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18 pages, 7989 KiB  
Article
Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China
by Fengnian Guo, Dengfeng Liu, Shuhong Mo, Qiang Li, Jingjing Meng and Qiang Huang
Plants 2024, 13(13), 1826; https://doi.org/10.3390/plants13131826 - 3 Jul 2024
Viewed by 946
Abstract
Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. [...] Read more.
Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau. Full article
(This article belongs to the Special Issue Remote Sensing-Based Monitoring of Vegetation Phenology in a Changing Environment)
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Figure 1

17 pages, 8328 KiB  
Article
Variation of the Start Date of the Vegetation Growing Season (SOS) and Its Climatic Drivers in the Tibetan Plateau
by Hanya Tang, Yongke Li, Xizao Sun, Xuelin Zhou, Cheng Li, Lei Ma, Jinlian Liu, Ke Jiang, Zhi Ding, Shiwei Liu, Pujia Yu, Luyao Jia and Feng Zhang
Plants 2024, 13(8), 1065; https://doi.org/10.3390/plants13081065 - 10 Apr 2024
Viewed by 988
Abstract
Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted [...] Read more.
Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY–170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP. Full article
(This article belongs to the Special Issue Remote Sensing-Based Monitoring of Vegetation Phenology in a Changing Environment)
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