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Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate (3rd Edition)

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 August 2025 | Viewed by 2383

Special Issue Editors

Dr. Xuejia Wang

E-Mail Website
Guest Editor
State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: vegetation change; land–atmosphere interactions; model simulation; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Tinghai Ou

E-Mail Website
Guest Editor
Department of Earth Science, University of Gothernburg, 405 30 Gothenburg, Sweden
Interests: climate extreme events; land–atmosphere interaction; climate modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Wenxin Zhang

E-Mail Website
Guest Editor
Department of Physical Geography and Ecosystem Science, Lund University, 221 00 Lund, Sweden
Interests: soil thermal regime; vegetation dynamics; biophysical properties of vegetaton; land surface models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As one of the crucial underlying land surfaces, vegetation plays a critical role in terrestrial ecosystems and the Earth's climate. As a result of climate warming, vegetation exhibits a range of responses, such as greening and browning, which have been reported by many remote sensing studies. Vegetation is an important and sensitive indicator of climate and environment evolutions, underscoring the need to improve our detection and understanding of the physiological and phenological responses of vegetation, analyze how changes in land surface properties (e.g., surface albedo and roughness length) are associated with vegetation dynamics, and identify the climate and ecological feedback provided by vegetation changes. The recent development of satellite remote sensing and its derived products have provided many opportunities to study vegetation dynamics and its interactions with the regional and global climate system. Moreover, newly developed state-of-the-art climate models, such as CMIP6 Earth system models, which include dynamic vegetation, allow us to conduct more extensive examinations of vegetation changes.

For this Special Issue, we are seeking contributions that apply a variety of high-resolution satellite data, global and regional numerical models, and machine learning methods to achieve detailed classification of vegetation, detect changes in vegetation dynamics, and investigate interactions between vegetation and climate/ecological systems, especially for high-latitude and high-altitude regions. Potential topics may include (but are not limited to)

  • Vegetation mapping;
  • Vegetation changes from various remote sensing data sources;
  • Response of vegetation to climate change;
  • Feedback of vegetation change to climate;
  • Dynamic vegetation modeling;
  • Ecological effect of vegetation change.

Dr. Xuejia Wang
Dr. Tinghai Ou
Dr. Wenxin Zhang
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

  • vegetation type
  • vegetation phenology
  • change detection
  • model simulation
  • response to climate change
  • feedback to climate change

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Related Special Issue

Published Papers (4 papers)

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Research

18 pages, 3069 KiB  
Article
Responses of Terrestrial GPP to Extreme Compound Heatwave and Drought Events of Different Intensities in the Yangtze River Basin
by Jiawen Zhu, Haokun Guo and Yankun Sun
Remote Sens. 2025, 17(5), 848; https://doi.org/10.3390/rs17050848 - 27 Feb 2025
Viewed by 309
Abstract
In 2022, a record-breaking extreme compound heatwave–drought (CHD) event occurred in China’s Yangtze River Basin (YRB), which significantly reduced terrestrial ecosystem Gross Primary Production (GPP), as many previous studies have shown. However, it remains uncertain how GPP responds to extreme CHD events of [...] Read more.
In 2022, a record-breaking extreme compound heatwave–drought (CHD) event occurred in China’s Yangtze River Basin (YRB), which significantly reduced terrestrial ecosystem Gross Primary Production (GPP), as many previous studies have shown. However, it remains uncertain how GPP responds to extreme CHD events of varying intensities, as well as the differences in GPP responses among different vegetation types in the YRB. This study used two independent GPP products (GPPGOSIF and GPPFluxSat) and Enhanced Vegetation Index (EVIMODIS) data to investigate these uncertainties during three identified CHD years: 2003, 2013, and 2022. In 2022, when the CHD event intensity was the strongest, the YRB experienced the most significant GPP reductions, with amplitudes of −0.37 gC m−2 day−1 (−3.05 standard deviation, hereafter STD) and −0.57 gC m−2 day−1 (−5.97 STD) for GPPGOSIF and GPPFluxSat, respectively. In the less intense years, the year 2003 had less than one-third of GPP reductions in 2022, while the year 2013 even showed an overall slight GPP increase. Moreover, the year 2022 also showed the widest extent with significant GPP reductions exceeding one STD, which was more than twice the grid points in 2003 and 2013. This study also revealed significant differences in GPP responses across different vegetation types. In 2022, GPP reductions were the most pronounced in shrub-dominated areas, followed by evergreen forests, while deciduous forests and mixed forests experienced relatively smaller decreases. However, there were no significant differences among vegetation types in 2003 and 2013. These findings enhance our understanding of the variability in the GPP responses to extreme CHD events of varying intensities in the YRB terrestrial ecosystems, and this enhancement provides an important scientific basis for optimizing related vegetation and carbon cycle models. Full article
(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate (3rd Edition))
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19 pages, 6740 KiB  
Article
Comparison of Spring Phenology from Solar-Induced Chlorophyll Fluorescence, Vegetation Index, and Ground Observations in Boreal Forests
by Dandan Shi, Yuan Jiang, Minghao Cui, Mengxi Guan, Xia Xu and Muyi Kang
Remote Sens. 2025, 17(4), 627; https://doi.org/10.3390/rs17040627 - 12 Feb 2025
Viewed by 304
Abstract
Spring phenology (start of growing season, SOS) in boreal forests plays a crucial role in the global carbon cycle. At present, more and more researchers are using solar-induced chlorophyll fluorescence (SIF) to evaluate the land surface phenology of boreal forests, but few studies [...] Read more.
Spring phenology (start of growing season, SOS) in boreal forests plays a crucial role in the global carbon cycle. At present, more and more researchers are using solar-induced chlorophyll fluorescence (SIF) to evaluate the land surface phenology of boreal forests, but few studies have utilized the primary SIF directly detected by satellites (e.g., GOME-2 SIF) to estimate phenology, and most SIF datasets used are high-resolution products (e.g., GOSIF and CSIF) constructed by models with vegetation indices (VIs) and meteorological data. Thus, the difference and consistency between them in detecting the seasonal dynamics of boreal forests remain unclear. In this study, a comparison of spring phenology from GOME-2 SIF, GOSIF, EVI2 (MCD12Q2), and FLUX tower sites, PEP725 phenology observation sites, was conducted. Compared with GOSIF and EVI2, the primary GOME-2 SIF indicated a slightly earlier spring phenology onset date (about 5 days earlier on average) in boreal forests, at a regional scale; however, SOSs and SOS-climate relationships from GOME-2 SIF, GOSIF, and EVI2 showed significant correlations with the ground observations at a site scale. Regarding the absolute values of spring phenology onset date, GOME-2 SIF and FLUX-GPP had an average difference of 8 days, while GOSIF and EVI2 differed from FLUX-GPP by 16 days and 12 days, respectively. GOME-2 SIF and PEP725 had an average difference of 38 days, while GOSIF and EVI2 differed from PEP725 by 24 days and 23 days, respectively. This demonstrated the complementary roles of the three remote sensing datasets when studying spring phenology and its relationship with climate in boreal forests, enriching the available remote sensing data sources for phenological research. Full article
(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate (3rd Edition))
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19 pages, 9258 KiB  
Article
Climate Warming Controls Vegetation Growth with Increasing Importance of Permafrost Degradation in the Northern Hemisphere During 1982–2022
by Yadong Liu, Xiaodong Wu, Tonghua Wu, Guojie Hu, Defu Zou, Yongping Qiao, Xianhua Wei, Xiaoying Fan and Xuchun Yan
Remote Sens. 2025, 17(1), 104; https://doi.org/10.3390/rs17010104 - 31 Dec 2024
Viewed by 663
Abstract
In permafrost regions, vegetation growth is influenced by both climate conditions and the effects of permafrost degradation. Climate factors affect multiple aspects of the environment, while permafrost degradation has a significant impact on soil moisture and nutrient availability, both of which are crucial [...] Read more.
In permafrost regions, vegetation growth is influenced by both climate conditions and the effects of permafrost degradation. Climate factors affect multiple aspects of the environment, while permafrost degradation has a significant impact on soil moisture and nutrient availability, both of which are crucial for ecosystem health and vegetation growth. However, the quantitative analysis of climate and permafrost remains largely unknown, hindering our ability to predict future vegetation changes in permafrost regions. Here, we used statistical methods to analyze the NDVI change in the permafrost region from 1982 to 2022. We employed correlation analysis, multiple regression residual analysis and partial least squares structural equation modeling (PLS-SEM) methods to examine the impacts of different environmental factors on NDVI changes. The results show that the average NDVI in the study area from 1982 to 2022 is 0.39, with NDVI values in 80% of the area remaining stable or exhibiting an increasing trend. NDVI had the highest correlation with air temperature, averaging 0.32, with active layer thickness coming in second at 0.25. Climate change plays a dominant role in NDVI variations, with a relative contribution rate of 89.6%. The changes in NDVI are positively influenced by air temperature, with correlation coefficients of 0.92. Although the active layer thickness accounted for only 7% of the NDVI changes, its influence demonstrated an increasing trend from 1982 to 2022. Overall, our results suggest that temperature is the primary factor influencing NDVI variations in this region. Full article
(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate (3rd Edition))
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24 pages, 8390 KiB  
Article
The Spatiotemporal Evolution of Vegetation in the Henan Section of the Yellow River Basin and Mining Areas Based on the Normalized Difference Vegetation Index
by Zhichao Chen, Xueqing Liu, Honghao Feng, Hongtao Wang and Chengyuan Hao
Remote Sens. 2024, 16(23), 4419; https://doi.org/10.3390/rs16234419 - 26 Nov 2024
Viewed by 653
Abstract
The Yellow River Basin is rich in coal resources, but the ecological environment is fragile, and the ecological degradation of vegetation is exacerbated by the disruption caused by high-intensity mining activities. Analyzing the dynamic evolution of vegetation in the Henan section of the [...] Read more.
The Yellow River Basin is rich in coal resources, but the ecological environment is fragile, and the ecological degradation of vegetation is exacerbated by the disruption caused by high-intensity mining activities. Analyzing the dynamic evolution of vegetation in the Henan section of the Yellow River Basin and its mining areas over the long term run reveals the regional ecological environment and offers a scientific foundation for the region’s sustainable development. In this study, we obtained a long time series of Landsat imageries from 1987 to 2023 on the Google Earth Engine (GEE) platform and utilized geographically weighted regression models, Sen (Theil–Sen median) trend analysis, M-K (Mann–Kendall) test, coefficient of variation (CV), and the Hurst index to investigate the evolution of vegetation cover based on the kNDVI (the normalized difference vegetation index). This index is used to explore the spatial and temporal characteristics of vegetation cover and its future development trend. Our results showed that (1) The kNDVI value in the Henan section of the Yellow River Basin exhibited a trend of fluctuating upward at a rate of 0.0509/10a from 1987 to 2023. The kNDVI trend in the mining areas of the region aligned closely with the overall trend of the Henan section; however, the annual kNDVI in each mining area consistently remained lower than that of the Henan section and displayed a degree of fluctuation, predominantly characterized by medium–high variability, with areas of moderate and high fluctuations accounting for 73.5% of the total. (2) The kNDVI in the study area showed a significant improvement in vegetation cover and its future development trends. We detected a significant improvement in the kNDVI index in the area; yet, significant improvement in this index in the future might cause vegetation degradation in 87% of the study area, which may be closely related to multiple factors such as the intensity of mining at the mine site, anthropogenic disturbances, and climate change. (3) The vegetation status of the Henan section of the Yellow River Basin shows a significant positive correlation with distance from mining areas, accounting for 90.9% of the total, indicating that mining has a strong impact on vegetation cover. This study provides a scientific basis for vegetation restoration, green development of mineral resources, and sustainable development in the Henan section of the Yellow River Basin. Full article
(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate (3rd Edition))
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