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Remote Sensing of Climate–Vegetation Dynamics and Their Effects on Ecosystems (Third Edition)

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

Deadline for manuscript submissions: 1 April 2026 | Viewed by 1558

Special Issue Editors


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Guest Editor
Taiwan International Graduate Program (TIGP) –Ph.D. Program on Biodiversity, Tunghai University, Taichung, Taiwan
Interests: geoinformatics; land surface phenology; long-term ecological study; biogeochemistry
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Guest Editor
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: plant and vegetation phenology; vegetation geography; global change and phenology; global change and plant geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Vegetation phenology plays an important role in regulating water cycles, carbon cycles, productivity, and more, and is significantly related to region-specific climatic and non-climatic factors. In the context of a warming climate, the dynamics of local regular climate and large-scale climatic variations, such as El Niño–Southern Oscillation (ENSO), are expected to become more dramatic and may have substantial effects on vegetation phenology. In addition, extreme climate events such as storms, tropical cyclones, and sporadic events alongside anthropogenic activities have abruptly altered the development of vegetation from regional to global scales. With the assistance of long-term in situ observations, PhenoCam monitoring networks, and multisource remotely sensed datasets, variations in vegetation phenology and its associations with regular climate, climatic fluctuations, and extremes can be captured and understood. Moreover, animal phenology, including migration, reproduction, and breeding cycles, is often closely coupled with vegetation dynamics. Consequently, remote sensing of vegetation phenology could provide valuable indirect insights into animal phenology and ecosystem interactions under climate change.

For this Special Issue, we invite scientists to apply remote sensing and spatial technology to explore the variations in vegetation phenology in relation to climate. Suitable topics include the combination of field observations with remote sensing techniques across scales, relationships between satellite-derived phenology (land surface phenology, LSP), and climate, including regional climate conditions and large-scale atmospheric anomalies. Studies on the effects of phenological variations in landscapes due to hydrological processes, water resources, and biogeochemical cycles are also significant contributions to this field. Research on the alterations of LSP alongside land-cover gradient and projections of phenology across scales is encouraged. We also welcome studies on animal phenology, such as migration, breeding, and reproduction cycles, which are tightly linked to vegetation dynamics. Remote sensing of these connections can reveal important cross-trophic interactions and ecosystem responses to climate change.

Prof. Dr. Chung-Te Chang
Prof. Dr. Junhu Dai
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 250 words) can be sent to the Editorial Office for assessment.

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 and animal phenology
  • regular climate
  • climatic fluctuation
  • disturbance
  • PhenoCam
  • multisource remotely sensed data
  • productivity
  • water and biogeochemical cycles
  • wildlife phenology derived from remote sensing

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Published Papers (2 papers)

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Research

26 pages, 12013 KB  
Article
Vegetation Greening Driven by Warming and Humidification Trends in the Upper Reaches of the Irtysh River
by Honghua Cao, Lu Li, Hongfan Xu, Yuting Fan, Huaming Shang, Li Qin and Heli Zhang
Remote Sens. 2026, 18(3), 482; https://doi.org/10.3390/rs18030482 - 2 Feb 2026
Viewed by 460
Abstract
To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. [...] Read more.
To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m3 m−3/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article
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26 pages, 7188 KB  
Article
Land Suitability Assessment and Gap Analysis for Sustainable Taro (Colocasia esculenta (L.) Schott) Production in Rwanda Using Remote Sensing Data and a Fuzzy AHP Model
by Jean Marie Vianney Nsigayehe, Xingguo Mo and Suxia Liu
Remote Sens. 2025, 17(24), 4062; https://doi.org/10.3390/rs17244062 - 18 Dec 2025
Viewed by 733
Abstract
Taro (Colocasia esculenta (L.) Schott) is a nutritionally important and climate-resilient crop with high potential for enhancing food security. Despite its significance, taro remains underutilized and excluded from major agricultural policies in Rwanda, resulting in low national yields. This gap hinders evidence-based [...] Read more.
Taro (Colocasia esculenta (L.) Schott) is a nutritionally important and climate-resilient crop with high potential for enhancing food security. Despite its significance, taro remains underutilized and excluded from major agricultural policies in Rwanda, resulting in low national yields. This gap hinders evidence-based planning and limits the crop contribution to resilience amidst population growth and climate change. By taking Rwanda as an example, a worldwide top 10 taro-producing country but still facing food insecurity issues, this study conducted a nationwide land suitability assessment to identify optimal areas for taro cultivation and quantify the production gap. The Fuzzy Analytic Hierarchy Process (AHP) model was integrated with GIS, where climatic, topographic, and a remotely sensed soil dataset were weighted and combined to generate a composite suitability index. Results revealed that 22.8% of Rwanda’s land is highly suitable (S1) and 55.7% is moderately suitable (S2) for taro cultivation. Within agricultural land, 30.2% is highly suitable, of which a significant portion (28.7%) remains largely underutilized, especially in the Eastern province. The national production gap was estimated at 32.4%, with over half of the districts exceeding 30%. The study highlights the importance of aligning taro cultivation with biophysical suitability and integrating spatial planning into national agricultural policies. The developed suitability map provides a critical decision-support tool for policymakers, agricultural planners, and extension services. By promoting sustainable taro production, improving farmer livelihoods and food security in Rwanda, it provides a global model for sustainable development for developing countries and advances research on orphan crops such as taro. The methodology offers a replicable framework for evaluating underutilized crops globally, contributing to sustainable agricultural diversification and food security. Full article
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