Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Quantitative Remote Sensing for Vegetation Phenology and Regional Landscape Patterns
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Quantitative Remote Sensing for Vegetation Phenology and Regional Landscape Patterns

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: 15 August 2025 | Viewed by 2046

Special Issue Editors

Dr. Ke Huang

E-Mail Website
Guest Editor
Department of Geosciences and Nature Management, University of Copenhagen, Copenhagen, Denmark
Interests: climate change; vegetation dynamic; remote sensing; deep learning
Dr. Jiaxing Zu

E-Mail Website
Guest Editor
Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning, China
Interests: remote sensing phenology; climate change; UAV lidar application
Dr. Ning Chen

E-Mail Website
Guest Editor
Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
Interests: carbon cycle and global change; ecological model; vegetation phenology
Dr. Xiaowei Tong

E-Mail Website
Guest Editor
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
Interests: remote sensing ecology; vegetation phenology

Special Issue Information

Dear Colleagues,

Vegetation phenology refers to specific life cycle events of plants, such as budbreak, flowering, leafout/greenup, fructification, and senescence. It is a crucial factor in assessing the impact of climate change on vegetation and influences terrestrial energy exchange and global carbon cycling. The rapid advancement in remote sensing technologies has broadened the scope of phenological studies by providing high-resolution, long-term, and diverse datasets that enable the precise monitoring and analysis of vegetation changes. These technologies facilitate a comprehensive understanding of land surface phenology across different scales and various ecosystems, thereby enhancing insights into ecological processes and the impacts of environmental changes. Over the past decades, extensive research has been conducted on the dynamics of vegetation phenology, its drivers and mechanisms, and its ecological implications and climatic feedback. This Special Issue is dedicated to studies that explore the latest advancements in the detection, understanding, and application of vegetation phenology based on various remote sensing techniques and datasets at landscape and regional scales. We invite researchers to submit papers on all aspects of vegetation phenology, including variation in landscape heterogeneity, cross-resolution comparison, relationships with carbon or hydrological cycling; and response to climate change. Related topics may include, but are not limited to, the following:

  • Innovative extraction algorithms for vegetation phenology, such as land surface phenology or leaf phenology, estimated from reflectance-based indices or solar-induced chlorophyll fluorescence observations.
  • Comparative studies of vegetation phenology across different scales using various datasets, including UAV imagery, Sentinel, Planetscope, Landsat, and MODIS.
  • Analysis of temporal variations and spatial patterns in remote sensing phenology and their determining factors at landscape or regional scales, including tropical forests, dryland woodlands, coastal forests, boreal forests, and commercial plantations.
  • Practical applications in agriculture, such as mapping or classification based on phenology stages using deep learning, carrying capacity assessment, and yield estimation.

Dr. Ke Huang
Dr. Jiaxing Zu
Dr. Ning Chen
Dr. Xiaowei Tong
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 dynamic
  • climate change
  • landscape pattern
  • cross-scale
  • vegetation phenology applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 25548 KiB  
Article
Improvement of FAPAR Estimation Under the Presence of Non-Green Vegetation Considering Fractional Vegetation Coverage
by Rui Li, Baolin Li, Yecheng Yuan, Wei Liu, Jie Zhu, Jiali Qi, Haijiang Liu, Guangwen Ma, Yuhao Jiang, Ying Li and Qiuyuan Tan
Remote Sens. 2025, 17(4), 603; https://doi.org/10.3390/rs17040603 - 10 Feb 2025
Viewed by 348
Abstract
The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed [...] Read more.
The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPARFVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPARFVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPARLAI) and MODIS FPAR products (FAPARMOD). The mean absolute percentage error of FAPARFVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPARLAI and FAPARMOD, respectively. The mean percentage error of FAPARFVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPARLAI and FAPARMOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPARFVC became more pronounced with increasing FVC compared to FAPARLAI. The findings suggested that the FAPARFVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing for Vegetation Phenology and Regional Landscape Patterns)
►▼ Show Figures

Figure 1

16 pages, 10159 KiB  
Article
Contribution of Climatic Change and Human Activities to Vegetation Dynamics over Southwest China during 2000–2020
by Gang Qi, Nan Cong, Man Luo, Tangzhen Qiu, Lei Rong, Ping Ren and Jiangtao Xiao
Remote Sens. 2024, 16(18), 3361; https://doi.org/10.3390/rs16183361 - 10 Sep 2024
Cited by 1 | Viewed by 1182
Abstract
Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI [...] Read more.
Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing for Vegetation Phenology and Regional Landscape Patterns)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop