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Monitoring Vegetation Response Based on Remote Sensing and Climate Data (Second 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: 15 February 2025 | Viewed by 2930

Special Issue Editors

Dr. Rui Yao

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Interests: urban heat island; urbanization; vegetation; climate change; land surface phenology
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Cunha

E-Mail Website
Guest Editor
1. Sciences Faculty, Porto University (FCUP) Rua do Campo Alegre, s.n. 4169-007 Porto, Portugal
2. Researcher at Institute for Systems and Computer Engineering, Technology (INESC TEC) Portugal, R. Dr. Roberto Frias, Porto, Portugal
Interests: remote sensing; crop modelling; climate change; precision agriculture; orchards/vineyards monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation plays a key role in the system of the earth and has a significant impact on carbon, water and energy cycles. Changes in vegetation can indirectly reflect changes in the environmental quality, and indicate climate and environmental evolutions. The accurate assessment of vegetation change (including vegetation cover, greeness, phenology and productivity, etc.) and its response to climate change and human activity is crucial to the realization of environmental protection and sustainable development. 

This Special Issue (Volume II) aims to enhance our understanding of vegetation change and its response to climate change and human activity, which is an important aspect of remote sensing with regard to vegetation. We invite authors to contribute to this Special Issue to improve and consolidate our current knowledge in this field. Manuscripts related to theories, methods and applications are welcome.

Submissions may include, but are not limited to:

  • Vegetation cover change;
  • Vegetation phenology change;
  • Vegetation productivity change;
  • Crop yield change;
  • Response of vegetation to climate and human activity;
  • Effect of vegetation on environment.

Dr. Rui Yao
Dr. Mario Cunha
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 cover
  • vegetation phenology
  • vegetation productivity
  • crop yield
  • NDVI/EVI/LAI
  • climate change
  • human activity detection/monitoring

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Related Special Issue

Published Papers (3 papers)

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Research

35 pages, 7235 KiB  
Article
Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China
by Xiehui Li, Yuting Liu and Lei Wang
Remote Sens. 2024, 16(19), 3623; https://doi.org/10.3390/rs16193623 - 28 Sep 2024
Viewed by 667
Abstract
Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, [...] Read more.
Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R2 scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models. Full article
(This article belongs to the Special Issue Monitoring Vegetation Response Based on Remote Sensing and Climate Data (Second Edition))
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20 pages, 16133 KiB  
Article
Changes in Vegetation Cover and the Relationship with Surface Temperature in the Cananéia–Iguape Coastal System, São Paulo, Brazil
by Jakeline Baratto, Paulo Miguel de Bodas Terassi and Emerson Galvani
Remote Sens. 2024, 16(18), 3460; https://doi.org/10.3390/rs16183460 - 18 Sep 2024
Viewed by 793
Abstract
The objective of this article is to investigate the possible correlations between vegetation indices and surface temperature in the Cananéia–Iguape Coastal System (CICS), in São Paulo (Brazil). Vegetation index data from MODIS orbital products were used to carry out this work. The Normalized [...] Read more.
The objective of this article is to investigate the possible correlations between vegetation indices and surface temperature in the Cananéia–Iguape Coastal System (CICS), in São Paulo (Brazil). Vegetation index data from MODIS orbital products were used to carry out this work. The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were acquired from the MODIS/Aqua sensor (MYD13Q1) and the leaf area index (LAI) from the MODIS/Terra (MOD15A2H). Surface temperature data were acquired from MODIS/Aqua (MYD11A2). The data were processed using Google Earth Engine and Google Colab. The data were collected, and spatial and temporal correlations were applied. Correlations were applied in the annual and seasonal period. The annual temporal correlation between vegetation indices and surface temperature was positive, but statistically significant for the LAI, with r = 0.43 (90% significance). In the seasonal period, positive correlations occurred in JFM for all indices (95% significance). Spatially, the results of this research indicate that the largest area showed a positive correlation between VI and LST. The hottest and rainiest periods (OND and JFM) had clearer and more significant correlations. In some regions, significant and clear correlations were observed, such as in some areas in the north, south and close to the city of Iguape. This highlights the complexity of the interactions between vegetation indices and climatic attributes, and highlights the importance of considering other environmental variables and processes when interpreting changes in vegetation. However, this research has significantly progressed the field, by establishing new correlations and demonstrating the importance of considering climate variability, for a more accurate understanding of the impacts on vegetation indices. Full article
(This article belongs to the Special Issue Monitoring Vegetation Response Based on Remote Sensing and Climate Data (Second Edition))
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17 pages, 5541 KiB  
Article
Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin
by Rongxi Wang, Hongtao Wang, Cheng Wang, Jingjing Duan and Shuting Zhang
Remote Sens. 2024, 16(14), 2575; https://doi.org/10.3390/rs16142575 - 13 Jul 2024
Viewed by 968
Abstract
Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect [...] Read more.
Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48). Full article
(This article belongs to the Special Issue Monitoring Vegetation Response Based on Remote Sensing and Climate Data (Second Edition))
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