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Land
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Vegetation Cover Changes Monitoring Using Remote Sensing Data
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Vegetation Cover Changes Monitoring Using Remote Sensing Data

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3273

Special Issue Editors

Dr. Dong Liang

E-Mail Website
Guest Editor
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Interests: earth observation; remote sensing; big earth data; synthetic aperture radar
Dr. Barjeece Bashir

E-Mail Website
Guest Editor
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Interests: vegetation dynamics; sustainable development; climate change; land degradation; remote sensing
Dr. Min Xu

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: quantitative remote sensing; environmental health; soil monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The alarming rates of global deforestation and the significant impacts of climate variations on crop production and vegetation health emphasize the critical need for precise vegetation monitoring. Current trends reveal that climate-induced changes are causing substantial shifts in vegetation patterns and are reducing agricultural productivity. The Special Issue “Vegetation Cover Changes Monitoring Using Remote Sensing Data” aims to investigate advanced remote sensing technologies to address these urgent challenges. By integrating big data to manage large datasets from high-resolution sensors, this issue explores advancements in satellite imagery and ground-based sensors for detailed vegetation analysis. Innovative data processing algorithms and enhanced analysis techniques are highlighted, focusing on the impacts of climate change and human activities on vegetation cover. This issue also addresses challenges such as data interoperability and the scalability of remote sensing applications, particularly in developing countries where high-resolution datasets are crucial. This collection aims to support the United Nations' Sustainable Development Goals (SDGs) by providing findings that contribute to sustainable environmental management and urban planning. We encourage contributions that tackle remote sensing data acquisition, processing, interpretation, and integration with other geospatial technologies and ground-based measurements.

We aim to answer questions such as the following:

  • What are the latest advancements in remote sensing technologies for vegetation cover monitoring, and how do these technologies enhance the detection and analysis of vegetation changes?
  • How can the fusion of multi-source remote sensing data improve the accuracy and reliability of vegetation cover assessments?
  • What novel data processing algorithms and methodologies are being developed for detecting and quantifying vegetation changes, and how do they compare efficiency and accuracy?
  • How can machine learning and artificial intelligence be utilized to improve the analysis and interpretation of remote sensing data for vegetation monitoring?
  • What are the challenges and solutions for ensuring data interoperability and scalability of remote sensing applications, particularly in developing countries with limited access to high-resolution datasets?
  • How do seasonal variations and extreme weather events influence vegetation cover changes, as observed through remote sensing data?
  • What are the long-term trends in vegetation cover changes across different biomes and ecosystems, and how can remote sensing data help predict future scenarios?
  • How can time-series analysis of vegetation cover changes be utilized to understand vegetation dynamics at different spatial scales, from local to global?

Dr. Dong Liang
Dr. Barjeece Bashir
Dr. Min Xu
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. Land is an international peer-reviewed open access monthly 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 2600 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 change
  • remote sensing
  • high spatial resolution
  • big data
  • satellite imagery
  • data processing algorithms
  • multi-source data integration
  • climate change impacts
  • biodiversity monitoring
  • land management

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Published Papers (4 papers)

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Research

17 pages, 2697 KiB  
Article
Conversion from Forest to Agriculture in the Brazilian Amazon from 1985 to 2021
by Hugo Tameirão Seixas, Hilton Luís Ferraz da Silveira, Alan Pereira da Silva Falcão Mendes, Fabiana Da Silva Soares and Ramon Felipe Bicudo da Silva
Land 2025, 14(2), 300; https://doi.org/10.3390/land14020300 - 31 Jan 2025
Viewed by 367
Abstract
Land-use and land-cover (LULC) changes in the Amazon biome are key processes that influence the environment and societies at local, national, and global scales. Numerous studies have already relied on land-cover and land-use maps to analyze change processes. This study presents a new [...] Read more.
Land-use and land-cover (LULC) changes in the Amazon biome are key processes that influence the environment and societies at local, national, and global scales. Numerous studies have already relied on land-cover and land-use maps to analyze change processes. This study presents a new dataset created by calculating the time required for deforested areas to transition to agriculture (annual and permanent crops) in the Brazilian Amazon biome. The calculations were performed over MapBiomas land-cover data (version 7), which range from 1985 to 2021, at a spatial resolution of 30 m. The method consists of basic algebraic operation and recursion to identify every conversion from forest to agriculture between 1985 and 2021. The results show a correlation between environmental policies and the time required for the conversion to be completed, such as the adoption of the soy moratorium and the New Forest Code, that were followed by a search for old cleared areas for the establishment of new agricultural sites. The new data can be useful in interdisciplinary studies focused on land-use and land-cover change analysis in Brazil, such as planning of forest restoration initiatives, and the evaluation of carbon stocks according to conversion length. Our accuracy assessment shows an opportunity to improve conversion length calculations by reducing errors in the classification of agriculture establishment. The major innovation of this study is the establishment of explicit links between the deforestation year of a given pixel and its respective year of agriculture establishment, which can provide new insights into understanding long-term land-use conversion processes in tropical ecosystems. Full article
(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)
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25 pages, 11764 KiB  
Article
Vegetation Classification in a Mountain–Plain Transition Zone in the Sichuan Basin, China
by Wenqian Bai, Zhengwei He, Yan Tan, Guy M. Robinson, Tingyu Zhang, Xueman Wang, Li He, Linlong Li and Shuang Wu
Land 2025, 14(1), 184; https://doi.org/10.3390/land14010184 - 17 Jan 2025
Viewed by 365
Abstract
Developing an effective vegetation classification method for mountain–plain transition zones is critical for understanding ecological patterns, evaluating ecosystem services, and guiding conservation efforts. Existing methods perform well in mountainous and plain areas but lack verification in mountain–plain transition zones. This study utilized terrain [...] Read more.
Developing an effective vegetation classification method for mountain–plain transition zones is critical for understanding ecological patterns, evaluating ecosystem services, and guiding conservation efforts. Existing methods perform well in mountainous and plain areas but lack verification in mountain–plain transition zones. This study utilized terrain data and Sentinel-1 and Sentinel-2 imagery to extract topographic, spectral, texture, and SAR features as well as the vegetation index. By combining feature sets and applying feature elimination algorithms, the classification performance of one-dimensional convolutional neural networks (1D-CNNs), Random Forest (RF), and Multilayer Perceptron (MLP) was evaluated to determine the optimal feature combinations and methods. The results show the following: (1) multi-feature combinations, especially spectral and topographic features, significantly improved classification accuracy; (2) Recursive Feature Elimination based on Random Forest (RF-RFE) outperformed ReliefF in feature selection, identifying more representative features; (3) all three algorithms performed well, with consistent spatial results. The MLP algorithm achieved the best overall accuracy (OA: 81.65%, Kappa: 77.75%), demonstrating robustness and lower dependence on feature quantity. This study presents an efficient and robust vegetation classification workflow, verifies its applicability in mountain–plain transition zones, and provides valuable insights for small-region vegetation classification under similar topographic conditions globally. Full article
(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)
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20 pages, 2217 KiB  
Article
Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China
by Zhiming Xia, Kaitao Liao, Liping Guo, Bin Wang, Hongsheng Huang, Xiulong Chen, Xiangmin Fang, Kuiling Zu, Zhijun Luo, Faxing Shen and Fusheng Chen
Land 2025, 14(1), 76; https://doi.org/10.3390/land14010076 - 3 Jan 2025
Viewed by 504
Abstract
Vegetation is a fundamental component of terrestrial ecosystems, and accurately assessing the effects of seasonal climate variations, extreme weather events, and land use changes on vegetation dynamics is crucial. The Ganjiang River Basin (GRB), a key region for water conservation and recharge in [...] Read more.
Vegetation is a fundamental component of terrestrial ecosystems, and accurately assessing the effects of seasonal climate variations, extreme weather events, and land use changes on vegetation dynamics is crucial. The Ganjiang River Basin (GRB), a key region for water conservation and recharge in southeastern China, has experienced significant land use changes and variable climate in the past. However, comprehensive evaluations of how these changes have impacted vegetation remain limited. To address this gap, we used machine learning models (random forest and XGBoost) to assess the impact of seasonal and extreme climate variables, land cover, topography, soil properties, atmospheric CO2, and night-time light intensity on vegetation dynamics. We found that the annual mean NDVI showed a slight increase from 1990 to 1999 but has decreased significantly over the last 8 years. XGBoost was better than the RF model in simulating the NDVI when using all five types of data source (R2 = 0.85; RMSE = 0.04). The most critical factors influencing the NDVI were forest and cropland ratio, followed by soil organic carbon content, elevation, cation exchange capacity, night-time light intensity, and CO2 concentration. Spring minimum temperature was the most important seasonal climate variable. Both linear and nonlinear relationships were identified between these variables and the NDVI, with most variables exhibiting threshold effects. These findings underscore the need to develop and implement effective land management strategies to enhance vegetation health and promote ecological balance in the region. Full article
(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)
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21 pages, 7047 KiB  
Article
Analysis of Spatiotemporal Variation Characteristics and Influencing Factors of Grassland Vegetation Coverage in the Qinghai–Tibet Plateau from 2000 to 2023 Based on MODIS Data
by Xiankun Shi, Dong Yang, Shijian Zhou, Hongwei Li, Siting Zeng, Chen Yin and Mingxin Yang
Land 2024, 13(12), 2127; https://doi.org/10.3390/land13122127 - 7 Dec 2024
Viewed by 869
Abstract
Changes in grassland fractional vegetation coverage (FVC) are important indicators of global climate change. Due to the unique characteristics of the Tibetan Plateau ecosystem, variations in grassland coverage are crucial to its ecological stability. This study utilizes the Google Earth Engine (GEE) platform [...] Read more.
Changes in grassland fractional vegetation coverage (FVC) are important indicators of global climate change. Due to the unique characteristics of the Tibetan Plateau ecosystem, variations in grassland coverage are crucial to its ecological stability. This study utilizes the Google Earth Engine (GEE) platform to retrieve long-term MODIS data and analyzes the spatiotemporal distribution of grassland FVC across the Qinghai–Tibet Plateau (QTP) over 24 years (2000–2023). The grassland growth index (GI) is used to evaluate the annual grassland growth at the pixel level. GI is an important indicator for measuring grassland growth status, which can effectively measure the changes in grassland growth in each year relative to the base year. FVC trends are monitored using Sen-Mann-Kendall slope estimation, the coefficient of variation, and the Hurst exponent. Geographic detectors and partial correlation analysis are then applied to explore the contribution rates of key driving factors to FVC. The results show: (1) From 2000 to 2023, FVC exhibited an overall upward trend, with an annual growth rate of 0.0881%. The distribution of FVC on the QTP follows a pattern of higher values in the east and lower values in the west; (2) Over the past 24 years, 54.05% of the total grassland area has shown a significant increase, 23.88% has remained stable, and only a small portion has shown a significant decrease. The overall trend is expected to continue with minimal variability, covering 82.36% of the total grassland area. The overall grassland GI suggests a balanced state of growth; (3) precipitation (Pre) and soil moisture (SM) are the main single factors affecting FVC changes in grasslands on the Tibetan Plateau (q = 0.59 and 0.46). In the interaction detection, in addition to the highest interaction between Pre and other factors, the interaction between SM and other factors also showed a significant impact on the changes in FVC of the QTP grassland; partial correlation analysis of hydrothermal factors and FVC of the QTP grassland. It shows that precipitation has a stronger correlation with QTP grassland FVC changes than temperature. This study has enhanced our understanding of grassland vegetation change and its driving factors on the QTP and quantitatively described the relationship between vegetation change and driving factors, which is of great significance for maintaining the sustainable development of grassland ecosystems. Full article
(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)
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