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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 7037

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Dr. Dong Liang

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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

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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

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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
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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

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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 (8 papers)

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Research

27 pages, 21677 KiB

Open AccessArticle

Monitoring Vegetation Dynamics and Driving Forces in the Baijiu Golden Triangle Using Multi-Decadal Landsat NDVI and Geodetector Modeling

by Miao Zhang, Yuanjie Deng, Yifeng Hai, Hang Chen, Aiting Ma, Wenjing Wang, Lu Ming, Huae Dang, Minghong Peng, Dingdi Jize, Cuicui Jiao and Mei Zhang

Land 2025, 14(5), 1111; https://doi.org/10.3390/land14051111 - 20 May 2025

Viewed by 190

Abstract

The China Baijiu Golden Triangle (BGT) serves as the core production hub of China’s Baijiu industry, where the ecological environment plays a pivotal role in ensuring the industry’s sustainable development. However, urbanization, industrial expansion, and climate change pose potential threats to the region’s vegetation dynamics. Utilizing Landsat remote sensing data from 2002 to 2022, this study integrates Theil–Sen trend analysis, the Mann–Kendall (MK) test, coefficient of variation (CV) analysis, and the Geodetector model (GD model) to investigate the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its underlying driving mechanisms within the BGT. The findings reveal an overall upward trend in vegetation NDVI, with the annual mean NDVI increasing from 0.45 to 0.67, corresponding to a growth rate of 0.49%. Spatially, areas of high vegetation cover are predominantly located in mountainous forest zones with favorable ecological conditions, whereas regions of low vegetation cover are concentrated in zones of urban expansion. Precipitation and topographic factors (elevation and slope) emerge as the primary natural drivers of vegetation change, while land use change and the night-time light index stand out as the most influential human-induced factors. Further analysis uncovers a nonlinear interactive enhancement effect between natural and anthropogenic factors, with the interaction between the night-time light index and precipitation being particularly pronounced. This suggests that urbanization not only directly impacts vegetation but may also exert indirect effects on the ecosystem by altering regional hydrological and climatic processes. The results indicate that ecological protection policies in the BGT have yielded some success; however, vegetation fragmentation and ecological pressures stemming from urban expansion remain significant challenges. Moving forward, optimizing land use policies and promoting eco-friendly development models will be essential to achieving ecosystem stability and sustaining industrial growth. Full article

(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)

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21 pages, 40005 KiB

Open AccessArticle

Vegetation Dynamics and Responses to Climate Variations and Human Activities in the Basin of the Yarlung Tsangpo, Lhasa, and Nianchu Rivers in the Tibetan Plateau

by Chunbo Su, Jingji Li, Ying Xiang, Shurong Yang, Xiaochao Zhang, Dinghui Xu, Shijun Wang, Tingbin Zhang, Peihao Peng and Xiaolu Tang

Land 2025, 14(5), 1027; https://doi.org/10.3390/land14051027 - 8 May 2025

Viewed by 291

Abstract

Terrestrial ecosystem vegetation are vulnerable to the joint impacts of human activities and climate change, particularly in ecologically fragile areas such as the Tibetan Plateau. Identifying vegetation cover changes and distinguishing their driving factors are crucial for ecological conservation in this region. This study utilized MODIS normalized difference vegetation index (NDVI) data from 2000 to 2019, combined with trend analysis (univariate linear regression and the Mann–Kendall test), partial correlation analysis, and residual analysis methods, to investigate the spatial and temporal dynamics of vegetation cover and its responses to climate change and human activities in the Yarlung Tsangpo River, Lhasa River, and Nianchu River Basin (YLN Basin) on the Tibetan Plateau. The results revealed significant differences in vegetation dynamics both in summer and the growing season: the average summer NDVI showed a significant decreasing trend during the study period, whereas the growing season NDVI exhibited no significant overall temporal trend, which highlighted the necessity of assessing vegetation dynamics seasonally to accurately capture their interannual complexity. Partial correlation analysis indicated that precipitation was the key limiting climatic factor for vegetation growth in this region, with its positive influence covering over 90% of the land area during summer and over 60% during the growing season. The residual analysis further indicated the dual and spatially heterogeneous roles of human activities: on the one hand, positive impacts, primarily from vegetation restoration projects, promoted NDVI increases in some areas; on the other hand, negative impacts, such as continuous grazing pressure, population growth, and associated land use changes, inhibited vegetation development in other areas. This study quantitatively assessed the combined effects of climate variability and complex human activities on the vegetation NDVI in the YLN Basin, emphasizing that the development of adaptive management and effective vegetation restoration strategies must fully consider seasonal differences, the key climatic limiting factor (water availability), and the spatial heterogeneity of human impacts to promote sustainable development in this ecologically fragile region. Full article

(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)

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20 pages, 3483 KiB

Open AccessArticle

Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis

by Hongjia Zhu, Ao Wang, Pengtao Wang, Chunguang Hu and Maomao Zhang

Land 2025, 14(3), 598; https://doi.org/10.3390/land14030598 - 12 Mar 2025

Cited by 1 | Viewed by 565

Abstract

As global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key ecological indicators, have garnered widespread attention. This study analyzes the spatiotemporal dynamics of LST and the Kernel Normalized Difference Vegetation Index (KNDVI) in 11 provinces along the Yangtze River and their response to climate change based on MODIS Terra satellite data from 2000 to 2020. The linear regression showed a significant KNDVI increase of 0.003/year (p < 0.05) and a LST rise of 0.065 °C/year (p < 0.01). The Principal Component Analysis (PCA) explained 74.5% of the variance, highlighting the dominant influence of vegetation cover and urbanization. The K-means clustering identified three regional patterns, with Shanghai forming a distinct group due to low KNDVI variability. The Generalized Additive Model (GAM) analysis revealed a nonlinear LST–KNDVI relationship, most evident in Hunan, where cooling effects weakened beyond a KNDVI threshold of 0.25. Despite a 0.07 KNDVI increase, high-temperature areas in Chongqing and Jiangsu expanded by over 2500 km², indicating limited LST mitigation. This study reveals the complex interaction between LST and the KNDVI, which may provide scientific basis for the development of regional ecological management and climate adaptation strategies. Full article

(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)

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18 pages, 10800 KiB

Open AccessArticle

An Automatic Algorithm for Mapping Algal Blooms and Aquatic Vegetation Using Sentinel-1 SAR and Sentinel-2 MSI Data

by Yihao Xin, Juhua Luo, Jinlong Zhai, Kang Wang, Ying Xu, Haitao Qin, Chao Chen, Bensheng You and Qing Cao

Land 2025, 14(3), 592; https://doi.org/10.3390/land14030592 - 12 Mar 2025

Viewed by 495

Abstract

Aquatic vegetation, including floating-leaved and emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV), and algal blooms (AB), are primary producers in eutrophic lake ecosystems and hold significant ecological importance. Aquatic vegetation and AB dominate in clear and turbid water states, respectively. Monitoring their dynamics is essential for understanding lake states and transitions. Sentinel imagery provides high-resolution data for capturing changes in aquatic vegetation and AB. However, the existing mapping algorithms for aquatic vegetation and AB based on Sentinel data only focused on one or two types. There are still limited algorithms that comprehensively reflect the dynamic changes of aquatic vegetation and AB. Additionally, the unique red-edge bands of Sentinel-2 MSI have not yet been fully exploited for mapping aquatic vegetation and AB. Therefore, we developed an automated mapping algorithm that utilizes Sentinel data, especially red-edge bands, to comprehensively reflect the dynamic changes of FEAV, SAV, and AB. The key indicator of the algorithm, the second principal component (PC2) derived from four red-edge bands and four other bands of Sentinel-2 MSI, can effectively distinguish between FEAV and AB. SAV was mapped by the Sentinel-based submerged aquatic vegetation index (SSAVI), which was constructed by fusing Sentinel-1 SAR and Sentinel-2 MSI data. The algorithm was tested in three representative lakes, including Lake Taihu, Lake Hongze, and Lake Chaohu, and yielded an average accuracy of 87.65%. The algorithm was also applied to track changes in aquatic vegetation and AB from 2019 to 2023. The results show that, over the past five years, AB coverage in all three lakes has decreased. The coverage of aquatic vegetation in Lake Taihu and Lake Hongze is also declining, while coverage remains relatively stable in Lake Chaohu. This algorithm leverages the high spatiotemporal resolution of Sentinel data, as well as its band advantages, and is expected to be applicable for large-scale monitoring of aquatic vegetation and AB dynamics. It will provide valuable technical support for future assessments of lake ecological health and state transitions. Full article

(This article belongs to the Special Issue Vegetation Cover Changes Monitoring Using Remote Sensing Data)

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17 pages, 2697 KiB

Open AccessArticle

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 1003

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 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

Open AccessArticle

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 1119

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 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

Open AccessArticle

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 768

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1096

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 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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