Xiufang Song

Understanding the spatiotemporal dynamics of vegetation and their driving mechanisms is essential for ecological assessment and management. However, current assessments of the Heihe River Basin (HRB) vegetation dynamics remain uncertain due to reliance on single indices without cross-validation and oversimplified attribution of residual variations. Here, we integrated four complementary vegetation indices (NDVI, EVI, kNDVI, and NIRv) with trend and abrupt change detection analyses to establish a framework for assessing vegetation changes in the HRB from 2004 to 2023. Given that the dominance of non-climatic factors is widely attributed to human water management and land use policies, land use change and other anthropogenic factors were incorporated together with topographic/edaphic factors into the optimal parameter-based geographical detector (OPGD), where vegetation changes induced by non-climatic factors were first isolated through residual trend analysis, thereby quantifying their explanatory power on vegetation index variations. The results demonstrate that vegetation in the HRB experienced a fluctuating upward trend (0.0013/yr) from 2004 to 2023, with significant improvement in 43% and degradation in 3% of the region. Climatic and non-climatic factors explained 26% and 74% of spatial variation, dominated by precipitation and land use change, respectively. Notably, the interaction of land use change and elevation accounted for 56% of NIRv variation, markedly exceeding single factors, as determined by the interaction detector in the OPGD. Additionally, large-scale ecological restoration projects and effective water resource management policies have played a pivotal role in facilitating vegetation recovery across the basin. This study enhances insight into vegetation dynamics and supports both sustainable restoration and development in the HRB.

Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation.

Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID’s epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management; Conclusions: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Addressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition.

Sustainable livelihoods (SL) have emerged as a crucial area of focus in global environmental change research, aligning with the Sustainable Development Goals (SDGs). This field is rapidly gaining prominence in sustainability science and has become one of the primary research paradigms. In our study, we conducted scientometrics analysis using the ISI Web of Science core collection database to examine research patterns and frontier areas in SL research. We selected 6441 papers and 265,759 references related to SL published from 1991 to 2020. To achieve this, we employed advanced quantitative analysis tools such as CiteSpace and VOSviewer to quantitatively analyze and visualize the evolution of literature in the SL research field. Our overarching objectives were to understand historical research characteristics, identify the knowledge base, and determine future research trends. The results revealed an exponential increase in SL research documentation since 1991, with the Consortium of International Agricultural Research Center (CGIAR) contributing the highest volume of research documents and citations. Key journals in this field included World Development, Global Environmental Change, Ecological Economics, and Ecology and Society. Notably, Singh RK and Shackleton CM emerged as prolific authors in SL research. Through our analysis, we identified six primary clusters of research areas: livelihoods, conservation, food security, management, climate change, and ecosystem services. Additionally, we found that tags such as rural household, agricultural intensification, cultural intensification, and livelihoods vulnerability remained relevant and represented active research hotspots. By analyzing keyword score relevance, we identified frontier areas in SL research, including mass tourism, solar home systems, artisanal and small-scale mining, forest quality, marine-protected areas, agricultural sustainability, sustainable rangeland management, and indigenous knowledge. These findings provide valuable insights to stakeholders regarding the historical, current, and future trends in SL research, offering strategic opportunities to enhance the sustainability of livelihoods for farmers and rural communities in alignment with the SDGs.

Purpose: Vegetation is a typical sensitive indicator of climate change, and therefore provides theoretical and valuable information for addressing issues arising from climate change including improving soil ecosystem services. Exploring how vegetation responses to climate change has become one of major hotspots of research. However, few scholars have performed bibliometric analyses of this field. This study investigated the current research activities and the trend developments of vegetation responses to climate change. Materials and methods: We conducted a quantitative bibliometric analysis of 2,310 publications on vegetation responses to climate change from 1991 to 2021 retrieved in the Web of Science Core Collection. The analysis comprised significant journals, disciplines, and scholars, as well as partnerships between countries and institutions, keyword co-occurrence and burst analysis. The bibliometric analysis tools, Histcite, Vosviewer, CiteSpace software, and R (Bibliometrix package), were applied. Results and discussion: The related publications on vegetation responses to climate change had been increasing exponentially in the past 30 years and its total global cited score reached its peak in 2010. The USA and China were the leading countries, with the Chinese Academy of Sciences having the highest number of publications and citations. The scholars who had the most citations were Allen CD, Bresears DD, and Running SW. Six research clusters were generated by keywords co-occurrence analysis, including impact, response, CO₂, growth, climate change, and vegetation. These clusters represented the current research topics that highlighted the responses of vegetation to climate change, the manifestation of its impact, and coping strategies. In future research on vegetation, the emphasis is expected to be placed on “human activities” and “N₂O emission”. Conclusion: This study has performed a comprehensive and systematic and quantitative analysis of the publications on the responses of vegetation to climate change. The results reveal the characteristics, development patterns, and research trends of studies on vegetation activity in response to climate change, which sheds new insights into understanding the relationship between soil and climate.

Purpose: Wildfire is one of the most important natural disturbances in forest and multi-vegetation ecosystems, directly or indirectly affecting the structural processes and functions of forest ecosystems with varying degrees. Wildfire releases vast amounts of carbon dioxide and other substances by destroying vegetation, making itself an important topic for the study of global change and environmental impacts. Therefore, a deeper understanding of this topic is particularly crucial for managing forest ecosystems. Methods: This paper was based on a literature search of the Web of Science database for international forest wildfire research, utilizing bibliometric and quantity statistical analysis methods. Results: The results show that forest wildfire research has been rapidly growing over the last 20 years, with the number of relevant articles generally increasing yearly at an average annual growth rate of about 22.45%. The US tops the list in terms of total and independent publications, with a total of 3111 articles (49.88%). The key journals publishing on this topic include 12 journals, Stephens S.L., Bergeron Y., and Lindenmayer D.B. are the key contributing authors to the field, and research institutions are primarily concentrated in the US Forest Service. Keyword co-occurrence analysis shows that current forest wildfire research is focused on seven main areas. This paper systematically reviewed the progress and hotspots of international forest wildfire research in recent decades, mainly focusing on occurrences, severity, management, and warning techniques for wildfires, as well as the impact of climate change and human activities on wildfires. Conclusions: The study concludes that research trends in this field have undergone a significant evolution in recent decades. The future forest wildfire research moves towards a combination of typical mechanisms and large-scale effects across spatial and temporal scales, deep integration of aerospace and earth observations and precise simulations, discipline fusion, and couplings research. We believe that this study provides a comprehensive and systematic overview for future forest wildfire observation, prediction, management, and investigation of ecological effects.

Grassland remote sensing (GRS) is an important research topic that applies remote sensing technology to grassland ecosystems, reflects the number of grassland resources and grassland health promptly, and provides inversion information used in sustainable development management. A scientometrics analysis based on Science Citation Index-Expanded (SCI-E) was performed to understand the research trends and areas of focus in GRS research studies. A total of 2692 papers related to GRS research studies and 82,208 references published from 1980 to 2020 were selected as the research objects. A comprehensive overview of the field based on the annual documents, research areas, institutions, influential journals, core authors, and temporal trends in keywords were presented in this study. The results showed that the annual number of documents increased exponentially, and more than 100 papers were published each year since 2010. Remote sensing, environmental sciences, and ecology were the most popular Web of Science research areas. The journal Remote Sensing was one of the most popular for researchers to publish documents and shows high development and publishing potential in GRS research studies. The institution with the greatest research documents and most citations was the Chinese Academy of Sciences. Guo X.L., Hill M.J., and Zhang L. were the most productive authors across the 40-year study period in terms of the number of articles published. Seven clusters of research areas were identified that generated contributions to this topic by keyword co-occurrence analysis. We also detected 17 main future directions of GRS research studies by document co-citation analysis. Emerging or underutilized methodologies and technologies, such as unmanned aerial systems (UASs), cloud computing, and deep learning, will continue to further enhance GRS research in the process of achieving sustainable development goals. These results can help related researchers better understand the past and future of GRS research studies.