Search Results (115)

The ecological urban agglomeration around Poyang Lake represents a critical development area in the Yangtze River basin. The spatiotemporal characteristics of the net primary productivity (NPP) of vegetation are explored from the perspective of the city’s functional position, providing important insights for the city to achieve the dual-carbon target and green development. The study evaluates the spatiotemporal variations in NPP from 2003 to 2022 in the eco-urban agglomeration around Poyang Lake, using the CASA model. Its variation characteristics were explored in detail from a completely new perspective and scope using indicators such as cycle amplitudes, CV coefficients, Hurst indices, and others. Results indicate seasonal fluctuations and significant variations between urban areas and vegetation, with implications for sustainable development. The annual NPP ranged from 200 to 800 gC/(m²·a), with a change rate of 0.58 gC/(m²·a) and evident seasonal fluctuations in the study area. Notably, urban core cities like Jiujiang and Nanchang exhibit lower NPP and decreasing trends. Scenic areas showed high forest cover and vigorous NPP changes, highlighting the need for targeted urban ecological management to enhance green development. Additionally, the seasonal fluctuations in NPP were notably influenced by specific land use types and local economic conditions. In areas with high vegetation cover, the seasonal characteristics of NPP are pronounced, while they are less evident in regions with strong urban economic conditions. Full article

The Arctic, characterised by severe climatic conditions and sparse vegetation, is experiencing rapid warming, with temperatures increasing by up to four times the global rate since 1979. Extensive impacts from these changes have far-reaching consequences for the global climate and energy balance. Satellite remote sensing is a valuable tool for monitoring Arctic vegetation dynamics, particularly in regions with limited ground observations. To investigate the ongoing impact of climate change on Arctic and sub-Arctic vegetation dynamics, a review of 162 studies published between 2000 and November 2024 was conducted. This review analyses the research objectives, spatial distribution of study areas, methods, and the temporal and spatial resolution of utilised satellite data. The key findings reveal circumpolar tendencies, including Arctic greening, lichen decline, shrub increase, and positive primary productivity trends. These changes impact the carbon balance in the tundra and affect specialised fauna and local communities. A large majority of studies conducted their analysis based on multispectral data, primarily using AVHRR, MODIS, and Landsat sensors. Although the warming of the Arctic is linked to greening trends, increased productivity, and shrub expansion, the diverse and localised ecological shifts are influenced by a multitude of complex factors. Furthermore, these changes can be challenging to observe due to difficult cloud cover and illumination conditions when acquiring optical satellite data. Additionally, the difficulty in validating these changes is compounded by the scarcity of in situ data. The fusion of satellite data with different spatial–temporal characteristics and sensor types, combined with methodological advancements, may help mitigate data gaps. This may be particularly crucial when assessing the Arctic’s potential role as a future carbon source or sink. Full article

The Great Green Wall (GGW) initiative, which started in 2007 and is still in development as of 2024, aims to combat desertification and enhance sustainability over 8000 km across Africa’s Sahel-Sahara region, encompassing 11 key countries and 7 countries associated with the initiative. Because of limited ground measurements for the GGW project, the progress and impacts of the GGW initiative have been a challenging problem to monitor and assess. This study aims to utilize satellite remote sensing data to analyze changes in the key factors related to the sustainability of the GGW region, including land cover type, vegetation index, precipitation rate, land surface temperature (LST), surface soil moisture, etc. Results from temporal analysis of these factors indicate that the deserts along the GGW are retreating and the regional mean of the Normalized Difference Vegetation Index (NDVI) has an increasing trend, although the precipitation has a slightly decreasing trend, over the past two decades. Further analysis shows spatial heterogeneity of vegetation, precipitation, and soil moisture changes. Desertification is still a challenging issue in some GGW countries. These results are helpful in understanding climate change in the GGW regions and the impacts of the Great Green Wall initiative. Full article

Alpine grassland is sensitive to climate change, and many studies have explored the trends in alpine vegetation. Most research focuses on the effects of climate warming and increased humidity on vegetation greening. However, less attention has been given to the positive impacts of human activities, particularly ecological restoration projects (ERPs). Our study utilized the CASA (Carnegie Ames Stanford Approach) model to simulate the net primary productivity (NPP) of alpine grasslands on the Tibetan Plateau (TP) from 2000 to 2020. Additionally, a moving window approach was employed to comparatively analyze the changes in the response characteristics of NPP to climate change before and after the implementation of ERPs. Our results indicated: (1) The NPP exhibited a fluctuating upward trend. The NPP growth rates of alpine meadow, alpine grassland, and desert grassland were found to be 2.38, 1.5, and 0.8 g C·m⁻²·a⁻¹, respectively. (2) The annual average NPP and annual growth rate of alpine grasslands after the implementation of ERPs were both higher than before, indicating that ERPs have intensified the growth trend of NPP in alpine grasslands. (3) ERPs have reduced the responsiveness of alpine grassland NPP to temperature variations and enhanced its responsiveness to changes in precipitation. In detail, ERPs enhanced the responsiveness of NPP in alpine meadow to both temperature and precipitation, reduced the responsiveness of NPP in alpine steppe to temperature while enhancing its responsiveness to precipitation, and mitigated the changes in the response of NPP in desert steppe to temperature and significantly enhanced its responsiveness to precipitation. Full article

The Yellow River Basin is rich in coal resources, but the ecological environment is fragile, and the ecological degradation of vegetation is exacerbated by the disruption caused by high-intensity mining activities. Analyzing the dynamic evolution of vegetation in the Henan section of the Yellow River Basin and its mining areas over the long term run reveals the regional ecological environment and offers a scientific foundation for the region’s sustainable development. In this study, we obtained a long time series of Landsat imageries from 1987 to 2023 on the Google Earth Engine (GEE) platform and utilized geographically weighted regression models, Sen (Theil–Sen median) trend analysis, M-K (Mann–Kendall) test, coefficient of variation (CV), and the Hurst index to investigate the evolution of vegetation cover based on the kNDVI (the normalized difference vegetation index). This index is used to explore the spatial and temporal characteristics of vegetation cover and its future development trend. Our results showed that (1) The kNDVI value in the Henan section of the Yellow River Basin exhibited a trend of fluctuating upward at a rate of 0.0509/10a from 1987 to 2023. The kNDVI trend in the mining areas of the region aligned closely with the overall trend of the Henan section; however, the annual kNDVI in each mining area consistently remained lower than that of the Henan section and displayed a degree of fluctuation, predominantly characterized by medium–high variability, with areas of moderate and high fluctuations accounting for 73.5% of the total. (2) The kNDVI in the study area showed a significant improvement in vegetation cover and its future development trends. We detected a significant improvement in the kNDVI index in the area; yet, significant improvement in this index in the future might cause vegetation degradation in 87% of the study area, which may be closely related to multiple factors such as the intensity of mining at the mine site, anthropogenic disturbances, and climate change. (3) The vegetation status of the Henan section of the Yellow River Basin shows a significant positive correlation with distance from mining areas, accounting for 90.9% of the total, indicating that mining has a strong impact on vegetation cover. This study provides a scientific basis for vegetation restoration, green development of mineral resources, and sustainable development in the Henan section of the Yellow River Basin. Full article

The Grain for Green Project (GGP) is a major national initiative aimed at ecological improvement and vegetation restoration in China, achieving substantial ecological and socio-economic benefits. Nevertheless, research on vegetation cover trends and the long-term restoration efficacy of the GGP in the Loess Plateau remains limited. This study examines the temporal–spatial evolution and sustainability of vegetation cover in this region, using NDVI data from Landsat (2000–2022) with medium-high spatial resolution. The analytical methods involve Sen’s slope, Mann–Kendall non-parametric test, and Hurst exponent to assess trends and forecast sustainability. The findings reveal that between 2000 and 2022, vegetation coverage in the Loess Plateau increased by an average of 0.86% per year (p < 0.01), marked by high vegetation cover expansion (173 × 10³ km², 26.49%) and low vegetation cover reduction (149 × 10³ km², 22.83%). The spatial pattern exhibited a northwest-to-southeast gradient, with a transition from low to high coverage levels, reflecting a persistent increase in high vegetation cover and decrease in low vegetation cover. Approximately 93% of the vegetation cover in the Loess Plateau showed significant improvement, while 5% (approximately 31 × 10³ km²) displayed a degradation trend, mainly in the urbanized and Yellow River Basin regions. Projections suggest that 90% of vegetation cover will continue to improve. In GGP-targeted areas, high and medium-high levels of vegetation cover increased significantly at rates of 0.456 ×10³ km²/year and 0.304 × 10³ km²/year, respectively, with approximately 75% of vegetation cover levels exhibiting positive trends. This study reveals the effectiveness of the GGP in promoting vegetation restoration in the Loess Plateau, offering valuable insights for vegetation recovery research and policy implementation in other ecologically fragile regions. Full article

Net primary production (NPP) serves as a critical proxy for monitoring changes in the global capacity for vegetation carbon sequestration. The assessment of the factors (i.e., human activities and climate changes) influencing NPP is of great value for the study of terrestrial systems. To investigate the influence of factors on grassland NPP, the ecologically vulnerable Qinghai–Tibet Plateau region was considered an appropriate study area for the period from 2000 to 2020. We innovated the use of the RICI index to quantitatively represent human activities and analyzed the effects of RICI and climatic factors on grassland NPP using the geographical detector. In addition, the future NPP was predicted through the integration of two modeling approaches: The Patch-Generating Land Use Simulation (PLUS) model and the Carnegie–Ames–Stanford Approach (CASA) model. The assessment revealed that the expanded grassland contributed 7.55 × 10⁴ Gg C (Gg = 10⁹ g) to the total NPP, whereas the deterioration of grassland resulted in a decline of 1.06 × 10⁵ Gg C. The climatic factor was identified as the dominant factor in grassland restoration, representing 70.85% of the total NPP, as well as the dominant factor in grassland degradation, representing 92.54% of the total NPP. By subdividing the climate change and human activity factors into sub-factors and detecting them with a geographical detector, the results show that climate change and anthropogenic factors have significant ability to explain geographic variation in NPP to a considerable extent, and the effect on NPP is greater when the factors interact. The q-values of the Relative Impact Contribution Index (RICI) and the RICI of the land use change NPP are consistently greater than 0.6, with the RICI of the human management practices NPP and the evapotranspiration remaining at approximately 0.5. The analysis of the interaction between climate and human activity factors reveals an average impact of greater than 0.8. By 2030, the NPP of the natural development scenario, economic development scenario (ED), and ecological protection scenario (EP) show a decreasing trend due to climate change, the dominant factor, causing them to decrease. Human activities play a role in the improvement. The EP indicates a positive expansion in the growth rate of forests, water, and wetlands, while the ED reveals rapid urbanization. It is notable that this is accompanied by a temporary suspension of urban greening. Full article

Vegetation seasonality is a critical indicator of ecological responses to global climate change, especially in the Iberian Peninsula, where the intersection of human activity and climate variability amplifies these effects. Understanding these changes is vital for adopting ecogeographical sustainability and developing effective climate adaptation strategies. This study examines trends in vegetation seasonality in the Iberian Peninsula from 1982 to 2023, based on weekly AVHRR NDVI data (2184 images). By integrating Seasonal Trend Analysis (STA) with Robust Trend Analysis (RTA)—including the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test (α = 0.05), and false discovery rate (FDR) control—we identified significant phenological shifts and widespread vegetation greening. The results reveal a regional response to global patterns of climate change, with 94.2% of the study area exhibiting significant trends, particularly in the Mediterranean ecoregion, where earlier growing seasons are becoming increasingly common. These shifts highlight the urgent need for sustainable land and resource management in the face of accelerating global change. Our findings provide critical insights into the ecological dynamics of the Iberian Peninsula, offering a robust foundation for formulating policies that promote environmental sustainability and enhance resilience to climate change. Full article

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors’ influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10⁻³ yr⁻¹ (p < 0.01), 1.30 g C m⁻² yr⁻² (p < 0.01) and 1.00 × 10⁻³ Wm⁻²μm⁻¹sr⁻¹yr⁻¹ (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia. Full article

Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China. Full article

Vegetation is an important component of an ecosystem, fulfilling various ecological functions in areas such as soil and water conservation, climate regulation, and water source maintenance. This study focuses on the Yongding River Basin as a research area. This study used vegetation indices with long time series as a data source in combination with Landsat land use data. This study applied linear trend estimation to analyze the interannual variation trend in vegetation greenness from 2002 to 2022 in the Yongding River Basin and quantitatively analyzed the impact of land use changes on vegetation greenness. The results show that, from 2002 to 2022, the vegetation greenness in the Yongding River Basin has shown an overall increasing trend. The average growth season and the maximum annual normalized difference vegetation index (NDVI) growth rates were 0.006/10a and 0.008/10a, respectively, and the area of increased vegetation greenness accounted for 90% of the total area. During the main growth season (April to October) in the Yongding River Basin, the NDVI generally showed a spatial pattern of being higher in mountainous areas and lower in water areas, with the largest coefficient of variation in vegetation in the river water areas, and the most stable vegetation in forest land. In terms of the changes in vegetation greenness, the contribution rate of arable land was between 36.73% and 38.63%, followed by grassland and forest land, with contribution rates of 26.86% to 27.11% and 23.94% to 26.43%, respectively. The total contribution rate of water areas, construction land, and unused land was around 10.18%. This study can provide a theoretical basis for environmental protection and rational land use in the Yongding River Basin. Full article

Resource-based cities (RBCs) in China are at a historic juncture in their transformative development. Observing and assessing the role of the resource curse in urban expansion and greening is crucial for the sustainable development of these cities. This study proposes a new framework to extract urban boundary data from 2000 to 2020 in China. Utilizing these data, we analyzed differences in urban expansion intensity and urban vegetation cover between 125 RBCs and 223 non-RBCs. We found the following: (1) While urban areas in China experienced steady growth from 2000 to 2020, the urban area expansion rates of RBCs lagged behind those non-RBCs located in the same geographical areas except in South China, with the lowest annual expansion rate of 1.18% occurring in the Northeast. (2) Although the existing urban areas in some cities show a greening trend, both existing and new urban areas in China are predominantly characterized overall by browning. Surprisingly, RBCs exhibit a stronger greening trend than non-RBCs, particularly in Northwestern China. (3) There is a nuanced interplay and coexistence between resource dependency and urban expansion, with a specific negative correlation when resource dependency is very high or very low. This study provides a novel method and approach for urban boundary delineation. It offers new insights into the developmental characteristics of RBCs, enriching the theoretical framework of resource curse research and supporting the green development of RBCs with robust data. Full article

The Greater Mekong Subregion (GMS) economic cooperation program is an effective and fruitful regional cooperation initiative for socioeconomic development in Asia; however, the vegetation change trends and directions in the GMS caused by rapid development remain unknown. In particular, there is a current lack of comparative studies on vegetation changes in various countries in the GMS. Based on the MODIS normalized difference vegetation index (NDVI) time series data, this study analyzed the spatiotemporal patterns of vegetation coverage and their trends in the GMS from 2000 to 2022 using the Theil–Sen slope estimation, the Mann–Kendall mutation test, and the gravity center migration model. The key findings were as follows: (1) the NDVI in the GMS showed an overall upward fluctuating trend over the past 23 years, with an annual growth rate of 0.11%. The NDVI changes varied slightly between seasons, with the greatest increases recorded in summer and winter. (2) The spatial distribution of NDVI in the GMS varied greatly, with higher NDVI values in the north–central region and lower NDVI values in the south. (3) A total of 66.03% of the GMS area showed increments in vegetation during the studied period, mainly in south–central Myanmar, northeastern Thailand, Vietnam, and China. (4) From 2000 to 2022, the gravity center of vegetation greenness shifted northward in the GMS, especially from 2000 to 2005, indicating that the growth rates of vegetation in the north–central part of the GMS were higher than those in the south. Furthermore, the vegetation coverage in all countries, except Cambodia, increased, with the most pronounced growth recorded in China. Overall, these findings can provide scientific evidence for the GMS to enhance ecological protection and sustainable development. Full article

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons’ NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced “greening” process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant “browning” trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of “greening in the east and browning in the west” of Central Asia. During spring, the dominant theme was the “greening” of vegetation NDVI, although there was noticeable “browning” observed in southwest region of Central Asia. During summer, the “browning” of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the “browning” trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the “greening” trend of vegetation NDVI in arid regions, which further aggravates the “browning” trend of vegetation NDVI. Full article

Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. This study used the annual mean Normalized Difference Vegetation Index (NDVI) as the vegetation greenness indicator. The variation in NDVI on Hainan Island was analyzed using the Theil–Sen median trend analysis and Mann–Kendall test during 2000–2019. The influence of natural and anthropogenic factors on the driving mechanism of the spatial pattern of NDVI was explored by the Multiscale Weighted Regression (MGWR) model. Additionally, we employed the Boosted Regression Tree (BRT) model to explore their contribution to NDVI. Then, the MGWR model was utilized to predict future greenness patterns based on precipitation and temperature data from different Shared Socioeconomic Pathway (SSP) scenarios for the period 2021–2100. The results showed that: (1) the NDVI of Hainan Island forests significantly increased from 2000 to 2019, with an average increase rate of 0.0026/year. (2) the R² of the MGWR model was 0.93, which is more effective than the OLS model (R² = 0.42) in explaining the spatial relationship. The spatial regression coefficients of the NDVI with temperature ranged from −10.05 to 0.8 (p < 0.05). Similarly, the coefficients of Gross Domestic Product (GDP) with the NDVI varied between −5.98 and 3.28 (p < 0.05); (3) The natural factors played the most dominant role in influencing vegetation activities as a result of the relative contributions of 83.2% of forest NDVI changes (16.8% contributed by anthropogenic activities). (4) under SSP119, SSP245, and SSP585 from 2021 to 2100, the NDVI is projected to have an overall decreasing pattern under all scenarios. This study reveals the trend of greenness change and the spatial relationship with natural and anthropogenic factors, which can guide the medium and long-term dynamic monitoring and evaluation of tropical forests on Hainan Island. Full article