Average Normalized Difference Vegetation Index Research Articles

Association between green space and multiple ambient air pollutants with depressive and anxiety symptoms among Chinese adolescents: The role of physical activity

ObjectiveTo explore the association between green space, multiple ambient air pollutants and depressive/anxiety symptoms and the mediating role of physical activity (PA) in Chinese adolescents. MethodA school-based health survey was conducted in eight provinces in China in 2021. 22,868 students aged 14.64 (±1.77) years completed standard questionnaires to record details of depressive, anxiety symptoms and PA. We calculated the average normalized difference vegetation index (NDVI) in circular buffers of 200 m, 500 m and 1000 m and estimated the concentrations of PM10, PM2.5, CO, NO2, O3, SO2 around the adolescents’ school addresses. ResultsThe exposure–response curves showed that the lower the NDVI value, the higher the risk of depressive and anxiety symptoms. CO, PM2.5 and SO2 and air pollution score were associated with increased risk of depressive and anxiety symptoms. NDVI in all circular buffers decreased the risk of depressive and anxiety symptoms at low levels of PA, but the associations were not significant at high levels of PA. In the subgroup analysis, PM10, PM2.5, CO, NO2, SO2, AQI and air pollution score increased the risk of depressive and anxiety symptoms at low PA levels, but the associations were not significant at high levels of PA. Mediation analysis indicated that the mediating effect of PA on the association between NDVI, NDVI-200 m NDVI-500 m, CO, PM10, PM2.5, SO2, AQI and depressive/anxiety symptoms was statistically significant（p < 0.05）. ConclusionMiddle-high level PA could reduce the strength of association between air pollution and depressive and anxiety symptoms. Meanwhile, the association between green space/air pollution and depressive/anxiety symptoms was partly mediated by PA.

Analysis of Spatial Differentiation of NDVI and Climate Factors on the Upper Limit of Montane Deciduous Broad-Leaved Forests in the East Monsoon Region of China

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m−2/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10−3/a). NDVI’s lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation’s vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation’s diverse adaptability to climatic variations.

Dynamic changes and impacts of accumulated temperature on the Tibetan plateau from 1980 to 2018 based on the fusion of simulation and observation data

In the context of global warming, the thermal conditions of the Tibetan Plateau have changed significantly in recent decades. In the present study, we analysed the spatiotemporal variation in T ≥ 0 °C accumulated temperature (AT0) on the Tibetan Plateau from 1980 to 2018 and its effect on normalized difference vegetation index (NDVI) changes by fusing climate model outputs and ground observations using the High Accuracy Surface Modeling (HASM). Cross-validation revealed that the root mean square error (RMSE) and R2 of the fused data from HASM were 1.593 °C and 0.719, respectively, which were greater than the 5.864 °C and 0.385, respectively, before fusion, indicating that HASM fusion improved the accuracy and that a more accurate AT0 could be obtained. Over the past 39 years, AT0 on the Tibetan Plateau had increased at a rate of 7.53 °C/year. The growth period was extended by 0.46 days/year, while the start and end of the growth period were 0.27 days/year earlier and 0.18 days/year later, respectively. Analysis of the decadal change in AT0 revealed that the areas with AT0 < 500 °C decreased by 5 % and that the areas with AT0 > 2000 °C increased by 6.2 %. However, a slower warming trend appeared after 2010 because of the decreasing rate of the daily mean temperature increase during the growth period. Increasing AT0 also promoted vegetation growth, especially in parts of the southern and eastern plateau regions, with a Pearson's correlation coefficient of 0.46 on the entire plateau between AT0 and the average NDVI during the growth periods. However, there was a significant negative correlation with a coefficient lower than −0.4 in the Qaidam Basin, and partial correlation analysis showed that the extension of the growth period was the main factor influencing the decrease in the NDVI in the Qaidam Basin.

Determining the impacts of climate change and human activities on vegetation change on the Chinese Loess Plateau considering human-induced vegetation type change and time-lag effects of climate on vegetation growth

ABSTRACT Since the initiation of the Grain for Green Project (GFGP) in 1999, dramatic change in vegetation status on the Loess Plateau. Spatially, geographical detector was employed to detect dominant variables influencing the spatial arrangement of normalized difference vegetation index (NDVI). Temporally, lagged or accumulated monthly precipitation, temperature and standardized precipitation evapotranspiration indices (SPEIs) sensitive to the monthly NDVI were first detected for every individual pixel, and the correlation between the NDVI and meteorological elements with time-lag effects was established a random forest model of unchanged land cover, followed by attributing impacts of climatic alterations and human interventions through residual examination across changed land cover. The findings indicate that (1) precipitation, slope, and soil dominantly influence the spatial arrangement of the NDVI. (2) Precipitation in current the month and cumulative temperatures of the previous 1–2 months steadily affect vegetation growth significantly, the optimal accumulation time interval for SPEI around 2000 are 8 months and 4 months, respectively. (3) Increases in the average NDVI within woodland and meadow vegetation on the Loess Plateau were primarily driven by climate change before 2000, accounting for 76.2%, whereas after 2000 it was dominantly driven by human activities, accounting for 64.16%.

Exposure to Neighborhood Greenness and Hypertension Incidence in Adults: A Longitudinal Cohort Study in Taiwan.

There are few studies on the health effects of long-term exposure to neighborhood greenness in a longitudinal setting, especially in Asian countries with high population densities. This study investigates the association between long-term exposure to neighborhood greenness and hypertension among adults in Taiwan. We selected 125,537 participants ( of age) without hypertension from Taiwan who had joined the standard medical examination program between 2001 and 2016. Neighborhood greenness was estimated using the normalized difference vegetation index (NDVI), derived from satellite images at a resolution of . The 2-y average NDVI value within a circular buffer around participants' residences was calculated. A time-varying Cox regression model was used to investigate the association between neighborhood greenness and incident hypertension. Mediation analyses were performed to examine whether the association was explained by air pollution, leisure-time physical exercise, or body mass index (BMI). Compared with living in areas within the first quartile of neighborhood greenness, living in areas within the second, third, and fourth quartiles of neighborhood greenness was found to be associated with a lower risk of hypertension, with hazard ratios (HRs) and 95% confidence intervals (CIs) of 0.95 (95% CI: 0.91, 1.00), 0.95 (95% CI: 0.90, 0.99), and 0.93 (95% CI: 0.88, 0.97), respectively. Each 0.1-unit increase in the NDVI was associated with a 24% lower risk of developing hypertension (0.76; 95% CI: 0.66, 0.87), with this associations being stronger among males and those with higher education levels. This association was slightly mediated by BMI but not by air pollution or leisure-time physical exercise. Our findings suggest the protective effects of neighborhood greenness on hypertension development, especially in males and well-educated individuals. Our results reinforced the importance of neighborhood greenness for supporting health. https://doi.org/10.1289/EHP13071.

Association of residential greenness with obstructive sleep apnea among Chinese old adults and the mediation role of PM2.5 and leisure-time physical activity

Few studies have investigated the association of residential greenness with obstructive sleep apnea (OSA). This study was to comprehensively examine the association of residential greenness exposure with OSA and explore the mediating effect of leisure-time physical activity (LTPA) and PM2.5 on the association among Chinese old adults. A prospective cohort study that enrolled 2027 adults aged ≥65 was conducted between 1st July 2015 and 30th September 2019 in Southern China. OSA was ascertained by Berlin Questionnaire. Greenness exposure was measured by contemporaneous and cumulative average normalized difference vegetation index (NDVI) in the 1000 m radius around each participant's residential address. Hazard ratios (HRs) with 95 % confidence intervals (CIs) were calculated by Cox proportional hazards model to assess the impact of greenness exposure on the incidence of OSA after adjusting for confounders. LTPA and PM2.5 were examined as potential mediators in the aforementioned models. A total of 293, nearly 14.5 %, participants developed OSA within 59,251 person-months of follow-up. When comparing the highest with lowest tertiles, both contemporaneous NDVI (>0.351 vs. ≤0.325: HR = 0.20, 95 % CI = 0.13–0.31) and cumulative NDVI (> 0.346 vs. ≤ 0.317: HR = 0.32, 95 % CI = 0.21–0.47) were associated with a reduced risk of OSA after adjusting for confounders. LTPA and PM2.5 significantly mediated the association between greenness and OSA. In conclusion, this study indicated that exposure to higher residential greenness could decrease OSA risk, and this benefit may be achieved by promoting physical activity and decreasing PM2.5 concentration. The findings suggest to formulate targeted interventional strategies by expanding residential greenness to prevent OSA and reduce disease burden.

Normalized Difference Vegetation Index (NDVI) in Assessment of Grain Crop State

The results of studying the dynamics of the NDVI of winter triticale crops in the conditions of the Northwest of Russia are presented. The NDVI was determined based on remote sensing data obtained by two methods including satellite imagery and aerial photography from an unmanned aerial vehicle (UAV). The analysis of the dynamics of the NDVI of the surveyed area for the period from May 15 to August 8, obtained from the Sentinel-2 L2A satellite, showed the dependence of the index on the increase in biomass of winter triticale by the periods of its growth and development, as well as on the meteorological conditions of the growing seasons. The sharpest increase in the vegetation index was noted in the period from the beginning of June to the beginning of July, which corresponds to the phase of entering the tube — filling of winter triticale grain. In the most favorable weather conditions, the vegetation index reached 1.0, which indicates the formation of dense vegetation by triticale crops. It is confirmed that the strong dependence of the average NDVI on yield occurs against the background of a decrease in the value of the vegetation index. The conducted aerial photography showed the presence of spatial heterogeneity in the studied fields, which caused uneven growth and development of winter triticale plants within the boundaries of one field. The shortage of grain from each hectare will be about 200 kg. In this regard, it is recommended to apply precision farming using NDVI mapping schemes with reference to point-based field surveys.

Spatial and temporal variation in normalized difference vegetation index of vegetation in Liaoning Province from the perspective of ecogeographic zoning

Normalized difference vegetation index (NDVI) is an important indicator reflecting vegetation cover and growth status. It is of significance for regional ecological conservation and natural resource management to investigate its spatial and temporal variation trends and response to ecological factors. We divided Liaoning Province into three ecological geographical regions, including northwest agro-pastoral zone, central agricultural zone, and eastern agroforestry zone. Based on remote sensing, vegetation, climate, topography and human activities, we used trend analysis and geographic probe model to examine the spatial and temporal trends of NDVI in Liaoning Province, and analyzed the intensity and interaction mechanism of each driver on the spatial distribution pattern of NDVI. The results showed that the annual average NDVI in Liaoning Province from 2001 to 2020 was 0-0.92, showing a distribution pattern of high in the east and low in the west, high in the inland and low in the coastal land. The overall trend of vegetation cover was increasing, and the NDVI increasing areas were mainly concentrated in the northwest agro-pastoral zone and the eastern agroforestry zone, the NDVI reduction areas were mainly concentrated at the border between the central agricultural zone and the eastern agroforestry zone, as well as in the coastal area of the eastern agroforestry zone. The annual average NDVI change varied among the three ecological-geographic zones. The NDVI of the northwest agro-pastoral zone from 2001 to 2020 were generally low, but showed a fluctuating trend of slow increase. The NDVI of the eastern agroforestry zone was high overall, and the interannual variation of NDVI was generally stable. The distribution of high and low NDVI in the central agricultural zone was staggered, and the interannual variation of NDVI showed a decreasing trend. Natural factors were the key drivers of NDVI changes in the three ecogeographic zones, with cumulative temperature and precipitation having the greatest influence. The interactions between the factors were all mutually and nonlinearly enhanced.

Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach

In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative.

Critical windows of greenness exposure during preconception and gestational periods in association with birthweight outcomes

Few studies have examined the association between greenness exposure and birth outcomes. This study aims to identify critical exposure time windows during preconception and pregnancy for the association between greenness exposure and birth weight. A cohort of 13 890 pregnant women and newborns in Shanghai, China from 2016–2019 were included in the study. We assessed greenness exposure using Normalized Difference Vegetation Index (NDVI) during the preconception and gestational periods, and evaluated the association with term birthweight, birthweight z-score, small-for-gestational age, and large-for-gestational age using linear and logistic regressions adjusting for key maternal and newborn covariates. Ambient temperature, relative humidity, ambient levels of fine particles (PM2.5) and nitrogen dioxide (NO2) assessed during the same period were adjusted for as sensitivity analyses. Furthermore, we explored the potential different effects by urbanicity and park accessibility through stratified analysis. We found that higher greenness exposure at the second trimester of pregnancy and averaged exposure during the entire pregnancy were associated with higher birthweight and birthweight Z-score. Specifically, a 0.1 unit increase in second trimester averaged NDVI value was associated with an increase in birthweight of 10.2 g (95% CI: 1.8–18.5 g) and in birthweight Z-score of 0.024 (0.003–0.045). A 0.1 unit increase in an averaged NDVI during the entire pregnancy was associated with 10.1 g (95% CI: 1.0–19.2 g) increase in birthweight and 0.025 (0.001–0.048) increase in birthweight Z-score. Moreover, the associations were larger in effect size among urban residents than suburban residents and among residents without park accessibility within 500 m compared to those with park accessibility within 500 m. Our findings suggest that increased greenness exposure, particularly during the second trimester, may be beneficial to birth weight in a metropolitan area.

Heterogeneous responses of wetland vegetation to climate change in the Amur River basin characterized by normalized difference vegetation index from 1982 to 2020.

Climate change affects wetland vegetation dramatically in mid- and high- latitudes, especially in the Amur River basin (ARB), straddling three countries and distributing abundance wetlands. In this study, spatiotemporal changes in average normalized difference vegetation index (NDVI) of wetland during the annual growing season were examined in the ARB from 1982 to 2020, and the responses of wetland vegetation to climatic change (temperature and precipitation) in different countries, geographic gradients, and time periods were analyzed by correlation analysis. The NDVI of wetland in the ARB increased significantly (p < 0.01) at the rate of 0.023 per decade from 1982 to 2020, and the NDVI on the Russian side (0.03 per decade) increased faster than that on the Chinese side (0.02 per decade). The NDVI of wetland was significantly positively correlated with daily mean temperature (p < 0.05, r = 0.701) and negatively correlated with precipitation, although the correlation was not significant (p > 0.05, r = -0.12). However, the asymmetric effects of diurnal warming on wetland vegetation were weak in the ARB. Correlations between the NDVI of wetland and climatic factors were zonal in latitudinal and longitudinal directions, and 49°N and 130°E were the points for a shift between increasing and decreasing correlation coefficients, closely related to the climatic zone. Under climate warming scenarios, the NDVI of wetland is predicted to continue to increase until 2080. The findings of this study are expected to deepen the understanding on response of wetland ecosystem to global change and promote regional wetland ecological protection.

Groundwater Level Dynamic Impacted by Land-Cover Change in the Desert Regions of Tarim Basin, Central Asia

Groundwater is essential to residents, ecology, agriculture, and industry. The depletion of groundwater impacted by climatic variability and intense human activities could threaten water, food, and socioeconomic security in arid regions. A thorough understanding of groundwater level dynamics and its response to land-cover change is necessary for groundwater management and ecosystem improvement, which are poorly understood in arid desert regions due to a scarcity of field monitoring data. In our study, spatiotemporal characteristics of groundwater level impacted by land-cover change and its relationship with vegetation were examined using 3-years in-situ monitoring data of 30 wells in the desert regions of Tarim Basin during 2019–2021. The results showed that the depth to groundwater level (DGL) exhibited obvious spatial and seasonal variations, and the fluctuation of DGL differed significantly among the wells. The cultivated land area increased by 1174.6, 638.0, and 732.2 km2 during 2000–2020 in the plains of Yarkand, Weigan-Kuqa, and Dina Rivers, respectively, mainly transferring from bare land and grassland. Annual average Normalized Difference Vegetation Index (NDVI) values increased with time during the period in the plains. DGL generally exhibited a weakly increasing trend from 2019 to 2021, mainly due to human activities. Land-cover change significantly affected the groundwater level dynamic. Generally, the groundwater system was in negative equilibrium near the oasis due to agricultural irrigation, was basically in dynamic equilibrium in the desert region, and was in positive equilibrium near the Tarim River Mainstream due to irrigation return water and streamflow. NDVI of natural desert vegetation was negatively correlated with DGL in the desert regions (R2 = 0.78, p &lt; 0.05). Large-scale land reclamation and groundwater overexploitation associated with water-saving irrigation agriculture development have caused groundwater level decline in arid oasis-desert regions. Hence, controlling groundwater extraction intensity, strengthening groundwater monitoring, and promoting water-saving technology would be viable methods to sustainably manage groundwater and maintain the ecological environment in arid areas.

Residential Greenness and Long-term Mortality Among Patients Who Underwent Coronary Artery Bypass Graft Surgery.

Studies have reported inverse associations between exposure to residential greenness and mortality. Greenness has also been associated with better surgical recovery. However, studies have had small sample sizes and have been restricted to clinical settings. We investigated the association between exposure to residential greenness and all-cause mortality among a cohort of cardiac patients who underwent coronary artery bypass graft (CABG) surgery. We studied this cohort of 3,128 CABG patients between 2004 and 2009 at seven cardiothoracic departments in Israel and followed patients until death or 1st May 2021. We collected covariate information at the time of surgery and calculated the patient-level average normalized difference vegetation index (NDVI) over the entire follow-up in a 300 m buffer from the home address. We used Cox proportional hazards regression models to estimate associations between greenness and death, adjusting for age, sex, origin, socioeconomic status, type of hospital admission, peripherality, air pollution, and distance from the sea. Mean age at surgery was 63.8 ± 10.6 for men and 69.5 ± 10.0 for women. During an average of 12.1 years of follow-up (37,912 person-years), 1,442 (46%) patients died. A fully adjusted Cox proportional hazards model estimated a 7% lower risk of mortality (HR: 0.93, 95% CI = [0.85, 1.00]) per 1 interquartile range width increase (0.04) in NDVI. Results were robust to the use of different buffer sizes (100 m-1,250 m from the home) and to the use of average NDVI exposure during the first versus the last 2 years of follow-up. Residential greenness was associated with lower risk of mortality in CABG patients.

Effects of greenness in university campuses on test anxiety among Chinese university students during COVID-19 lockdowns: a correlational and mediation analysis

ABSTRACT The purpose of this study is to investigate the correlations of greenness exposure with test anxiety among university students during COVID-19 lockdowns and to explore their mechanisms. We conducted a cross-sectional study with 2609 university students in Anhui and Shandong provinces, China. We assessed perceived campus greenness using a five-point Likert scale for quality, visibility, abundance, usage, and accessibility. Objective greenness was estimated via average normalised difference vegetation index (NDVI) in 1,000-, 1,500-, and 2,000-m radius zones around each of the campuses. A generalised linear mixed model examined the associations between greenness and test anxiety and to evaluate the mediation effects of physical activity, body mass index (BMI), and air pollution. Results showed that higher NDVI1500-m correlated with lower test anxiety (OR = 0.871; 95% CI: 0.851, 0.891), physical activity may partially mediate this association. Increased campus greenness may alleviate test anxiety among Chinese university students.

Assessing the extent and impacts of linear infrastructure on Sri Lanka's natural and protected areas: Implications for future development planning.

Linear infrastructure (LI) has varying effects on landscapes depending on different ecosystems' sensitivity and threat levels. Economically developing tropical countries are particularly at risk from LI. Therefore, understanding a country's current LI network and planning future developments to avoid further fragmentation and disturbance is crucial. This study aimed to assess the extent of Sri Lanka's LI network (i.e., roads, railroads, and powerlines), given that it is both a biodiversity hotspot and an economically developing country in the tropics. First, we calculated the average normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) indices and examined their spatial autocorrelation per divisional secretariat division. Then a multivariate cluster analysis was used to determine clusters of natural and protected areas that may receive similar impacts from different LI and their combinations. Results indicated that roads are the most widespread LI type in Sri Lanka, followed by powerlines and railroads. Over 80% of Sri Lanka's total land area falls within 1 km of either a national or a provincial/local road. Areas with high NDVI were primarily manmade habitats, with less than 20% contribution from protected areas. Over 50% of the total protected area of Sri Lanka is being impacted by all three types of LI. Powerlines were the most common LI type in protected areas in proportion to their total network lengths. To minimize environmental impact while achieving development goals, future LI development activities should use a landscape approach to identify development needs and strategies informed by these findings.

Vegetation Dynamics of Sub-Mediterranean Low-Mountain Landscapes under Climate Change (on the Example of Southeastern Crimea)

In the context of a changing environment, understanding the interaction between vegetation and climate is crucial for assessing, predicting, and adapting to future changes in different vegetation types. Vegetation exhibits high sensitivity to external environmental factors, making this understanding particularly significant. This study utilizes geospatial analysis techniques, such as geographic information systems, to investigate vegetation dynamics based on remote sensing data and climatic variables, including annual air temperature, annual precipitation, and annual solar radiation. The research methodology encompasses data collection, processing, and analysis, incorporating multispectral imagery and multilayered maps of various parameters. The calculation of the normalized difference vegetation index serves to evaluate changes in vegetation cover, identify areas experiencing variations in green biomass, and establish strategies for the future development of different vegetation types. During the period from 2001 to 2022, the average normalized difference vegetation index value in the Southeastern Crimea region amounted to 0.443. The highest average values were recorded in the year 2006, reaching a magnitude of 0.469. Conversely, the lowest values were observed in the years 2001–2002, constituting 0.397. It has been ascertained that an overarching positive trend in the evolution of NDVI values from 2001 to 2022 is apparent, thus implying a notable augmentation in vegetative biomass. However, adversarial trends manifest in discrete locales adjacent to the cities of Sudak and Feodosia, along with the coastal stretches of the Black Sea. Correlation analysis is employed to establish relationships between vegetation changes and climatic indicators. The findings contribute to our understanding of the vulnerability of various vegetation types and ecosystems in the Southeastern Crimea region. The obtained data provide valuable insights for the development of sustainable vegetation resource management strategies and climate change adaptation in the region.

Assessing the Vegetation Dynamics and Its Influencing Factors in Central Asia from 2001 to 2020

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation.

Polar-facing slopes showed stronger greening trend than equatorial-facing slopes in Tibetan plateau grasslands

The orientation of slopes in alpine zones creates microclimates, e.g. equatorial-facing slopes (EFSs) are generally drier and warmer than are polar-facing slopes (PFSs). The vegetation growing in these microhabitats responds divergently to climatic warming depending on the slope orientation. We proposed a spatial metric, the greenness asymmetric index (GAI), defined as the ratio between the average normalized difference vegetation index (NDVI) on PFSs and EFSs within a given spatial window, to quantify the asymmetry of greenness across aspects. We calculated GAI for each non-overlapping 3 × 3 km2 (100 × 100 Landsat pixels) grid, and seamlessly mapped it on Tibetan Plateau (TP) grassland using NDVI time series from the Landsat-5, -7 and -8 satellites. PFSs were greener than EFSs (GAI > 1) in warm and dry areas, and EFSs were greener than PFSs (GAI < 1) in cold and wet areas. We also detected a stronger greening trend (0.0040 vs 0.0034 y−1) and a higher sensitivity of NDVI to temperature (0.031 vs 0.026 °C−1) on PFSs than EFSs, leading to a significant positive trend in GAI (0.00065 y−1, P < 0.01) in the TP from 1991 to 2020. Our results suggest that global warming exacerbated the greenness asymmetry associated with the slope orientation: PFSs are more sensitive to warming and have been greening at a faster rate than EFSs. The gradient of EFSs and PFSs provided a “natural laboratory” to study interaction of water and temperature limitations on vegetation growth. Our study is the first to detect the effect of aspect on the greening trend in the TP. Future research needs to clarify the full biotic and abiotic determinants for this spatial and temporal asymmetry of greenness across aspects with the support of extensive field measurements and refined high-resolution NDVI products.

Temporal and spatial analysis of vegetation cover change in the Yellow River Delta based on Landsat and MODIS time series data.

Based on the Landsat normalized difference vegetation index (NDVI) and the NDVI product of MODIS, this study synthesized two kinds of time-series images. The features were selected according to the characteristics of the time series, and the random forest algorithm was used for classification. Based on the classification results and GIS spatial analysis, the temporal and spatial changes in vegetation cover in the Yellow River Delta from 2000 to 2020 were studied. The results showed that from 2000 to 2020, the vegetation first increased and then decreased, and the dynamic degree of land cover change was generally low. The monthly average minimum NDVI values during the vegetation growth period mostly occurred before 2010, and the maximum values occurred after 2010. From the spatial perspective, the average vegetation area of the Yellow River Delta accounted for 31.54% of the total study area; specifically, the spatial pattern of vegetation distribution was relatively fixed, and the fixed vegetation area accounted for 63.90% of the total vegetation area. The spatial distribution had significant differences, and the vegetation was distributed radially from the center of the Yellow River to the periphery, with significant fragmentation found outside the watershed. The Yellow River had a strong interference with vegetation growth, and the stable vegetation distribution areas were concentrated near the Yellow River. The correlation coefficient between vegetation distribution and the location of the Yellow River was - 0.9964.

Monitoring intertidal golden tides dominated by Ectocarpus siliculosus using Sentinel-2 imagery

Golden seaweed tides are a global environmental and social problem, that have been occurring along Ireland's eastern coastline since the 1990s. This study focused on analysing the spatiotemporal dynamics of golden seaweed (Ectocarpus siliculosus) coverage at Dollymount Strand in Dublin Bay (Ireland), between 2016 and 2022, and its relationship with meteorological conditions. Hyperspectral measurements in the field and Sentinel-2 imagery were utilized to monitor macroalgal blooms with a spatiotemporal resolution of 10 m and minimum costs. The normalized difference vegetation index (NDVI) values filtered between 0.1 and 1 were used to identify the coverage of golden seaweeds. The results showed that the golden seaweed coverage extended over 99% of the study area (54 ha) in June 2020 with an average NDVI of 0.25. A seasonal pattern of golden seaweed abundance was observed and modelled from May to October using a Generalized Additive Model. Approximately 28% of the coverage was correlated with daily average global radiance, and 38% of the mean NDVI was associated with daily average maximum air temperatures and wind direction by means of the Generalized Linear Models. A greater biomass of Ectocarpus spp. was accumulated on the beach when the wind direction was from the north-east and south-east. The results also suggested that freshwater nutrient inputs in winter from nearby estuaries may be contributing to golden tides on the shoreline. These findings are useful in developing strategies aimed at controlling and managing golden tides globally, as well as using them as bioindicators of ecological status in coastal environments.