Enhanced Vegetation Index Research Articles

Assessing the Spatiotemporal Patterns and Impacts of Droughts in the Orinoco River Basin Using Earth Observations Data and Surface Observations

Droughts impact the water cycle, ecological balance, and socio-economic development in various regions around the world. The Orinoco River Basin is a region highly susceptible to droughts. The basin supports diverse ecosystems and supplies valuable resources to local communities. We assess the spatiotemporal patterns and impacts of droughts in the basin using remote sensing data and surface observations. We use monthly precipitation (P), air temperature near the surface (T2M), enhanced vegetation index (EVI) derived from Earth observations, and average daily flow (Q) data to quantify drought characteristics and impacts. We also investigated the association between drought and global warming by correlating the drought intensity and the percentage of dry area with sea surface temperature (SST) anomalies in the Pacific (Niño 3.4 index), Atlantic (North Atlantic Index [NATL]), and South Atlantic Index [SATL]) oceans. We evaluate the modulating effect of droughts on the hydrological regime of the most relevant tributaries by calculating the trend and significance of the regional standardized precipitation index (SPI) and percentage area affected by dry conditions. El Niño events worsen the region’s drought conditions (SPI vs. Niño 3.4 index, r = −0.221), while Atlantic SST variability has less influence on the basin’s precipitation regime (SPI vs. NATL and SATL, r = 0.117 and −0.045, respectively). We also found that long-term surface warming trends aggravate drought conditions (SPI vs. T2M anomalies, r = −0.473), but vegetation greenness increases despite high surface temperatures (SPI vs. EVI anomalies, r = 0.284). We emphasize the irregular spatial-temporal patterns of droughts in the region and their profound effects on the ecological flow of rivers during prolonged hydrological droughts. This approach provides crucial insights into potential implications for water availability, agricultural productivity, and overall ecosystem health. Our study underlines the urgent need for adaptive management strategies to mitigate the adverse effects of droughts on ecosystems and human populations. The insights derived from our study have practical implications for developing strategies to address the impacts of droughts and ensure the protection of this ecologically significant region.

Green-blue space exposure changes and impact on individual-level well-being and mental health: a population-wide dynamic longitudinal panel study with linked survey data.

Cross-sectional evidence suggests that living near green and blue spaces benefits mental health; longitudinal evidence is limited. To quantify the impact of changes in green and blue spaces on common mental health disorders, well-being and health service use. A retrospective, dynamic longitudinal panel study. Wales, UK. An e-cohort comprising 99,682,902 observations of 2,801,483 adults (≥ 16 years) registered with a general practice in Wales (2008-2019). A 5312-strong 'National Survey for Wales (NSW) subgroup' was surveyed on well-being and visits to green and blue spaces. Common mental health disorders, general practice records; subjective well-being, Warwick-Edinburgh Mental Well-being Scale. Common mental health disorder and use of general practice services were extracted quarterly from the Welsh Longitudinal General Practice Dataset. Annual ambient greenness exposure, enhanced vegetation index and access to green and blue spaces (2018) from planning and satellite data. Data were linked within the Secure Anonymised Information Linkage Databank. Multilevel regression models examined associations between exposure to green and blue spaces and common mental health disorders and use of general practice. For the National Survey for Wales subgroup, generalised linear models examined associations between exposure to green and blue spaces and subjective well-being and common mental health disorders. Our longitudinal analyses found no evidence that changes in green and blue spaces through time impacted on common mental health disorders. However, time-aggregated exposure to green and blue spaces contrasting differences between people were associated with subsequent common mental health disorders. Similarly, our cross-sectional findings add to growing evidence that residential green and blue spaces and visits are associated with well-being benefits: Greater ambient greenness (+ 1 enhanced vegetation index) was associated with lower likelihood of subsequently seeking care for a common mental health disorder [adjusted odds ratio (AOR) 0.80, 95% confidence interval, (CI) 0.80 to 0.81] and with well-being with a U-shaped relationship [Warwick-Edinburgh Mental Well-being Scale; enhanced vegetation index beta (adjusted) -10.15, 95% CI -17.13 to -3.17; EVI2 beta (quadratic term; adj.) 12.49, 95% CI 3.02 to 21.97]. Those who used green and blue spaces for leisure reported better well-being, with diminishing extra benefit with increasing time (Warwick-Edinburgh Mental Well-being Scale: time outdoors (hours) beta 0.88, 95% CI 0.53 to 1.24, time outdoors2 beta -0.06, 95% CI -0.11 to -0.01) and had 4% lower odds of seeking help for common mental health disorders (AOR 0.96, 95% CI 0.93 to 0.99). Those in urban areas benefited most from greater access to green and blue spaces (AOR 0.89, 95% CI 0.89 to 0.89). Those in material deprivation benefited most from leisure time outdoors (until approximately four hours per week; Warwick-Edinburgh Mental Well-being Scale: time outdoors × in material deprivation: 1.41, 95% CI 0.39 to 2.43; time outdoors2 × in material deprivation -0.18, 95% CI -0.33 to -0.04) although well-being remained generally lower. Longitudinal analyses were restricted by high baseline levels and limited temporal variation in ambient greenness in Wales. Changes in access to green and blue spaces could not be captured annually due to technical issues with national-level planning datasets. Further analyses could investigate mental health impacts in population subgroups potentially most sensitive to local changes in access to specific types of green and blue spaces. Deriving green and blue spaces changes from planning data is needed to overcome temporal uncertainties. This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (Project number 16/07/07) and will be published in full in Public Health Research; Vol. 11, No. 10. Sarah Rodgers is part-funded by the NIHR Applied Research Collaboration North West Coast.

Ambient greenness, access to local green spaces, and subsequent mental health: a 10-year longitudinal dynamic panel study of 2·3 million adults in Wales

Living in greener areas, or close to green and blue spaces (GBS; eg, parks, lakes, or beaches), is associated with better mental health, but longitudinal evidence when GBS exposures precede outcomes is less available. We aimed to analyse the effect of living in or moving to areas with more green space or better access to GBS on subsequent adult mental health over time, while explicitly considering health inequalities. A cohort of the people in Wales, UK (≥16 years; n=2 341 591) was constructed from electronic health record data sources from Jan 1, 2008 to Oct 31, 2019, comprising 19 141 896 person-years of follow-up. Household ambient greenness (Enhanced Vegetation Index [EVI]), access to GBS (counts, distance to nearest), and common mental health disorders (CMD, based on a validated algorithm combining current diagnoses or symptoms of anxiety or depression [treated or untreated in the preceding 1-year period], or treatment of historical diagnoses from before the current cohort [up to 8 years previously, to 2000], where diagnosis preceded treatment) were record-linked. Cumulative exposure values were created for each adult, censoring for CMD, migration out of Wales, death, or end of cohort. Exposure and CMD associations were evaluated using multivariate logistic regression, stratified by area-level deprivation. After adjustment, exposure to greater ambient greenness over time (+0·1 increased EVI on a 0-1 scale) was associated with lower odds of subsequent CMD (adjusted odds ratio 0·80, 95% CI 0·80-0·81), where CMD was based on a combination of current diagnoses or symptoms (treated or untreated in the preceding 1-year period), or treatments. Ten percentile points more access to GBS was associated with lower odds of a later CMD (0·93, 0·93-0·93). Every additional 360 m to the nearest GBS was associated with higher odds of CMD (1·05, 1·04-1·05). We found that positive effects of GBS on mental health appeared to be greater in more deprived quintiles. Ambient exposure is associated with the greatest reduced risk of CMD, particularly for those who live in deprived communities. These findings support authorities responsible for GBS, who are attempting to engage planners and policy makers, to ensure GBS meets residents' needs. National Institute for Health and Care Research Public Health Research programme.

A multitemporal index for the automatic identification of winter wheat based on Sentinel-2 imagery time series

ABSTRACT Timely and accurate monitoring of the spatial distribution of wheat is crucial for wheat field management, growth monitoring, yield estimation and prediction. In this study, a multitemporal index, termed the winter wheat mapping index (WWMI), was constructed for automatic winter wheat mapping on the basis of Sentinel-2 enhanced vegetation index (EVI) time series and wheat phenological features. Henan, an important winter wheat production province in China, was selected as the study area. Zhumadian, the primary wheat-growing city in Henan, was the test area. Both empirical and automatic threshold (Otsu) methods were adopted to calculate the optimal threshold of the WWMI. The performance of WWMI in winter wheat mapping was compared at object-oriented and pixel-based levels. The proposed WWMI separated winter wheat and nonwinter wheat areas well, thus achieving highly accurate winter wheat mapping. In Zhumadian, the empirical threshold method performed better than the Otsu method, but the former relied on official statistics to iteratively adjust the WWMI threshold. In Henan, the mapping accuracy achieved by the Otsu method was higher than that achieved by the empirical threshold method, with mean relative errors (MREs) of 6.78% and 19.87% at the municipal and county levels, respectively. This was because, compared with the empirical threshold method, the Otsu method did not rely on official statistics and adaptively determined the optimal threshold of the WWMI for each city in Henan, thus fully considering wheat growth state differences in different cities. In addition, the object-oriented WWMI performed better than the pixel-based WWMI in wheat mapping. The results further demonstrated the feasibility of combining the WWMI with the Otsu method for automatic winter wheat mapping at large extents, which will provide a theoretical basis for identifying other food crops.

The influence of enhanced vegetation index, proximity to national borders, proximity to protected areas, and proximity to water on malaria case prevalence in Sub-Saharan Africa, 2000-2020

Background: This study aimed to assess the effect of enhanced vegetation index, proximity to national borders, proximity to protected areas, and water on malaria morbidity in Sub-Saharan Africa. Methods: Data were pooled from the Demographic and Health Surveys (DHS) conducted in 32 Sub-Saharan African countries spanning 2000 to 2020. Women's standard weights were denormalized and bivariate analyses were conducted to identify potential confounders. Two models were fitted; model 1 involved primary exposure variables and model 2 involved primary exposure variables adjusted for significant confounders. The final interpretation of the results was based on model 2. Results: The prevalence of malaria cases was 23.1%, The risk of children suffering malaria in households that belong to the 3rd quantile of the vegetation index is 27% less compared to children that belong to the households in the 1st quantile of the vegetation index [aOR=0.73, 95%CI: 0.55-0.99; p-value<0.05]. Children belonging to households in the 4th quantile of proximity to water have a 33% higher risk of suffering from malaria compared to households that are closer to water [aOR=1.33, 95%CI:1.08-1.63; p-value<0.01]. Conclusions: Environmental factors have been found to influence malaria morbidity among children in SSA. Intervention should be targeted at households especially those that are closer to water with more children under five to ensure full access and use of ITNs among all children under five as part of the overall goal of achieving the health-related SDGs.

Effects of Climate Change and Urbanization on Vegetation Phenology in the Bucharest Metropolitan Area

Being an essential issue in global warming, the response of urban vegetation to climate change and urbanization has become an increasing concern at both the local and global levels. This study aims to investigate the effect of the urban environment on vegetation phenology for the Bucharest metropolitan area in Romania and to identify the potential climate drivers that influence key phenology in the urban environment. In this study, we comprehensively analyzed the response of urban vegetation phenology shifts due to climate variability and urbanization in the Bucharest metropolitan area from a spatiotemporal perspective during the 2002- 2022 period. Through synergy use of time series of the main climate variables, Air temperature -AT, land surface temperature (LST), and biophysical variables derived from MODIS Terra/Aqua satellite and in-situ data, this study developed a complex statistical and spatial regression analysis. Green space was measured with satellite-derived vegetation indicators Normalized Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI), Net Primary Production (NPP) data, which captures the combined availability of urban parks, street trees, forest, and periurban agricultural areas. Leaf Area Index (LAI) and Photosynthetically active radiation (FPAR) indicators have been used to characterize the effects of meteorological parameters and urbanization impacts on vegetation phenology and their changes. The results show that the response of vegetation phenology to urbanization level and climate parameters variability has a distinct spatiotemporal difference across the urban/periurban gradient. The findings of this study show that the land surface temperature anomalies associated with urbanization-induced climate warming, especially during strong summer heat waves and under urban heat islands alter urban vegetation biophysical properties, directly impacting its phenology shifts. At the metropolitan scale, the urban thermal environment directly impacts vegetation phenology patterns. The quantitative findings of this study are of great importance for understanding the complex impacts of urbanization and climate changes on vegetation phenology and for developing models to predict vegetation phenological changes under future urbanization.

Association between residential greenspace and mental health among cancer survivors in Shanghai, China

BackgroundLiving near and enjoying visually green landscapes is associated with better mental health, but evidence focusing on vulnerable populations (such as cancer survivors) is sparse. The purpose of this study was to explore the association between residential greenspace and anxiety and depressive symptoms among cancer survivors in Shanghai, China. MethodsIn total, 4195 cancer survivors participated in this study from the 2022 Shanghai Cancer Patient Needs Survey. The estimation of residential greenspaces was based on Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The presence and severity of depressive and anxiety symptoms were assessed by using the Patient Health Questionnaire-2 (PHQ-2) and Generalized Anxiety Disorder-2 (GAD-2). The relation between mental health and green space was assessed using the Generalized Additive Model (GAM) after controlling for relevant individual covariates and contextual characteristics. ResultsThe prevalence of anxiety and depression in cancer survivors was 36.2% and 28.3% respectively. After multivariate adjustment, each increase in inter-quartile range (IQR) for NDVI in the 250 m buffer (NDVI-250m) was associated with a decrease in PHQ-2 score (△score (95%CI): 0.018 (−0.034, −0.002)) and GAD-2 score (△score (95%CI): 0.018 (−0.034, −0.002)), respectively. We observed that an increase in IQR for NDVI-250m was associated with a 3.3% (Odds ratio (OR) (95%CI):0.967 (0.943, 0.991)) reduction in anxiety symptoms. More pronounced greenspace-mental health effects were found among young adults (18–65 years) and participants living in suburban areas, compared to young people over 65 and those living in urban areas (P-interaction < 0.05). ConclusionsHigher levels of residential green space are associated with lower risk of depression and anxiety disorders. Our findings will fill the gap in the relationship between green space and mental health among cancer survivors in urban China, and provide new evidence for garden afforestation, community planning and policy-making. To better understand this association, more longitudinal studies are necessary to investigate the mechanisms involved.

Vegetation Greenness Sensitivity to Precipitation and Its Oceanic and Terrestrial Component in Selected Biomes and Ecoregions of the World

In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 139 ecoregions during the period of 2001–2018. This analysis aimed to diagnose the vegetative response of vegetation to the dominant component of precipitation, which is of particular interest considering the hydroclimatic characteristics of each ecoregion, climate variability, and changes in the origin of precipitation that may occur in the context of climate change. The enhanced vegetation index (EVI) was used as an indicator of vegetation greenness. Without consideration of semi-arid and arid regions and removing the role of temperature and radiation, the results show the maximum VGS to precipitation in boreal high-latitude ecoregions that belong to boreal forest/taiga: temperate grasslands, savannas, and shrublands. Few ecoregions, mainly in the Amazon basin, show a negative sensitivity. We also found that vegetation greenness is generally more sensitive to the component that contributes the least to precipitation and is less stable throughout the year. Therefore, most vegetation greenness in Europe is sensitive to changes in PLT and less to PLO. In contrast, the boreal forest/taiga in northeast Asia and North America is more sensitive to changes in PLO. Finally, in most South American and African ecoregions, where PLT is crucial, the vegetation is more sensitive to PLO, whereas the contrast occurs in the northern and eastern ecoregions of Australia.

Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin

ABSTRACT Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.

Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes

Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a simple, but effective “Light and Temperature-Driven Growth model and Double Logistic function fusion algorithm” (LTDG_DL). The empirical basis of the LTDG_DL algorithm was traced from the de Wit crop growth simulation model and the commonly observed nonlinear correlation between the EVI and the Leaf Area Index (LAI). It assimilates the ground daily solar radiation and air temperature to generate seasonal profiles of the empirical LAI and EVI and conducts the within-season calibration of the empirical EVI by adjusting crop growth using cloud-free Landsat EVI observations. The initial date of seedling emergence (DOYini) and the accumulated Growing Degree Days for completion of the vegetative and Flowering stage (FGDDs) were variables to which the algorithm’s accuracy was most sensitive. The variable-constrained optimization of the LTDG_DL algorithm was performed by loading the seedling emergence calendar of local prevailing crops and establishing an FGDD lookup table with an exhaustive sampling without replication method. Compared to temporal interpolation functions and Landsat–MODIS spatiotemporal fusion algorithms, the LTDG_DL algorithm had superior performance in the predictions of the EVI increment slope at the vegetative growth stage, the timing of the peak EVI, and the protection of key Landsat EVI observations over cloud-contaminated and complex landscape agricultural systems. Finally, the advantages and limitations of the LTDG_DL algorithm are discussed.

Associations between residential greenness and air pollution and the incident metabolic syndrome in a Thai worker cohort.

Increasing air pollution and decreasing exposure to greenness may contribute to the metabolic syndrome (MetS). We examined associations between long-term exposure to residential greenness and air pollution and MetS incidence in the Bangkok Metropolitan Region, Thailand. Data from 1369 employees (aged 52-71years) from the Electricity Generating Authority of Thailand cohort from 2002 to 2017 were analyzed. The greenness level within 500m of each participant's residence was measured using the satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The kriging approach was used to generate the average concentration of each air pollutant (PM10, CO, SO2, NO2, and O3) at the sub-district level. The average long-term exposure to air pollution and greenness for each participant was calculated over the same period of person-time. Cox proportional hazards models were used to analyze the greenness-air pollution-MetS associations. The adjusted hazard ratio of MetS was 1.42 (95% confidence interval (CI): 1.32, 1.53), 1.22 (95% CI: 1.15, 1.30), and 2.0 (95% CI: 1.82, 2.20), per interquartile range increase in PM10 (9.5μg/m3), SO2 (0.9ppb), and CO (0.3ppm), respectively. We found no clear association between NDVI or EVI and the incidence of MetS. On the contrary, the incident MetS was positively associated with NDVI and EVI for participants exposed to PM10 at concentrations more than 50μg/m3. In summary, the incidence of MetS was positively associated with long-term exposure to air pollution. In areas with high levels of air pollution, green spaces may not benefit health outcomes.

A linear positive relationship between tree species diversity and forest productivity across forest-dominated natural reserves on a large spatial scale

Exploring diversity-productivity relationships (DPRs) of various ecosystems is essential to forest management and conservation, as it can inform us about the dynamics and future trend of forest productivity and carbon sequestration under the change of species diversity. However, the DPR of natural reserve and its formation mechanisms are poorly understood, although such ecosystem plays a key role in protecting species diversity and maintaining carbon sink and other ecological service functions. By integrating ground survey data of 113,007 trees of 163 tree species from 1029 plots and remotely sensed vegetation data, we investigated the DPR features as well as the mechanisms behind them of 25 forest-dominated national natural reserves across the large part of northeastern China. We found a significant (P < 0.01) positive relationship between species diversity index and aboveground biomass across the analyzed natural reserves on a large spatial scale. In addition, the species diversity index and aboveground biomass obtained from the ground survey data were significantly (P < 0.01) and positively correlated with satellite-derived enhanced vegetation index linked to forest productivity, further confirming the robustness of the linear positive DPR observed. These results indicate that forest productivity and carbon sequestration would probably decline if a decrease in tree species number, highlighting the importance of species diversity protection in natural reserves. A structural equation modeling analysis suggested that heat-related and water-related climate factors played predominant roles in modulating species diversity index and aboveground biomass respectively and hence DPR across the natural reserves studied. Meanwhile, geographical location (i.e. latitude and longitude) can indirectly affect DPR by regulating the climate factors above. The findings of this study can contribute to understanding the DPR and predicting the future trends of forest carbon sink under climate change and thus to sustainable forest management for our study region as well as other similar natural reserves.

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period.

Monitoring long-term vegetation condition dynamics in persistent semi-arid wetland communities using time series of Landsat data

Wetlands in arid and semi-arid regions are characterized by dry- and wet-phase vegetation expression which responds to variable water resources. Monitoring condition trends in these wetlands is challenging because transitions may be rapid and short-lived, and identification of meaningful condition change requires longitudinal study. Remotely-sensed data provide cost effective, multi-decadal information with sufficient temporal and spatial scale to explore wetland condition. In this study, we used a time series of Enhanced Vegetation Index (EVI) derived from 34 years (1988–2021) of Landsat imagery, to investigate the long-term condition dynamics of six broad vegetation groups (communities) in a large floodplain wetland system, the Macquarie Marshes in Australia. These communities were persistently mapped as River Red Gum wetland, Black Box/Coolibah woodland, Lignum shrubland, Semi-permanent wetland, Terrestrial grassland and Terrestrial woodland. We used generalized additive models (GAM) to explore the response of vegetation to seasonality, river flow and climatic conditions. We found that EVI was a useful metric to monitor both wetland condition and response to climatic and hydrological drivers. Wetland communities were particularly responsive to river flow and seasonality, while terrestrial communities were responsive to climate and seasonality. Our results indicate asymptotic condition responses, and therefore evidence of hydrological thresholds, by some wetland communities to river flows. We did not observe a long-term trend of declining condition although an apparent increase in condition variability towards the end of the time series requires continued monitoring. Our remotely-sensed, landscape-scale monitoring approach merits further ground validation. We discuss how it can be used to provide a management tool which continuously assesses short and long-term wetland condition and informs conservation decisions about water management for environmental flows.

Vegetation response to changes in climate across different climate zones in China

Vegetation growth is sensitive to climate change. The complex climate types of China pose great challenges to the sustainable management of vegetation on global change. Therefore, this study used Enhanced Vegetation Index (EVI) as an indicator to explore the spatiotemporal dynamics of vegetation and their driving factors in different climatic zones of China to provide theoretical support for sustainable vegetation management in different climate zones in the future. The results showed that vegetation exhibited considerable clustering patterns in the country, with high and low values concentrated in the eastern and western regions, respectively. From 2001 to 2020, both at regional and pixel scales, vegetation in China showed a significant greening trend. EVI displayed a noticeable increase within temperate and subtropical areas. The only exception is observed in the eastern coastal area of the North China Plain and Yangtze River Delta region, which experienced evident degradation trend. During this period, China's climate showed an overall trend towards warming and humidification with drying trends observed mainly over the western regions. The impact of climate changes resulted in EVI dynamics that vary over time and space. The vegetation change in China was mainly derived by changes in precipitation and radiation rather than temperature, especially in temperate and subfrigid regions. Precipitation was the main driving factor for vegetation greening in tropical and temperate regions, while radiation and temperature were the dominant climate factor for vegetation greening in subfrigid and subtropical regions, respectively. When precipitation was no longer a limiting factor for vegetation growth, the effect of temperature or radiation increases. In addition, the positive impact of precipitation on plant growth in temperate regions was much greater than that of radiation and temperature, and this difference was much greater than in tropical, subtropical, and subfrigid regions.

Residential greenness mitigates mortality risk from short-term airborne particulate exposure: An individual-level case-crossover study

BackgroundStudies suggested that greenness could reduce death risks related to ambient exposure to particulate matter (PM), while the available evidence was mixed across the globe and substantially exiguous in low- and middle-income countries. By conceiving an individual-level case-crossover study in central China, this analysis primarily aimed to quantify PM-mortality associations and examined the modification effect of greenness on the relationship. MethodsWe investigated a total of 177,058 nonaccidental death cases from 12 counties in central China, 2008–2012. Daily residential exposures to PM2.5 (aerodynamic diameter <2.5 µm), PMc (aerodynamic diameter between 2.5 and 10 µm), and PM10 (aerodynamic diameter <10 µm) were assessed at a 1 × 1-km resolution through satellite-derived machine-learning models. Residential surrounding greenness was assessed using satellite-derived enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) at multiple buffer sizes (250, 500, and 1000 m). To quantify the acute mortality risks associated with short-term exposure to PM2.5, PMc, and PM10, a time-stratified case-crossover design was utilized in conjunction with a conditional logistic regression model in our main analyses. To investigate the effect modification of greenness on PM-mortality associations, we grouped death cases into low, medium, and high greenness levels using cutoffs of 25th and 75th percentiles of NDVI or EVI exposure, and examined potential effect heterogeneity in PM-related mortality risks among these groups. ResultsMean concentrations (standard deviation) on the day of death were 73.8 (33.4) μg/m3 for PM2.5, 43.9 (17.3) μg/m3 for PMc, and 117.5 (44.9) μg/m3 for PM10. Size-fractional PM exposures were consistently exhibited significant associations with elevated risks of nonaccidental and circulatory mortality. For every increase of 10-μg/m3 in PM exposure, percent excess risks of nonaccidental and circulatory mortality were 0.271 (95% confidence interval [CI]: 0.010, 0.533) and 0.487 (95% CI: 0.125, 0.851) for PM2.5 at lag-01 day, 0.731 (95% CI: 0.108, 1.359) and 1.140 (95% CI: 0.267, 2.019) for PMc at lag-02 day, and 0.271 (95% CI: 0.010, 0.533) and 0.386 (95% CI: 0.111, 0.662) for PM10 at lag-01 day, respectively. Compared to participants in the low-level greenness areas, those being exposed to higher greenness were found to be at lower PM-associated risks of nonaccidental and circulatory mortality. Consistent evidence for alleviated risks in medium or high greenness group was observed in subpopulations of female and younger groups (age <75). ConclusionsShort-term exposure to particulate air pollution was associated with elevated risks of nonaccidental and circulatory death, and individuals residing in higher neighborhood greenness possessed lower risk of PM-related mortality. These findings emphasized the potential public health advantages through incorporating green spaces into urban design and planning.

Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020

The widespread distribution of karst landforms has led to a shortage of water resources in Southwest China. Understanding the spatiotemporal variations in and driving factors of evapotranspiration (ET) in this area is crucial for understanding and predicting severe water resource shortage. This study conducted trend analysis using meteorological data from 2003 to 2020 as well as remote sensing products such as Penman–Monteith–Leuning equation version 2 (PML-V2) ET. The factors influencing the spatial distribution pattern of average ET were identified using a geographical detector. Partial correlation analysis was performed to characterize the relationships between ET and the factors governing its variations, determined using the random forest model. The results demonstrated the following: (1) The average ET decreased with increasing latitude and altitude, primarily affected by the landform type in terms of longitude and displaying “W”-shaped fluctuations. Overall, the annual ET exhibited a significant (p &lt; 0.05) increasing trend, with 72.63% of its area under the increasing trend. (2) The results of the geographic detector indicated sunshine duration as the strongest explanatory factor of the spatial distribution of ET, followed by enhanced vegetation index (EVI), landform type, precipitation, elevation, slope, and aspect. Instead of an individual factor, the interplay between multiple factors more considerably influenced the spatial distribution pattern of ET. (3) The EVI exhibited an overall increasing trend, with a significant increase over 73.59% of the study area and a positive correlation with ET. Thus, the increase in EVI had the strongest impact on ET in the study area, which was further confirmed by the results of the random forest model for 42.92% of the study area. Thus, the present findings clarify the spatiotemporal variations in and driving factors of ET in Southwest China and can serve as a benchmark for policies aiming to develop and manage water resources in this region.

Enhancing resilience against geological hazards and soil erosion through sustainable vegetation management: A case study in Shaanxi Province

As an important component of terrestrial ecosystems, vegetation performs a necessary function in linking the atmosphere, hydrosphere, biosphere as well as soil sphere. The response of vegetation to its driving factors has been a hot research issue that needs to be further explored. Here, the temporal and spatial changes in vegetation cover in Shaanxi Province from 2000 to 2020 was characterized, and the responses to topographic elements, climate factors, geological hazards, and human activities was addressed. The outcomes proved that the average annual vegetation coverage in Shaanxi Province displayed a slight upward trend with a growing rate of 0.0545/10a. Specifically, enhanced vegetation index (EVI) increased significantly in most parts of the study area, while EVI decreased significantly in the border areas of Xi'an and Xianyang. There are differences in the effects of the driving factors on vegetation change during the study period. According to correlation analysis, vegetation was positively associated with temperature and negatively associated with precipitation. In addition, when the elevation was under 2500 m, EVI increased with elevation, while when the elevation used to be above 2500 m, EVI decreased with elevation. Moreover, geological disasters and soil erosion are the major driving factors in the reduction of vegetation cover. Human activities and national policies are the main driving factors for vegetation recovery in the study area. The results of the study are of great significance for ecosystem monitoring and provide a scientific basis for ecosystem restoration.

Warming and increasing precipitation induced greening on the northern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) has a fragile ecosystem that is sensitive to climate change. Due to the amplifying effect of climate change, the QTP has experienced rapid warming and shifting precipitation in recent decades, profoundly impacting the local ecosystem. However, the specific details of how vegetation responds to these changes were unclear, and the corresponding contributions were poorly quantified. Here, we employed an elastic net regression model to investigate the sensitivity of vegetation to climate factors across multiple time scales and various seasons. The vegetation activity was represented by the enhanced vegetation index (EVI), while climate change was represented by temperature, precipitation, photosynthetically active radiation (PAR), and soil moisture fraction (SMF). During 2000–2020, approximately 50 % of the QTP area showed greening, mainly concentrated in the northern region. Climate change explained approximately 70 % of the variation in vegetation during the growing season, 39 % in spring and 66 % in autumn. Grasslands exhibited the highest sensitive to climate change, with a relative contribution of 83 %, followed by mixed forests (70 %), forests (53 %) and deserts (52 %). Both temperature and precipitation significantly affected vegetation, with relative contributions of 29 % and 22 %, respectively, during the growing season. PAR and SMF had less impact on vegetation, with relative contributions of 8 % and 12 %, respectively. In the greening region, precipitation (26 %) was more important for vegetation growth compared to temperature (25 %). These findings emphasize the importance of precipitation on vegetation on the QTP, providing valuable insights for improving regional ecosystem assessment model and promoting the restoration of fragile ecosystems.