Association between greenery and health indicators in urban patients with symptomatic heart failure: a retrospective cohort study in Lithuania

Urban green spaces benefit physical, mental health, and reduses the risk of cardiovascular disease. A study in Kaunas, Lithuania collected health data from 100 patients with symptomatic heart failure (HF) during 2006-2009. Residential greenness was measured by the normalized difference vegetation index (NDVI). We assessed the impact of greenness on health indicators and on changes in health markers after 6 months. Higher greenness levels based on the NDVI 1-km radius were related to higher mean values of heart rate (HR) and ejection fraction and lower left ventricular (LV) end-diastolic diameter index (LV EDDI), LV end-systolic volume (ESV), left atrium size (LAS), and right atrium size (RAS) at baseline. After 6 months, a decrease in DBP and HR and an improvement in spiroergometric parameters were associated with exposure to high levels of greenness.  The long-term rehabilitation group experienced significant changes in spiroergometric indicators. The results confirm that the greenness of the residential environment can improve health indicators in patients with HF.

Community-based assessment of the dynamics of urban landscape characteristics and ecosystem services in the rainforest and guinea savanna ecoregions of Nigeria

Understanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI). A household survey involving the administration of a semi-structured questionnaire to 1552 participants was conducted. Diminishing NDVI and increasing NDBI were observed due to the rising trend of urban expansion, corroborating the perception of over 54% of the respondents who noted a decline in landscape ecological health. Residential expansion, agricultural practices, transport and infrastructural development, and fuelwood production were recognised as the principal drivers of landscape changes. Climate variability/change reportedly makes a 28.5%-34.4% (Negelkerke R2) contribution to the changing status of natural landscapes in Akure and Makurdi as modelled by multinomial logistic regression, while population growth/in-migration and economic activities reportedly account for 19.9%-36.3% in Owerri and Minna. Consequently, ecosystem services were perceived to have declined in their potential to regulate air and water pollution, reduce soil erosion and flooding, and mitigate urban heat stress, with a corresponding reduction in access to social services. We recommend that urban residents be integrated into management policies geared towards effectively developing and enforcing urban planning regulations, promoting urban afforestation, and establishing sustainable waste management systems.

Analysis of spatial and temporal changes in vegetation cover and its drivers in the Aksu River Basin, China

Exploring vegetation dynamics in arid areas and their responses to different natural and anthropogenic factors is critical for understanding ecosystems. Based on the monthly MOD13Q1 (250 m) remote sensing data from 2000 to 2019, this study analyzed spatio-temporal changes in vegetation cover in the Aksu River Basin and predicted future change trends using one-dimensional linear regression, the Mann-Kendall test, and the Hurst index. Quantitative assessment of the magnitude of anthropogenic and natural drivers was performed using the Geodetector model. Eleven natural and anthropogenic factors were quantified and analyzed within five time periods. The influence of the driving factors on the changes in the normalized difference vegetation index (NDVI) in each period was calculated and analyzed. Four main results were found. (1) The overall vegetation cover in the region significantly grew from 2000 to 2019. The vegetation cover changes were dominated by expected future improvements, with a Hurst index average of 0.45. (2) Land use type, soil moisture, surface temperature, and potential vapor dispersion were the main drivers of NDVI changes, with annual average q-values above 0.2. (3) The driving effect of two-factor interactions was significantly greater than that of single factors, especially land use type interacts with other factors to a greater extent on vegetation cover. (4) The magnitude of the interaction between soil moisture and potential vapor dispersion and the magnitude of the interaction between anthropogenic factors and other factors showed an obvious increasing trend. Current soil moisture and human activities had a positive influence on the growth of vegetation in the area. The findings of this study are important for ecological monitoring and security as well as land desertification control.

Revealing climatic and groundwater impacts on the spatiotemporal variations in vegetation coverage in marine sedimentary basins of the North China Plain, China

The North China Plain (NCP) is one of the three great plains in China and also serves as a vital region for grain, cotton, and oil production. Under the influence of regional hydrothermal changes, groundwater overexploitation, and seawater intrusion, the vegetation coverage is undergoing continuous alterations. However, a comprehensive assessment of impacts of precipitation, temperature, and groundwater on vegetation in marine sedimentary regions of the NCP is lacking. Heilonggang Basin (HB) is located in the low-lying plain area in the east of NCP, which is part of the NCP. In this study, the HB was chosen as a typical area of interest. We collected a series of data, including the Normalized Difference Vegetation Index (NDVI), precipitation, temperature, groundwater depth, and Total Dissolved Solids (TDS) from 2001 to 2020. Then the spatiotemporal variation in vegetation was analyzed, and the underlying driving mechanisms of vegetation variation were explored in this paper. The results show that NDVI experiences a rapid increase from 2001 to 2004, followed by stable fluctuations from 2004 to 2020. The vegetation in the HB has achieved an overall improvement in the past two decades, with 76% showing improvement, mainly in the central and eastern areas, and 24% exhibiting deterioration in other areas. From 2001 to 2020, NDVI correlates positively with precipitation, whereas its relationship with temperature fluctuates between positive and negative, and is not statistically significant. There is a threshold for the synergistic change of NDVI and groundwater depth. When the groundwater depth is lower than 3.8 m, NDVI increases sharply with groundwater depth. However, beyond this threshold, NDVI tends to stabilize and fluctuate. In the eastern coastal areas, NDVI exhibits a strong positive correlation with groundwater depth, influenced by the surface soil TDS controlled by groundwater depth. In the central regions, a strong negative correlation is observed, where NDVI is primarily impacted by soil moisture under the control of groundwater. In the west and south, a strong positive correlation exists, with NDVI primarily influenced by the intensity of groundwater exploitation. Thus, precipitation and groundwater are the primary driving forces behind the spatiotemporal variability of vegetation in the HB, while in contrast, the influence of temperature is uncertain. This study has elucidated the mechanism of vegetation response, providing a theoretical basis for mitigating adverse factors affecting vegetation growth and formulating rational water usage regulations in the NCP.

Why do avian responses to change in Arctic green-up vary?

Global climate change has altered the timing of seasonal events (i.e., phenology) for a diverse range of biota. Within and among species, however, the degree to which alterations in phenology match climate variability differ substantially. To better understand factors driving these differences, we evaluated variation in timing of nesting of eight Arctic-breeding shorebird species at 18 sites over a 23-year period. We used the Normalized Difference Vegetation Index as a proxy to determine the start of spring (SOS) growing season and quantified relationships between SOS and nest initiation dates as a measure of phenological responsiveness. Among species, we tested four life history traits (migration distance, seasonal timing of breeding, female body mass, expected female reproductive effort) as species-level predictors of responsiveness. For one species (Semipalmated Sandpiper), we also evaluated whether responsiveness varied across sites. Although no species in our study completely tracked annual variation in SOS, phenological responses were strongest for Western Sandpipers, Pectoral Sandpipers, and Red Phalaropes. Migration distance was the strongest additional predictor of responsiveness, with longer-distance migrant species generally tracking variation in SOS more closely than species that migrate shorter distances. Semipalmated Sandpipers are a widely distributed species, but adjustments in timing of nesting relative to variability in SOS did not vary across sites, suggesting that different breeding populations of this species were equally responsive to climate cues despite differing migration strategies. Our results unexpectedly show that long-distance migrants are more sensitive to local environmental conditions, which may help them to adapt to ongoing changes in climate.

Intellligent sustainable agricultural water practice using multi sensor spatiotemporal evolution

The amount of water taken from non-renewable resources such as aquifers to fulfill irrigation requirements is rarely monitored, putting sustainable agriculture under threat in the face of changing climate. In the present research, an attempt was made to apply multi-sensor (Landsat suite, GRACE, GRACE-FO) satellite data to monitor spatiotemporal evolution of agriculture for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). For this purpose, time series of NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), and MSAVI2 (Modified Soil-Adjusted Vegetation 2) was utilized to assess vegetation pattern change in the study area. The present investigation used High-resolution Planetscope (PS) nanosatellite data to validate the vegetation results. Mann Kendall trend analysis and linear regression were performed to study the temporal pattern, and the relationship between vegetation, GRACE, and climate variables was performed from 1984 to 2020. Water extraction based on the averaged value of JPL GWS and CSR GWS showed a decreasing trend of -10.24 ± 1.4 mm/year from 2003-2020. The annual rainfall showed a decreasing trend, while the annual temperature showed an increasing trend from 1982-2020. The correlation of vegetation indices with rainfall of one-month lag showed a significantly better relationship of 0.74, 0.74, and 0.75, respectively, for NDVI, SAVI, and MSAVI2. The correlation between temperature and all three vegetation indices is a strong negative correlation: -0.85 for NDVI and -0.9 for SAVI and MSAVI.

3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications.

The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).

Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

Monitoring and evaluation of vegetation restoration in the Ebinur Lake Wetland National Nature Reserve under lockdown protection

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Responses of vegetation cover to hydro-climatic variations in Bosten Lake Watershed, NW China

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9-16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake's level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake's water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.

Different greenness exposure in Europe and respiratory outcomes in youths. A systematic review and meta-analysis

The existing evidence on the association between greenness and respiratory outcomes remains inconclusive. We aimed at systematically summarizing existing literature on greenness exposure and respiratory outcomes in European children and adolescents, with a preliminary attempt to qualify the distribution of dominant tree species across different geographical areas and bioclimatic regions. Overall, 4049 studies were firstly identified by searching PubMed/MEDLINE, EMBASE, Scopus, Web of Science, GreenFile and CAB direct, up to 29 August 2023. Eighteen primary studies were included in the systematic review and six were meta-analyzed. No overall significant association was observed between the Normalized Difference Vegetation Index, assessed within 500-m buffers (i.e. NDVI-500), and the odds of asthma for 0.3-increase in the exposure (OR: 0.97, 95% CI from 0.53 to 1.78). Similarly, an overall exposure to the NDVI-300 highest tertile, as compared to the lowest tertile, was not significantly associated with asthma (OR: 0.65, 95% CI from 0.22 to 1.91): heterogeneity among studies was significant (p = 0.021). We delineated some key elements that might have mostly contributed to the lack of scientific consensus on this topic, starting from the urgent need of harmonized approaches for the operational definition of greenness. Additionally, the complex interplay between greenness and respiratory health may vary across different geographical regions and climatic conditions. At last, the inconsistent findings may reflect the heterogeneity and complexity of this relationship, rather than a lack of scientific consensus itself. Future research should compare geographical areas with similar bioclimatic parameters and dominant or potentially present vegetation species, in order to achieve a higher inter-study comparability.

Associations between community green view index and fine particulate matter from Airboxes

Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012-2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha-1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (-25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

Decadal-scale variability and warming affect spring timing and forest growth across the western Great Lakes region

The Great Lakes region of North America has warmed by 1-2 °C on average since pre-industrial times, with the most pronounced changes observable during winter and spring. Interannual variability in temperatures remains high, however, due to the influence of ocean-atmosphere circulation patterns that modulate the warming trend across years. Variations in spring temperatures determine growing season length and plant phenology, with implications for whole ecosystem function. Studying how both internal climate variability and the "secular" warming trend interact to produce trends in temperature is necessary to estimate potential ecological responses to future warming scenarios. This study examines how external anthropogenic forcing and decadal-scale variability influence spring temperatures across the western Great Lakes region and estimates the sensitivity of regional forests to temperature using long-term growth records from tree-rings and satellite data. Using a modeling approach designed to test for regime shifts in dynamic time series, this work shows that mid-continent spring climatology was strongly influenced by the 1976/1977 phase change in North Pacific atmospheric circulation, and that regional forests show a strengthening response to spring temperatures during the last half-century.

Exposure to green spaces, cardiovascular risk biomarkers and incident cardiovascular disease in older adults: The Seniors-Enrica II cohort

INTRODUCTION

The impact of residential green spaces on cardiovascular health in older adults remains uncertain.

METHODS

Cohort study involving 2114 adults aged ≥ 65 years without cardiovascular disease (CVD), residing in five dense municipalities (Prince et al., 2015) of the Madrid region and with detailed characterization of their socioeconomic background, health behaviors, CVD biological risk factors, and mental, physical, and cognitive health. Greenness exposure was measured using the Normalized Difference Vegetation Index (NDVI) at varying distances from participants' homes. Traffic exposure, neighborhood environment, neighborhood walkability, and socioeconomic deprivation at the census level were also assessed. Serum N-terminal pro-B-type natriuretic peptide (NT-ProBNP), high-sensitivity troponin T (hs-TnT), interleukin 6 (IL-6), and Growth Differentiation Factor 15 (GDF-15) were measured at baseline, and incident CVD events identified through electronic medical records (International Classification of Primary Care-2 codes K74, K75, K77, K90, and K92).

RESULTS

After adjusting for sex, age, educational attainment, financial hardship and socioeconomic deprivation at the census level, an interquartile range (IQR) increase in NDVI at 250, 500, 750, and 1000 m around participants' homes was associated with mean differences in ProBNP of -5.56 % (95 %CI: -9.77; -1.35), -5.05 % (-9.58; -0.53), -4.24 % (-8.19, -0.19), and -4.16 % (-7.59; -0.74), respectively; and mean differences in hs-TnT among diabetic participants of -8.03 % (95 %CI: -13.30; -2.77), -9.52 % (-16.08; -2.96), -8.05 % (-13.94, -2.16) and -5.56 % (-10.75; -0.54), respectively. Of similar magnitude, although only statistically significant at 250 and 500 m, were the observed lower IL-6 levels with increasing greenness. GDF-15 levels were independent of NDVI. In prospective analyses (median follow-up 6.29 years), an IQR increase in residential greenness at 500, 750, and 1000 m was associated with a lower risk of incident CVD. The variables that contributed most to the apparent beneficial effects of greenness on CVD were lower exposure to traffic, improved cardiovascular risk factors, and enhanced physical performance. Additionally, neighborhood walkability and increased physical activity were notable contributors among individuals with diabetes.

CONCLUSION

Increased exposure to residential green space was associated with a moderate reduction in CVD risk in older adults residing in densely populated areas.

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.

Beneficial impacts of residential greenness on sleep deprivation in adults aged 19 or older living in South Korea: A nationwide community health survey in 2011-2018

BACKGROUND

Evidence of the relationship between greenness and sleep is limited, and, given the worsening sleep insufficiency worldwide, this relationship needs elucidation. In this study, we investigated the association of greenness with sleep deprivation using nationwide survey data.

METHODS

This study included 1,727,273 participants in the Korea Community Health Survey who resided in all 229 districts of South Korea from 2011 to 2018. Sleep deprivation variables were defined as strong deprivation or mild deprivation, based on average daily sleep duration of <5 or 5-6 h, respectively. District-specific annual average of satellite-derived enhanced vegetation index (EVI) was used as a green space exposure. A logistic regression with complex survey weights was used to estimate the association between greenness and sleep deprivation, and it was further examined by sex, age group, educational status, income level, and population density. The regression analysis was performed annually, and the annual estimates were pooled by a combined data analysis.

RESULTS

A higher level of greenness was associated (odds ratio [95 % confidence interval]) with strong and mild sleep deprivation (0.96 [0.93-0.99] and 0.96 [0.95-0.97]), respectively, and males and the younger age group (<65 years) showed a more prominent association with greenness than in females and the elderly group (65 years or older). In addition, only high-population-density areas showed evident associations of greenness with both strong and mild sleep deprivation.

CONCLUSIONS

This large population-based study provides important epidemiological evidence for improving sleep quantity through an increase in greenness exposure and supports policymakers in establishing strategies for urban planning.

Exposure to urban green spaces and mental health during the COVID-19 pandemic: evidence from two low and lower-middle-income countries

Introduction

The COVID-19 pandemic has had a significant impact on mental health globally, with limited access to mental health care affecting low- and middle-income countries (LMICs) the most. In response, alternative strategies to support mental health have been necessary, with access to green spaces being a potential solution. While studies have highlighted the role of green spaces in promoting mental health during pandemic lockdowns, few studies have focused on the role of green spaces in mental health recovery after lockdowns. This study investigated changes in green space access and associations with mental health recovery in Bangladesh and Egypt across the pandemic.

Methods

An online survey was conducted between January and April 2021 after the first lockdown was lifted in Bangladesh (n = 556) and Egypt (n = 660). We evaluated indoor and outdoor greenery, including the number of household plants, window views, and duration of outdoor visits. The quantity of greenness was estimated using the normalized difference vegetation index (NDVI). This index was estimated using satellite images with a resolution of 10x10m during the survey period (January-April 2021) with Sentinel-2 satellite in the Google Earth Engine platform. We calculated averages within 250m, 300m, 500m and 1000m buffers of the survey check-in locations using ArcGIS 10.3. Multiple linear regression models were used to evaluate relationships between changes in natural exposure and changes in mental health.

Results

The results showed that mental health improved in both countries after the lockdown period. People in both countries increased their time spent outdoors in green spaces after the lockdown period, and these increases in time outdoors were associated with improved mental health. Unexpectedly, changes in the number of indoor plants after the lockdown period were associated with contrasting mental health outcomes; more plants translated to increased anxiety and decreased depression. Refocusing lives after the pandemic on areas other than maintaining indoor plants may assist with worrying and feeling panicked. Still, indoor plants may assist with depressive symptoms for people remaining isolated.

Conclusion

These findings have important implications for policymakers and urban planners in LMICs, highlighting the need to increase access to natural environments in urban areas to improve mental health and well-being in public health emergencies.

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.

Spatiotemporal evolution characteristics and driving forces of vegetation cover variations in the Chengdu-Chongqing region of China under the background of rapid urbanization

The research on the spatiotemporal changes and driving factors of ecosystems in rapidly urbanizing regions has always been a topic of widespread concern. As the fourth pole of China's economic development, the research on the Chengdu-Chongqing region has reference significance for the urbanization process of developing countries such as India, Brazil, and South Africa.The normalized difference vegetation index (NDVI) has been widely applied in studies of plant and ecosystem changes. Based on MODIS NDVI data from 2001 to 2020 and meteorological data of the same period, this study reveals the evolution of NDVI in the Chengdu-Chongqing region from three aspects: the spatiotemporal variation characteristics of NDVI, the prediction of future trends in vegetation coverage, and the response of vegetation to climate change and human activities. During the period of plant growth, the mean NDVI achieved a value of 0.78, and the vegetation coverage rate is increasing year by year. According to the Hurst index, the future NDVI in Chengdu-Chongqing region will tend to decrease, and its trend is opposite to that of the past period of time. The Chengdu-Chongqing region vegetation positively affected by human activities is greater than those negatively affected, and in terms of vegetation degradation, the impact of human activities is greater than climate change.

Green spaces exposure and the risk of common psychiatric disorders: A meta-analysis

Objective

To explore the effects of green spaces exposure on common psychiatric disorders.

Methods

PubMed, Embase, Web of Science and MEDLINE were screened and articles published prior to November 15, 2023 were included. Analyses were performed on common psychiatric disorders, categorized into depression, anxiety, dementia, schizophrenia, and attention deficit hyperactivity disorder (ADHD). And the subgroup analyses were conducted for depression, anxiety, dementia, and schizophrenia.

Results

In total, 2,0064 studies were retrieved, 59 of which were included in our study; 37 for depression, 14 for anxiety, 8 for dementia, 7 for schizophrenia and 5 for ADHD. Green spaces were found to benefit the moderation of psychiatric disorders (OR = 0.91, 95% CI: 0.89 to 0.92). Green spaces positively influence depression (OR = 0.89, 95% CI: 0.86 to 0.93), regardless of the cross-sectional or cohort studies. Green spaces can also help mitigate the risk of anxiety (OR = 0.94, 95%CI:0.92 to 0.96). As an important index for measuring green spaces, a higher normalized difference vegetation index (NDVI) level related to a lower level of depression (OR = 0.95, 95%CI:0.91 to 0.98) and anxiety (OR = 0.95, 95%:0.92 to 0.98). The protection was also found in dementia (OR = 0.95, 95% CI: 0.93 to 0.96), schizophrenia (OR = 0.74, 95% CI: 0.67 to 0.82), and ADHD (OR = 0.89, 95% CI: 0.86 to 0.92) results.

Conclusion

Green spaces decrease the risk of psychiatric disorders, including depression, anxiety, dementia, schizophrenia, and ADHD. Further studies on green spaces and psychiatric disorders are needed, and more green spaces should be considered in city planning.

Impacts of the Three Gorges Dam on riparian vegetation in the Yangtze River Basin under climate change

As the largest hydroelectric project in the world, the Three Gorges Dam (TGD) is expected to have significant environmental and ecological impacts on riparian vegetation in the Yangtze River Basin (YRB). However, existing studies have mainly focused on small segments of the YRB. In addition, few studies have quantified the responses of riparian vegetation to both climatic factors and dam construction. In this study, we investigated riparian vegetation dynamics over the entire YRB before, during, and after the construction of TGD from 1982 to 2015 using the normalized difference vegetation index (NDVI). Furthermore, the effects of climatic factors and dam construction on riparian vegetation were quantitatively analyzed using path analysis. The results demonstrate that the YRB has experienced a generally greening trend after TGD construction. The impacts of climate change on riparian vegetation have exhibited notable spatial heterogeneity and temperature is the main climatic factor that affects riparian vegetation growth. Moreover, TGD becomes the major contributor to riparian vegetation dynamics in the YRB after TGD construction. TGD has not only directly enhanced riparian vegetation but also indirectly affected riparian vegetation by regulating the microclimate. This study highlights the significance of anthropogenic interference when evaluating the relationships between riparian vegetation and climatic factors, providing useful insights for the effective management and conservation of large-scale riparian ecosystems.

Temperate grassland vegetation restoration influenced by grazing exclusion and climate change

Grasslands are one of the most important terrestrial biomes, supporting a wide range of ecological functions and services. Grassland degradation due to overgrazing is a severe issue worldwide, especially in developing regions. However, observations from multiple sources have shown that temperate grasslands in China have significantly increased during the past two decades. It remains controversial what factors have driven the vegetation restoration in this region. In this study, we combined remote-sensing images and field survey datasets to quantify the contributions of different factors to vegetation restoration in six temperate grasslands in northern China. Across the six grasslands, the Normalized Difference Vegetation Index (NDVI) increased by 0.003-0.0319 year-1. The average contributions of grazing exclusion and climate change to the NDVI increase were 49.23 % and 50.77 %, respectively. Precipitation change was the primary climate factor driving vegetation restoration, contributing 50.76 % to the NDVI variance. By contrast, climate warming tended to slow vegetation restoration, and atmospheric CO2 concentration change contributed little to the NDVI increase in the temperate grasslands. These results emphasize the significant contributions of both climate change and human management to grassland vegetation restoration.

Duration and variability of spring green-up mediate population consequences of climate change

Single phenological measures, like the average rate of phenological advancement, may be insufficient to explain how climate change is driving trends in animal populations. Here, we develop a multifactorial concept of spring phenology-including the onset of spring, spring duration, interannual variability, and their temporal changes-as a driver for population dynamics of migratory terrestrial species in seasonal environments. Using this conceptual model, we found that effects of advancing spring phenology on animal populations may be buffered or amplified depending on the duration and interannual variability of spring green-up, and those effects are modified by evolutionary and plastic adaptations of species. Furthermore, we compared our modelling results with empirical data on normalized difference vegetation index-based spring green-up phenology and population trends of 106 European landbird finding similar associations. We conclude how phenological changes are expected to affect migratory bird populations across Europe and identify regions that are particularly prone to suffer population declines.

Mapping soil suitability using phenological information derived from MODIS time series data in a semi-arid region: A case study of Khouribga, Morocco

To address the increasing global demand for food, it is crucial to implement sustainable agricultural practices, which include effective soil management techniques for enhancing productivity and environmental conditions. In this regard, a study was conducted to assess the efficacy of utilizing phenological metrics derived from satellite data in order to map and identify suitable agricultural soil within a semi-arid region. Two distinct methodologies were compared: one based on physicochemical soil parameters and the other utilizing the phenological response of vegetation through the application of the Normalized Difference Vegetation Index (NDVI) Modis-time series. The study findings indicated that the NDVI-based approach successfully identified a specific class of soil suitability for agriculture (referred to as S1) that could not be effectively mapped using the multi-criteria analysis (MCAD) method relying on soil physicochemical parameters. This S1 class of soil suitability accounted for approximately 5 % of the total study area. These outcomes suggest that phenological-based approaches offer greater potential for spatio-temporal monitoring of soil suitability status compared to MCAD, which heavily relies on discrete observations and necessitates frequent updates of soil parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.

Estimation of forest canopy density through Geospatial Technology-a case study on Sathyamangalam Forest, Erode District, Tamil Nadu

The term forest canopy density (FCD) refers to one of the important criteria used to evaluate forest's ecological health. It plays a significant role in assessing the health of the forest and serves as a key landmark for potential management actions. The canopy coverage or crown cover is referred to the percentage of the forest floor that is covered by the vertical projection of tree crowns and necessary for monitoring the condition of the forest. The present study aims to estimate the forest canopy density (FCD) through Geospatial Techniques for Sathyamangalam Forest for the period between 2016 and 2022 with SENTINEL 2A satellite data. The weighted overlay analysis method was implemented with biophysical parameters, namely, Normalize Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI), Shadow Index (SI), and Soil Bareness Index (SBI) to analyze the state of the forest and its activity. The results observed significantly that the forest canopy with 158.60 km2 in 2016 which is increased to 190.37 km2 in 2018 (1.14%) then suddenly decreased to 134.85 km2 in 2020 (2.47%). The forest canopy has recovered some of its original area with 168.83 km2 through better environmental conditions during 2021-2022 (1.52%). Therefore, Geospatial Technology plays a significant role in estimating recent changes in regional forest.

Spatial and Temporal Variations in Richness, Diversity and Abundance of Floral Visitors of Curry Plants (Bergera koenigii L.): Insights on Plant-Pollinator Interactions

The reproductive success of flowering plants relates to flower-visitor communities and plant-pollinator interactions. These traits are species- and region-specific and vary across regions, pollinator groups, and plant species. However, little literature exists on the spatiotemporal variation in visitor activity, especially in India. Here, we aimed to depict the spatial and temporal variation in visitor activity on the curry plants (Bergera koenigii). Data were collected at different daytime slots from three vegetation zones (confirmed by field surveys and normalized difference vegetation index values in remote sensing)-dense, medium-density, and low-density vegetation in West Bengal, India. The visitors' richness, diversity, and abundance were higher in the area with dense vegetation. Considering daytime patterns, higher values for these parameters were obtained during 10.00-14.00 h. For most visitors, the flower handling time was shorter, and the visitation rate was higher in dense vegetation areas (at 10.00-14.00 h) than in medium- and low-density vegetation areas. The proportions of different foraging categories varied over time. Vital pollinators were Apis cerana, Apis dorsata, Appias libythea, Halictus acrocephalus, Nomia iridescens, and Tetragonula iridipennis. However, the effectiveness of pollinators remained region-specific. Therefore, it can be concluded that floral visitors' richness, diversity, abundance, and plant-visitor interactions varied spatially with their surrounding vegetation types and also changed daytime-wise.

Exploiting satellite data for total direct runoff prediction using CN-based MSME model

This paper explores the potential to enhance the functionality of the modified Sahu-Mishra-Eldho model (MSME-CN) using indirect soil moisture measurements derived from satellite data. The current version of the MSME-CN model is not applicable in ungauged watersheds due to the necessity of calibrating the crucial parameter α, which reflects soil saturation, based on measured rainfall-runoff events. We hypothesize that the Normalized Difference Vegetation Index (NDVI) can serve as an indirect indicator of soil moisture to assess the soil saturation parameter α in the MSME model. This hypothesis was tested across five different watersheds, three located in the southeastern USA and two in southern Poland. The NDVI product, developed from data obtained from the Advanced Very High-Resolution Radiometer (AVHRR), was utilized in this study. Results indicate that NDVI is a robust indicator of soil moisture for representing the α parameter in the MSME model. The correlation coefficient between α and NDVI a day prior to a rainfall event was around 0.80 for the WS80 and Kamienica watersheds and nearly 0.60 for the other watersheds. The analysis corroborates the hypothesis that NDVI can serve as an indirect parameter of soil moisture to assess the soil saturation parameter α in the MSME-CN model. Based on Nash-Sutcliffe Efficiency (NSE) statistics, the total direct runoff predicted by the MSME-CN model, with the α parameter updated using NDVI, was rated 'very good' for the WS80 and AC11 watersheds, 'good' for the Kamienica watershed, 'satisfactory' for Stobnica, and 'unsatisfactory' for the high forest density WS14 watershed, potentially highlighting the model's limitation in such watersheds.

Disentangling associations between multiple environmental exposures and all-cause mortality: an analysis of European administrative and traditional cohorts

Background

We evaluated the independent and joint effects of air pollution, land/built environment characteristics, and ambient temperature on all-cause mortality as part of the EXPANSE project.

Methods

We collected data from six administrative cohorts covering Catalonia, Greece, the Netherlands, Rome, Sweden, and Switzerland and three traditional cohorts in Sweden, the Netherlands, and Germany. Participants were linked to spatial exposure estimates derived from hybrid land use regression models and satellite data for: air pollution [fine particulate matter (PM2.5), nitrogen dioxide (NO₂), black carbon (BC), warm season ozone (O3)], land/built environment [normalized difference vegetation index (NDVI), distance to water, impervious surfaces], and ambient temperature (the mean and standard deviation of warm and cool season temperature). We applied Cox proportional hazard models accounting for several cohort-specific individual and area-level variables. We evaluated the associations through single and multiexposure models, and interactions between exposures. The joint effects were estimated using the cumulative risk index (CRI). Cohort-specific hazard ratios (HR) were combined using random-effects meta-analyses.

Results

We observed over 3.1 million deaths out of approximately 204 million person-years. In administrative cohorts, increased exposure to PM2.5, NO2, and BC was significantly associated with all-cause mortality (pooled HRs: 1.054, 1.033, and 1.032, respectively). We observed an adverse effect of increased impervious surface and mean season-specific temperature, and a protective effect of increased O3, NDVI, distance to water, and temperature variation on all-cause mortality. The effects of PM2.5 were higher in areas with lower (10th percentile) compared to higher (90th percentile) NDVI levels [pooled HRs: 1.054 (95% confidence interval (CI) 1.030-1.079) vs. 1.038 (95% CI 0.964-1.118)]. A similar pattern was observed for NO2. The CRI of air pollutants (PM2.5 or NO2) plus NDVI and mean warm season temperature resulted in a stronger effect compared to single-exposure HRs: [PM2.5 pooled HR: 1.061 (95% CI 1.021-1.102); NO2 pooled HR: 1.041 (95% CI 1.025-1.057)]. Non-significant effects of similar patterns were observed in traditional cohorts.

Discussion

The findings of our study not only support the independent effects of long-term exposure to air pollution and greenness, but also highlight the increased effect when interplaying with other environmental exposures.

Environmental flow assessment for intermittent rivers supporting the most poleward mangroves

The most vulnerable and dynamic ecosystems in terms of response to climate change and fluctuations in hydrological conditions are mangroves, particularly those located on the edge of their latitudinal range limits. The four primary Iranian mangrove forest sites: Nayband, Qeshm, Gabrik, and Govatr, located in the northern part of the Persian Gulf and the Gulf of Oman already exist near the limit of their tolerance to extreme temperature, precipitation, and salinity. Due to extreme climate conditions at these locations, the mangrove trees are usually smaller and less dense as compared with mangroves closer to the equator complicating their monitoring and mapping efforts. Despite the growing attention to the ecological benefits of mangrove forests and their importance in climate change mitigation, there are still a few studies on these marginal mangroves. Therefore, we investigated whether the variation in mangrove ecosystem health is related to the changes in physical parameters and differs between estuarine and sea-side locations. We developed a comprehensive database on NDVI values, associated rainfall, temperature, and river flow based on in-situ and remote sensing measurements. By understanding the normal hydrologic patterns that control the distribution and growth of mangroves in arid and semi-arid regions, we are questioning the need for environmental flow allocation to restore mangrove ecosystem health. This brings us to the second gap in the literature and the need for further studies on Environmental Flow assessment for intermittent and ephemeral rivers. Alike other mangroves studied, forests showed greenness seasonality, positively correlated with rainfall, and negatively correlated with temperature. As there was no clear difference between estuarine and marine sites, freshwater influence in the form of river flow, unlike temperature, cannot be considered a major limiting factor. Nevertheless, during prolonged droughts mangroves could benefit from the recommended allocation of Environmental Flow during the cold period (November-March).

Dynamic Evolution of Aquaculture along the Bohai Sea Coastline and Implications for Eco-Coastal Vegetation Restoration Based on Remote Sensing

The expansion and intensification of coastal aquaculture around the Bohai Sea in China has reduced the tidal flats and damaged the coastal vegetation environment. However, there are few studies on the relationship between the evolution of coastal aquaculture and the variability of coastal vegetation, which limits our understanding of the impact of human activities on the coastal ecosystem. In this study, based on remote sensing technology, we firstly used a combination of a neural network classifier and manual correction to monitor the long-term dynamic changes in aquaculture in the Bohai Sea from 1984 to 2022. We then analyzed its evolution, as well as the relationship between the evolution of coastal aquaculture and the variability of coastal vegetation, in detail. Our study had three main conclusions. Firstly, the aquaculture along the coast of the Bohai Sea showed an expanding trend from 1984 to 2022, with an increase of 538%. Secondly, the spatiotemporal changes in the aquaculture centroids in different provinces and cities varied. The centroid of aquaculture in Liaoning Province was mainly distributed in the Liaodong Peninsula, and moved northwest; that in Hebei Province was distributed in the northeast and moved with no apparent pattern; the centroid of aquaculture in Tianjin was mainly distributed in the southeast and moved westward; and the centroid of aquaculture in Shandong Province was mainly distributed in the northwest and moved in a northwesterly direction. Finally, the expansion of aquaculture of the Bohai Sea has increased the regional NDVI and length of the corresponding coastline, and has made coastlines move toward the sea. Our results provide reliable data support and reference for ecologically managing aquaculture and coastal environmental protection in the Bohai Sea.

Long-term changes in the daytime growing season carbon dioxide exchange following increased temperature and snow cover in arctic tundra

Increasing temperatures and winter precipitation can influence the carbon (C) exchange rates in arctic ecosystems. Feedbacks can be both positive and negative, but the net effects are unclear and expected to vary strongly across the Arctic. There is a lack of understanding of the combined effects of increased summer warming and winter precipitation on the C balance in these ecosystems. Here we assess the short-term (1-3 years) and long-term (5-8 years) effects of increased snow depth (snow fences) (on average + 70 cm) and warming (open top chambers; 1-3°C increase) and the combination in a factorial design on all key components of the daytime carbon dioxide (CO2 ) fluxes in a wide-spread heath tundra ecosystem in West Greenland. The warming treatment increased ecosystem respiration (ER) on a short- and long-term basis, while gross ecosystem photosynthesis (GEP) was only increased in the long term. Despite the difference in the timing of responses of ER and GEP to the warming treatment, the net ecosystem exchange (NEE) of CO2 was unaffected in the short term and in the long term. Although the structural equation model (SEM) indicates a direct relationship between seasonal accumulated snow depth and ER and GEP, there were no significant effects of the snow addition treatment on ER or GEP measured over the summer period. The combination of warming and snow addition turned the plots into net daytime CO2 sources during the growing season. Interestingly, despite no significant changes in air temperature during the snow-free time during the experiment, control plots as well as warming plots revealed significantly higher ER and GEP in the long term compared to the short term. This was in line with the satellite-derived time-integrated normalized difference vegetation index of the study area, suggesting that more factors than air temperature are drivers for changes in arctic tundra ecosystems.

Thirty-six years of butterfly monitoring, snow cover, and plant productivity reveal negative impacts of warmer winters and increased productivity on montane species

Climate change is contributing to declines of insects through rising temperatures, altered precipitation patterns, and an increasing frequency of extreme events. The impacts of both gradual and sudden shifts in weather patterns are realized directly on insect physiology and indirectly through impacts on other trophic levels. Here, we investigated direct effects of seasonal weather on butterfly occurrences and indirect effects mediated by plant productivity using a temporally intensive butterfly monitoring dataset, in combination with high-resolution climate data and a remotely sensed indicator of plant primary productivity. Specifically, we used Bayesian hierarchical path analysis to quantify relationships between weather and weather-driven plant productivity on the occurrence of 94 butterfly species from three localities distributed across an elevational gradient. We found that snow pack exerted a strong direct positive effect on butterfly occurrence and that low snow pack was the primary driver of reductions during drought. Additionally, we found that plant primary productivity had a consistently negative effect on butterfly occurrence. These results highlight mechanisms of weather-driven declines in insect populations and the nuances of climate change effects involving snow melt, which have implications for ecological theories linking topographic complexity to ecological resilience in montane systems.

An evaluation framework for quantifying vegetation loss and recovery in response to meteorological drought based on SPEI and NDVI

Drought affects vegetation growth to a large extent. Understanding the dynamic changes of vegetation during drought is of great significance for agricultural and ecological management and climate change adaptation. The relations between vegetation and drought have been widely investigated, but how vegetation loss and restoration in response to drought remains unclear. Using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) data, this study developed an evaluation framework for exploring the responses of vegetation loss and recovery to meteorological drought, and applied it to the humid subtropical Pearl River basin (PRB) in southern China for estimating the loss and recovery of three vegetation types (forest, grassland, cropland) during drought using the observed NDVI changes. Results indicate that vegetation is more sensitive to drought in high-elevation areas (lag time < 3 months) than that in low-elevation areas (lag time > 8 months). Vegetation loss (especially in cropland) is found to be more sensitive to drought duration than drought severity and peak. No obvious linear relationship between drought intensity and the extent of vegetation loss is found. Regardless of the intensity, drought can cause the largest probability of mild loss of vegetation, followed by moderate loss, and the least probability of severe loss. Large spatial variability in the probability of vegetation loss and recovery time is found over the study domain, with a higher probability (up to 50 %) of drought-induced vegetation loss and a longer recovery time (>7 months) mostly in the high-elevation areas. Further analysis suggests that forest shows higher but cropland shows lower drought resistance than other vegetation types, and grassland requires a shorter recovery time (4.2-month) after loss than forest (5.1-month) and cropland (4.8-month).

Residential greenspace and blood lipids in an essential hypertension population: Mediation through PM2.5 and chemical constituents

Fine particulate matter (PM2.5) adversely affects blood lipids, while residential greenspace exposure may improve blood lipids levels. However, the association between exposure to residential greenspace and blood lipids has not been adequately studied, especially in vulnerable populations (e.g. people with essential hypertension). This study aimed to assess the association between residential greenspace exposure and blood lipids, and to clarify whether PM2.5 and chemical constituents was mediator of it. We used a period (May 2010 to December 2011) from the Chinese national hypertension project. The residential greenspace was estimated using satellite-derived normalized difference vegetation index (NDVI). The generalized additive mixed model (GAMM) was used to assess the association between exposure to residential greenspace and blood lipids, and the mediation model was used to examine whether there was a mediating effect of PM2.5 and chemical constituents on that association. The exposure to residential greenspace was negatively associated with the decreased risk of dyslipidemia, especially short-term exposure. For example, the odd ratioshort-term for dyslipidemia was 0.915 (95% CI：0.880 to 0.950). This association was strengthened by physical activity and participants living in the North. PM2.5 and chemical constituents were important mediators in this association, with the proportion of mediators ranging from -5.02% to 26.33%. The association between exposure to residential greenspace and dyslipidemia in this essential hypertensive population, especially participants living in the North and doing daily physical activity, was mediated by PM2.5 and chemical constituents.

Longitudinal Impacts of Precision Greenness on Alzheimer's Disease

BACKGROUND

The potential for greenness as a novel protective factor for Alzheimer's disease (AD) requires further exploration.

OBJECTIVES

This study assesses prospectively and longitudinally the association between precision greenness - greenness measured at the micro-environmental level, defined as the Census block - and AD incidence.

DESIGN

Older adults living in consistently high greenness Census blocks across 2011 and 2016 were compared to those living in consistently low greenness blocks on AD incidence during 2012-2016.

SETTING

Miami-Dade County, Florida, USA.

PARTICIPANTS

230,738 U.S. Medicare beneficiaries.

MEASUREMENTS

U.S. Centers for Medicare and Medicaid Services Chronic Condition Algorithm for AD based on ICD-9 codes, Normalized Difference Vegetation Index, age, sex, race/ethnicity, neighborhood income, and walkability.

RESULTS

Older adults living in the consistently high greenness tertile, compared to those in the consistently low greenness tertile, had 16% lower odds of AD incidence (OR=0.84, 95% CI: 0.76-0.94, p=0.0014), adjusting for age, sex, race/ethnicity, and neighborhood income. Age, neighborhood income and walkability moderated greenness' relationship to odds of AD incidence, such that younger ages (65-74), lower-income, and non-car dependent neighborhoods may benefit most from high greenness.

CONCLUSIONS

High greenness, compared to low greenness, is associated with lower 5-year AD incidence. Residents who are younger and/or who reside in lower-income, walkable neighborhoods may benefit the most from high greenness. These findings suggest that consistently high greenness at the Census block-level, may be associated with reduced odds of AD incidence at a population level.

Vegetation cover change and its response to climate extremes in the Yellow River Basin

Extreme climate events have increased in frequency and severity under the background of climate change, with vegetation growth exhibiting a sensitive response to them. By assimilating GIMMS NDVI and MODIS NDVI using the Residual Network, we obtained a long time series and high resolution NDVI dataset of the Yellow River Basin (YRB). The dataset was utilized for examining the spatiotemporal variability of NDVI and analyzing the response of vegetation cover to climate extremes with meteorological data. Our findings reveal the following: (1) A significant rise in NDVI was seen in the YRB, displaying a mean growth rate of 0.019/10a (p < 0.001). However, seasonal differences exist. The mean NDVI of multi-year declines from southeast to northwest, while the overall trend of vegetation cover improves. (2) The NDVI response to extreme temperature exhibits noticeable spatiotemporal differences. Daytime extreme high temperature in the northern YRB is negatively correlated with NDVI, while they are positively correlated in the lower YRB and the southern part of the middle YRB. Nighttime extreme high temperature exhibits a positive correlation with NDVI. Overall, NDVI displays a stronger response to extreme precipitation than to extreme temperature, with a negative correlation with CWD and a positive correlation with PRCPTOT. (3) The NDVI demonstrates a lagged response to climate extremes in the YRB, with a greater lag in response to extreme temperature than extreme precipitation. The research findings can provide scientific support for the future management and planning of vegetation in the YRB, as well as contribute to the promotion of ecological environment regulation and sustainable development.

Linkage between growth phenology and climate-growth responses along landscape gradients in boreal forests

Boreal forests represent an important carbon sink and, therefore, significantly contribute to climate change mitigation. Tree-ring width series of boreal species reflect climate variation at the moment of tree-ring formation but also lagged climatic effects from dormancy preceding tree-ring formation and antecedent growing seasons. However, little is known about how the growth sensitivity to climate in specific intra-annual periods varies across the landscape. Here, we assessed growth responses to climate variation during the 45 months preceding the tree-ring formation for nine boreal stands of Picea glauca and Picea mariana distributed along the gradients of elevation and slope aspect. We combined process-based modeling of wood formation and remote sensing data to determine growth phenology at each site. Next, we classified intra-annual seasons with significant climate-growth correlations based on the timing of dormancy and growth periods. Both the phenology and the climate-growth relationships systematically shifted with elevation and, to a lower extent, also with slope orientation at the treeline. The mean duration of the growing season varied between 100 days at treelines above 900 m and 160 days at lowlands below 500 m. The growth at treelines was stimulated by temperature in the summer of the tree-ring formation year and two years before tree-ring formation. The period of significant climate-growth correlations during the current summer did not exceed three months in agreement with the local duration of the growing season. The growth of trees in lower elevations was instead stimulated by high temperature during the dormancy periods but restricted by high temperature in antecedent summer seasons. In conclusion, our study highlights the linkage between the timing of climate-growth sensitivity and growth phenology, primarily determined by proximity to the treeline. Consequently, accounting for landscape gradients in growth phenology is crucial for upscaling the climatic limits of boreal stands' growth as climate change progresses.

Past and future trends of diurnal temperature range and their correlation with vegetation assessed by MODIS and CMIP6

Temperature anomalies and changes in the diurnal temperature range (DTR) are expected to pose physiological challenges to biota; hence, both spatial and temporal variations in DTR provide important insights into temperature-induced stress in humans, animals, and vegetation. Furthermore, vegetation could dampen temperature variability. Here, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data of Land Surface Temperature (LST) to evaluate the global variation in DTR and its rate of change in spatial and temporal scales for the two decades spanning from 2001 to 2020. We show that North America, Africa, and Antarctica, as well as the global mean, experienced statistically significant DTR rates of change over the last 20 years in either summer, winter, or the annual mean. The rates were all negative, indicating the day-night temperature differences are decreasing in those regions because night temperatures are increasing at a faster rate than day temperatures. MODIS data of the Normalized Difference Vegetation Index (NDVI) revealed a strongly negative correlation with DTR, with a spatial correlation coefficient of -0.61. This correlation demonstrates a prominent dampening effect of vegetation on diurnal temperature oscillations. For future DTR projections, we used 19 models in the Coupled Model Intercomparison Project 6 (CMIP6) to predict global DTR trends from 2021 to 2050 with low and high CO2 concentration scenarios. The high CO2 emission scenario projects significant decreases in DTR in circumpolar regions, central Africa, and India compared to the low CO2 scenario. This difference in the two scenarios underscores the substantial influence of increased global temperatures and elevated CO2 concentration on DTR and, consequently, on the ecosystems in certain regions.

Association of residential greenness with chronotype among children

BACKGROUND

The association between residential greenness and chronotype remains unclear, especially among children. The current study aimed to explore the associations between residential greenness and chronotype parameters in children and examine potential pathways for these associations.

METHODS

In this cross-sectional study, 16,421 children ages 3-12 were included. Two satellite-derived vegetation indices, i.e., the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. The mid-sleep point on a workday (MSW) and the mid-sleep point on free days (MSF) were considered. And mid-sleep time on free days adjusted for sleep debt (MSFsc) was used as an indicator of chronotype. In addition to multivariable linear regression models, subgroup analyses were conducted to explore effect modifiers, and mediation analyses were used to explore possible mediating mechanisms of air pollutants underlying the associations.

RESULTS

An interquartile range (IQR) increase in both NDVI500-m and EVI500-m was significantly associated with an earlier MSFsc of -0.061 (95 % confidence interval (CI): -0.072, -0.049) and -0.054 (95 % CI: -0.066, -0.042), respectively. Non-linear dose response relationships were discovered between greenness indices and MSFsc and MSF. The results of stratified analyses showed the effect of residential greenness on MSW was stronger among primary school children and individuals with higher household income than among kindergarten children and those with lower household income. The joint mediation effects of PM2.5, PM1, PM10, NO2, and SO2 on the associations of NDVI500-m and EVI500-m with MSFsc were 89.6 % and 76.0 %, respectively.

CONCLUSIONS

Higher levels of residential greenness may have beneficial effects on an earlier chronotype in the child population, by reducing the effects of air pollutants, especially PM2.5. Our research hopes to promote the deployment of green infrastructure and healthy urban design strategies.

Assessment of seasonal warming trends at the Nile Delta: a paradigm for human-induced climate change

Given its modern geographical and geomorphological characteristics, along with rapid socio-economic changes, the Nile Delta stands out as one of the world's most dynamic landscapes. The key drivers of the land use change in this region have been the reclamation of delta margins, changes in agricultural practices, and urban expansion. The present study aims to explore the variations in the seasonal daytime and nighttime trends of the land surface temperatures (LST) at this active agronomic system in response to the seasonal variations of vegetation cover as revealed by the normalized difference vegetation index (NDVI) during the past two decades. The data were exclusively acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument for the period from January 2001 to December 2021, where geospatial and statistical analyses were accomplished to construct a LST/NDVI spatio-temporal pattern throughout the Nile Delta. Results revealed a robust negative and a significant relationship between the NDVI and the diurnal LST with high regression coefficients (R2) ranging from 0.78 to 0.97 (p value < 0.05). Maximal seasonal warming trends occurred during harvesting seasons (springs and falls), while the least warming was recorded during winters (the growing seasons). It was also observed that the nocturnal warming (0.72°C/decade) was almost as double as the corresponding value of the daytime trend (0.33°C/decade). The study recognized a seasonal climatic warming throughout the Nile Delta influenced by the human-induced land use change and agricultural practices.

Spatiotemporal variations and its driving factors of NDVI in Northwest China during 2000-2021

Northwest China (WTL) is an essential ecological barrier zone of China, an important node of the "Silk Road Economic Belt," and a crucial bearing area for China's execution of the "One Road and One Belt" and "Going Global" strategies. However, its ecology is exceedingly fragile and particularly vulnerable to climate change and human interference. This study explored the spatiotemporal evolution characteristics of vegetation in WTL using NDVI data and investigated its drive mechanisms by geodetector, partial correlation analysis, and residual trend analysis methods. As well as forecasting the trend for vegetation changes. The findings demonstrated that (1) the change in NDVI manifested an overall improvement trend and the distribution in space of NDVI rose from the center to the periphery. 57.07% of the area had a sparse cover of vegetation (NDVI between 0 and 0.2). In addition, about 49% of regions had deterioration tendencies, which were mainly aggregated in HX, QCXDB, QCXDN, and the eastern of QCXQN and QCXXB. (2) The NDVI's shifting trend was unsustainability, and the region of uncertain future accounted for 57.45% of the total, with apparent unsustainability features. (3) The key parameters influencing NDVI spatial distribution were Pre (precipitation), vegetation type, land use type, and soil type. The interaction between two factors enhanced the influence of any single element, which appeared as bivariate and nonlinear enhancements. (4) Both climate variations and human activities have been recognized as key variables affecting NDVI growth. NDVI variance in 73.02% of areas was influenced by the combined effects of climate variations and human activities. However, human activities were the most influential element in NDVI growth, with the relative contributions of 80.28% (19.72% of which was caused by climate variations). These results can be conducive to deepening insights into the local vegetation status, identifying the mechanisms driving vegetation change, and providing scientific recommendations for WTL's ecosystem restoration measures based on actual situations.

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 52-71 years) from the Electricity Generating Authority of Thailand cohort from 2002 to 2017 were analyzed. The greenness level within 500 m 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 (9.5 μg/m3), SO2 (0.9 ppb), and CO (0.3 ppm), 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 50 μg/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.

Associations of PM2.5 composition and green space with metabolic syndrome in a Chinese essential hypertensive population

BACKGROUND

Metabolic syndrome (MetS) has emerged as a significant global public health concern. While environmental factors, including PM2.5, have been identified as important risk factors for MetS in the general population, limited studies have investigated their impact on individuals with essential hypertension. Therefore, our study aims to explore the relationship between PM2.5 composition, green space, and their combined effects on MetS among a Chinese essential hypertensive population.

METHOD

A total of 20,131 participants diagnosed with essential hypertension from 10 provinces in China were included in this study. Individual level exposure to various environmental factors (including PM2.5, PM2.5 composition, green space and temperature) were evaluated using spatiotemporal models based on satellites data. Participants were defined as MetS according to the definition issued by the International Diabetes Federation. Generalized additive mixed models were used to analyze the individual air pollutants, green space and their interaction on MetS.

RESULT

The prevalence of MetS in this population was 44.33%. The adjusted odd ratio (OR) of MetS, with each one unit increase in SO42-, BC and NO3- were 1.077 (1.049, 1.106), 1.126 (1.077, 1.177) and 0.977 (0.958, 0.996) respectively. Additionally, each unit increase of the Normalized Difference Vegetation Index (NDVI) was associated with a decreased risk of MetS (OR: 0.988, 95% CI: 0.984-0.993). In particular, green space was found to mitigate the adverse impacts of PM2.5 on MetS (OR: 0.988, 95% CI: 0.984-0.993).

CONCLUSION

Our results suggested that there was a positive association between PM2.5 and its composition (SO42-, BC) with MetS in the essential hypertensive population, while green space might play a protective role. Moreover, green space could effectively weaken the positive relationship between air pollutants and MetS, especially in males and participants younger than 60 years old.

Effects of Ecological Restoration and Climate Change on Herbaceous and Arboreal Phenology

With global climate change, changes in vegetation phenology have become increasingly evident. Horqin Sandy Land is located near the eastern part of the West Liaohe River. It is the largest sandy land in China and its ecological environment is fragile. Investigating the changes in vegetation phenology in these sandy areas and determining the relationship between vegetation phenology and meteorological factors are of great importance for predicting the impacts of future climate change and understanding the response mechanisms of ecosystems. In this study, we used the time series of the Normalized Difference Vegetation Index (NDVI) from 2000 to 2021 and extracted the vegetation phenology in the Horqin Sandy Land using high-order curve fitting methods, including the start date of the growing season (SOS), the end date of the growing season (EOS), and the length of the growing season (LOS). We analyzed their temporal variation and used partial correlation analysis to determine their relationship with meteorological factors (temperature and precipitation). In addition, we compared the phenology and microclimate of forest and grassland within the study area. In the Horqin Sandy Land, the vegetation SOS was concentrated between the 115th and 150th day, the EOS was concentrated between the 260th and 305th day, and the LOS ranged from 125 to 190 days. Over the past 22 years, the SOS, EOS, and LOS of vegetation in the Horqin Sandy Land showed trends of delay, shift, and extension, with rates of change of 0.82 d/10a, 5.82 d/10a, and 5.00 d/10a, respectively. The start date of the growing season in the Horqin Sandy Land was mainly influenced by precipitation in April of the current year, while the end date was mainly influenced by precipitation in August of the current year. Overall, the SOS in the forested areas of the Horqin Sandy Land was slightly later than in the grasslands, but the EOS in the forested areas was significantly later than in the grasslands, resulting in a longer LOS in the forests. In addition, annual precipitation and the rate of precipitation increase were higher in the forested areas than in the grasslands, but soil temperature was higher in the grasslands than in the forests. Vegetation phenology in the Horqin Sandy Land has undergone significant changes, mainly manifested in the delayed end date of the growing season, the extended length of the growing season, and the differences between forest and grassland. This indicates that climate change has indeed affected phenological changes and provides a theoretical basis for subsequent ecological restoration and desertification prevention efforts in the region.

Land degradation neutrality: Testing the indicator in a temperate agricultural landscape

Land degradation directly affects around 25% of land globally, undermining progress on most of the UN Sustainable Development Goals (SDG), particularly target 15.3. To assess land degradation, SDG indicator 15.3.1 combines sub-indicators of productivity, soil carbon and land cover. Over 100 countries have set Land Degradation Neutrality (LDN) targets. Here, we demonstrate application of the indicator for a well-established agricultural landscape using the case study of Great Britain. We explore detection of degradation in such landscapes by: 1) transparently evaluating land cover transitions; 2) comparing assessments using global and national data; 3) identifying misleading trends; and 4) including extra sub-indicators for additional forms of degradation. Our results demonstrate significant impacts on the indicator both from the land cover transition evaluation and choice or availability of data. Critically, we identify a misleading improvement trend due to a trade-off between improvement detected by the productivity sub-indicator, and 30-year soil carbon loss trends in croplands (11% from 1978 to 2007). This carbon loss trend would not be identified without additional data from Countryside Survey (CS). Thus, without incorporating field survey data we risk overlooking the degradation of regulating and supporting ecosystem services (linked to soil carbon), in favour of signals from improving provisioning services (productivity sub-indicator). Relative importance of these services will vary between socioeconomic contexts. Including extra sub-indicators for erosion or critical load exceedance, as additional forms of degradation, produced a switch from net area improving (9%) to net area degraded (58%). CS data also identified additional degradation for soil health, including 44% arable soils exceeding bulk density thresholds and 35% of CS squares exceeding contamination thresholds for metals.

Determinants of residential greenness and its association with prostate cancer risk: A case-control study in Singapore

BACKGROUND

Exposure to greenness has been shown to be beneficial to health, but few studies have examined the association between residential greenness and prostate cancer (PCa) risk. Our main objectives were to identify the determinants of residential greenness, and to investigate if residential greenness was associated with PCa risk in Singapore.

METHODS

The hospital-based case-control study was conducted between April 2007 and May 2009. The Singapore Prostate Cancer Study (SPCS) comprised 240 prostate cancer cases and 268 controls, whose demographics and residential address were collected using questionnaires. Residential greenness was measured by normalized difference vegetation index (NDVI) around the participants' homes using a buffer size of 1 km. Determinants of NDVI were identified using a multivariable linear regression model. Logistic regression models were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of associations between NDVI and PCa risk, adjusting for potential confounders.

RESULTS

Having a BMI within the second quartile, as compared to the lowest quartile, was associated with higher levels of NDVI (β-coefficient = 0.263; 95% CI = 0.040-0.485) after adjusting for covariates. Additionally, being widowed or separated, as compared to being married, was associated with lower levels of NDVI (β-coefficient = -0.393; 95% CI = -0.723, -0.063). An interquartile range (IQR) increase in NDVI was positively associated with prostate cancer risk OR = 1.45; 95% CI = 1.02-2.07). Stratified analysis by tumour grade and stage showed that higher NDVI was associated with higher risk of low grade PCa.

CONCLUSION

Our findings suggested that residential greenness was associated with higher risk of PCa in Singapore. Future studies on the quality and type of green spaces, as well as other factors of residential greenness, in association with PCa risk should be conducted to better understand this relationship.

Spatiotemporal variation in the vegetation cover of Peshawar Basin in response to climate change

Climate factors like temperature, precipitation, humidity, and sunshine time exert a profound influence on vegetation. The intricate interplay between the two is crucial to understand in the face of changing climate to develop mitigation strategies. In the current exploration, we delve how climate variability (CV) has impacted the vegetation in the Peshawar Basin (PB) using remote sensing data tools. The trend of climatic variability was investigated using the modified Mann-Kendall test and Sen's slope statistics. The changing climatic parameters were regressed on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The NDVI was further analyzed for spatiotemporal variability under land surface temperature (LST) influence. Results revealed that among the climate factors, average annual temperature and solar radiation have a significant (p < 0.05) negative impact on vegetation while precipitation and relative humidity significantly (p < 0.05) influence NDVI positively. The overall positive trend shows that vegetation improved between 2001 and 2020 with time, however some years (2010, 2012, 2014, 2016, and 2017) with low NDVI. NDVI varied in space considerably due to climatic extremes brought on by CV and the urbanization of agricultural land. NDVI regressed on LST showed that there was no or very little vegetation in the grids with high LST. The study concluded that the region is significantly impacted by both CV-related extreme weather events and anthropogenic activities. The vegetation is improving, but it is in danger of being destroyed by deforestation due to CV and human activities that exacerbate the risk of future calamities. To protect vegetation and avoid disasters, there is an immense need for adaptation and mitigation measures to deal with the region's fast-changing environment. The study urges local authorities to create climate-resilient governmental policies and supports regional sustainable development and vegetation restoration.