Cumulative Normalized Difference Vegetation Index Research Articles

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.

Residential greenness during pregnancy and early life and development of asthma up to 27 years of age: The Espoo Cohort Study

Previous studies have suggested that living close to green spaces has protective health effects, but potential effects on asthma are contradictory. We investigated the association between the amount of greenness in the residential area during pregnancy and early life and development of asthma in the first 27 years of life. The study population included all 2568 members of the Espoo Cohort Study, Finland. We calculated individual-level exposure to green space measured as cumulative Normalized Difference Vegetation Index (cumNDVI in unit-months) within 300 m of the participant's residence during pregnancy and the first two years of life in both spring and summer seasons. The onset of asthma was assessed using information from the baseline and follow-up surveys. Exposure to residential greenness in the spring season during pregnancy was associated with an increased risk of asthma up to 6 years of age, with an adjusted hazard ratio (aHR) of 3.72 (95% confidence interval (CI): 1.11, 12.47) per 1 unit increase in cumNDVI. Increased greenness in the summer during pregnancy associated with asthma up to 6 years, with an aHR of 1.41 (95% CI: 0.85, 2.32). The effect was found to be related to increased greenness particularly during the third trimester of pregnancy, with an aHR of 2.37 (95% CI: 1.36, 4.14) per 1 unit increase of cumNDVI. These associations were weaker at the ages of 12 and 27 years. No association was found between NDVI in the first two years of life and the development of asthma. Our findings provide novel evidence that exposure to greenness during pregnancy increases the risk of developing asthma. The adverse effects were strongest for the prenatal greenness in the spring season and in the third trimester of pregnancy. Both the season and trimester of exposure to greenness are critical in the development of asthma.

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

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

Scalable growth models for time‐series multispectral images

AbstractVegetation indices (VIs) are produced as a combination of different reflectance bands that are captured by multispectral images (MSIs). These indices, such as normalized difference vegetation index (NDVI), are reported to be proxy indicators of photosynthetic activity, plant canopy biomass, and leaf area index. To determine the utility of using VI derived from MSI to model plant growth, random regression (RR) models with linear splines and different orders of Legendre polynomials were applied to data collected (years 2019 and 2020) as part of the Genome‐to‐Fields initiative. Growth curves of maize (Zea mays L.) hybrids were modeled using both NDVI and cumulative NDVI (cNDVI) phenotypes. Due to the difference in MSI recording dates, and sparse overlap in hybrids between years, all the analyses were nested within a year. Results indicate that RR models using Legendre polynomials provide a robust and scalable method for modeling growth curves using phenotypes extracted from MSI; however, RR models using linear splines showed inconsistent convergence. Growth curves estimated using NDVI and cNDVI showed low‐to‐moderate heritability (0.11–0.44) and a range of genetic correlations (−0.15 to 0.97) with grain yield. This study demonstrates the utility of MSI for modeling genetic growth trends, with the best modeling results obtained when using Legendre polynomials and cNDVI.

Long-term exposure to residential surrounding greenness and incidence of diabetes: A prospective cohort study

Exposure to residential greenness might affect population health through increasing physical activity and social engagement, improving mental health, and reducing harmful environmental exposure. However, evidence on the association of greenness with risk of diabetes is still controversial. In this study, we recruited a total of 22,535 participants aged ≥18 years from Yinzhou District, Ningbo, Zhejiang Province, China to investigate the associations between residential greenness and risk of diabetes incidence. Residential greenness was estimated using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Vegetation Continuous Field (VCF). We also calculated cumulative average NDVI, EVI and VCF values, and changes in NDVI, EVI and VCF during the follow-up period. We used Cox proportional hazards models controlling for demographic characteristics, lifestyles, individual socioeconomic status, history of diseases and particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) to examine hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assessed physical activity, body mass index (BMI) or PM2.5 as potential mediators. During 84,992.64 person-years of follow-up, a total of 1,154 incident cases of diabetes occurred. In multivariable models, living in the highest quartile of cumulative average NDVI, EVI and VCF within 250-m buffer was associated with 57% (HR = 0.43, 95% CI: 0.36, 0.52), 62% (HR = 0.38, 95% CI: 0.32, 0.45), and 55% (HR = 0.45, 95% CI: 0.38, 0.54) reduction in diabetes risk compared with the lowest quartile, respectively. Results remained similar for NDVI, EVI, and VCF within 500-m and 1000-m buffers. Stratified analyses showed stronger association for residential greenness and diabetes among older people. The association between greenness and diabetes did not appear to be mediated by physical activity, PM2.5 or BMI. Our findings suggested that higher residential greenness was significantly associated with lower risk of diabetes.

Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework

Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application.

Assessing the impacts of natural disasters on rice production in Jiangxi, China

ABSTRACT Agricultural productivity is affected by natural disasters, such as drought and flood, which can be assessed by remote sensing (RS) technology. The main objective of this research is to develop an operational methodology for predicting rice yield that is utilized to assess the impacts of drought and flood disasters on agricultural productivity taking Jiangxi, China as an example. For this purpose, we first calculated the Standardized Precipitation Index (SPI) using monthly rainfall data of 83 stations from 1960 to 2020 to identify the disastrous years and their spatial extent of impact. Then, time-series MODIS data from 2000 to 2020, Landsat images, digital elevation model (DEM) were obtained and utilized for rice plantation mapping using decision-tree algorithm and yield estimation. The identified 3-cropping rice plantations were converted into rice masks for calculating the county-level cumulative NDVI (normalized difference vegetation index) to match the reported county-level annual rice yield for building remote sensing-based rice yield models taking the averages of 2016–2019 as test. By comparing the determination coefficient (R2), root mean square error (RMSE) and prediction accuracy of the models, we found that the best model for rice yield estimation is Y D = 4.899 × 10−6×NDVI 2 + 2.891 × NDVI + 98511.218 (R2 = 0.898). This model was applied to estimate the county-level annual rice production of 2014 and 2015, and provincial annual rice production of 2010–2019 for validation and prediction. The agreement with the government-reported yield reaches 97–98% at provincial level and the models were hence considered reliable. After verification, the models were applied to other remained years after 2010 for estimating the provincial annual rice production. Then, a new disaster indicator, that is, the disaster composite index (DCI) was developed for assessing the disaster impact on annual rice production in a pilot area around Nanchang City. Results show that developed models are capable of achieving rice yield estimation using both MODIS data for regional and Landsat data for local scale. In comparison with the normal or least affected year, impacts of drought and flood were successfully evaluated using DCI for pilot area and their relationship was created. We hence believe the developed methodology is extendable to South China and other rice plantation area in the world.

Urban greenness and survival in lung cancer patients: A registry-based cohort study in Beijing

BackgroundPrevious studies have reported inverse associations between urban greenness and mortality. However, findings regarding contemporaneous and cumulative average greenness exposure are inconsistent. A potential explanation is the weak effects of greenness among the general population. This study assessed the associations between contemporaneous and cumulative average greenness exposure and survival in lung cancer patients. MethodsAn open cohort consisting of lung cancer patients enrolled from 1 January 2001 was studied. The cohort was followed up until 31 December 2018. We evaluated contemporaneous and cumulative greenness exposure using the normalized difference vegetation index (NDVI) within a 250 m buffer of each patient’s geocoded residential address. The association between NDVI and mortality was evaluated using both time-fixed and time-varying Cox models. Their difference was compared either. ResultsIn time-fixed Cox models, a 0.1-unit increment in contemporaneous and cumulative average NDVI was associated with 17% (95% confidence interval [CI], 15–18%) and 7% (95% CI, 5–8%) reductions in mortality rate, respectively. Time-varying Cox models consistently revealed significant associations, but their magnitudes were lower, at 2% (95% CI, 1–3%) for both contemporaneous and cumulative average NDVI. Compared to the time-fixed models, the time-varying ones exhibited better fit for the proportional hazard assumption in Cox regressions with more conservative results. ConclusionUrban greenness is positively associated with an increased chance of survival in lung cancer patients. Compared to time-fixed Cox models, the results of time-varying Cox models were more conservative. This study confirms the beneficial health effects of urban greenness, which may facilitate the improvement of public health by urban green space planning.

Effect of heatwaves and greenness on mortality among Chinese older adults

Heatwaves and greenness have been shown to affect health, but the evidence on their joint effects is limited. We aim to assess the associations of the combined exposure to greenness and heatwaves. We utilized five waves (February 2000–October 2014) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort of older adults aged 65. We defined heatwaves as the daily maximum temperature ≥92.5th percentile with duration ≥3 days. We calculated the number of heatwave days in one year before death to and cumulative Normalized Difference Vegetation Index (NDVI) during follow-up to assess individual long-term exposure to heatwaves and greenness. Cox proportional hazards models were used to assess the effects of greenness, heatwaves, and their interaction on mortality, adjusted for covariates. We conducted subgroup analyses by residence, gender, and age. There were 20,758 participants in our study, totaling 67,312 person-years of follow-up. The mean NDVI was 0·41 (SD 0.13), and the mean number of heatwave days was 8.92 (2.04). In the adjusted model, the mortality hazard ratio (HR) for each 3-day increase in heatwave days was 1.04 (95% CI 1.04, 1.05), each 0.1-unit decrease in cumulative NDVI was 1.06 (1.05, 1.07). In the adjusted model with an interaction term, the HR for the interaction term was 1.01 (1.01, 1.02) with a p-value less than 0.001. In our subgroup analyses, the HR for each 3-day increase in heatwave days was higher in urban areas than in rural areas (1.06 vs. 1.03), and the HR for 0.1-unit decrease in NDVI was higher in urban areas than in rural areas (1.08 vs. 1.04). Greenness can protect against the effect of heatwaves on mortality, and heatwaves affect the health effects of greenness. Urban dwellers have a higher response to the detrimental effect of heatwaves and a higher marginal benefit from greenness exposure.

Effect of heatwaves and greenness on mortality among Chinese elderly people

BACKGROUND AND AIM: Both heatwaves and greenness have been shown to affect health, but the evidence on their joint effects is needed to be better elucidated. We aim to assess the associations of the combined exposure to greenness and heatwaves and whether the effect varies between different subgroups. METHODS: We utilized the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort of older adults aged 65. We used the number of heatwave days in one year before death to estimate individual long-term heatwaves exposure and cumulative Normalized Difference Vegetation Index (NDVI) during follow-up to assess individual greenness exposure. Cox proportional hazards models were used to assess the effects of greenness, heatwaves, and their interaction on mortality, adjusted for a range of covariates. We conducted subgroup analyses by residence, gender, and age. RESULTS:There were 20,758 participants in our study, totaling 67,312 person-years of follow-up. The mean NDVI was 0·41 (SD 0.13), and the mean number of heatwave days was 8.92 (2.04). In the adjusted model, the mortality hazard ratio (HR) for each 3-day increase in heatwave days was 1.04 (95% CI 1.04,1.05), each 0.1-unit decrease in cumulative NDVI was 1.06 (1.05,1.07). In the adjusted model with an interaction term, the HR for the interaction term was 1.01 (1.01,1.02) with a p-value less than 0.001. In our subgroup analyses, the HR for each 3-day increase in heatwave days was higher in urban areas than in rural areas (1.06 vs. 1.03), and the HR for 0.1-unit decrease in NDVI was higher in urban areas than in rural areas (1.08 vs. 1.04). CONCLUSIONS:Greenness can protect against the effect of heatwaves on mortality, and the number of heatwave days will affect the health effects of greenness. Urban dwellers have a higher response to the detrimental effect of heatwaves and have a higher marginal benefit from greenness exposure. KEYWORDS: Green space, Temperature, Environmental epidemiology

Early drought warning based on chlorophyll fluorescence and normalized difference vegetation index in Xilingol League of China

Drought is one of the most serious natural disasters affecting global food security and human life. Traditional drought monitoring is based on the impact of water deficit on crop morphology. Due to the time lag of traditional drought monitoring based on vegetation index and land surface temperature, crop yield has been adversely affected when drought detection is delayed. Early drought warning is an effective way to prevent disasters and reduce damage. An early warning model, using the percentage of anomaly vegetation and fluorescence (PAVF) was proposed to determine drought risk level and development trends. The principle of PAVF is to analyze the change in the percentage of anomaly vegetation (PAV) using the normalized difference vegetation index (NDVI), from the moderate-resolution imaging spectroradiometer sensor, and percentage anomaly fluorescence (PAF), from the Global Ozone Monitoring Experiment. A standard score of seasonal cumulative NDVI and Sun-induced chlorophyll fluorescence based on the data from 2007 to 2017 was used as a proxy for the evaluation index. Because PAV and PAF have different sensitivities to monitoring vegetation physiological changes, these parameters can reflect the occurrence and development trend of drought from different perspectives. Drought risk analysis in Xilingol League of China in 2018 demonstrated that the drought warning model could depict the evolution process of agricultural drought and can efficiently predict the occurrence of drought in the next half month. The proposed PAVF provides an available and innovative way to address the complexity of agricultural drought warnings.

Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images

Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study’s main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calculating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014–2016 using ground observations. Kappa coefficients ranged from 0.81–0.96 for 2014–2015 and 0.62–0.89 for 2015–2016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6% to 16.5% of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions.

Changes in the Tree-Ring Width-Derived Cumulative Normalized Difference Vegetation Index over Northeast China during 1825 to 2013 CE

Vegetation coverage is very important in terrestrial ecosystems and climate systems. However, the observational record of the normalized difference vegetation index (NDVI), which started in the 1980s when satellites became widely used, is too short to investigate the history of variation in vegetation coverage beyond the modern observation period. Here, we present a 189 y vegetation coverage series based on a total of 349 Mongolian pine (Pinus sylvestris var. mongolica Litv) cores from seven locations from the central–western Da Hinggan Mountains (CW–DHM), northeastern China. We found a significant relationship between tree-ring width and the regional cumulative normalized difference vegetation index (CNDVI). The correlation between the ring-width chronology and the regional June–July CNDVI (CNDVIJJ) was significant, with r = 0.68 (n = 32, p &lt; 0.001) and an explained variance of 45.8% (44.0% after the adjustment for the loss of the degree of freedom). On this basis, we designed a transfer function to reconstruct the CNDVIJJ for the CW–DHM region from 1825 to 2013 CE (Common Era). During the last 189 years, there were 28 years with high CNDVIJJ values, and another 28 years with low values. We also observed CNDVIJJ fluctuations at the inter-annual and decadal time scales, including eight low value periods and nine high value periods. Based on our analysis, the variation in CNDVI is associated with climatic factors, such as temperature, precipitation and the Palmer Drought Severity Index (PDSI), which combines both temperature and precipitation. From 1950 to 2002 CE, the CNDVI showed a noticeable decreasing trend in the CW–DHM region, whereas after 2003 CE, the CNDVI exhibited an apparent increase, which has also been observed in southern Central Siberia, eastern Mongolia and northeastern and eastern China, indicating that the CNDVI change in the CW–DHM is related to climate change in the local region and in some parts of Asia.

Surface reflectance calculation and predictive models of biophysical parameters of maize crop from RG-NIR sensor on board a UAV

Unmanned aerial vehicles (UAVs) present themselves as an alternative to overcome the limitations of satellite sensors in monitoring agricultural crops, motivating many studies with UAVs. They can carry sensors, which need studies for better understanding. The present study aimed to vicariously calibrate a Red-Green-Near infrared (RG-NIR) low-cost sensor on board a UAV, and to develop predictive models of biophysical parameters for a maize crop. To achieve this purpose, 15 sets of images were captured over 61 days after emergence (DAE) of the maize crop plantation. Each set of images was mosaicked and had their digital numbers (DN) converted to reflectance. After calibration, normalized difference vegetation index (NDVI) and cumulative NDVI (cNDVI) were calculated to serve as an independent variable in the models for estimating crop parameters. In the field, 54 plants were collected and evaluated for height, leaf area and dry biomass. It was observed that the NIR band had an influence on the red band, but this influence was attenuated with the empirical line calibration. NDVI was able to detect seasonal and spatial variations in maize. The NDVI model obtained on the collection day to estimate the total dry above ground biomass had better results, generating RMSE of 68.68 g m−2 and R2 of 0.81, in comparison with cNDVI. For productivity, the result was satisfactory with cNDVI, showing RMSE of 134.00 g m−2 and R2 of 0.63. Calibration of the sensor was shown to be important to attenuate influence between bands.

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

Gene-Environment Interaction of Residential Greenness and FOXO on Mortality among Older Adults

Background The Forkhead box O (FOXO) gene is a candidate longevity gene. Residential greenness is an important built environment factor strongly associated with mortality. There was no previous study on the interaction between FOXO and residential greenness based on our knowledge. Methods We studied 3,179 participants aged 65 and older from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We measured residential greenness by satellite derived Normalized Difference Vegetation Index (NDVI) using a 500-m radius around each residential address. We calculated contemporaneous NDVI, cumulative NDVI and changes in NDVI over time. We used adjusted Cox-proportional hazard regression models to assess main effects and interaction of FOXO SNPs and residential greenness on mortality risk. Results We found participants with two minor allele copies of FOXO3A SNPs had lower mortality risk than those with zero copy (HR: 0.803 95% CI: 0.654, 0.987 for rs4946936, HR: 0.807 95% CI: 0.669, 0.974 for rs2802292, HR: 0.803 95%CI: 0.666, 0.968 for rs2253310). We found no mortality difference among different genotypes for FOXO1A SNPs rs17630266, rs2755209 and rs2755213. Higher contemporaneous NDVI was associated with lower mortality risk (HR: 0.887 95% CI: 0.863, 0.911 for 0.1 unit of NDVI). The protective effect of NDVI was stronger among participants with two minor allele copies of rs2802292 SNP compared with the ones with zero copy (Interaction term P&lt;0.05), while not different between participants with one copy and zero copy.Conclusions We found gene-environment interaction between FOXO and residential greenness on mortality in this population study. A higher level of greenness may interact with FOXO pathways.

Temperature, topography, soil characteristics, and NDVI drive habitat preferences of a shade-tolerant invasive grass.

AimDespite the large literature documenting the negative effects of invasive grasses, we lack an understanding of the drivers of their habitat suitability, especially for shade‐tolerant species that do not respond positively to canopy disturbance. We aimed to understand the environmental niche and potential spatial distribution of a relatively new invasive species, wavyleaf basketgrass (Oplismenus undulatifolius (Ard.) Roem. & Schult, WLBG) by leveraging data available at two different spatial scales.LocationMid‐Atlantic region of the United States.MethodsMaximum entropy modeling (Maxent) was used to predict the habitat suitability of WLBG at the regional scale and the landscape scale. Following variable evaluation, model calibration, and model evaluation, final models were created using 1,000 replicates and projected to each study area.ResultsAt the regional scale, our best models show that suitability for WLBG was driven by relatively high annual mean temperatures, low temperature seasonality and monthly range, low slope, and high cumulative Normalized Difference Vegetation Index (NDVI). At the landscape scale, suitability was highest near roads and streams, far from trails, at low elevations, in sandy, moist soil, and in areas with high NDVI.Main ConclusionsWe found that invasion potential of this relatively new invader appears high in productive, mesic habitats at low slope and elevations. At the regional scale, our model predicted areas of suitable habitat far outside areas where WLBG has been reported, including large portions of Virginia and West Virginia, suggests serious potential for spread. However, large portions of this area carry a high extrapolation risk and should therefore be interpreted with caution. In contrast, at the landscape level, the suitability of WLBG is largely restricted to areas near current presence points, suggesting that the expansion risk of this species within Shenandoah National Park is somewhat limited.

Variation in the vegetation fade stage and its relationships with climate and vegetation productivity in Inner Mongolia, China

Based on the MODIS NDVI data from 2001 to 2018, we extracted the fade stage of vegetation in Inner Mongolia and the characteristics of its temporal and spatial changes using the Logistic curve maximum curvature method of the cumulative normalized difference vegetation index (NDVI). We analyzed the responses of fade stage to climatic factors and NDVI at the ecological zone level. The results showed that the fade stage in Inner Mongolia occurred between the day of year (DOY) 260 and 280 during the study period. It was DOY 270-280 in the forest ecological zone, and was delayed from the south to north. The fade stage in the grassland ecological zone was the earliest and ranged between DOY 257 and 273, and was delayed from the northeast to southwest. The fade stage in the desert ecological zone ranged between DOY 270 and 283, which was delayed from the northeast to southwest. From 2001 to 2018, the fade stage in three ecological zones showed an insignificant delaying trend. Spatially, vegetation productivity decreased from the northeast to southwest, with a larger area of an increasing temporal trend than that of a decreasing trend. The fade stage in the entire Inner Mongolia and three ecological zones was greatly affected by precipitation during the two or three months before the fade stage and was positively correlated with the pre-season average temperature, maximum temperature, and minimum temperature, respectively. The increases/decreases in vegetation productivity in August and September delayed/advanced the fade stage in entire Inner Mongolia and three ecological zones. In contrast, the increases/decreases in vegetation productivity in June and July advanced/delayed the fade stage in the grassland and desert ecological zone.

Gene-Environment Interaction of FOXO and Residential Greenness on Mortality Among Older Adults.

Residential greenness is an important environmental factor that is strongly associated with mortality. To our knowledge, there was no previous study on the gene-environment interaction analysis between residential greenness and forkhead box O (FOXO) gene, a candidate longevity gene. Our sample consisted of 3179 participants aged 65 and older from the Chinese Longitudinal Healthy Longevity Survey. Residential greenness was measured by satellite-derived normalized difference vegetation index (NDVI) using a 500-m radius around each residential location. Contemporaneous NDVI, cumulative NDVI, and changes in NDVI over time were calculated. We used Cox-proportional hazard regression models to assess the main effect and gene-environment interaction effect of FOXO single nucleotide polymorphism (SNP) and residential greenness on mortality. We found that participants carrying two minor alleles of the three studied FOXO3A SNPs had lower mortality risk than those without minor allele (hazard ratio [HR]: 0.803 95% confidence interval [CI]: 0.654-0.987 for rs4946936, HR: 0.807 95% CI: 0.669-0.974 for rs2802292, HR: 0.803 95% CI: 0.666-0.968 for rs2253310). We found no difference in mortality among the genotypes of the other three FOXO1A SNPs (rs17630266, rs2755213, or rs2755209). Higher contemporaneous NDVI was associated with lower mortality risk (HR: 0.887 95% CI: 0.863-0.911 for 0.1-U of NDVI). The protective effect of both contemporaneous NDVI and cumulative NDVI was stronger for two minor allele carriers compared with zero minor allele carriers of the three FOXO3A SNPs. Compared with the zero minor allele genotype of the three FOXO3A SNPs, the protective effect on the mortality risk of minor allele homozygotes also increased with the increasing NDVI level at percentile 25, 50, and 75 (interaction term coefficient p < 0.05). We found gene-environment interaction between FOXO and residential greenness on mortality in this population study. A higher level of greenness may interact with FOXO pathways.

Patterns of helminth infection in Kenyan elephant populations

BackgroundThe dynamics of helminth infection in African elephant populations are poorly known. We examined the effects of age, sex, social structure and the normalized difference vegetation index (NDVI) as primary drivers of infection patterns within and between elephant populations.MethodsCoprological methods were used to identify helminths and determine infection patterns in distinct elephant populations in Maasai Mara National Reserve, Tsavo East National Park, Amboseli National Park and Laikipia-Samburu Ecosystem. Gaussian finite mixture cluster analyses of egg dimensions were used to classify helminth eggs according to genera. Generalized linear models (GLM) and Chi-square analyses were used to test for variation in helminth infection patterns and to identify drivers in elephant populations.ResultsHelminth prevalence varied significantly between the studied populations. Nematode prevalence (96.3%) was over twice as high as that of trematodes (39.1%) in elephants. Trematode prevalence but not nematode prevalence varied between populations. Although we found no associations between helminth infection and elephant social groups (male vs family groups), the median helminth egg output (eggs per gram, epg) did vary between social groups: family groups had significantly higher median epg than solitary males or males in bachelor groups. Young males in mixed sex family groups had lower epg than females when controlling for population and age; these differences, however, were not statistically significant. The average NDVI over a three-month period varied between study locations. Cluster analyses based on egg measurements revealed the presence of Protofasciola sp., Brumptia sp., Murshidia sp., Quilonia sp. and Mammomonogamus sp. GLM analyses showed that the mean epg was positively influenced by a three-month cumulative mean NDVI and by social group; female social groups had higher epg than male groups. GLM analyses also revealed that epg varied between elephant populations: Samburu-Laikipia elephants had a higher and Tsavo elephants a lower epg than Amboseli elephants.ConclusionsElephants had infection patterns characterized by within- and between-population variation in prevalence and worm burden. Sociality and NDVI were the major drivers of epg but not of helminth prevalence. Gastrointestinal parasites can have a negative impact on the health of wild elephants, especially during resource scarcity. Thus, our results will be important when deciding intervention strategies.