Residential greenness is considered beneficial to human health, and its association with respiratory function has been found in previous studies. However, its link with pneumonia remains unclear. To explore the association of residential greenness with incident pneumonia, we conducted a prospective cohort study based on participants of the UK Biobank, followed from 2006 to 2010 to the end of 2019. Residential greenness was measured by Normalized Difference Vegetation Index (NDVI) within 500 m and 1000 m buffer. Cox proportional hazard models were conducted to assess the association, and restricted cubic spline models were also constructed to estimate their exposure-response relationship. Results demonstrate that residential greenness was negatively related to the risk of incident pneumonia. An interquartile (IQR) increase in NDVI 500-m buffer was associated with 4 % [HR (95 % CI) =0.96 (0.94, 0.97), P < 0.001] lower risk of incident pneumonia. Compared to the lowest greenness quartile (Q1), the highest quartile (Q4) had a lower risk of incident pneumonia, with the HR (95 % CI) estimated to be 0.91 (0.87, 0.95) (P values <0.001). Analyses based on NDVI 1000-m buffer obtained similar results. Furthermore, a significant effect of modifications by age and income on the linkage between residential greenness and incident pneumonia was found. These findings propose a potential effective prevention of incident pneumonia and provide the scientific basis for promoting the construction of residential greenness.

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.

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.

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.

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.

Despite the significance of the associations of air pollution and greenness with the risk of breast cancer, this topic has not been investigated on a global scale. We conducted an ecological study using 7 years of data from 162 countries. Disability-adjusted life years (DALYs) and incidence data were used to represent the breast cancer disease burden. Particulate matter with a diameter < 2.5 μm (PM2.5), ozone (O3), nitrogen dioxide (NO2), and the normalized difference vegetation index (NDVI) were adopted as our exposures. We employed generalized linear mixed models to explore the relationship between air pollution and greenness on breast cancer disease burden. The rate ratio (RR) and its 95% confidence interval (CI) indicate the effect size. There is a positive association between air pollution and the burden of breast cancer disease. Contrarily, per interquartile range increment in NDVI was negatively associated with DALYs and incidence. In terms of air pollutants and breast cancer, NDVI seems to have a significant influence on the relationship between these two conditions. A higher amount of greenness helps to alleviate the negative association of air pollution on breast cancer. PM2.5 and O3 play a mediating role in the relationship between greenness and breast cancer disease burden. In areas with higher levels of greenness, there is a possibility that the inverse association between air pollutants and the burden of breast cancer may be influenced.

UNASSIGNED: While the adverse effects of short-term ambient ozone exposure on lung function are well-documented, the impact of long-term exposure remains poorly understood, especially in adults.
UNASSIGNED: We aimed to investigate the association between long-term ozone exposure and lung function decline. The 3014 participants were drawn from 17 centers across eight countries, all of which were from the European Community Respiratory Health Survey (ECRHS). Spirometry was conducted to measure pre-bronchodilation forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) at approximately 35, 44, and 55 years of age. We assigned annual mean values of daily maximum running 8-h average ozone concentrations to individual residential addresses. Adjustments were made for PM2.5, NO2, and greenness. To capture the ozone-related change in spirometric parameters, our linear mixed effects regression models included an interaction term between long-term ozone exposure and age.
UNASSIGNED: Mean ambient ozone concentrations were approximately 65 μg/m³. A one interquartile range increase of 7 μg/m³ in ozone was associated with a faster decline in FEV1 of -2.08 mL/year (95% confidence interval: -2.79, -1.36) and in FVC of -2.86 mL/year (-3.73, -1.99) mL/year over the study period. Associations were robust after adjusting for PM2.5, NO2, and greenness. The associations were more pronounced in residents of northern Europe and individuals who were older at baseline. No consistent associations were detected with the FEV1/FVC ratio.
UNASSIGNED: Long-term exposure to elevated ambient ozone concentrations was associated with a faster decline of spirometric lung function among middle-aged European adults over a 20-year period.
UNASSIGNED: German Research Foundation.

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.
CONCLUSIONS: 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.

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

Rapid assessment of crop damage is essential for successful management of insect pest outbreaks. In this study, we investigated the use of an unmanned aircraft system (UAS) and image analyses to assess an outbreak of the beet armyworm, Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae), that occurred in soybean fields in South Korea. A rotary-wing UAS was deployed to obtain a series of aerial images over 31 soybean blocks. The images were stitched together to generate composite imagery, followed by image analyses to quantify soybean defoliation. An economic analysis was conducted to compare the cost of the aerial survey with that of a conventional ground survey. The results showed that the aerial survey precisely estimated the defoliation compared to the ground survey, with an estimated defoliation of 78.3% and a range of 22.4-99.8% in the 31 blocks. Moreover, the aerial survey followed by image analyses was found to be more economical than the conventional ground survey when the number of target soybean blocks subject to the survey was more than 15 blocks. Our study clearly demonstrated the effectiveness of using an autonomous UAS and image analysis to conduct a low-cost aerial survey of soybean damage caused by S. exigua outbreaks, which can inform decision-making for S. exigua management.