scholarly journals Effect of Residential Greenness and Nearby Parks on Respiratory and Allergic Diseases among Middle School Adolescents in a Chinese City

2019 ◽  
Vol 16 (6) ◽  
pp. 991 ◽  
Author(s):  
Linyan Li ◽  
Jaime Hart ◽  
Brent Coull ◽  
Shi-jie Cao ◽  
John Spengler ◽  
...  
Keyword(s):  
Middle School ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Health Effect ◽  
Allergic Diseases ◽  
Developed Countries ◽  
School Students ◽  
Urban Forms ◽  
Chinese City

Research on the health impacts of green environments has mainly been conducted in developed countries. Differences in the urban forms between China and Western countries make it essential to understand the role of greenspace in Chinese settings. From 2014 to 2015, middle school students (n = 5643) in Suzhou, China were enrolled in a study on the health effect of residential greenness. The normalized difference vegetation index (NDVI) and distance to the nearest park were calculated for each home address. Logistic regression was performed to test associations between exposure and self-reported doctor diagnoses of asthma, pneumonia, rhinitis, and eczema, adjusting for important confounders. No statistically significant associations were observed for any seasonal NDVI-based measures. However, the proximity of the participants’ residences to the closest park showed an inverse relationship to reported symptoms. The odds ratios for the furthest quartile compared to the closest quartile based on the distance to the nearest park were 0.58 (95% CI: 0.35, 0.99), 0.70 (95% CI: 0.50, 0.96), 0.92 (95% CI: 0.74, 1.15), 0.97 (95% CI: 0.76, 1.24), 0.86 (95% CI: 0.68, 1.10) for current asthma, ever asthma, ever pneumonia, ever rhinitis, and ever eczema, respectively. These findings focused on a single Chinese city and suggest that exposure to natural vegetation in urban areas may affect health through various pathways.

scholarly journals Soil-transmitted helminth infection and skin prick test reactivity in children

2014 ◽  
Vol 54 (1) ◽  
pp. 52
Author(s):  
Schenny Regina Lubis ◽  
Lily Lrsa ◽  
Rita Evalina ◽  
Supriatmo Supriatmo ◽  
M. Sjabaroeddin
Keyword(s):  
Skin Prick Test ◽  
Urban Areas ◽  
Allergic Diseases ◽  
Developed Countries ◽  
Cross Sectional Study ◽  
Stool Examination ◽  
School Students ◽  
Chi Square ◽  
Cross Sectional ◽  
Prick Test

Background Allergic diseases cause an increasingly largeburden in developed countries and in urban areas of middleincomecountries . Paras itic infections may induce allergicresponses in humans, particularly soil-transmitted helminth(STH) infections that are prevalent in childhood in developingcountries. Although soil-transmitted helminth infec tions havebeen associated with lower prevalence of allergen skin testreactivity, study outcomes remain inconclusive.Objective To analyze for an association between STH infectionsand skin prick test reactivity in children.Methods We conducted a cross-sectional study in August 2009among primary school students aged 7- 12 years, at SecanggangSubdistrict, Langkat District, North Sumatera Province. Sixtyeight children were recruited in this study consisted of 34 childrenwith STH infections and the other 34 children without any STHinfection. Soil-transmitted helminth infections were determinedby Kato-Katz stool examination s. All subjects underwent skinprick tests for seven allergens. Results were con sidered to bepositive if wheal diameters 2: 3 mm and negative when whealdiameters < 3 mm. Data was an alysed by Chi-square test.Results Stool examinations revealed that the most commoninfec tion was T. trichiura (18/34 subjects), followed by mixedinfections (T. trichiura and A lumbricoides; 12/34 subjects), andA. lumbricoides (4134 subjects). There was a significant associationbetween STH infections and negative skin prick test (P= 0.002).In addition, there were significant associations with negative skinprick tests for each helminth type: A. lumbricoides (P=0.001) ,T. trichiura (P=0.01) and mixed infection (P = 0.006). Severeinfection intensity was also significantly associated with negativeskin prick tests (P=0.031) .Conclusion Children with STH infections tend to have negativeskin prick test results.

scholarly journals An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

2021 ◽  
Vol 13 (4) ◽  
pp. 766
Author(s):  
Yuanmao Zheng ◽  
Qiang Zhou ◽  
Yuanrong He ◽  
Cuiping Wang ◽  
Xiaorong Wang ◽  
...  
Keyword(s):  
Urban Development ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Development Planning ◽  
Urban Land ◽  
Sustainable Urban Development ◽  
Land Use Patterns ◽  
Water Index ◽  
Nighttime Light

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

Comparing Effects of FOXO3A and Residing in Urban Areas on Longevity: A Gene-Environment Interaction Study

2021 ◽  
Author(s):  
John S Ji ◽  
Linxin Liu ◽  
Lijing Yan ◽  
Yi Zeng
Keyword(s):  
Protective Effect ◽  
Effect Size ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fine Particulate Matter ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Gene Environment Interaction ◽  
Gene Environment

Abstract Forkhead box O3 (FOXO3A) is a candidate longevity gene. Urban residents are also positively associated with longer life expectancy. We conducted a gene-environment interaction to assess the synergistic effect of FOXO3A and urban/rural environments on mortality. We included 3085 older adults from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used single nucleotide polymorphisms (SNPs) rs2253310, rs2802292, and rs4946936 to identify the FOXO3A gene and classified residential locations as "urban" and "rural." Given the open cohort design, we used the Cox-proportional hazard regression models to assess the mortality risk. We found the minor allele homozygotes of FOXO3A to have a protective effect on mortality [HR (95% CI) for rs4946936 TT vs. CC: 0.807 (0.653, 0.996); rs2802292 GG vs TT: 0.812 (0.67, 0.985); rs2253310 CC vs. GG: 0.808 (0.667, 0.978)]. Participants living in urban areas had a lower risk of mortality [HR of the urban vs. the rural: 0.854 (0.759, 0.962)]. The interaction between FOXO3A and urban and rural regions was statistically significant (pinteraction&lt;0.01). Higher air pollution (fine particulate matter: PM2.5) and lower residential greenness (Normalized Difference Vegetation Index: NDVI) both contributed to higher mortality. After adjusting for NDVI and PM2.5, the protective effect size of FOXO3A SNPs was slightly attenuated while the protective effect size of living in an urban environment increased. The effect size of the beneficial effect of FOXO3 on mortality is roughly equivalent to that of living in urban areas. Our research findings indicate the effect of places of residence and genetic predisposition of longevity are intertwined.

scholarly journals Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 548 ◽  
Author(s):  
Xinpeng Tian ◽  
Zhiqiang Gao
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Aerosol Loading ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis ◽  
Reflecting Surface

The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined DT/DB AOD products for the years 2000–2016 are used. These products are validated using AErosol RObotic NETwork (AERONET) data from twenty-three ground sites situated in high aerosol loading areas and with available measurements at least 500 days. The results show that the numbers of collections (N) of DB and DT/DB retrievals were much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright reflecting surface and heavy pollution conditions. The percentage falling within the expected error (PWE) of the DT retrievals (45.6%) is lower than that for the DB (53.4%) and DT/DB (53.1%) retrievals. The DB retrievals have 5.3% less average overestimation, and 25.7% higher match ratio than DT/DB retrievals. It is found that the current merged aerosol algorithm will miss some cases if it is determined only on the basis of normalized difference vegetation index. As the AOD increases, the value of PWE of the three products decreases significantly; the undervaluation is suppressed, and the overestimation is aggravated. The retrieval accuracy shows distinct seasonality: the PWE is largest in autumn or winter, and smallest in summer. The most severe overestimation and underestimation occurred in the summer. Moreover, the DT, DB and DT/DB products over different land cover types still exhibit obvious deviations. In urban areas, the PWE of DB product (52.6%) is higher than for the DT/DB (46.3%) and DT (25.2%) products. The DT retrievals perform poorly over the barren or sparsely vegetated area (N = 52). However, the performance of three products is similar over vegetated area. On the whole, the DB product performs better than the DT product over the heavy aerosol loading area.

Landslide hazard risk mapping based on Random Forest Classification in Ruijin, Jiangxi, China

2020 ◽  
Author(s):  
Zhou Xiaoting ◽  
Weicheng Wu ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
Renxiang Chen ◽  
...  
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Road Traffic ◽  
Road Construction ◽  
Buffer Zone ◽  
Landslide Risk ◽  
Risk Area ◽  
Hazard Risk ◽  
Forest Classification

&lt;p&gt;Landslides are common geological hazards that not only&amp;#160;affect the normal road traffic but also pose a great threat and damage to human&amp;#160;lives&amp;#160;and properties. This study aims to&amp;#160;conduct such&amp;#160;a hazard risk&amp;#160;mapping&amp;#160;using&amp;#160;Random&amp;#160;Forest Classification&amp;#160;(RFC)&amp;#160;approach taking Ruijin County in Jiangxi, China&amp;#160;as an example. Multi-source data&amp;#160;namely terrain&amp;#160;(DEM, slope and aspect),&amp;#160;precipitation, the normalized difference vegetation index (NDVI)&amp;#160;representing vegetation condition and abundance, strata and their lithology, distance to roads, distance to rivers, distance to faults,&amp;#160;thickness of weathering crust, soil type and&amp;#160;texture, etc., were employed for this study. The non-numeric data such as geological strata, soil units, faults, were spatialized and assigned values in terms of their susceptibility to landslide. Similarly, linear features such as roads, rivers and faults were buffered with distances of 0-30, 30-60, 60-90 and 90-120 m and each buffer zone was assigned a susceptibility value of landslide, e.g., zones&amp;#160;0-30,&amp;#160;30-60, 60-90 and 90-120 of road buffers were assigned respectively 10, 7, 4, and 1, meaning that the closer to the road, the higher risk of landslide. In total, 16&amp;#160;hazard&amp;#160;factor&amp;#160;layers were derived and converted into raster. 156 landslide&amp;#160;hazards&amp;#160;that have truly taken places (points) and been verified in field were used to create&amp;#160;a&amp;#160;training set (TS, 70% of total landslides)&amp;#160;and a&amp;#160;validation set (VS, 30%)&amp;#160;by buffering-based rasterization&amp;#160;procedure. A number of polygons were defined in places where landslide is unlikely to occur, e.g., water bodies, zero-slope plain, and urban areas. These polygons were added to the TS as non-risk area. Then, RFC&amp;#160;was conducted to model&amp;#160;the probability of landslide risk using these 16&amp;#160;factor layers as predictors and TS for training. The obtained RF model was applied&amp;#160;back to the 16 factor layers to predict&amp;#160;the&amp;#160;probability of landslide&amp;#160;risk at each pixel&amp;#160;in the&amp;#160;whole county. The prediction map was checked against the VS and found that the Overall Accuracy&amp;#160;and&amp;#160;Kappa Coefficient are respectively 92.18% and 0.8432, and the landslide-prone areas are mainly distributed on two&amp;#160;sides of the roads. The results reveal that extremely high-risk zones&amp;#160;with a probability&amp;#160;of&amp;#160;more&amp;#160;than 0.9&amp;#160;take up 76.70&amp;#160;km&lt;sup&gt;2&lt;/sup&gt;&amp;#160;in the county, and the distance to roads&amp;#160;is the most important factor followed by precipitation among all factors causing&amp;#160;landslides as road construction and housing development cut off slopes leading to instability of the weathered crust; and heavy rainfalls trigger the instability. Our study shows that the RFC prediction&amp;#160;has high accuracy and in good consistency with field observation.&lt;/p&gt;

scholarly journals NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

2020 ◽  
Author(s):  
Perminder Singh ◽  
Ovais Javeed
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Spectral Signature ◽  
Landsat 8 ◽  
Thematic Mapping

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining  various features based upon their spectral signature  such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli  was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also  used for the mapping of changes in land use  and land cover.  NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature 

scholarly journals A Calculation and Compiling Models of Land Cover Quality Index 2019 uses the Geographic Information System in Pariaman City, West Sumatra Province, Indonesia

2019 ◽  
Vol 8 (3) ◽  
pp. 6406-6411
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Quality Index ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Director General ◽  
Environmental Damage ◽  
Vegetation Density ◽  
Forest Region

The purpose of calculation and compiling the Land Cover Quality Index (LCQI) is to evaluate the value of natural and environmental resources based on land cover conditions in an administrative region such as city, regency and province in Indonesia referring to the Regulation Director General of Pollution Control and Environmental Damage Number P.1/PPKL/PKLA.4/2018. The analytical method used in the calculation of the Normalized Difference Vegetation Index (NDVI), the Maximum likelihood classification approach, and the preparation of LCQI calculation methods based on 1) sufficiency area (forest region) and forest cover at minimal 30% on rivers and islands; 2) Ability and suitability of land minimal 25%; and 3) a link with the direction of land use in urban areas of at minimal 30%. The results showed the vegetation density index value in Pariaman city was classified as a good category with a value of 0.474903 μm, the results of a land cover classification in Pariaman City with the largest region are found in mixed gardens land of 2,736.57 ha or 37%. Whereas the smallest region is found in cypress vegetation land as a greenbelt at the coastal border 12.06 ha or 0,16%. and the results of the LCQI calculation indicate the LCQI value in 2019 (24,06) which is in the alert classification (<50). The increase in land cover outside the forest region is mainly directed at increasing green open space because Pariaman City does not have natural forest which are vulnerable to changes in land cover because of its high population density

scholarly journals VENµS-Derived NDVI and REIP at Different View Azimuth Angles

2022 ◽  
Vol 14 (1) ◽  
pp. 184
Author(s):  
Manuel Salvoldi ◽  
Yaniv Tubul ◽  
Arnon Karnieli ◽  
Ittai Herrmann
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Crop ◽  
Bidirectional Reflectance ◽  
Reflected Light ◽  
Change Vector ◽  
The Stability ◽  
Bidirectional Reflectance Distribution

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth’s surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles’ effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles.

scholarly journals Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

2020 ◽  
Vol 12 (7) ◽  
pp. 1082 ◽  
Author(s):  
Jianhui Xu ◽  
Feifei Zhang ◽  
Hao Jiang ◽  
Hongda Hu ◽  
Kaiwen Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Independent Variables

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

Sign in / Sign up

Export Citation Format

Share Document