scholarly journals Distribution of Urban Blue and Green Space in Beijing and Its Influence Factors

2020 ◽  
Vol 12 (6) ◽  
pp. 2252
Author(s):  
Haoying Wang ◽  
Yunfeng Hu ◽  
Li Tang ◽  
Qi Zhuo
Keyword(s):  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Urban Landscape ◽  
Housing Prices ◽  
Influence Factors ◽  
Normal Operation ◽  
Web Crawler ◽  
Green Vegetation ◽  
Urban Landscape Ecology

Urban blue and green space is a key element supporting the normal operation of urban landscape ecosystems and guaranteeing and improving people's lives. In this paper, 97.1k photos of Beijing were captured by using web crawler technology, and the blue sky and green vegetation objects in the photos were extracted by using the Image Cascade Network (ICNet) neural network model. We analyzed the distribution characteristics of the blue–green space area proportion index and its relationships with the background economic and social factors. The results showed the following. (1) The spatial distribution of Beijing's blue–green space area proportion index showed a pattern of being higher in the west and lower in the middle and east. (2) There was a positive correlation between the satellite remote sensing normalized difference vegetation index (NDVI) and the proportion index of green space area, but the fitting degree of geospatial weighted regression decreased with an increasing analysis scale. (3) There were differences in the relationship between the housing prices in different regions and the proportion index of blue–green space, but the spatial fitting degree of the two increased with the increase of study scale. (4) There was a negative correlation between the proportion index of blue–green space and population density, and the low-population areas per unit blue–green space were mainly distributed in the south of the city and the urban fringe areas beyond the Third Ring Road. The urban blue–green space analysis that was constructed by this study provides new aspect for urban landscape ecology study, and the results proposed here also provide support for government decision-makers to optimize urban ecological layouts.

scholarly journals Association between Green Space Structure and the Prevalence of Asthma: A Case Study of Toronto

2021 ◽  
Vol 18 (11) ◽  
pp. 5852
Author(s):  
Yuping Dong ◽  
Helin Liu ◽  
Tianming Zheng
Keyword(s):  
Air Pollution ◽  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Chronic Inflammatory Disease ◽  
Space Structure ◽  
Tree Diversity ◽  
Underlying Mechanisms ◽  
Index Tree

Asthma is a chronic inflammatory disease that can be caused by various factors, such as asthma-related genes, lifestyle, and air pollution, and it can result in adverse impacts on asthmatics’ mental health and quality of life. Hence, asthma issues have been widely studied, mainly from demographic, socioeconomic, and genetic perspectives. Although it is becoming increasingly clear that asthma is likely influenced by green spaces, the underlying mechanisms are still unclear and inconsistent. Moreover, green space influences the prevalence of asthma concurrently in multiple ways, but most existing studies have explored only one pathway or a partial pathway, rather than the multi-pathways. Compared to greenness (measured by Normalized Difference Vegetation Index, tree density, etc.), green space structure—which has the potential to impact the concentration of air pollution and microbial diversity—is still less investigated in studies on the influence of green space on asthma. Given this research gap, this research took Toronto, Canada, as a case study to explore the two pathways between green space structure and the prevalence of asthma based on controlling the related covariates. Using regression analysis, it was found that green space structure can protect those aged 0–19 years from a high risk of developing asthma, and this direct protective effect can be enhanced by high tree diversity. For adults, green space structure does not influence the prevalence of asthma unless moderated by tree diversity (a measurement of the richness and diversity of trees). However, this impact was not found in adult females. Moreover, the hypothesis that green space structure influences the prevalence of asthma by reducing air pollution was not confirmed in this study, which can be attributed to a variety of causes.

scholarly journals Lower Noise Annoyance Associated with GIS-Derived Greenspace: Pathways through Perceived Greenspace and Residential Noise

2018 ◽  
Vol 15 (7) ◽  
pp. 1533 ◽  
Author(s):  
Angel M. Dzhambov ◽  
Iana Markevych ◽  
Boris Tilov ◽  
Zlatoslav Arabadzhiev ◽  
Drozdstoj Stoyanov ◽  
...  
Keyword(s):  
Noise Level ◽  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Buffer Zone ◽  
Tree Cover ◽  
Noise Sources ◽  
Noise Abatement ◽  
Noise Annoyance ◽  
Lower Noise

Growing amounts of evidence support an association between self-reported greenspace near the home and lower noise annoyance; however, objectively defined greenspace has rarely been considered. In the present study, we tested the association between objective measures of greenspace and noise annoyance, with a focus on underpinning pathways through noise level and perceived greenspace. We sampled 720 students aged 18 to 35 years from the city of Plovdiv, Bulgaria. Objective greenspace was defined by several Geographic Information System (GIS)-derived metrics: Normalized Difference Vegetation Index (NDVI), tree cover density, percentage of green space in circular buffers of 100, 300 and 500 m, and the Euclidean distance to the nearest structured green space. Perceived greenspace was defined by the mean of responses to five items asking about its quantity, accessibility, visibility, usage, and quality. We assessed noise annoyance due to transportation and other neighborhood noise sources and daytime noise level (Lday) at the residence. Tests of the parallel mediation models showed that higher NDVI and percentage of green space in all buffers were associated with lower noise annoyance, whereas for higher tree cover this association was observed only in the 100 m buffer zone. In addition, the effects of NDVI and percentage of green space were mediated by higher perceived greenspace and lower Lday. In the case of tree cover, only perceived greenspace was a mediator. Our findings suggest that the potential for greenspace to reduce noise annoyance extends beyond noise abatement. Applying a combination of GIS-derived and perceptual measures should enable researchers to better tap individuals’ experience of residential greenspace and noise.

scholarly journals A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2757 ◽  
Author(s):  
Akash Ashapure ◽  
Jinha Jung ◽  
Anjin Chang ◽  
Sungchan Oh ◽  
Murilo Maeda ◽  
...  
Keyword(s):  
Comparative Study ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Canopy Cover ◽  
Unmanned Aircraft ◽  
Estimation Methods ◽  
Green Vegetation ◽  
Multi Temporal ◽  
Rgb Images ◽  
Multispectral Sensors

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

scholarly journals Do Exposures to Green Space Reduce the Risk of Hypertension?

2020 ◽  
Author(s):  
Jie Jiang ◽  
Gongbo Chen ◽  
Baojing Li ◽  
Yuanan Lu ◽  
Yuming Guo ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Cross Sectional Study ◽  
Central China ◽  
Mixed Linear Models ◽  
Cross Sectional ◽  
Standardized Protocol

Abstract Background: Few epidemiological research examined the effects of greenness on cardiovascular diseases in developing countries. We aimed to explore the relationships between green space and hypertension and blood pressure in China.Methods: This cross-sectional study recruited 39, 259 adults from five counties in central China. Blood pressure measurements were performed according to a standardized protocol. Normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) was used to assess the exposure to greenness. We used mixed linear models to test greenspace-cardiovascular disease outcome pathways.Results: Higher green space was related to decreased hypertension prevalence and blood pressure. After fully adjusting the covariates, each interquartile range increase in NDVI500m and EVI500m were related to an 8% decrease in odds of hypertension. The changes in SBP and DBP (95% CI) were - 0.88 mm Hg (- 1.17, - 0.58) and - 0.64 mm Hg (- 0.82, - 0.46) for NDVI, and - 0.79 mm Hg (- 1.14, - 0.45) and - 0.67 mm Hg (- 0.87, - 0.46) for EVI, respectively. Subgroup analyses showed that the effects of green space were more pronounced in males, smokers, and drinkers.Conclusions: The effects of green space may reduce the risk of hypertension. Also, behavioral factors may affect this potential pathway.

scholarly journals Applying Remote Sensing Technologies in Urban Landscapes of the Mediterranean

2020 ◽  
Vol 2 (1) ◽  
pp. 27-36
Author(s):  
Celestina M. G. Pedras ◽  
Helena Maria Fernandez ◽  
Rui Lança ◽  
Fernando Granja-Martins
Keyword(s):  
Mediterranean Climate ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Landscape ◽  
Urban Landscapes ◽  
Climate Data ◽  
Water Requirements ◽  
The Mediterranean ◽  
Almost All

There has been increasing pressure on water resources in cities due to the proliferation of urban green areas. In the Mediterranean climate, only a small part of the plants’ water needs is supplied by rainfall during the winter months. Thus, in Algarve (Portugal) irrigation of the urban landscapes is required almost all year round. The aims of this study were to evaluate the maintenance of the urban landscapes of São Brás de Alportel (Algarve) during a year, based on the characterization of the vegetation of the urban gardens, the climate data, the analysis of the irrigation systems, the calculation of the plants water requirements and the normalized difference vegetation index (NDVI). By crossing all this information, it was possible to understand if the current maintenance level is the most suitable for sustainable irrigated urban landscapes. In most of the gardens, it was possible to establish a relationship between the gross irrigation water requirements and NDVI. In general, the NDVI allowed us to study the urban landscape, through the monthly observation of the differences in the appearance and development of the vegetation.

scholarly journals Spectral variables, growth analysis and yield of sugarcane

2005 ◽  
Vol 62 (3) ◽  
pp. 199-207 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Lower Amount ◽  
Total Biomass ◽  
Area Index ◽  
Spectral Vegetation Indices ◽  
Green Vegetation ◽  
Highly Correlated ◽  
Simple Ratio

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

scholarly journals Spatial autocorrelation of ndvi and gvi indices derived from landsat/tm images for soybean crops in the western of the state of Paraná in 2004/2005 crop season

2013 ◽  
Vol 33 (3) ◽  
pp. 525-537 ◽  
Author(s):  
Gustavo H. Dalposso ◽  
Miguel A. Uribe-Opazo ◽  
Erivelto Mercante ◽  
Rubens A. C. Lamparelli
Keyword(s):  
Spatial Autocorrelation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The State ◽  
Landsat Tm ◽  
Crop Season ◽  
Yield Data ◽  
Green Vegetation ◽  
Crop Cycle ◽  
Global And Local

This research aims at studying spatial autocorrelation of Landsat/TM based on normalized difference vegetation index (NDVI) and green vegetation index (GVI) of soybean of the western region of the State of Paraná. The images were collected during the 2004/2005 crop season. The data were grouped into five vegetation index classes of equal amplitude, to create a temporal map of soybean within the crop cycle. Moran I and Local Indicators of Spatial Autocorrelation (LISA) indices were applied to study the spatial correlation at the global and local levels, respectively. According to these indices, it was possible to understand the municipality-based profiles of tillage as well as to identify different sowing periods, providing important information to producers who use soybean yield data in their planning.

scholarly journals Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation

2020 ◽  
Vol 12 (18) ◽  
pp. 7434 ◽  
Author(s):  
Yusuke Kumakoshi ◽  
Sau Yee Chan ◽  
Hideki Koizumi ◽  
Xiaojiang Li ◽  
Yuji Yoshimura
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Landscapes ◽  
Site Location ◽  
Urban Green ◽  
Urban Greenery ◽  
Geographical Scale ◽  
Green Vegetation ◽  
Block Level ◽  
The City

Urban greenery is considered an important factor in sustainable development and people’s quality of life in the city. To account for urban green vegetation, Green View Index (GVI), which captures the visibility of greenery at street level, has been used. However, as GVI is point-based estimation, when aggregated at an area-level by mean or median, it is sensitive to the location of sampled sites, overweighing the values of densely located sites. To make estimation at area-level more robust, this study aims to (1) propose an improved indicator of greenery visibility (standardized GVI; sGVI), and (2) quantify the relation between sGVI and other green metrics. Experiment on an hypothetical setting confirmed that bias from site location can be mitigated by sGVI. Furthermore, comparing sGVI and Normalized Difference Vegetation Index (NDVI) at the city block level in Yokohama city, Japan, we found that sGVI captures the presence of vegetation better in the city center, whereas NDVI is better at capturing vegetation in parks and forests, principally due to the different viewpoints (eye-level perception and top-down eyesight). These tools provide a foundation for accessing the effect of vegetation in urban landscapes in a more robust matter, enabling comparison on any arbitrary geographical scale.

scholarly journals Association between Surrounding Greenness and Mortality: An Ecological Study in Taiwan

2020 ◽  
Vol 17 (12) ◽  
pp. 4525
Author(s):  
Hsiao-Yun Lee ◽  
Chih-Da Wu ◽  
Yi-Tsai Chang ◽  
Yinq-Rong Chern ◽  
Shih-Chun Candice Lung ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Mixed Model ◽  
Normalized Difference Vegetation Index ◽  
Underlying Mechanism ◽  
Risk Of Death ◽  
Asian Populations ◽  
Green Vegetation ◽  
Moderate Resolution ◽  
Green Index ◽  
The Relationship

Exposure to surrounding greenness is associated with reduced mortality in Caucasian populations. Little is known however about the relationship between green vegetation and the risk of death in Asian populations. Therefore, we opted to evaluate the association of greenness with mortality in Taiwan. Death information was retrieved from the Taiwan Death Certificate database between 2006 to 2014 (3287 days). Exposure to green vegetation was based on the normalized difference vegetation index (NDVI) collected by the Moderate Resolution Imagine Spectroradiometer (MODIS). A generalized additive mixed model was utilized to assess the association between NDVI exposure and mortality. A total of 1,173,773 deaths were identified from 2006 to 2014. We found one unit increment on NDVI was associated with a reduced mortality due to all-cause (risk ratio [RR] = 0.901; 95% confidence interval = 0.862–0.941), cardiovascular diseases (RR = 0.892; 95% CI = 0.817–0.975), respiratory diseases (RR = 0.721; 95% CI = 0.632–0.824), and lung cancer (RR = 0.871; 95% CI = 0.735–1.032). Using the green land cover as the alternative green index showed the protective relationship on all-cause mortality. Exposure to surrounding greenness was negatively associated with mortality in Taiwan. Further research is needed to uncover the underlying mechanism.

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