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 Effects of Windthrows on Forest Cover, Tree Growth and Soil Characteristics in Drought-Prone Pine Plantations

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 817
Author(s):  
Jesús Julio Camarero ◽  
Michele Colangelo ◽  
Antonio Gazol ◽  
Manuel Pizarro ◽  
Cristina Valeriano ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Pinus Halepensis ◽  
Soil Characteristics ◽  
Tree Cover ◽  
Mediterranean Forests ◽  
Soil Microbial Community Structure ◽  
Soil Features ◽  
Disturbance Intensity

Windstorms are forest disturbances which generate canopy gaps. However, their effects on Mediterranean forests are understudied. To fill that research gap, changes in tree, cover, growth and soil features in Pinus halepensis and Pinus sylvestris plantations affected by windthrows were quantified. In each plantation, trees and soils in closed-canopy stands and gaps created by the windthrow were sampled. Changes in tree cover and radial growth were assessed by using the Normalized Difference Vegetation Index (NDVI) and dendrochronology, respectively. Soil features including texture, nutrients concentration and soil microbial community structure were also analyzed. Windthrows reduced tree cover and enhanced growth, particularly in the P. halepensis site, which was probably more severely impacted. Soil characteristics were also more altered by the windthrow in this site: the clay percentage increased in gaps, whereas K and Mg concentrations decreased. The biomass of Gram positive bacteria and actinomycetes increased in gaps, but the biomass of Gram negative bacteria and fungi decreased. Soil gaps became less fertile and dominated by bacteria after the windthrow in the P. halepensis site. We emphasize the relevance of considering post-disturbance time recovery and disturbance intensity to assess forest resilience within a multi-scale approach.

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 A Spatial and Temporal Assessment of Vegetation Greening and Precipitation Changes for Monitoring Vegetation Dynamics in Climate Zones over Africa

2021 ◽  
Vol 10 (3) ◽  
pp. 129
Author(s):  
Vincent Nzabarinda ◽  
Anming Bao ◽  
Wenqiang Xu ◽  
Solange Uwamahoro ◽  
Madeleine Udahogora ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Type ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Plant Health ◽  
Tree Cover ◽  
Climatic Zones ◽  
Dramatic Decline ◽  
Precipitation Changes

Vegetation is vital, and its greening depends on access to water. Thus, precipitation has a considerable influence on the health and condition of vegetation and its amount and timing depend on the climatic zone. Therefore, it is extremely important to monitor the state of vegetation according to the movements of precipitation in climatic zones. Although a lot of research has been conducted, most of it has not paid much attention to climatic zones in the study of plant health and precipitation. Thus, this paper aims to study the plant health in five African climatic zones. The linear regression model, the persistence index (PI), and the Pearson correlation coefficients were applied for the third generation Normalized Difference Vegetation Index (NDVI3g), with Climate Hazard Group infrared precipitation and Climate Change Initiative Land Cover for 34 years (1982 to 2015). This involves identifying plants in danger of extinction or in dramatic decline and the relationship between vegetation and rainfall by climate zone. The forest type classified as tree cover, broadleaved, deciduous, closed to open (>15%) has been degraded to 74% of its initial total area. The results also revealed that, during the study period, the vegetation of the tropical, polar, and warm temperate zones showed a higher rate of strong improvement. Although arid and boreal zones show a low rate of strong improvement, they are those that experience a low percentage of strong degradation. The continental vegetation is drastically decreasing, especially forests, and in areas with low vegetation, compared to more vegetated areas, there is more emphasis on the conservation of existing plants. The variability in precipitation is excessively hard to tolerate for more types of vegetation.

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

<p>Landslides are common geological hazards that not only affect the normal road traffic but also pose a great threat and damage to human lives and properties. This study aims to conduct such a hazard risk mapping using Random Forest Classification (RFC) approach taking Ruijin County in Jiangxi, China as an example. Multi-source data namely terrain (DEM, slope and aspect), precipitation, the normalized difference vegetation index (NDVI) representing vegetation condition and abundance, strata and their lithology, distance to roads, distance to rivers, distance to faults, thickness of weathering crust, soil type and 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 0-30, 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 hazard factor layers were derived and converted into raster. 156 landslide hazards that have truly taken places (points) and been verified in field were used to create a training set (TS, 70% of total landslides) and a validation set (VS, 30%) by buffering-based rasterization 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 was conducted to model the probability of landslide risk using these 16 factor layers as predictors and TS for training. The obtained RF model was applied back to the 16 factor layers to predict the probability of landslide risk at each pixel in the whole county. The prediction map was checked against the VS and found that the Overall Accuracy and Kappa Coefficient are respectively 92.18% and 0.8432, and the landslide-prone areas are mainly distributed on two sides of the roads. The results reveal that extremely high-risk zones with a probability of more than 0.9 take up 76.70 km<sup>2</sup> in the county, and the distance to roads is the most important factor followed by precipitation among all factors causing 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 has high accuracy and in good consistency with field observation.</p>

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 Habitat monitoring and conservation prioritization of Western Hoolock Gibbon in upper Brahmaputra Valley, Assam, India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kuladip Sarma ◽  
Malabika Kakati Saikia ◽  
Bidyut Sarania ◽  
Himolin Basumatary ◽  
Siddhartha Sankar Baruah ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Potential ◽  
Tree Cover ◽  
Conservation Priority ◽  
Conservation Prioritization ◽  
Potential Habitat ◽  
Brahmaputra Valley ◽  
Dense Forest ◽  
Open Forest

AbstractThe present study aimed at predicting the potential habitat of Western Hoolock Gibbon (Hoolock hoolock) in the upper Brahmaputra River Valley, Assam, India, and identifying priority conservation areas for the species, taking canopy cover into account. We used the maximum entropy algorithm for the prediction of the potential habitat of the gibbon using its current distribution with 19 environmental parameters as primary predictors. Spatio-temporal analyses of the habitat were carried out using satellite-based remote sensing and GIS techniques for two decades (1998–2018) along with Terra Modis Vegetation Continuous Field product to examine land use land cover (LULC), habitat fragmentation, Normalized Difference Vegetation Index (NDVI) and tree cover percentage of the study area. To identify the conservation priority area, we applied a cost-effective decision-making analysis using systematic conservation prioritization in R programming. The model predicted an area of 6025 km2 under high potential habitat, a major part of which was found to overlap with dense forest (80%), followed by moderately open forest (74%) and open forest (66%). The LULC change matrix showed a reduction of forest area in the predicted high potential habitat during the study period, while agricultural class showed an increasing trend. The fragmentation analysis indicated that the number of patches and patch density increased from 2008 to 2018 in the ‘very dense’ and ‘dense’ canopy regions of the gibbon habitat. Based on the conservation priority analysis, a 640 km2 area has been proposed to conserve a minimum of 10% of gibbon habitat. The current analysis revealed that in the upper Brahmaputra Valley most areas under dense forest and dense canopy have remained intact over the last two decades, at least within the high potential habitat zone of gibbons independent of the degree of area change in forest, agriculture and plantation.

scholarly journals A multi-biome study of tree cover detection using the Forest Cover Index

2021 ◽  
Author(s):  
Sarah Becker ◽  
Megan Maloney ◽  
Andrew Griffin
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Tree Cover ◽  
Future Research ◽  
Vegetative Cover ◽  
Ancillary Data ◽  
Land Covers ◽  
Sentinel 2

Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.

scholarly journals Global Forest Types Based on Climatic and Vegetation Data

2022 ◽  
Vol 14 (2) ◽  
pp. 634
Author(s):  
Chen Xu ◽  
Xianliang Zhang ◽  
Rocío Hernandez-Clemente ◽  
Wei Lu ◽  
Rubén D. Manzanedo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Type ◽  
Tree Cover ◽  
Small Range ◽  
Vegetation Types ◽  
Forest Types ◽  
Type Distribution ◽  
Forest Attributes ◽  
Descriptive Names

Forest types are generally identified using vegetation or land-use types. However, vegetation classifications less frequently consider the actual forest attributes within each type. To address this in an objective way across different regions and to link forest attributes with their climate, we aimed to improve the distribution of forest types to be more realistic and useful for biodiversity preservation, forest management, and ecological and forestry research. The forest types were classified using an unsupervised cluster analysis method by combining climate variables with normalized difference vegetation index (NDVI) data. Unforested regions were masked out to constrict our study to forest type distributions, using a 20% tree cover threshold. Descriptive names were given to the defined forest types based on annual temperature, precipitation, and NDVI values. Forest types had distinct climate and vegetation characteristics. Regions with similar NDVI values, but with different climate characteristics, which would be merged in previous classifications, could be clearly distinguished. However, small-range forest types, such as montane forests, were challenging to differentiate. At macroscale, the resulting forest types are largely consistent with land-cover types or vegetation types defined in previous studies. However, considering both potential and current vegetation data allowed us to create a more realistic type distribution that differentiates actual vegetation types and thus can be more informative for forest managers, conservationists, and forest ecologists. The newly generated forest type distribution is freely available to download and use for non-commercial purposes as a GeoTIFF file via doi: 10.13140/RG.2.2.19197.90082).

scholarly journals Fig. 3. Relation between per capita income and measures of private (A, B) and public (C) green infrastructure, including the normalized difference vegetation index (NDVI) and fractional tree cover. NDVI and tree cover values are expressed as percentage relative differences to respective district municipality means. Data points represent census tracts and are faded to highlight densities.

2021 ◽  
Author(s):  
Zander S Venter ◽  
Charlie M. Shackleton ◽  
Francini Van Staden ◽  
Odirilwe Selomane ◽  
Vanessa A Masterson
Keyword(s):  
South Africa ◽  
Low Income ◽  
Green Infrastructure ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tree Cover ◽  
Racial Groups ◽  
Public And Private ◽  
Data Points ◽  
Relative Differences

<p>Urban green infrastructure provides ecosystem services that are essential to human wellbeing. A dearth of national-scale assessments in the Global South has precluded the ability to explore how political regimes, such as the forced racial segregation in South Africa during and after Apartheid, have influenced the extent of and access to green infrastructure over time. We investigate whether there are disparities in green infrastructure distributions across race and income geographies in urban South Africa. Using open-source satellite imagery and geographic information, along with national census statistics, we find that public and private green infrastructure is more abundant, accessible, greener and more treed in high-income relative to low-income areas, and in areas where previously advantaged racial groups (i.e. White citizens) reside.</p>

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