Abstract:The aim of this study was to identify the major variables identified as important for considering the stabilization of slope revegetation based on hydroseeding applications and evaluate weights of each variable using the analytic hierarchy process (AHP) with both environmental experts and civil engineers. Twenty-five variables were selected by the experts' survey from a total of 65 from the existing literature, with each variable considered as an important factor for slope stabilization in South Korea. The final results from the AHP method showed that variables associated with the driving force of water resources showed higher values in all expert groups such as rain intensity, seepage water and drainage condition. Other important variables were related to plant growth such as vegetation community, vegetation coverage and quality of soil ameliorant produced in an artificial factory such as tensile strength, permeability coefficient, soil texture and organic matter. The five highest-ranked variables that satisfied both environmental experts and civil engineers were rain intensity, seepage water, slope angle, drainage condition and ground layer. The findings of this research could be helpful for developing a more accurate rating system to evaluate the stability of slope revegetation.
Many empirical studies assessing the economic benefits of urban green space have continually documented that green space tends to increase both value and sale price of nearby residential properties. Previous studies, however, have not fully captured the quality of neighborhood level landscape spatial patterns on housing prices. To fill this literature gap, this study examined the association between landscape spatial patterns of urban green spaces and single-family home sale transactions using a spatial regression model. The research was conducted through the analysis of 11,326 housing transaction records from 2010 to 2012 in Austin, TX, USA. Variables measuring the structural, locational and neighborhood characteristics of housing were coupled with Geographic Information Systems, remote sensing and FRAGSTATS to calculate several landscape indices measuring the quality of existing landscape spatial patterns. After controlling for any spatial autocorrelation effects, we found that that larger tree and urban forest areas surrounding single-family homes positively contributed to property values, while more fragmented, isolated and irregularly shaped landscape spatial patterns resulted in the inverse. The results of this research increase awareness of the role of urban green spaces while informing community design/planning practices about the linkages between landscape spatial structure and economic benefits.
Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.
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