Impacts of land use changes from the Hanoi Master Plan 2030 on urban heat islands: Part 1. Cooling effects of proposed green strategies

2017 ◽  
Vol 32 ◽  
pp. 295-317 ◽  
Author(s):  
Tetsu Kubota ◽  
Han Soo Lee ◽  
Andhang Rakhmat Trihamdani ◽  
Tran Thi Thu Phuong ◽  
Takahiro Tanaka ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Urban Heat Islands ◽  
Master Plan ◽  
Heat Islands ◽  
Urban Heat ◽  
Cooling Effects

Impacts of land use changes from the Hanoi Master Plan 2030 on urban heat islands: Part 2. Influence of global warming

2017 ◽  
Vol 31 ◽  
pp. 95-108 ◽  
Author(s):  
Han Soo Lee ◽  
Andhang Rakhmat Trihamdani ◽  
Tetsu Kubota ◽  
Satoru Iizuka ◽  
Tran Thi Thu Phuong
Keyword(s):  
Land Use ◽  
Global Warming ◽  
Land Use Changes ◽  
Urban Heat Islands ◽  
Master Plan ◽  
Heat Islands ◽  
Urban Heat

scholarly journals Impacts of Land use Changes on Urban Heat Islands in Hanoi, Vietnam: Scenario Analysis

2017 ◽  
Vol 198 ◽  
pp. 525-529 ◽  
Author(s):  
Andhang Rakhmat Trihamdani ◽  
Tetsu Kubota ◽  
Han Soo Lee ◽  
Kento Sumida ◽  
Tran Thi Thu Phuong
Keyword(s):  
Land Use ◽  
Scenario Analysis ◽  
Land Use Changes ◽  
Urban Heat Islands ◽  
Heat Islands ◽  
Urban Heat

scholarly journals Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

Land ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 28 ◽  
Author(s):  
Charlotte Shade ◽  
Peleg Kremer
Keyword(s):  
Land Use ◽  
Green Infrastructure ◽  
Land Use Changes ◽  
Temporal Trends ◽  
Urban Heat Islands ◽  
Kappa Statistics ◽  
Heat Islands ◽  
Chain Analysis ◽  
Global Trend ◽  
Urban Heat

Urbanization is a rapid global trend, leading to consequences such as urban heat islands and local flooding. Imminent climate change is predicted to intensify these consequences, forcing cities to rethink common infrastructure practices. One popular method of adaptation is green infrastructure implementation, which has been found to reduce local temperatures and alleviate excess runoff when installed effectively. As cities continue to change and adapt, land use/landcover modeling becomes an important tool for city officials in planning future land usage. This study uses a combination of cellular automata, machine learning, and Markov chain analysis to predict high resolution land use/landcover changes in Philadelphia, PA, USA for the year 2036. The 2036 landcover model assumes full implementation of Philadelphia’s green infrastructure program and past temporal trends of urbanization. The methodology used to create the 2036 model was validated by creating an intermediate prediction of a 2015 landcover that was then compared to an existing 2015 landcover. The accuracy of the validation was determined using Kappa statistics and disagreement scores. The 2036 model successfully met Philadelphia’s green infrastructure goals. A variety of landscape metrics demonstrated an overall decrease in fragmentation throughout the landscape due to increases in urban landcover.

scholarly journals Characterizing the Hourly Variation of Urban Heat Islands in a Snowy Climate City during Summer

2019 ◽  
Vol 16 (14) ◽  
pp. 2467 ◽  
Author(s):  
Chaobin Yang ◽  
Ranghu Wang ◽  
Shuwen Zhang ◽  
Caoxiang Ji ◽  
Xie Fu
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Air Temperature ◽  
Temporal Variability ◽  
Spatial Information ◽  
Thermal Environment ◽  
Thermal Characteristics ◽  
Urban Heat Islands ◽  
Heat Islands ◽  
Urban Heat

Temporal variation of urban heat island (UHI) intensity is one of the most important themes in UHI studies. However, fine-scale temporal variability of UHI with explicit spatial information is sparse in the literature. Based on the hourly air temperature from 195 meteorological stations during August 2015 in Changchun, China, hourly spatiotemporal patterns of UHI were mapped to explore the temporal variability and the effects of land use on the thermal environment using time series analysis, air temperature profiling, and spatial analysis. The results showed that: (1) high air temperature does not indicate strong UHI intensity. The nighttime UHI intensity (1.51 °C) was much stronger than that in the daytime (0.49 °C). (2) The urban area was the hottest during most of the day except the period from late morning to around 13:00 when there was about a 40% possibility for an “inverse UHI intensity” to appear. Paddy land was the coolest in the daytime, while woodland had the lowest temperature during the nighttime. (3) The rural area had higher warming and cooling rates than the urban area after sunrise and sunset. It appeared that 23 °C was the threshold at which the thermal characteristics of different land use types changed significantly.

scholarly journals Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

2020 ◽  
pp. 1-21
Author(s):  
Ehsan Kamali Maskooni ◽  
Hossein Hashemi ◽  
Ronny Berndtsson ◽  
Peyman Daneshkar Arasteh ◽  
Mohammad Kazemi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Heat Islands ◽  
Semiarid Region ◽  
Land Cover Changes ◽  
Landsat Data ◽  
Heat Islands ◽  
Urban Heat

scholarly journals A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI

2019 ◽  
Vol 33 (2) ◽  
pp. 162-172
Author(s):  
Iswari Nur Hidayati ◽  
R Suharyadi
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Spectral Characteristics ◽  
Impervious Surface ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Landsat 8 Oli ◽  
Heat Islands ◽  
Urban Heat

Impervious surface is one of the major land cover types of urban and suburban environment. Conversion of rural landscapes and vegetation area to urban and suburban land use is directly related to the increase of the impervious surface area. The impervious surface expansion is straight-lined with decreasing green spaces in urban areas. Impervious surface is one of indicator for detecting urban heat islands. This study compares various indices for mapping impervious surfaces using Landsat 8 OLI imagery by optimizing the different spectral characteristics of Landsat 8 OLI imagery. The research objectives are (1) to apply various indices for impervious surface mapping and (2) identifies impervious surfaces in urban areas based on multiple indices and provide recommendations and find the best index for mapping impervious surface in urban areas. In addition to utilizing the index, land use supervised classification method, maximum likelihood classification used for extracting built-up, and non-built-up areas. Accuracy assessment of this research used field data collection as primary data for calculating kappa coefficient, producer accuracy, and user accuracy. The study can also be extended to find the land surface temperature and correlate the impervious surface extraction data with urban heat islands.

Spatiotemporal impact of land use/land cover changes on urban heat islands: A case study of Paço do Lumiar, Brazil

2018 ◽  
Vol 136 ◽  
pp. 279-292 ◽  
Author(s):  
Janilci Serra Silva ◽  
Richarde Marques da Silva ◽  
Celso Augusto Guimarães Santos
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Heat Islands ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Heat Islands ◽  
Urban Heat

scholarly journals Quantifying the effect of waterways and green areas on the surface temperature

2017 ◽  
Vol 39 (1) ◽  
pp. 89 ◽  
Author(s):  
Elis Dener Lima Alves
Keyword(s):  
Surface Temperature ◽  
Temporal Variability ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Natural Area ◽  
Heat Islands ◽  
Green Areas ◽  
Urban Heat ◽  
Cooling Effects ◽  
The Relationship

The cooling effects of urban parks and green areas, which form the “Park Cool Island” (PCI) can help decrease the surface temperature and mitigate the effects of urban heat islands (UHI). Therefore, the objective of this research was to know the temporal variability of PCI intensity, as well as analyze the factors that determines it and propose an equation to predict the PCI intensity in Iporá, Goiás State, Brazil. To this purpose, the PCI intensity values were obtained using the Landsat-8 satellite (band 10), and then correlated with the NDVI and the LAI, in which proposes equations through multiple linear regression to estimate the PCI intensity. The results indicated that: 1) the greater the distance of the natural area, greater the surface temperature; 2) there is a great seasonality in PCI, in which the intensity of PCI is much higher in the spring (or close to it); 3) the relationship between NDVI and LAI variables, showed good coefficients of determination; 4) the equations for the buffer of 200 and 500 m, had low RMSE with high coefficients of determination (r2 = 0.924 and r2 = 0.957 respectively). 

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