Remote Sensing, Heat Island Effect and Housing Price Prediction via AutoML

2020 ◽  
pp. 113-118
Author(s):  
Rita Yi Man Li ◽  
Kwong Wing Chau ◽  
Herru Ching Yu Li ◽  
Fanjie Zeng ◽  
Beiqi Tang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Housing Price ◽  
Heat Island ◽  
Price Prediction ◽  
Island Effect

scholarly journals Detecting the Cool Island Effect of Urban Parks in Wuhan: A City on Rivers

2020 ◽  
Vol 18 (1) ◽  
pp. 132
Author(s):  
Qijiao Xie ◽  
Jing Li
Keyword(s):  
Remote Sensing ◽  
Negative Impact ◽  
Remote Sensing Data ◽  
Urban Parks ◽  
High Water ◽  
Water Bodies ◽  
Green Spaces ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat

As a nature-based solution, development of urban blue-green spaces is widely accepted for mitigating the urban heat island (UHI) effect. It is of great significance to determine the main driving factors of the park cool island (PCI) effect for optimizing park layout and achieving a maximum cooling benefit of urban parks. However, there have been obviously controversial conclusions in previous studies due to varied case contexts. This study was conducted in Wuhan, a city with high water coverage, which has significant differences in context with the previous case cities. The PCI intensity and its correlation with park characteristics were investigated based on remote sensing data. The results indicated that 36 out of 40 urban parks expressed a PCI effect, with a PCI intensity of 0.08~7.29 °C. As expected, larger parks with enough width had stronger PCI intensity. An increased density of hardened elements in a park could significantly weaken PCI effect. Noticeably, in this study, water bodies in a park contributed the most to the PCI effect of urban parks, while the vegetated areas showed a negative impact on the PCI intensity. It implied that in a context with higher water coverage, the cooling effect of vegetation was weakened or even masked by water bodies, due to the interaction effect of different variables on PCI intensity.

Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing

2012 ◽  
Vol 34 (9-10) ◽  
pp. 3177-3192 ◽  
Author(s):  
José A. Sobrino ◽  
Rosa Oltra-Carrió ◽  
Guillem Sòria ◽  
Juan Carlos Jiménez-Muñoz ◽  
Belén Franch ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Thermal Remote Sensing ◽  
Island Effect ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The City

scholarly journals Remote sensing of the urban heat island effect across biomes in the continental USA

2010 ◽  
Vol 114 (3) ◽  
pp. 504-513 ◽  
Author(s):  
Marc L. Imhoff ◽  
Ping Zhang ◽  
Robert E. Wolfe ◽  
Lahouari Bounoua
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat

scholarly journals Data and techniques for studying the urban heat island effect in Johannesburg

2015 ◽  
Vol XL-7/W3 ◽  
pp. 203-206 ◽  
Author(s):  
C. H. Hardy ◽  
A. L. Nel
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Sensing Data ◽  
Island Effect ◽  
Urban Heat ◽  
The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

scholarly journals Analisis Heat Island pada Perkebunan Kelapa Sawit: Studi Kasus di Kabupaten Kayong Utara, Kalimantan Barat

2020 ◽  
Vol 8 (2) ◽  
pp. 106-115
Author(s):  
Nurul Ihsan Fawzi ◽  
Marindah Yulia Iswari
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Palm Oil ◽  
Secondary Forest ◽  
Remote Sensing Data ◽  
Primary Forest ◽  
Landsat 8 ◽  
Heat Island ◽  
Local Climate ◽  
Island Effect

Between 2000 – 2017, 3.06 million hectares of primary forest in Kalimantan have been converted into palm oil plantation. This change impacts local climate changes. This study aims is to analyze the heat island in palm oil plantation. The analytical method used surface temperature estimation through remote sensing and zonal statistics. The remote sensing data that are used is Landsat 8 images acquired on 15 July 2018 and 3 August 2019. From this research, we found that young palm oil plantations have an average IHI value of 2.1 ± 1.7oC in 2018 and 1.7 ± 1.4oC in 2019. The IHI value is close to the heat island in a built-up area. IHI for mature palm oil plantation (11-12 years) created a cool island with an intensity close to secondary forest. The decreasing value of IHI for 2018 and 2019 in palm oil plantations is due to the growth of palm oil trees, which decreases surface temperature. The implication of this research is to know heat island effect due to deforestation or land cover changes, especially change into palm oil plantations.

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