Landsat Time-series Images-based Urban Heat Island Analysis: The Effects of Changes in Vegetation and Built-up Land on Land Surface Temperature in Summer in the Hanoi Metropolitan Area, Vietnam

Abstract

Rapid and unplanned urbanization leads to temperature rise, urban vegetation decrease and built-up land increase, forming an urban heat island (UHI). This study investigated the effects of changes in vegetation and built-up land on land surface temperature (LST) in summer, based on remotely sensed images. LST was first retrieved by means of the Radiative Transfer Model (RTM). Scatterplots and an univariate linear regression model (ULRM) were first employed to independently measure the influence of NDVI on LST, and of NDBI on LST, respectively. In order to assess the effects of changes in vegetation and built-up land on LST, a multivariate linear regression model (MLRM) was finally employed to improve the accuracy of the predicted model in the identification of the joint effect of both the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) on LST. The result from the case from the Hanoi Metropolitan Area (HMA), Vietnam using Landsat-5 TM and Landsat-8 OLI/TIRS time-series images during the 1996-2016 period shows that there exists a negative effect of built-up land and a positive effect of vegetation on LST. In addition, indications of intensifying UHI effects were detected in the HMA, especially tending to expand faster and wider to the parts of western, northwestern and southwestern HMA during the 1996-2007 period. These findings suggest that vegetation weakens the effect of UHIs, whereas, built-up land greatly strengthens the effect of UHIs in the HMA.

Modelo de regressão para identificação de ilha de calor

“…Por sua vez, o número de variáveis consideradas variou entre os diferentes trabalhos. Observou-se que, de um modo geral, ocorreu a avaliação entre duas variáveis dependentes (Li et al, 2017;Isa;Wan Mohd;Salleh, 2017;Agathangelidis;Cartalis;Santamouris, 2020;NGUYEN, 2020) e 36 (Alonso;Renard, 2020). A Figura 10 apresenta a distribuição do número de variáveis na amostra aderente.…”

A systematic literature review of applications of geographic information system in COVID-19 studies

“…GIS can be used to detect geographic patterns in other data, such as disease clusters resulting from toxins, sub-optimal water access, etc. GIS has been commonly used for monitoring and management of natural resouces (4-7), environment (8)(9)(10) and climate change (11,12). The applications of GIS have been widely applied in health sciences such as effective and fast decision making (13,14), prediction and analysis of diseases (15,16), management of health programs (17,18), diseases (19,20) and environmental health (21,22), accessing mental health services (23,24), disease mapping (25,26), and investigating epidemics (27,28).…”

Rural–Urban Transition of Hanoi (Vietnam): Using Landsat Imagery to Map Its Recent Peri-Urbanization

“…The temporal series from 1992 to 2010 of a low-spatial resolution satellite (a weather sensor mounted on the Defense Meteorological satellite) shed light upon the constant growth of urban areas and rural villages, but mainly upon the big increase in periurban areas over the same period [27]. With regard to Hanoi, on the other hand, the studies made using remote sensing data are quite heterogeneous: for instance, they have used Landsat imagery to investigate the changes in the River Red section flowing though Hanoi [28]; to trace the increasing impact of urban sprawl on the urban heat islands in the metropolitan area [29], sometimes accompanied by a decrease in vegetation [30]; to map the chlorophyll-a concentration in hypertrophic waters like those of West Lake in Hanoi [31], etc. However, studies have usually turned their attention to the recent urban expansion of this city, also in comparison with other urban areas like Nagoya, Hartford and Shanghai [32].…”