scholarly journals Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

2021 ◽  
Vol 13 (21) ◽  
pp. 4428
Author(s):  
Lu Niu ◽  
Zhengfeng Zhang ◽  
Zhong Peng ◽  
Yingzi Liang ◽  
Meng Liu ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Geographically Weighted Regression ◽  
Vegetation Index ◽  
Coastal Region ◽  
Least Square ◽  
Weighted Regression ◽  
Coefficient Of Determination ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.

scholarly journals Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

2017 ◽  
Vol 11 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Paul Macarof ◽  
Florian Statescu
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Heat Island ◽  
Sensing Technology ◽  
Urban Heat ◽  
Surface Urban Heat Island

Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

scholarly journals A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon

2018 ◽  
Vol 10 (9) ◽  
pp. 1428 ◽  
Author(s):  
Chunhong Zhao ◽  
Jennifer Jensen ◽  
Qihao Weng ◽  
Russell Weaver
Keyword(s):  
Land Cover ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Metropolitan Areas ◽  
San Antonio ◽  
Weighted Regression ◽  
Heat Island ◽  
Global Regression ◽  
Urban Heat ◽  
Surface Urban Heat Island

This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.

Geographically weighted regression of the urban heat island of a small city

2014 ◽  
Vol 53 ◽  
pp. 341-353 ◽  
Author(s):  
Danijel Ivajnšič ◽  
Mitja Kaligarič ◽  
Igor Žiberna
Keyword(s):  
Urban Heat Island ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Small City ◽  
Heat Island ◽  
Urban Heat

scholarly journals Surface Urban Heat Island Assessment of a Cold Desert City: A Case Study over the Isfahan Metropolitan Area of Iran

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1368
Author(s):  
Alireza Karimi ◽  
Pir Mohammad ◽  
Sadaf Gachkar ◽  
Darya Gachkar ◽  
Antonio García-Martínez ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Metropolitan Area ◽  
Negative Correlation ◽  
Land Surface ◽  
Vegetation Index ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Cold Desert ◽  
Urban Heat ◽  
Surface Urban Heat Island

This study investigates the diurnal, seasonal, monthly and temporal variation of land surface temperature (LST) and surface urban heat island intensity (SUHII) over the Isfahan metropolitan area, Iran, during 2003–2019 using MODIS data. It also examines the driving factors of SUHII like cropland, built-up areas (BI), the urban–rural difference in enhanced vegetation index (ΔEVI), evapotranspiration (ΔET), and white sky albedo (ΔWSA). The results reveal the presence of urban cool islands during the daytime and urban heat islands at night. The maximum SUHII was observed at 22:30 pm, while the minimum was at 10:30 am. The summer months (June to September) show higher SUHII compared to the winter months (February to May). The daytime SUHII demonstrates a robust positive correlation with cropland and ΔWSA, and a negative correlation with ΔET, ΔEVI, and BI. The nighttime SUHII displays a negative correlation with ΔET and ΔEVI.

scholarly journals Hubungan Kerapatan Vegetasi dan Bangunan terhadap UHI (Urban Heat Island) di Kota Magelang

Ruang ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 83
Author(s):  
Febriyan Riyadi ◽  
Sri Rahayu
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Least Square ◽  
Heat Island ◽  
Ordinary Least Square ◽  
Urban Heat

Urban Heat Island (UHI) adalah fenomena dimana suatu wilayah perkotaan lebih panas daripada wilayah disekitarnya. Faktor utama yang mempengaruhi terjadinya UHI adalah terjadinya konversi tutupan lahan vegetasi menjadi daerah terbangun akibat perkembangan kota. Hal tersebut mengakibatkan peningkatan suhu permukaan, dikarenakan kerapatan vegetasi yang berkurang dan meningkatnya kerapatan bangunan. Analisis yang digunakan adalah klasifikasi tak terbimbing untuk melihat perubahan tutupan lahan, analisis NDVI (Normalized Difference Vegetation Index) untuk mengetahui perubahan vegetasi, analisis NDBI (Normalized Difference Vegetation Index) untuk mengetahui perubahan kerapatan bangunan, serta menggunakan LST (Land Surface Temperature) untuk mengetahui suhu permukaan suatu kota dan OLS (Ordinary Least Square) merupakan permodelan regresi berganda pada aplikasi ArcGis  digunakan untuk mengetahui hubungan antar variabel tersebut. Hasil dalam penelitian ini menunjukkan bahwa suhu rata-rata Kota Magelang pada tahun 2000 sebesar 22,58°C meningkat menjadi 27,11°C pada tahun 2016. Artinya suhu rata-rata Kota Magelang mengalami kenaikan sebesar 4,53°C. Hubungan antara kerapatan bangunan (x1) dan kerapatan vegetasi (x2) terhadap suhu permukaan (y) diketahui melalui formula OLS yang dihasilkan yaitu Y= 5,61 X1 – 1,34 X2 + 2,4.Hal ini berarti jika kerapatan bangunan meningkat dan kerapatan vegetasi berkurang, maka suhu permukaan meningkat.

scholarly journals The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

2021 ◽  
Vol 10 (6) ◽  
pp. 416
Author(s):  
Nagihan Aslan ◽  
Dilek Koc-San
Keyword(s):  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Island ◽  
Landsat Images ◽  
Multi Temporal ◽  
Urban Heat ◽  
Surface Urban Heat Island

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

scholarly journals Analysis of Land Surface Temperature Driving Factors and Spatial Heterogeneity Research Based on Geographically Weighted Regression Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yin Zhi ◽  
Liang Shan ◽  
Lina Ke ◽  
Ruxin Yang
Keyword(s):  
Spatial Heterogeneity ◽  
Urban Heat Island ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Driving Factors ◽  
Weighted Regression ◽  
Heat Island ◽  
Moran's I ◽  
Urban Heat ◽  
Gwr Model

Acceleration of urbanization has brought about a series of problems, which include irreversible changes to urban surfaces and continuous increases in land surface temperatures (LSTs). In this context, analysis of the driving factors and spatial heterogeneity of urban LST is of considerable importance for mitigating urban heat island effects and promoting healthy and comfortable urban living environments. This study explored the relationship between the spatial characteristics and driving factors of the LST by using a geographically weighted regression (GWR) model to analyze multisource data from the Xigang District of Dalian City. The results showed that the urban heat island effect in Xigang District is significant, with LSTs generally above 28°C at the end of August, mostly concentrated in the range of 38–40°C. The highest LST values were detected in northern port and harbor areas; the lowest LST values occurred in mountainous forest areas. The global Moran’s I value was 0.994, which was indicative of a very high positive correlation, and local Moran’s I values formed H-H and L-L type clusters concentrated in the northern harbor area and southern mountainous area, respectively. Finally, the GWR model could reflect the spatial heterogeneity of the relationships between LST and its driving factors well. Among these, in terms of natural physical factors, digital elevation model, normalized difference vegetation index, and modified normalized difference water index data were found to be negatively correlated with LSTs in most cases; in the social dimension, the point-of-interest number and building-coverage ratio were generally positively correlated with LSTs.

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