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 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 MODEL KEKRITISAN INDEKS LINGKUNGAN DENGAN ALGORITMA URBAN HEAT ISLAND DI KOTA SEMARANG

2017 ◽  
Vol 19 (1) ◽  
pp. 45 ◽  
Author(s):  
Bandi Sasmito ◽  
Andri Suprayogi
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Criticality Index

<p align="center"> <strong>ABSTRAK</strong></p><p class="JudulABSInd"><span lang="IN"> </span></p><p class="abstrak"><span lang="IN">Pembangunan infrastruktur di Kota Semarang berkembang sangat pesat sebagai pusat bisnis, ekonomi, industri, hiburan, dan pendidikan. Pembangunan memberikan dampak positif bagi masyarakat kota, namun terdapat juga dampak negatif yang terjadi yaitu penurunan kualitas lingkungan. Meningkatnya suhu udara adalah salah satu dampak dari penurunan kualitas lingkungan. Puncak atap dan dinding dari gedung bertingkat, tempat parkir, jalan, dan trotoar cenderung memiliki albedo yang rendah. Permukaan rendah albedo menyerap energi panas radiasi matahari lebih tinggi dari objek sekitarnya. Akibatnya, jumlah kelebihan energi panas menumpuk di sekitarnya menjadi pulau-pulau panas atau <em>Urban Heat Island</em> (UHI). Penelitian ini bertujuan untuk mendeteksi terjadinya fenomena kekritisan lingkungan akibat UHI dengan menganalisis suhu permukaan dan sebaran vegetasi di wilayah studi. Ada dua langkah metode dalam penelitian ini, pertama adalah membuat peta sebaran suhu permukaan tanah dan peta sebaran kerapatan vegetasi di tahun 2013 sampai 2016. Peta suhu permukaan dibuat dengan model algoritma <em>Land Surface Temperature</em> (LST) dan sebaran vegetasi adalah dengan algoritma <em>Normalized Difference Vegetation Index</em> (NDVI). LST didapatkan dengan mengolah Citra Landsat-8 band TIRS (<em>Thermal Infrared Red Sensor</em>), sedangkan NDVI  didapatkan dengan mengolah Citra Landsat-8 band OLI (<em>Operation Land Imager</em>). Langkah kedua adalah membuat peta kekritisan lingkungan dengan algoritma ECI (<em>Environmental Criticality Index</em>). ECI didapatkan dari nilai LST dibagi NDVI yang direntangkan histogram spektralnya menjadi 8 bit. </span>Melalui<span lang="IN"> hasil penelitian ini dapat disimpulkan bahwa suhu permukaan di Kota Semarang meningkat dan sebaran kelas suhu tinggi meluas setiap tahun. Kekritisan lingkungan akibat UHI terdeteksi di pusat kota, yaitu wilayah Utara Kota Semarang.</span></p><p class="abstrak"><strong><span lang="IN">Kata kunci</span></strong><span lang="IN">: </span><em><span lang="IN">Urban Heat Island</span></em><span lang="IN"> (UHI), </span><em><span lang="IN">Land Surface Temperature</span></em><span lang="IN"> (LST), <em>Normalized Difference Vegetation Index</em> (NDVI)</span><span lang="IN">, </span><em><span lang="IN">Environmental Criticality Index</span></em><span lang="IN"> (ECI)</span></p><p align="center"><strong><br /></strong></p><p align="center"><strong><em>ABSTRACT</em></strong></p><p class="Abstrakeng">Infrastructure in Semarang City developes rapidly as a center of business, economics, industry, entertainment, and education. Development gives positive impact to citizen, however environmental degradation as the negative impact also occured. Temperatures rising is one of environmental degradation impact. Roof top and wall of a building, parking lot, road, and sidewalk tend to have a low albedo. The low surface albedo absorbs thermal energy from solar radiation higher than the surrounding objects. As a result, the amount of excess heat accumulate in the vicinity into heat islands or Urban Heat Island (UHI). This study aims to detect the occurrence of environmental criticality due to UHI phenomenon by analyzing the surface temperature and the distribution of vegetation in the study area. There are two steps in this research, first step is to create land surface temperature distribution map and vegetation density distribution map in the year of 2013 to 2016. The surface temperature map created by Land Surface Temperature (LST) algorithm model and vegetation distribution created by Normalized Difference Vegetation Index (NDVI) algorithm. LST is obtained by processing Landsat-8 band TIRS (Thermal Infrared Sensor Red), while the NDVI obtained by processing Landsat-8 band OLI (Operation Land Imager). The second step is to create environmental criticality map with  ECI (Environmental Criticality Index) algorithm. ECI is obtained from LST value divided by NDVI spectral histogram stretched to 8 bits. From this research, can be concluded that the heat coverage in Semarang City increase and distribution of vegetation density index spread every year. Environmental criticality due to UHI occurred in downtown area, specifically in the northern side of Semarang City.</p><p><strong><em>Keywords</em></strong><em>:</em><em>   </em><em>Urban Heat Island</em> (UHI), <em>Land Surface Temperature</em> (LST), <em>Normalized Difference Vegetation Index</em> (NDVI), <em>Environmental Criticality Index</em> (ECI)</p><p align="center"><strong><em><br /></em></strong></p><p> </p>

scholarly journals Evaluating the roles of green and built-up areas in reducing a surface urban heat island using remote sensing data

Urbani izziv ◽  
2019 ◽  
Vol 2 (30) ◽  
pp. 105-112
Author(s):  
Gordana Kaplan
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Heat Island ◽  
Negative Effects ◽  
Rapid Urbanization ◽  
Green Areas ◽  
Urban Heat ◽  
Surface Urban Heat Island

Rapid urbanization has several negative effects on both the environment and human health. Urbanization has also become an important contributor to global warming. One of these effects is the urban heat island (UHI), which is caused by human activities and defined as the temperature difference between urban and surrounding rural areas. With rapid urbanization in the past few decades, Skopje has experienced remarkable UHI effects. To investigate the roles of built-up and green areas in a surface UHI, this article uses satellite data from Landsat ETM+ to analyse the land surface temperature and high-resolution Planet Scope DOVE data to analyse built-up and green areas. For geostatistical analyses, seventeen randomly selected subareas in Skopje were used. The results show a significant correlation between the UHI and built-up areas, and strong correlation between green areas and areas not affected by the UHI, indicating that the UHI effect can be significantly weakened with additional green areas. One of the significant findings in the study is the ideal proportion of built-up (40%) and green areas (60%), where the UHI effect is weak, or in some cases prevented. For future studies, investigating other factors that may contribute to the UHI phenomenon is suggested.

Measuring UHI using Landsat 8 OLI and TIRS data with NDVI and NDBI in Municipality of Prishtina

2021 ◽  
Vol 14 (11) ◽  
pp. 25-36
Author(s):  
Florim Isufi ◽  
Albert Berila ◽  
Shpejtim Bulliqi
Keyword(s):  
Regression Analysis ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Strong Positive Correlation ◽  
Strong Negative Correlation ◽  
Urban Heat ◽  
Surface Urban Heat Island

The study is aimed at investigating the phenomenon of the Surface Urban Heat Island (SUHI) over the municipality of Prishtina. The SUHI was investigated based on the relationship between Land Surface Temperature (LST) estimated from Landsat 8 Thermal Infrared Sensor (TIRS) band with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager (OLI) bands using Geographic Information System (GIS). To understand this relationship, a regression analysis was performed. Regression analysis in both cases showed high relationships between LST, NDVI and NDBI. LST relationships with NDVI showed a strong negative correlation having an R2 value of 0.7638 highlighting the extraordinary role of vegetation towards reducing the SUHI effect while LST relationships with NDBI showed a strong positive correlation having an R2 value of 0.8038 highlighting the role that built-up areas have in strengthening the SUHI effect. Built-up areas and bare surfaces are responsible for generating the SUHI effect while vegetation and water bodies minimize this effect by creating freshness. The maps in which the SUHI phenomenon are identified, are extremely important and should be paid great attention by the city leaders themselves. This should be done in order for urban planning policies to go to those areas where such a harmful phenomenon occurs in order for the lives of citizens to be as healthy as possible.

scholarly journals Urban Thermal Environment and Heat Island in Ho Chi Minh City, Vietnam from Remote Sensing Data

2017 ◽  
Author(s):  
Van Tran Thi ◽  
Bao Ha Duong Xuan ◽  
Mai Nguyen Thi Tuyet
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Urban Areas ◽  
Ho Chi Minh City ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The Impact

In urban area, one of the great problem is the rise of temperature, which leads to form the urban heat island effect. This paper refers to the trend of the urban surface temperature extracted from the Landsat images from which to consider changes in the formation of surface urban heat island for the north of Ho Chi Minh city in period 1995-2015. Research has identified land surface temperature from thermal infrared band, according to the ability of the surface emission based on characteristics of normalized difference vegetation index NDVI. The results showed that temperature fluctuated over the city with a growing trend and the gradual expansion of the area of the high-temperature zone towards the suburbs. Within 20 years, the trend of the formation of surface urban heat island with two typical locations showed a clear difference between the surface temperature of urban areas and rural areas with space expansion of heat island in 4 times in 2015 compared to 1995. An extreme heat island located in the inner city has an area of approximately 18% compared to the total area of the region. Since then, the solution to reduce the impact of urban heat island has been proposed, in order to protect the urban environment and the lives of residents in Ho Chi Minh City becoming better

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 The localization of urban heat island in the Katowice conurbation (Poland) using the combination of land surface temperature, Normalized Difference Vegetation Index and Normalized Difference Built-up Index

2021 ◽  
Vol 94 (1) ◽  
pp. 111-129
Author(s):  
Ádám Nádudvari
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Surface ◽  
Temperature Measurements ◽  
Heat Island ◽  
Urban Heat

The localization of Surface Urban Heat Island (SUHI) as a potential heat risk for the urban population was evaluated. The paper aimed to propose an approach to quantify and localize (SUHI) based on Landsat series TM, ETM+, OLI satellite imageries from the period 1996-2018 and recognize the Atmospheric Urban Heat Island (AUHI) effects from long term temperature measurements. Using the theoretical relation between the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the LST (Land Surface Temperature), SUHIintensity and SUHIrisk maps were created from the combination of LST, NDVI, NDBI using threshold values to localize urban heat island in the Katowice conurbation. Negative values of SUHI intensity characterize areas where there is no vegetation, highly built-up areas, and areas with high surface temperatures. The urban grow – revealed from SUHI – and global climate change are acting together to strengthen the global AUHI effect in the region as the temperature measurements were indicated.

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 Analisis Fenomena Pulau Panas Perkotaan Kota Bandung Menggunakan Google Earth Engine

2019 ◽  
Author(s):  
Muhammad Malik Ar-Rahiem ◽  
Muhamad Riza Fakhlevi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Google Earth Engine

Pulau Panas Perkotaan (Urban Heat Island) adalah fenomena antropogenik akibat pengaruh urbanisasi. Kawasan perkotaan yang terbangun memiliki temperatur yang lebih hangat dibandingkan kawasan sekitarnya. Fenomena Pulau Panas Perkotaan di Kota Bandung diteliti menggunakan data Suhu Permukaan Tanah (Land Surface Temperature) yang diakuisisi dari satelit Landsat 8. Lima tahun data satelit dianalisis menggunakan piranti daring Google Earth Engine untuk menganalisis variasi temporal Pulau Panas Perkotaan di Kota Bandung dan sekitarnya. Suhu yang diakuisisi dari satelit dikonversi menjadi estimasi suhu permukaan dengan mempertimbangkan nilai Normalized Difference Vegetation Index. Hasil dari penelitian ini adalah peta persebaran rata-rata dan median suhu permukaan di Cekungan Bandung tahun 2013-2018, serta grafik seri waktu suhu permukaan di 3 jenis tata guna lahan yang mewakili daerah kota (sekitar Jalan Sudirman), hutan kota (Hutan Babakan Siliwangi), dan hutan (Tamah Hutan Raya Djuanda). Suhu rata-rata Kota Bandung pada tahun 2013-2018 adalah 26,93 oC (median seluruh data) dan 25,57oC (rata-rata seluruh data). Sementara perbandingan berdasarkan tata guna lahan; daerah kota memiliki suhu permukaan rata-rata 27,30 oC, daerah hutan kota memiliki suhu 21,31oC, dan daerah hutan memiliki suhu 18,60oC. Peta persebaran suhu panas permukaan dari citra Landsat 8 menunjukkan bahwa daerah hutan secara konsisten memiliki suhu paling rendah, diikuti dengan hutan kota, dan kemudian daerah kota menjadi area yang paling panas dengan suhu maksimal hingga 33,73oC. Penggunaan Google Earth Engine yang berbasis komputasi awan sangat memudahkan pengolahan data citra satelit dalam jumlah besar yang selama ini tidak memungkinkan dilakukan dengan cara konvensional (mengunduh dan memproses di komputer).

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