scholarly journals MENGUKUR URBAN HEAT ISLAND MENGGUNAKAN PENGINDERAAN JAUH, KASUS DI KOTA YOGYAKARTA

2017 ◽  
Vol 19 (2) ◽  
pp. 195 ◽  
Author(s):  
Nurul Ihsan Fawzi
Keyword(s):  
Radiative Transfer ◽  
Urban Heat Island ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat

<p>Peningkatan suhu di kawasan perkotaan adalah salah satu dampak dari urbanisasi. Fenomena <em>Urban Heat Island</em> (UHI) merupakan salah satu akibat yang dihasilkan akibat peningkatan suhu tersebut. Diperlukan upaya untuk menilai perubahan suhu apakah dapat berbahaya bagi manusia atau tidak. Melalui pengukuran UHI, pengaruh pembangunan kota terhadap peningkatan suhu dapat diukur. Tujuan penelitian ini adalah untuk memberikan panduan ilmiah tentang perolehan intensitas dan distribusi UHI menggunakan teknik penginderaan jauh. Studi kasus yang dilakukan berada di Kota Yogyakarta, Daerah Istimewa Yogyakarta. Citra penginderaan jauh yang digunakan adalah citra Landsat 8 perekaman tanggal 24 Juni 2013, <em>path/row</em> 120/65. Metode ekstraksi suhu permukaan menggunakan inversi persamaan Planck dengan koreksi emisivitas dan atmosfer menggunakan <em>radiative transfer equation</em>. Hasil pengolahan didapatkan intensitas UHI sebesar <em>±</em>2,5<sup>o</sup>C pada Kota Yogyakarta dan intensitas UHI sebesar <em>±</em>3,23<sup>o</sup>C pada area yang diperluas dengan buffer 1 km dari batas Kota Yogyakarta. Hal ini menunjukkan bahwa pemilihan area penelitian untuk analisis UHI menjadi penting karena mempengaruhi nilai intensitas UHI dan distribusi UHI. Ditemukan kawasan Malioboro dan sekitarnya berpotensi kuat terjadi UHI, sehingga perlu upaya mitigasi UHI pada kawasan tersebut. Pengukuran UHI menggunakan penginderaan jauh dipengaruhi oleh hal-hal yang berkaitan dengan sistem penginderaan jauh, seperti validasi dan akurasi hasil estimasi, pengaruh atmosfer, dan perbedaan waktu intensitas puncak UHI. Metode perolehan UHI dalam penelitian ini dapat menjadi panduan ilmiah yang digunakan untuk dasar evaluasi pembangunan perkotaan untuk lebih baik. </p>

scholarly journals DINAMIKA SUHU PERMUKAAN DAN KERAPATAN VEGETASI DI KOTA CIREBON

2019 ◽  
Vol 6 (1) ◽  
pp. 23-31
Author(s):  
Moh Dede ◽  
Galuh Putri Pramulatsih ◽  
Millary Agung Widiawaty ◽  
Yanuar Rizky Rizky Ramadhan ◽  
Amniar Ati
Keyword(s):  
Transfer Equation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Radiative Transfer Equation ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Multi Temporal ◽  
Urban Heat ◽  
Landsat 5 Tm

Peningkatan suhu udara merupakan dampak dari pemanasan global serta berkurangnya vegetasi. Pada kawasan perkotaan, peningkatan suhu udara secara signifikan dapat memunculkan fenomena urban heat island yang dalam jangka panjang mampu mengubah iklim mikro. Estimasi suhu permukaan dan kerapatan vegetasi diperoleh dari data satelit penginderaan jauh secara multi-temporal. Penelitian ini bertujuan untuk menganalisis dinamika suhu permukaan dan kerapatan vegetasi di Kota Cirebon. Penelitian ini memanfaatkan data citra Landsat-5 TM dan Landsat-8 OLI yang divalidasi dengan data MODIS pada periode tahun 1998, 2008, serta 2018. Nilai suhu permukaan diekstraksi dengan radiative transfer equation, sedangkan informasi kerapatan vegetasi diperoleh dengan normalized difference vegetation index (NDVI). Interaksi antara suhu permukaan dan kerapatan vegetasi diketahui melalui analisis korelasi spasial. Sepanjang tahun 1998 hingga 2018 terjadi peningkatan suhu permukaan sebesar 1.18 oC yang disertai dengan menurunnya area bervegetasi rapat hingga 12.683 km2. Penelitian ini juga menunjukkan korelasi negatif yang signifikan antara suhu permukaan dan kerapatan vegetasi di Kota Cirebon. Suhu permukaan tertinggi terpusat pada CBD, pelabuhan, area rawan kemacetan, kawasan industri, dan terminal. Berdasarkan kajian ini, upaya menanggulangi suhu permukaan di Kota Cirebon perlu ditangani melalui penyediaan ruang terbuka hijau, green belt, maupun reforestrasi.

Analisis Pengaruh Kerapatan Vegetasi Terhadap Suhu Permukaan dan Keterkaitannya Dengan Fenomena UHI

2020 ◽  
Vol 21 (1) ◽  
pp. 99
Author(s):  
Dewi Miska Indrawati ◽  
Suharyadi Suharyadi ◽  
Prima Widayani
Keyword(s):  
Urban Heat Island ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Urban Heat ◽  
Split Window Algorithm

Kota Mataram adalahpusat dan ibukota dari provinsi Nusa Tenggara Barat yang tentunya menjadi pusat semua aktivitas masyarakat disekitar daerah tersebut sehingga menyebabkan peningkatan urbanisasi. Semakin meningkatnya peningkatan urbanisasi yan terjadi di perkotaan akan menyebabkan perubahan penutup lahan, dari awalnya daerah bervegetasi berubah menjadi lahan terbangun. Oleh karena itu, akan memicu peningkatan suhu dan menyebabkan adanya fenomena UHI dikota Mataram.Tujuan dari penelitian ini untuk mengetahui hubungan kerapatan vegetasi dengan kondisi suhu permukaan yang ada diwilayah penelitian dan memetakan fenomena UHI di Kota Mataram. Citra Landsat 8 OLI tahun 2018 yang digunakan terlebih dahulu dikoreksi radiometrik dan geometrik. Metode untuk memperoleh data kerapatan vegetasi menggunakan transformasi NDVI, LST menggunakan metode Split Window Algorithm (SWA) dan identifikasi fenomena urban heat island. Hasil penelitian yang diperoleh menunjukkan kerapatan vegetasi mempunyai korelasi dengan nilai LST. Hasil korelasi dari analisis pearson yang didapatkan antara kerapatan vegetasi terhadap suhu permukaan menghasilkan nilai -0,744. Fenomena UHIterjadi di pusat Kota Mataram dapat dilihat dengan adanya nilai UHI yaitu 0-100C. Semakin besar nilai UHI, semakin tinggi perbedaan LSTnya.

A comparative study of urban heat island effects in two Belarusian cities from satellite and ground-based observations

2021 ◽  
Author(s):  
Heorhi Burchanka ◽  
Yahor Prakopchyk ◽  
Tsimafei Schlender ◽  
Aleh Baravik ◽  
Siarhei Barodka
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
City Planning ◽  
Thermal Characteristics ◽  
Data Availability ◽  
Eastern Bloc ◽  
Temperature Fields ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat

&lt;p&gt;This study is devoted to analysis of urban development effects on surface thermal characteristics for the case of Belarusian cities of Minsk and Mahilo&amp;#365;. Both cities being situated on the same latitude (53.90 N) and not far from each other (~180 km distance), while also sharing a number of similar features typical for cities in Belarus (and in some other former Eastern Bloc countries as well), Minsk and Mahilo&amp;#365; nevertheless differ significantly in terms of their population, size and structure. It is therefore of interest to perform urban climate studies for these two cities in parallel.&lt;/p&gt;&lt;p&gt;First, we use geoinformation systems (QGIS), centralized city planning databases and Open Street Maps (OSM) vector data to implement description of Minsk and Mahilo&amp;#365; urban territories in terms of functional zones, taking into account such features as buildings density and urban area category (industrial, residential, business, recreational and other types).&lt;/p&gt;&lt;p&gt;Furthermore, we perform analysis of surface temperature fields for both cities from satellite data (Landsat-8) and ground-based observations, the latter including both regular meteorological stations (in urban as well as surrounding rural areas) and a volunteer network of weather and air quality sensors distributed in both cities as part of the AirMQ project [1]. We analyze observations for several months in the 2019-2021 period (depending on data availability), paying special attention to days with specific weather conditions (e.g. blocking anticyclones).&lt;/p&gt;&lt;p&gt;Analysis demonstrates clear evidence of significant urban heat island effects in thermal regimes of both cities, with specific areas of increased temperature related to urban zoning, industrial and green areas, buildings heights and density. However, the selected method of surface urban heat island (SUHI) detection turns out to be somewhat limited for the purposes of studying the effects of blocking anticyclones on urban heat island phenomena development, thereby calling for application of atmospheric numerical modelling techniques.&lt;/p&gt;&lt;p&gt;[1] AirMQ project, URL: https://airmq.by/&lt;/p&gt;

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 Utilization of thermal bands of Landsat 8 data and geographic information system for analysis of urban heat island in Baghdad governorate 2016

2018 ◽  
Vol 162 ◽  
pp. 03026 ◽  
Author(s):  
Alaa Khalaf
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Urban Heat Island ◽  
Water Body ◽  
Geographic Information ◽  
Landsat 8 ◽  
Heat Island ◽  
Green Area ◽  
Bare Land ◽  
Urban Heat

This study aimed to determine Urban Heat Island in Baghdad Governorate capital of Iraq by thermal bands of Landsat 8 images and Geographic Information System. Two cloud free Landsat 8 scenes covering the study area were selected for analysis. These scenes were located at path 168/37 and path 169/37. Images were acquired during daytime at 10:33 AM and it were dated August 19, 2016 and August 26, 2016, respectively. All images mentioned above were registered in Universal Transverse Mercator projection zone 38. Temperatures have been extracted from thermal bands 10 and 11 for each scene and then these results have been merged by mosaic process to produce one scene for band 10 and band 11. Finally, urban heat island has been extracted for study area by taking the average temperatures of band 10 and band 11. The final results shown that Baghdad Governorate contains four heat islands: water body, built-up area, green area, and bare land area. The Heat Island of bare land was highest, followed by green area, built-up area and water body. The highest summer daytime Heat Island was found on bare land (57°C), followed by green area (45 °C),built-up area (43 °C), and water body (37 °C).

scholarly journals Urban Heat Island Analysis Using the Landsat 8 Satellite Data: A Case Study in Skopje, Macedonia

Proceedings ◽  
2018 ◽  
Vol 2 (7) ◽  
pp. 358 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan ◽  
Zehra Yigit Avdan
Keyword(s):  
Urban Heat Island ◽  
Satellite Data ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat

scholarly journals Urban Heat Island Analysis over the Land Use Zoning Plan of Bangkok by Means of Landsat 8 Imagery

2018 ◽  
Vol 10 (3) ◽  
pp. 440 ◽  
Author(s):  
Chaiyapon Keeratikasikorn ◽  
Stefania Bonafoni
Keyword(s):  
Land Use ◽  
Urban Heat Island ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat

scholarly journals ANALYSIS OF THE INTRA-CITY VARIATION OF URBAN HEAT ISLAND AND ITS RELATION TO LAND SURFACE/COVER PARAMETERS

2016 ◽  
Vol III-8 ◽  
pp. 123-129
Author(s):  
D. Gerçek ◽  
İ. T. Güven ◽  
İ. Ç. Oktay
Keyword(s):  
Air Pollution ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface ◽  
Urban Form ◽  
View Factor ◽  
Landsat 8 ◽  
Heat Island ◽  
Surface Cover ◽  
Urban Heat

Along with urbanization, sealing of vegetated land and evaporation surfaces by impermeable materials, lead to changes in urban climate. This phenomenon is observed as temperatures several degrees higher in densely urbanized areas compared to the rural land at the urban fringe particularly at nights, so-called Urban Heat Island. Urban Heat Island (UHI) effect is related with urban form, pattern and building materials so far as it is associated with meteorological conditions, air pollution, excess heat from cooling. UHI effect has negative influences on human health, as well as other environmental problems such as higher energy demand, air pollution, and water shortage. <br><br> Urban Heat Island (UHI) effect has long been studied by observations of air temperature from thermometers. However, with the advent and proliferation of remote sensing technology, synoptic coverage and better representations of spatial variation of surface temperature became possible. This has opened new avenues for the observation capabilities and research of UHIs. <br><br> In this study, "UHI effect and its relation to factors that cause it" is explored for İzmit city which has been subject to excess urbanization and industrialization during the past decades. Spatial distribution and variation of UHI effect in İzmit is analysed using Landsat 8 and ASTER day & night images of 2015 summer. Surface temperature data derived from thermal bands of the images were analysed for UHI effect. Higher temperatures were classified into 4 grades of UHIs and mapped both for day and night. <br><br> Inadequate urban form, pattern, density, high buildings and paved surfaces at the expanse of soil ground and vegetation cover are the main factors that cause microclimates giving rise to spatial variations in temperatures across cities. These factors quantified as land surface/cover parameters for the study include vegetation index (NDVI), imperviousness (NDISI), albedo, solar insolation, Sky View Factor (SVF), building envelope, distance to sea, and traffic space density. These parameters that cause variation in intra-city temperatures were evaluated for their relationship with different grades of UHIs. Zonal statistics of UHI classes and variations in average value of parameters were interpreted. The outcomes that highlight local temperature peaks are proposed to the attention of the decision makers for mitigation of Urban Heat Island effect in the city at local and neighbourhood scale.

scholarly journals SOCIO-ECONOMIC VULNERABILITY TO URBAN HEAT IN PHOENIX, ARIZONA AND DALLAS, TEXAS DURING JUNE 2020

2020 ◽  
Vol VI-3/W1-2020 ◽  
pp. 59-66
Author(s):  
T. Moss ◽  
B. Kar
Keyword(s):  
Urban Heat Island ◽  
Temperature Difference ◽  
Hot Spot ◽  
Urban Morphology ◽  
Landsat 8 ◽  
Economic Factors ◽  
Heat Island ◽  
Economic Vulnerability ◽  
Urban Heat ◽  
Socio Economic Factors

Abstract. Urban expansion compounded by climate change appears to exacerbate the temperature difference between urban and rural areas. This temperature difference, known as the urban heat island (UHI) effect, results from lack of vegetation, increased impervious surfaces, excess heat released from human activities, and changing radiation and wind dynamics due to urban morphology. UHI has been found to increase heat-related illnesses, and in some instances, mortalities among vulnerable populations. Heat exposure is particularly pertinent in 2020, as stay-at-home orders and higher unemployment rates due to the COVID-19 pandemic have further exposed urban residents to local temperatures. Certain socio-economic groups that are more affected by COVID-19 are disproportionately exposed to high urban temperatures. We investigated the relationships between urban heat island intensity (UHII), normalized difference vegetation index (NDVI), and selected socio-economic factors for Dallas, TX and Phoenix, AZ for June 2020. We used an equal-weighting approach to combine socio-economic factors obtained from 2018 US Census Bureau data to determine socio-economic vulnerability, and used Landsat 8 imagery to derive NDVI and land surface temperature. Pearson’s correlation, hot spot analysis, and Moran’s I tests revealed that socio-economic vulnerability was higher in areas with high urban temperatures and decreased vegetation.

Sign in / Sign up

Export Citation Format

Share Document