Remote sensing based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM+ data

Senanayake, I. P.; Welivitiya, W. D. D. P.; Nadeeka, PM

doi:10.1016/j.uclim.2013.07.004

Cited by 152 publications

(125 citation statements)

References 28 publications

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“…The first step of estimating a UHI pattern using satellite images is to extract LST [56]. Herein, the LST from Landsat TM was estimated using its thermal band (band 6), based on the method adopted by Li et al [48].…”

Section: Derivation Of Lst From the Satellite Imagesmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

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Section: Derivation Of Lst From the Satellite Imagesmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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“…Akibatnya, jumlah kelebihan energi panas menumpuk di sekitarnya. Fenomena ini menyebabkan daerah perkotaan memiliki suhu yang lebih tinggi dibandingkan dengan wilayah pedesaan (Comarazamy et al, 2010;Senanayake et al, 2013).…”

Section: Pendahuluanunclassified

“…Vegetasi menurunkan suhu udara melalui proses evapotranspirasi, yaitu uap air dilepaskan ke atmosfer sekitarnya. Sayangnya vegetasi digantikan oleh permukaan tanah buatan seperti jalan, bangunan, trotoar, lapangan parkir beton, dan lain-lain (Hung et al, 2010;Senanayake et al, 2013). Sifat bahan yang digunakan dalam pembangunan struktur perkotaan menimbulkan emisivitas panas, sebagai sebab utama dalam pembentukan UHI.…”

Section: Pendahuluanunclassified

“…Sifat bahan yang digunakan dalam pembangunan struktur perkotaan menimbulkan emisivitas panas, sebagai sebab utama dalam pembentukan UHI. Limbah panas yang dihasilkan oleh pabrik, pendingin ruangan (AC) dan kendaraan bermotor di daerah perkotaan berkontribusi juga dalam pembentukan UHI (Senanayake et al, 2013;Isima et al, 2016;Li & Norford, 2016;Fazeli et al, 2016).…”

Section: Pendahuluanunclassified

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Model Kekritisan Indeks Lingkungan Dengan Algoritma Urban Heat Island Di Kota Semarang

Sasmito¹,

Suprayogi²

2017

MIG

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ABSTRAKPembangunan 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 yangterjadi 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 Urban Heat Island (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 Land Surface Temperature (LST) dan sebaran vegetasi adalah dengan algoritma Normalized Difference Vegetation Index (NDVI). LST didapatkan dengan mengolah Citra Landsat-8 band TIRS (Thermal Infrared Red Sensor), sedangkan NDVI didapatkan dengan mengolah Citra Landsat-8 band OLI (Operation Land Imager). Langkah kedua adalah membuat peta kekritisan lingkungan dengan algoritma ECI (Environmental Criticality Index). ECI didapatkan dari nilai LST dibagi NDVI yang direntangkan histogram spektralnya menjadi 8 bit. Melalui 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. Kata kunci: Urban Heat Island (UHI), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Environmental Criticality Index (ECI) ABSTRACT 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

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“…Chen et al (2006) used NDWI as one of indexes to know the characteristics of land cover in an area. Besides, Senanayake et al, (2013) also used NDWI in analyzing the vegetated land coverto separate the objects of water and cloud. Index value NDWI was big or increase on vegetation that had a lot of water content, or index increased from the dry ground object to the open water (Molidena et al, 2012).…”

mentioning

confidence: 99%

Detection of Green Open Space Using Combination Index of Landsat 8 Data (Case Study: Dki Jakarta)

Sulma

Nugroho

Zubaidah

et al. 2017

IJReSES

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Remote sensing based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM+ data

Cited by 152 publications

References 28 publications

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Model Kekritisan Indeks Lingkungan Dengan Algoritma Urban Heat Island Di Kota Semarang

Detection of Green Open Space Using Combination Index of Landsat 8 Data (Case Study: Dki Jakarta)

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