scholarly journals Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology

2020 ◽  
Vol 12 (17) ◽  
pp. 2854 ◽  
Author(s):  
Mohammad Karimi Firozjaei ◽  
Solmaz Fathololoumi ◽  
Naeim Mijani ◽  
Majid Kiavarz ◽  
Salman Qureshi ◽  
...  
Keyword(s):  
Los Angeles ◽  
Surface Temperature ◽  
Feature Space ◽  
Least Square ◽  
Anthropogenic Heat ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Heat Island ◽  
Spectral Indices ◽  
Spatial Coverage

The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R2) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R2 = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability.

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

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 EXTRACTION OF BUILT-UP AREA BY COMBINING TEXTURAL FEATURES AND SPECTRAL INDICES FROM LANDSAT-8 MULTISPECTRAL IMAGE

2018 ◽  
Vol XLII-5 ◽  
pp. 727-733 ◽  
Author(s):  
V. S. Bramhe ◽  
S. K. Ghosh ◽  
P. K. Garg
Keyword(s):  
Correlation Energy ◽  
Feature Space ◽  
Cost Effective ◽  
Misclassification Error ◽  
Support Vector ◽  
Svm Classifier ◽  
Landsat 8 ◽  
Textural Features ◽  
Spectral Indices ◽  
Spectral Channels

<p><strong>Abstract.</strong> Remote sensing techniques provide efficient and cost-effective approach to monitor the expansion of built-up area, in comparison to other traditional approaches. For extracting built-up class, one of the common approaches is to use spectral and spatial features such as, Normalized Difference Built- up index (NDBI), GLCM texture, Gabor filters etc. However, it is observed that classes such as river soil and fallow land usually mix up with built-up class due to their close spectral similarity. Intermixing of classes have been observed in the classified image when using spectral channels. In this paper, an approach has been proposed which uses urban based spectral indices and textural features to extract built-up areas. Three well known spectral indices i.e. NDBI, Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBai) have been used in this work. Along with spectral indices, local spatial dependency of neighborhood regions is captured using eight GLCM based textural feature, such as, Contrast, Correlation, Energy and Homogeneity etc. for each image band. All textural and spectral indices bands are combined and used for extracting built-up areas using Support Vector Machine (SVM) classifier. Results suggest 4.91% increase in overall accuracy when using texture and spectral indices in comparison with 84.38% overall accuracy achieved when using spectral data only. It is observed that built-up class are more separable in the projected spectral-spatial feature space in comparison to spectral channels. Incorporation of textural features with spectral features reduces the misclassification error and provides results with less salt and pepper noise.</p>

scholarly journals Analisis Heat Island pada Perkebunan Kelapa Sawit: Studi Kasus di Kabupaten Kayong Utara, Kalimantan Barat

2020 ◽  
Vol 8 (2) ◽  
pp. 106-115
Author(s):  
Nurul Ihsan Fawzi ◽  
Marindah Yulia Iswari
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Palm Oil ◽  
Secondary Forest ◽  
Remote Sensing Data ◽  
Primary Forest ◽  
Landsat 8 ◽  
Heat Island ◽  
Local Climate ◽  
Island Effect

Between 2000 – 2017, 3.06 million hectares of primary forest in Kalimantan have been converted into palm oil plantation. This change impacts local climate changes. This study aims is to analyze the heat island in palm oil plantation. The analytical method used surface temperature estimation through remote sensing and zonal statistics. The remote sensing data that are used is Landsat 8 images acquired on 15 July 2018 and 3 August 2019. From this research, we found that young palm oil plantations have an average IHI value of 2.1 ± 1.7oC in 2018 and 1.7 ± 1.4oC in 2019. The IHI value is close to the heat island in a built-up area. IHI for mature palm oil plantation (11-12 years) created a cool island with an intensity close to secondary forest. The decreasing value of IHI for 2018 and 2019 in palm oil plantations is due to the growth of palm oil trees, which decreases surface temperature. The implication of this research is to know heat island effect due to deforestation or land cover changes, especially change into palm oil plantations.

scholarly journals MODELING THE IMPACT OF SURFACE CHARACTERISTICS ON THE NEAR SURFACE TEMPERATURE LAPSE RATE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 395-399 ◽  
Author(s):  
M. K. Firozjaei ◽  
S. Fathololuomi ◽  
S. K. Alavipanah ◽  
M. Kiavarz ◽  
A. Vaezi ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Feature Space ◽  
Surface Characteristics ◽  
Lapse Rate ◽  
Landsat 8 ◽  
Near Surface ◽  
Temperature Lapse Rate ◽  
Topographic Characteristics ◽  
The Impact

Abstract. Modeling of Near-Surface Temperature Lapse Rate (NSTLR) is very important in various environmental applications. The Land Surface Temperature (LST) is influenced by many properties and conditions including surface biophysical and topographic characteristics. Some researches have considered the LST - Digital Elevation Model (DEM) feature space to model NSTLR. However, the influence of detailed surface characteristics is rare. This study investigated the impact of surface characteristics on the LST-DEM feature space for NSTLR modeling. A set of remote sensing data including Landsat 8 images, MODIS products, and surface features including DEM and land use of the Balikhli-Chay on 01/07/2018, 18/08/2018 and 03/09/2018 were collected and used in this study. First, Split Window (SW) algorithm was used to estimate LST, and spectral indices were employed to model surface biophysical characteristics. Owing to the impact of surface biophysical and topographic characteristics on the LST-DEM feature space, the NSTLR was calculated for different classes of surface biophysical characteristics, land use, and solar local incident angle. The modeled NSTLR values based on the LST-DEM feature space on 01/07/2018, 18/08/2018 and 03/09/2018 were 8.5, 1.5 and 2.4 °C Km−1; respectively. The NSTLR in different classes of surface biophysical characteristics, land use type and topographical parameters were variable between 0.5 to 14 °C Km−1. This clearly showed the dependence of NSTLR on topographic and biophysical conditions. This provides a new way of calculating surface characteristic specific NSTLR.

scholarly journals Impact of urban environment on land surface temperature in local climate zones

2021 ◽  
Vol 333 ◽  
pp. 02008
Author(s):  
Anna Gosteva ◽  
Sofia Ilina ◽  
Aleksandra Matuzko
Keyword(s):  
Surface Temperature ◽  
Urban Environment ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Landsat 8 ◽  
Local Climate ◽  
Spatial Coverage ◽  
Artificial Environment ◽  
Local Climate Zones

The replacement of the natural landscape by artificial environment has led to changes in the ecosystem and physical properties of the surface, such as heat storage capacity, and thermal conductivity properties. These changes increase the difficulty of heat transfer between urban areas and the environment. Land surface temperature (LST) images from various satellites are widely used to represent urban thermal environments, which are more convenient and intuitive way. LST maps provide full spatial coverage, which distinguishes them from air temperature data obtained from meteorological stations. The study of LST according to the Landsat 8 data of Krasnoyarsk city over the past 10 years allowed the authors to talk about the observation of constant seasonal urban heat islands (UHI). For a more detailed consideration of the urban environment, this study further considers urban landscapes, thus the idea of local climate zone (LCZ) is introduced to study these diverse impacts in addition to the traditional map of LST. And analysis of the interaction of UHI and LCZ.

scholarly journals Estimation of Land Surface Temperature Using LANDSAT 8 Satellite Data of Panchkula District, Haryana

2020 ◽  
pp. 47-66
Author(s):  
Ravi Kumar ◽  
Anup Kumar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Global Scale ◽  
Sensor Data ◽  
Landsat 8 ◽  
Heat Island ◽  
The Earth ◽  
Satellite Sensor Data

Land surface temperature (LST) represents hotness of the surface of the Earth at a particular location. Land surface temperature is useful for meteorological, climatological changes, heat island, agriculture, hydrological processes at local, regional and global scale. Presently many satellite sensor data are available for calculation of land surface temperature like Landsat 8 and MODIS. In the present study land surface temperature in Panchkula district of Haryana have been calculated using Landsat 8 satellite data of 5th May 2019 and 28th October 2019. Already available equations were used for computation of LST in the study area. LST in the study area varies from 18°C to 56°C. High LST is observed in cultivation land, urban area while low LST is observed in hilly forest area in the study area. In the study validation of LST could not be done because of not available of temperature data of studied dates, however, the result gives idea of land surface temperature on a particular day and location.

scholarly journals FEATURE SELECTION OF VARIOUS LAND COVER INDICES FOR MONITORING SURFACE HEAT ISLAND IN TEHRAN CITY USING LANDSAT 8 IMAGERY

2017 ◽  
Vol 25 (3) ◽  
pp. 241-250 ◽  
Author(s):  
Nikrouz MOSTOFI ◽  
Mahdi HASANLOU
Keyword(s):  
Feature Selection ◽  
Land Cover ◽  
Surface Temperature ◽  
Rural Areas ◽  
Land Surface ◽  
Vegetation Indices ◽  
Surface Heat ◽  
Support Vector ◽  
Landsat 8 ◽  
Heat Island

Recently, scientists have been taking a great interest in Global warming issue, since the global surface temperature has been significantly increased all through last century. The surface heat island (SHI) refers to an urban area that has higher surface temperatures than its surrounding rural areas due to urbanization. In this paper, Tehran city is used as case study area. This paper tries to employ a quantitative approach to explore the relationship between land surface temperature and the most widespread land cover indices, and select proper (urban and vegetation) indices by incorporating supervised feature selection procedures using Landsat 8 imageries. In this regards, genetic algorithm is incorporated to choose best indices by employing kernel base one, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE = 0.9324, NRMSE = 0.2695 and R2 = 0.9315).

scholarly journals Relationship between urban heat island and population density of districts in Ho Chi Minh City derived from Sentinel - 3 SLSTR data

2021 ◽  
Vol 62 (5) ◽  
pp. 67-75
Author(s):  
Ha Thu Thi Le ◽  
Trung Van Nguyen ◽  
Khoa Ngoc Nguyen ◽  
Phuong Dang Nguyen ◽  
Tuyet Thi Vo ◽  
...  
Keyword(s):  
Population Density ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface ◽  
Ground Surface ◽  
Ho Chi Minh City ◽  
Coefficient Of Determination ◽  
Heat Island ◽  
Infrared Spectral ◽  
Urban Heat

The urban heat island occurs due to the causes of the urbanization process, of which the main reason is an increase in population density leading to the changes in artificial objects on the ground surface. Recently, using the Split - Window algorithm for two thermal infrared spectral channels with wavelengths of 11 µm and 12 µm to calculate the daily surface temperature with two times of day and night serves to determine the change of land surface temperature. This method is intended to improve the reliability of the results and high technical efficiency. This study uses Sentinel - 3 SLSTR data to determine urban heat island in the districts of Ho Chi Minh City compared to areas bordering the city on May 15th 2020. In addition, population density is calculated according to the results of the census in 2020. The linear relationship between the urban heat island and population density was built with the coefficient of determination R2=0.64.

scholarly journals Identifikasi Urban Heat Island di Kota Solok menggunakan Algoritma Landsat-8 OLI Landsurface Temperature

2018 ◽  
Vol 19 (1) ◽  
pp. 31
Author(s):  
Adenan Yandra Nofrizal
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Urban Heat

Pembangunan yang terjadi di Kota Solok akan menyebabkan terjadinya perubahan penggunaan lahan. Perubahan penggunaan lahan yang terjadi dengan meningkatnya lahan terbangun akan menyebabkan naiknya suhu permukaan (surface temperature) yang dapat menyebabkan terjadinya urban heat island. Penelitian ini bertujuan untuk mengetahui suhu permukaan yang ada di Kota Solok dan daerah fenomena urban heat island dan hubungan antara perubahan penggunaan lahan terhadap suhu permukaan yang menyebabkan terjadinya urban heat island di daerah Kota Solok. Metode yang digunakan dalam penelitian ini yaitu dengan menggunakan salah satu model Land Surface Temperature untuk mengetahui suhu permukaan dengan menggunakan aplikasi pengolahan citra digital selain itu juga menggunakan metode Object Base Image Analyst (OBIA) untuk mendapatkan penggunaan lahan yang ada di Kota Solok. Dengan menggunakan metode yang digunakan akan didapatkan suhu permukaan yang ada di Kota Solok dan daerah fenomena Urban Heat Island serta hubungannya penggunaan lahan dengan suhu permukaan.Kata Kunci : Suhu Permukaan, OBIA, Penggunaan Lahan

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