scholarly journals Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy

2020 ◽  
Vol 12 (8) ◽  
pp. 3186 ◽  
Author(s):  
Sabrina Lai ◽  
Federica Leone ◽  
Corrado Zoppi
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Spatial Data ◽  
Land Surface ◽  
Urban Areas ◽  
Regional Scale ◽  
Landsat 8 ◽  
Policy Recommendations ◽  
Mediterranean Shrubs ◽  
Land Covers

Land surface temperature (LST) is a key climate variable that has been studied mainly at the urban scale and in the context of urban heat islands. By analyzing the connection between LST and land cover, this study shows the potential of LST to analyze the relation between urbanization and heating phenomena at the regional level. Land cover data, drawn from Copernicus, and LST, retrieved from Landsat 8 satellite images, are analyzed through a methodology that couples GIS and regression analysis. By looking at the Italian island of Sardinia as a case study, this research shows that urbanization and the spatial dynamics of heating phenomena are closely connected, and that intensively farmed areas behave quite similarly to urban areas, whereas forests are the most effective land covers in mitigating LST, followed by areas covered with Mediterranean shrubs. This leads to key policy recommendations that decision-makers could implement to mitigate LST at the regional scale and that can, in principle, be exported to regions with similar climate and land covers. The significance of this study can be summed up in its novel approach to analyzing the relationship between LST and land covers that uses freely available spatial data and, therefore, can easily be replicated in other regional contexts to derive appropriate policy recommendations.

scholarly journals Analysis of Land Use Land Cover Changes with Land Surface Temperature Using Spatial-Temporal Data for Nagpur City, India

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Farhan Khan ◽  
Bhumika Das ◽  
R. K. Mishra ◽  
Brijesh Patel
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Spatial Data ◽  
Land Surface ◽  
Water Bodies ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Barren Land

Abstract Remote sensing and Geographic Information System (GIS) are the most efficient tools for spatial data processing. This Spatial technique helps in generating data on natural resources such as land, forests, water, and their management with planning. The study focuses on assessing land change and surface temperature for Nagpur city, Maharashtra, for two decades. Land surface temperature and land use land cover (LULC) are determined using Landsat 8 and Landsat 7 imageries for the years 2000 and 2020. The supervised classification technique is used with a maximum likelihood algorithm for performing land classification. Four significant classes are determined for classification, i.e., barren land, built-up, vegetation and water bodies. Thermal bands are used for the calculation of land surface temperature. The land use land cover map reveals that the built-up and water bodies are increasing with a decrease in vegetation and barren land. Likewise, the land surface temperature map showed increased temperature for all classes from 2000 to 2020. The overall accuracy of classification is 98 %, and the kappa coefficients are 0.98 and 0.9 for the years 2000 and 2020, respectively. Due to urban sprawl and changes in land use patterns, the increase in land surface temperature is documented, which is a global issue that needs to be addressed.

Impact of Urban Land Cover Types on Surface Temperature

2021 ◽  
Author(s):  
Marzie Naserikia ◽  
Melissa Hart ◽  
Negin Nazarian
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Urban Areas ◽  
Climatic Conditions ◽  
Landsat 8 ◽  
Local Climate ◽  
Land Cover Types ◽  
Cooling Effects

<p>The conversion of natural land to built-up surfaces has been widely documented as the main determinant of warming across urban areas. However, uncertainties remain regarding which primary land cover variables control urban heat in different climatic conditions at a global scale. While there is a very little understanding of how the cooling effects of vegetation cover vary over different cities, there is a deep knowledge gap in realizing how other land covers (such as soil, water, and built-up areas) are associated with urban warming and how this relationship is varied in different background climates. Accordingly, using a high spatial resolution dataset, a global synthetic investigation is needed to find the underlying factors influencing intra-urban temperature variability in various climates. To address this shortcoming, this study focuses on exploring the relationship between land surface temperature and land cover in different cities (using Landsat 8 imagery) and aims to investigate the effects of these land cover types on thermal environments in different climatic backgrounds. Preliminary analysis shows that different land cover types have different roles in different climate classes due to their various surface characteristics and in particular, the performance of green spaces to reduce LST is highly dependent on its background climate. For example, the efficiency of vegetation cover to reduce urban surface warming in temperate and tropical climates is more than that in arid and semi-arid areas. In this climate class, since baren soil is the main contributor to the intensity of LST, increasing the area of a green space presents an effective method to mitigate the adverse effects of local warming. Our findings provide helpful information for future urban climate-sensitive planning oriented at mitigating local climate warming in cities.</p>

scholarly journals A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 389-394 ◽  
Author(s):  
R. Bala ◽  
R. Prasad ◽  
V. P. Yadav ◽  
J. Sharma
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sensor Data ◽  
Landsat 8 ◽  
Temperature Relation

<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>

scholarly journals LAND USE ANALYSIS ON LAND SURFACE TEMPERATURE IN URBAN AREAS USING A GEOGRAPHICALLY WEIGHTED REGRESSION AND LANDSAT 8 IMAGERY, A CASE STUDY: TEHRAN, IRAN

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 117-122 ◽  
Author(s):  
A. Karimi ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Urban Areas ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Spatial Factors

Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R<sup>2</sup> value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial,military, transportation, and roads areas have the highest surface temperature.

scholarly journals Method of Land Cover Change and Number of Vehicles Increasing on Land Surface Temperature: Case Research of Surabaya City, East Java Province, Indonesia

2020 ◽  
Vol 9 (3S) ◽  
pp. 177-181
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface ◽  
Urban Areas ◽  
Motor Vehicles ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Urban Heat ◽  
The City

Urban Heat island phenomenon that occurs in some major cities to Indonesia can be caused by several factors like a construction of high building, land cover change and air pollution by motor vehicles. The phenomenon of small climate change in urban areas such as the city of Surabaya, the province of Jawa Timur can cause rising surface temperatures that can impact the decline in health and environmental degradation. This research was conducted to know the dynamics of land cover change, and dynamics of the vehicle to surface temperature in Surabaya city. Methods that are in use are a spatial analysis of multi-temporal imagery and analysis by using statistical data. The data used in surface temperature analysis and land cover analysis in the form of vegetation is Landsat-8 (OLI/TIRS) during the recording period from 2014 to 2019 and vehicle dynamics data. The results showed a change of land cover with an estimated area of 2989,8 ha, a rise in surface temperature with an estimate of 3.95 0C, and with the increase in a several of vehicles by 929.225 units from 2013 until 2018. Large surface temperature shows the areas with crowded activities such as settlements area, industrial zone, and government zone.

scholarly journals Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

2021 ◽  
Vol 10 (12) ◽  
pp. 809
Author(s):  
Jing Sun ◽  
Suwit Ongsomwang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
Lulc Change ◽  
Main Components ◽  
The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

scholarly journals Land Surface Temperature Retrieval from LANDSAT-8 Thermal Infrared Sensor Data and Validation with Infrared Thermometer Camera

2018 ◽  
Vol 7 (4.20) ◽  
pp. 608 ◽  
Author(s):  
Muhammad Mejbel Salih ◽  
Oday Zakariya Jasim ◽  
Khalid I. Hassoon ◽  
Aysar Jameel Abdalkadhum
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Infrared Sensor ◽  
Infrared Thermometer ◽  
Ground Truth Data ◽  
Land Cover Types

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LANDSAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the measurements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the imaged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measurement taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LANDSAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.   

Correlation of air temperature and land surface temperature of Landsat-8 in cities of South Korea

2020 ◽  
Author(s):  
Sungwon Choi ◽  
Donghyun Jin ◽  
Noh-hun Seong ◽  
Daeseong Jung ◽  
Kyung-soo Han
Keyword(s):  
Correlation Analysis ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Data ◽  
Landsat 8 ◽  
In Situ Data ◽  
Urban Greenery ◽  
Important Position

&lt;p&gt;Recently, there are many problems in urban area such as urban thermal island phenomenon, changes in urban green area, changes in urban weather and various urban types. And surface temperature data have been utilized in many areas to identify these phenomena. This means that surface temperatures is an important position in urban greenery and weather. High temporal and spatial resolution satellite data are needed to continuously observe the phenomenon in urban areas. In addition, the surface temperature varies from type of indicator, topography, and various factors, so there is a limit to the in-situ data for observing changes throughout the city. Therefore, various organizations around the world are currently conducting surface temperature measurements using satellites. However, the use of data in clear pixel is essential for accurate surface temperature calculations using satellites, but the accuracy of results will be reduced if the data from in the pixel which conclude clouds.&lt;/p&gt;&lt;p&gt;Therefore, we tried to solve these problems by analyzing the correlation between the air temperature data and the Landsat-8 LST data. The variables used in the correlation analysis are air temperature, Landsat-8 LST, NDVI and NDWI, and the study period is 2014 to 2016 and the study area is South Korea's five cities (Seoul, Busan, Daejeon, Daegu, Gwangju). For correlation analysis, the air temperature data points provided by the Korea Meteorological Administration and the Landsat-8 pixels were matched, and the correlation coefficient calculated by the correlation analysis was applied to the Landsat-8 satellite to calculate the LST. We validated by direct comparison the re-produced Landsat-8 LST with observed Landsat-8 LST. And the result of validation showed a high correlation of 0.9. It shows that compensation for the satellite's shortcomings from clouds by using the correlation between temperature and LST.&lt;/p&gt;

scholarly journals SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

2020 ◽  
Vol 11 (2) ◽  
pp. 94-110 ◽  
Author(s):  
Syed Riad Morshed Riad Morshed ◽  
Md. Abdul Fattah ◽  
Asma Amin Rimi ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Moisture Index ◽  
Type Transformation ◽  
Spatial Environment ◽  
Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

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