Geospatial assessment of land surface temperature in Nagpur, India: an impact of urbanization

Sakhre, Saurabh; Dey, Jaydip; Vijay, Ritesh; Kumar, Rakesh

doi:10.1007/s12665-020-08952-1

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…In this process, the cities take up the areas which were formerly used for agricultural purposes. These suburbs of the cities now serve residential and industrial functions [7]. This sprawl calls for infrastructural development, especially for transportation, since people are likely to live far from the workplace and need daily commuting to and from one end of the city to another.…”

Section: Urbanizationmentioning

confidence: 99%

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Islam¹,

Jan²,

Waheed³

et al. 2022

Computers, Materials &Amp; Continua

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Spatial and temporal information on urban infrastructure is essential and requires various land-cover/land-use planning and management applications. Besides, a change in infrastructure has a direct impact on other land-cover and climatic conditions. This study assessed changes in the rate and spatial distribution of Peshawar district's infrastructure and its effects on Land Surface Temperature (LST) during the years 1996 and 2019. For this purpose, firstly, satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper) plus and OLI (Operational Land Imager) of 30 m resolution were taken. Secondly, for classification and image processing, remote sensing (RS) applications ENVI (Environment for Visualising Images) and GIS (Geographic Information System) were used. Thirdly, for better visualization and more in-depth analysis of land sat images, pre-processing techniques were employed. For Land use and Land cover (LU/LC) four types of land cover areas were identified -vegetation area, water cover, urbanized area, and infertile land for the years under research. The composition of red, green, and near infra-red bands was used for supervised classification. Classified images were extracted for analyzing the relative infrastructure change. A comparative analysis for the classification of images is performed for SVM (Support Vector Machine) and ANN (Artificial Neural Network). Based on analyzing these images, the result shows the rise in the average temperature from 30.04 • C to 45.25 • C. This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km 2 from 1996 to 2019. It has also been witnessed that

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Section: Urbanizationmentioning

confidence: 99%

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Islam¹,

Jan²,

Waheed³

et al. 2022

Computers, Materials &Amp; Continua

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“…In this study, satellite images were classified into six distinct categories namely built-up, beach, grassland, open land, vegetation, and waterbody. An emphasis has been given to the built-up area because alteration of other land use categories into built-up helps to increase LST (Sakhre et al, 2020). LULC was performed using the maximum likelihood algorithm for supervised classification technique in ERDAS Imagine software.…”

Section: Section I: Spectral Indices and Lulc Analysismentioning

confidence: 99%

“…Temperature is one of the significant climatic parameters that influenced micro as well as global climate (Baede et al, 2001;Zaksek and Ostir, 2002;Weng, 2009). Literature is telling that the world's temperature is increasing continuously for the last 150 years (Sakhre et al, 2020;Vayssade et al, 2021). In this era of globalization, urbanization especially migration toward the city is increasing drastically.…”

Section: Introductionmentioning

confidence: 99%

Impact of land-use dynamics on land surface temperature in Mumbai city, India: A geospatial approach

Waghchaure,

Vijay,

Dey

et al. 2022

J ENVIRON INFORM LET

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To assess and monitor the environmental dynamics on a regional or global scale, Land Surface Temperature (LST) has been estimated for South Mumbai, using Landsat data for the years 2000, 2010, 2015, and 2020. The urban heat island (UHI) effect has also been assessed by analysing the LST pattern in the study area. The normalized difference vegetation index (NDVI) analysis shows that LST and UHI effects are less when vegetation cover is high. On the contrary, the normalized difference built-up index (NDBI) is directly proportional to LST which indicates the impact of human activities on LST as well as UHI. The relationship between LST of the study area and ambient air temperature has shown a strong correlation with an increasing trend of LST from 2000 to 2020. The study reveals that the average LST of Mumbai has been increased from 27.1 to 32.7 °C in the last twenty years. The ward-wise temperature profile analysis has been carried out to address the worst thermal discomfort zone and associated population. The study suggests increasing the green space for maintaining the average LST in Mumbai. This study provides a baseline for future studies like LST and human health, climate change, assessment of the ecological status, etc. of the urban environment.

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“…It has a tropical rainy and dry atmosphere, with dry weather for most of the year. Nagpur has an average annual precipitation of 1000 to 1300 mm, with 80 percent of it coming during the monsoon season (Sakhre et al, 2020). Summers are scorching, march to June is the summer with May being the hottest month of the year.…”

Section: Study Areamentioning

confidence: 99%

“…For the past 150 years, annual temperatures have been rising continuously all across the world. However, different ground surfaces have different emissivity, temperature-bearing properties Sakhre et al, 2020). Some research shows that on a global scale, land and temperature changes have a negative impact on climatic conditions (Alves & L Skole, 1996).…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Das

Mishra

et al. 2021

Journal of Landscape Ecology

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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.

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Geospatial assessment of land surface temperature in Nagpur, India: an impact of urbanization

Cited by 9 publications

References 40 publications

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Impact of land-use dynamics on land surface temperature in Mumbai city, India: A geospatial approach

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

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