Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

Alexander, Cici

doi:10.1016/j.jag.2019.102013

Cited by 105 publications

(55 citation statements)

References 51 publications

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“…From a comparison of the spatial patterns of LST and driving factors in Figure 6, a general conclusion could be made: that high LST was related to ISA, and that lower LST was usually located in the vegetation and snow areas, which was consistent with previous studies [11,26]. However, the quantitative analysis of their relationships in different seasons should be explored in detail.…”

Section: Relationships Between Lst and Driving Factorssupporting

confidence: 84%

“…To investigate the formation and driving mechanism of SUHI, numerous studies examined the relationships between LST and its driving factors [20,[25][26][27][28][29][30][31][32]. The driving factors could be roughly classified into three categories: surface biophysical parameters, landscape component and configuration factors, and socioeconomic factors [27].…”

Section: Introductionmentioning

confidence: 99%

“…The driving factors could be roughly classified into three categories: surface biophysical parameters, landscape component and configuration factors, and socioeconomic factors [27]. The surface biophysical parameters were usually composed of many spectral indices, among which normalized difference vegetation index (NDVI), normalized difference building index (NDBI), and normalized difference water index (NDWI) were the most used because they are easily calculated [26,33]. However, although they exhibited good linear relationships with LST, the relationship between LST and NDVI suffers from evident seasonal changes [11].…”

Section: Introductionmentioning

confidence: 99%

“…First, relevant SUHI research has been focused on tropical, subtropical, and temperate cities, whereas a snow-climate city like Changchun should be given more attention, although several studies have been conducted in the city [26,[43][44][45][46][47]. In Cici Alexander's paper, AlgAarhus Kommune in Denmark was selected as the study area, as relevant research is under-represented and sensitive at high latitudes [26]. The city has high latitudes, above 55 • , and has an average annual temperature of 7.8 • C, which is even higher than that of Changchun (5.5 • C).…”

Section: Introductionmentioning

confidence: 99%

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Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

et al. 2020

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Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.

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Section: Relationships Between Lst and Driving Factorssupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

et al. 2020

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“…Segundo Alexander (2020), as resoluções espaciais de bandas termais são menores que as do visível e do infravermelho, a exemplo do sensor Espectrorradiômetro de Imagem de Resolução Moderada (MODIS). No entanto, os dados produzidos por meio desse sensor constituem uma alternativa para a obtenção de dados em larga escala por possuir uma frequência diária de imageamento.…”

Section: Introductionunclassified

Uso de Sensoriamento Remoto na análise da temperatura da superfície em áreas de floresta tropical sazonalmente seca

Santos¹,

Carvalho²,

Silva³

et al. 2020

Rev. Bras. Geog. Fis.

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Pesquisas com o foco na criação de modelos para analisar a temperatura da superfície com sensoriamento remoto são muito importantes, pois servem como base de informações que auxiliarão no gerenciamento hidrológico, agrícola e ambiental. O presente estudo utilizou dados remotos na estimativa de temperatura da superfície em áreas de florestas secas, e seu objetivo foi verificar qual entre os produtos MODIS melhor representa a temperatura da superfície em áreas de caatinga. A área de estudo compreende uma caatinga preservada e outra em sucessão ecológica. Foram utilizadas como base espacial, dados de temperatura da superfície e de NDVI obtidos a partir do sensor MODIS e dados de temperatura do ar mensuradas por meio de estações agrometeorológicas de superfície. A análise se deu por regressão linear e coeficiente de correlação de Pearson entre a temperatura estimada pelo satélite e do ar na superfície. Os resultados apresentaram variações temporais nas estimativas pelo MODIS, sendo que os produtos diurnos do Terra e noturnos do Aqua foram os que melhor representaram a temperatura máxima e mínima. Para isso, equações de ajustes são propostas para áreas de caatinga com diferentes tipos de cobertura. As variações na temperatura estavam de acordo com a sazonalidade do NDVI da floresta e, ao longo dos dezessete anos, foi verificado aumento nos valores de temperatura nas áreas de estudo. Use of Remote Sensing in Surface Temperature Analysis in Seasonally Dry Tropical Forest A B S T R A C TResearch with a focus on creating models to analyze the surface temperature with remote sensing is very important, as they serve as a base of information that will assist in hydrological, agricultural, and environmental management. The present study used remote data to estimate surface temperature in dry forest areas, and its objective was to verify which of the MODIS products best represents the surface temperature in caatinga areas. The study area comprises a preserved caatinga and another in ecological succession. Surface temperature data and NDVI obtained by MODIS sensor and air temperature data measured using surface agrometeorological stations at each site were used. The analysis was done by linear regression and Pearson's correlation coefficient between the temperature estimated by the satellite and the air temperature on the surface. The results showed seasonal variations on the MODIS data, and the data that best fitted the maximum and minimum temperatures were obtained from Terra daytime and Aqua nighttime. Equations are proposed to best adjustments of the data according to the caatinga surface vegetation cover. The temperature variations were, according to the NDVI forest seasonality, and over the seventeen years, the temperature increased in both studied sites.Keywords: Caatinga Biome, LST, NDVI, MODIS

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Agricultural Field Analysis Using Satellite Surface Reflectance Data and Machine Learning Technique

Wyawahare¹,

Kulkarni²,

Kulkarni³

et al. 2020

Communications in Computer and Information Science

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Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

Cited by 105 publications

References 51 publications

Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

Uso de Sensoriamento Remoto na análise da temperatura da superfície em áreas de floresta tropical sazonalmente seca

Agricultural Field Analysis Using Satellite Surface Reflectance Data and Machine Learning Technique

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