Analysis of the Relationship between Land Surface Temperature and Vegetation and Built-Up Indices in Upper-Hill, Nairobi

Mwangi, P. W.; Karanja, Faith; Kamau, Paul

doi:10.4236/gep.2018.61001

Cited by 28 publications

(18 citation statements)

References 9 publications

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“…Linear regression has been commonly used for studying the correlation of LST with NDBI and NDVI [25,33,45,46] resulting in useful equations. In this study, multiple linear regression was used to model the relationship between LST (dependent variable), NDBI and NDVI (independent variables) and data used in the regression model are pictured in a graphical form with a 3D space in Figure 6.…”

Section: Resultsmentioning

confidence: 99%

The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran

Naserikia

Shamsabadi

Rafieian

et al. 2019

IJERPH

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In this study, the spatio-temporal changes of urban heat island (UHI) in a mega city located in a semi-arid region and the relationships with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) are appraised using Landsat TM/OLI images with the help of ENVI and ArcGIS software. The results reveal that the relationships between NDBI, NDVI and land surface temperature (LST) varied by year in the study area and they are not suitable indices to study the land surface temperature in arid and semi-arid regions. The study also highlights the importance of weather conditions when appraising the relationship of these indices with land surface temperature. Overall, it can be concluded that LST in arid and steppe regions is most influenced by barren soil. As a result, built-up areas surrounded by soil or bituminous asphalt experience higher land surface temperatures compared to densely built-up areas. Therefore, apart from setting-up more green areas, an effective way to reduce the intensity of UHI in these regions is to develop the use of cool and smart pavements. The experiences from this paper may be of use to cities, many of which are struggling to adapt to a changing climate.

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Section: Resultsmentioning

confidence: 99%

The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran

Naserikia

Shamsabadi

Rafieian

et al. 2019

IJERPH

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“…The high LST is recorded in the southwestern and central parts where there are low vegetation, high built-up land, and low elevation. LST has indirect proportion to vegetation [ 41 , 42 ] and slope [ 37 , 43 , 44 ] but direct proportion to built-up land [ 45 – 47 ]. The multiple regression model is very useful for the responsible predictor of land surface temperature.…”

Section: Discussionmentioning

confidence: 99%

Extents of Predictors for Land Surface Temperature Using Multiple Regression Model

2020

The Scientific World Journal

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Land surface temperature (LST) is a key factor in numerous areas such as climate change, land use/land cover in the urban areas, and heat balance and is also a significant participant in the creation of climate models. Landsat data has given numerous possibilities to understand the land processes by means of remote sensing. The present study has been performed to identify the LST of the study region using Landsat 8 OLI/TIRS satellite images for two time periods in order to compare the data. The study also attempted to identify and predict the role and importance of NDVI, NDBI, and the slope of the region on LST. The study concludes that the maximum and minimum temperatures of 40.44 C and 20.78 C were recorded during the November month whereas the maximum and minimum LST for month March has increased to 42.44 C and 24.57 C respectively. The result indicates that LST is inversely proportional to NDVI (−6.369) and slope (−0.077) whereas LST is directly proportional to NDBI (+14.74). Multiple linear regression model has been applied to calculate the extents of NDVI, NDBI, and slope on the LST. It concludes that the increase in vegetation and slope would result in slight decrease in temperature whereas the increase in built-up will result in a huge increase in temperature.

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“…Converting to Top of Atmosphere (TOA) from DN: The digital number (DN) spectral information was transformed into TOA radiometric values for Landsat 7 (Equation 2) and Landsat 8 (Equation 3). Band 6a in Landsat 7 was used for the analysis as it has a low radiance variance compared to band 6b (Patricia Wanjiku Mwangi, Karanja, & Kamau, 2018 The effective temperature viewed by a satellite with an assumption of unity emission is the at-satellite brightness. This is calculated for thermal bands only in equation for both Landsat 7 and Landsat 8 (Equation 7).…”

Section: 41mentioning

confidence: 99%

Contribution Index of Land Cover and Land Surface Temperature Changes in Upper Hill Nairobi, Kenya

Mwangi

Karanja

Kamau

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Urban heat island is the difference in thermal temperature between rural and urban areas. The urbanization process alters the material type with impervious surfaces being absorbers of incoming radiation during the day and emitting it at night. The research involved the use of time-series satellite imagery from Sentinel, Landsat, ASTER and MODIS for the period 1986, 1995, 2000, 2005, 2011, 2015 and 2017 over the Upper Hill, Nairobi. Morning, afternoon and night land surface temperatures (LST) were calculated for each of these years and analyzed together with the land cover. The mean albedo was calculated to determine the relationship between each land cover and mean LST. The contribution index was calculated to determine whether a land contributed positively or negatively to the mean LST in Upper Hill. Results indicated that built-up land cover had increased from 1986 to 2017 by 0.86% per annum while forest land cover had decreased by 0.99% per annum. Sparse grassland had higher albedo and LST values of 0.81 and 27.9 °C respectively, whereas water had lower albedo and LST values of 0.09 and 25.1 °C. Water had the lowest mean LST during the day but highest mean LST in the afternoon and night in each of the years due to its high thermal capacity. Bare ground tends to have a higher contribution index compared to other land covers, while forest land cover has a negative contribution index, indicating the impact land cover types have on LST and the urban heat island effect.

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Analysis of the Relationship between Land Surface Temperature and Vegetation and Built-Up Indices in Upper-Hill, Nairobi

Cited by 28 publications

References 9 publications

The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran

The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran

Extents of Predictors for Land Surface Temperature Using Multiple Regression Model

Contribution Index of Land Cover and Land Surface Temperature Changes in Upper Hill Nairobi, Kenya

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