Keywords

Introduction

The atmospheric systems and energy balance of the earth are gradually being altered as a result of chaotic urbanization, which has a direct impact on human thermal discomfort. Such issues are exacerbated in the cities, since the urban environment is the object of man’s most arbitrary landscape-modifying actions (Gomes and Caracristi 2021). In the light of the challenges of global warming and its characterizing dynamics related to earth’s surface alterations, such as agricultural expansion, desertification, urban development, and so on, examining the surface urban heat intensity is critical. In this regard, a lot of effort has gone into determining land surface temperature using remote sensing data (Garouani et al. 2021). The removal of vegetation within urban areas, changes in urban thermal and physical properties of construction materials, building, morphology, surface roughness, urbanization and anthropogenic heat sources, all modifies, alters, or affects local energy and leads to increase in atmospheric temperature in urban areas compared to their surroundings (Ayanlade et al. 2021). Adequate and accurate information about the status of the land surface temperature (LST) of specific areas of interest is required for successful geo-environmental management, which involves the monitoring and modelling of the environment (Agbor and Makinde 2018). There are many natural and anthropogenic factors responsible for the increase or decrease in LST, while the degree of LST is seasonal and location dependent. Climate change and urbanization have been reported as one of the critically significant factors responsible for the change in land use and LST (Argueso et al. 2015; Elhadi et al. 2020).

The heat intensity effect of solar radiation varies significantly across urban and rural areas. It has been observed to be higher in urban or metropolitan areas than in rural areas. The term urban heat island (UHI) is used to describe this phenomenon, which is primarily impacted by the amount of plant and water pervious surfaces present in an urbanized area. Because water pervious surfaces and vegetation have been replaced by impervious surfaces in urban environments, there is less evaporation to reduce LST (Michael et al. 2012).

In an urban environment, natural vegetation is eliminated and replaced by non-transpiring and non-evaporating surfaces that have low capacity for solar reflectivity and high capacity for heat absorption such as concrete, asphalt, and metals in most cases, resulting in a significant modification of the earth’s surface (Andrew 2012; Ridwan et al. 2021). This change eventually causes incoming solar energy to be redistributed, resulting in the rural–urban disparity in air temperatures and surface radiance (Guiling et al. 2008).

The influence of urbanization is tremendous, and it affects or alters the natural ecosystem; therefore, understanding UHI is vital for a variety of applications in earth and physical sciences as well as environmental management techniques (Aneeqa et al. 2016). The demand for agricultural production, food, and shelter is increasing as the global population grows. As a result of anthropogenic activities, land cover characteristics are shifting to satisfy rising population need and replacing vegetated areas with impermeable surfaces, inadvertently leading to climate change (Imran et al. 2021). Increased heat intensity in the urban climate has major consequences on human health and the usage of outdoor areas, as well as many activities that contribute to the liveability and vitality of cities. It causes different multifaceted issues such as skin cancer and greater energy consumption because air conditioners are often required (Michael et al. 2012; Naserikia et al. 2019).

LST is defined by how hot the “surface” of the earth would feel when touched in a particular region (Przyborski 2021) while the surface in this context and as used in satellite remote sensing refers to whatever a satellite observes as its signal pierces through the atmosphere to the earth. Surface heat fluxes, which are affected by urbanization, influence the LST in an area (Dousset and Gourmelon 2003). Therefore, understanding the spatiotemporal distribution of LST will aid in deciphering its mechanism and determining possible mitigation techniques (Sun et al. 2009).

Apart from the LST, other indices that have been reported to contribute to the urban heat island of a location include vegetation which is often measured using the Normalized Difference Vegetative Indices (NDVI), urbanization or built-up areas which is often measured using the Normalized Difference Building Indices (NDBI), surface emissivity, etc.

The NDVI is an index used to detect and ascertain the existence or presence of live green vegetation. Most visible light (0.4–0.7 m) is absorbed by healthy vegetation, whereas most near-infrared light (0.7–1.1 m) is reflected. In contrast, unhealthy or sparse vegetation will reflect less near-infrared light and more visible light (Weirer and Herring 2010). As a result, greater radiation that is reflected in the near-infrared wavelengths than the visible wavelengths indicates the existence of green vegetation, while minimal variation in intensity between the two wavelengths suggests the existence of either non-vegetated surfaces or sparse vegetation (Weirer and Herring 2010). The built-up density for each area is described by the NDBI, which is synonymous to the vegetation density described by the NDVI. The ratio of short red infrared (SWIR) to near infrared (NIR) is calculated as NDBI, with indices ranging from −1 to 1 (Kshetri 2018).

The impact of the relationship or connection between the NDVI and LST, especially in locations where the urban heat intensity phenomenon is more prevalent and mitigating efforts are required, cannot be overemphasized. This is primarily because denser vegetation lowers LST by ensuring the transfer of latent heat to the atmosphere from the surface via evapotranspiration. The NDVI is used to investigate this relationship and, as a result, provides insight into how plants or vegetation’s natural cooling mechanism can be exploited to improve urban thermal settings. In general, it is expected that lower LSTs are recorded or observed in locations or places with a high NDVI, implying that the two have an indirect relationship. However, surface evapotranspiration and soil moisture levels may significantly alter or modify the dynamics of this relationship (Yuan and Bauer 2007).

A property or attribute of a surface that determines the volume of energy that is emitted by an object at a particular temperature when compared to a blackbody at the same temperature is known as surface emissivity (EXERGEN 2021). It can also be described as the ability of a surface or an object to convert heat energy it receives into radiant energy (Sekertekin and Bonafoni 2020). The emissivity is gotten from NDVI after the fractional vegetation (Pv) cover has been estimated and then calculated from the reflectance values of the materials on the earth surface based on the results of the NDVI. The emissivity of materials on the earth’s surface reflects how well they absorb all incident radiation and convert it to internal energy before emitting (re-radiating) the received energy at the highest rate feasible per unit area (Isa et al. 2016).

One of the important features or characteristics that can be observed by satellite remote sensing is the surface temperature of an area. This data (surface temperature) has a wide range of applications in ecology, environmental studies as well as spatial data modelling, which is frequently employed in web-based GIS applications (Sameen and Al Kubaisy 2014).

Recently, the connection and correlation between LST and other factors or indices have received significant research attention (Peng et al. 2020). Researchers frequently look into the individual interaction between LST, vegetation, surface emissivity, and water, as well as the impact of urban land growth on temperature change, while little known effort has been invested in the combined effects of some of these interactions, a gap this research seeks to fill. The fundamental goal of this study is to investigate the combined effect and interdependence of vegetation (using the Normalized Difference Vegetation Index (NDVI)), built-up area (using Normalized Difference Built-up Index (NDBI)), surface emissivity, and the LST in the assessment of urban heat intensity using Bosso Local Government Area of Niger State, Nigeria, as a case study.

Materials and Methods

Bosso, which is the project site of this study, is one of the Local Government Areas (LGA) in Niger State, Nigeria (see Fig. 14.1). With its administrative headquarters situated in Maikunkele, it covers an area of about 1592 km2 and a population of about 147,359 according to the 2006 census. Bosso LGA has a high heat flow (Mohammed et al. 2019) which could be as a result of increased urban activities, hence, making it a suitable location for this study.

Fig. 14.1
Three maps of the Kaduna state, Niger, and study area Bosso L G A. Niger and the study area Bosso L G A are highlighted with a circle.

Cartographic description of the study area

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The study area experiences both wet (rainy) and dry seasons annually, and an attempt was made to assess the effect of seasonal variation of the LST or the heat intensity of the study area. Therefore, two images were downloaded for each year: one at dry season (February) and the second at rainy season (September), for the years 2015, 2017, 2019, and 2021, making a total of eight images. The properties of the Landsat-8 images, generated from their metadata, are presented in Table 14.1.

Table 14.1 Characteristics of the used Landsat-8 OLI images
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The step-by-step or detailed procedure adopted for the execution of this study is presented in Fig. 14.2. The used Landsat-8 OLI/TIRS C2 L1 band images for 2015, 2017, 2019, and 2021 were downloaded from https://earthexplorer.usgs.gov/.

Fig. 14.2
A flowchart of the estimation of urban heat intensity. It begins with Landsat imagery, followed by pan sharpening, to L S T, N D V I, and N D B I to estimate the urban heat intensity.

Methodology workflow of estimating urban heat intensity from Landsat images

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Radiometric and Atmospheric Correction

On satellite images, radiometric and atmospheric correction is frequently used to reduce the atmosphere’s absorption and scattering effects. As the electromagnetic (EM) energy travels from the sun to the earth and back to the sensor, through the atmosphere, absorption diminishes the intensity of EM energy resulting in haziness, while the energy is redirected in the atmosphere by scattering resulting in an adjacent effect in which neighbouring pixels are shared and thus affecting image quality (GISgeography 2021). Radiometric and atmospheric correction removes the effect of sensor influence and atmospheric effect on the reflectance value of satellite images. Radiometric and atmospheric correction was done by computing the Top of Atmospheric (TOA) spectral reflectance (Eq. 14.1), followed by the correction of sun angle (Eq. 14.2) for all the satellite images downloaded for this study.

Procedure for Obtaining LST

LST can be estimated using the Landsat-8 OLI/TIRS C2 L1 thermal bands by applying Eqs. (14.1)–(14.8) (Rosado et al. 2020) presented in the simplified procedure described by the following five (5) steps.

  1. I.

    Top of Atmospheric (TOA) spectral reflectance.

On a given surface, the ratio of reflected solar radiation to incident solar radiation is often referred to as the ratio of TOA radiance (Eq. 14.1), which is a unitless measurement. The mean solar spectral irradiance and the solar zenith angle derived from satellite-measured spectral radiance are used for the estimation of TOA (Rosado et al. 2020).

$$TOA (L) = ML*Qcal + AL$$
(14.1)

where ML is the band-specific multiplicative rescaling factor from the metadata, Qcal corresponds to band 10, and AL is the band-specific additive rescaling factor from the metadata.

  1. II.

    Calculation of Brightness Temperature

The thermal band detectors record TOA’s brightness temperature in the form of digital numbers (DNs), which is then converted to surface temperature using the single channel algorithm. Surface temperatures obtained with Eq. (14.3) are deemed to be highly accurate (Michael et al. 2012).

$$BT = (K2 / (ln (K1 / L) + 1)) - 273.15$$
(14.2)

where K1 and K2 are the band-specific thermal conversion constant from the metadata and L is the TOA. In order to obtain the results in degree Celsius, absolute zero is added to the radiant temperature as presented in Eq. (14.3).

$$BT = \left( {1321.0789/Ln\left( {\left( {777.8853/^{\prime\prime}\% TOA\% ^{\prime\prime}} \right) + 1} \right)} \right) - 273.15$$
(14.3)
  1. III.

    Extracting the NDVI

Reflectance data of Landsat-8 images, i.e. the visible red (Red) and the near-infrared (NIR) bands (bands 4 and 5, respectively) were used to extract the NDVI.

The NDVI was extracted using the expression presented in Eqs. (14.4) and (14.5).

$${\text{NDVI}} = ({\text{Band}}\;5 - {\text{Band}}\;4)/({\text{Band}}\;5 + {\text{Band}}\;4)$$
(14.4)

It should be noted that calculating the NDVI is crucial since it is necessary to estimate the proportion of vegetation (Pv), which is related to the NDVI, and emissivity (ε), which is related to the proportion of vegetation.

$${\text{NDVI}} = {\text{Float}}({\text{Band}}\;5 - {\text{Band}}\;4)/{\text{Float}}({\text{Band}}\;5 + {\text{Band}}\;4)$$
(14.5)
  1. IV.

    Estimating vegetation proportion (P\({\varvec{v}}\))

The proportion of vegetation was estimated using Eq. (14.6) (Carlson and Ripley 1997).

$$Pv = {\text{Square}}\left( {\left( {{\text{NDVI}} - {\text{NDVImin}}} \right)/\left( {{\text{NDVImax}} - {\text{NDVImin}}} \right)} \right)$$
(14.6)

The minimum and maximum values of the NDVI are gotten from the properties of NDVI under the source tab or the highest and lowest value range in ArcGIS 10.8.

  1. V.

    Estimating Surface Emissivity (ε)

The effectiveness of a material’s surface in emitting energy as thermal radiation is known as its emissivity. Thermal radiation is electromagnetic radiation that includes both visible (light) and infrared (infrared) wavelengths that are invisible to the human eye (Sobrino et al. 2013). The mathematical expression used for the estimation of emissivity is presented in Eq. (14.7).

$$\varepsilon = m*Pv + n$$
(14.7)

where m = emissivity of vegetation (0.004), \(Pv\) = percentage of vegetation, and \(n\) = soil emissivity value (0.986).

  1. VI

    Estimating the Land Surface Temperature

Equation (14.8) presents the mathematical expression used for the estimation of LST.

$$\textit{LST} = (BT / (1 + (0.00115 *BT / 1.4388) *Ln(\varepsilon )))$$
(14.8)

where \(BT\) = Brightness temperature and \(\varepsilon\) = Emissivity.

Estimating the NDBI

The NDBI was extracted using the reflectance data (short red infrared (SWIR) and near infrared (NIR) bands) of Landsat-8 images. The NDBI is estimated as the ratio between short red infrared (SWIR) and near infrared (NIR) and has indices ranging from −1 to 1. Generally, the mathematical expression for estimating the NDBI for an area is shown in Eq. (14.9), while Eq. (14.10) presents the equation used to extract the NDBI specifically from Landsat-8 image.

$$\textit{NDBI} = (\textit{SWIR}-\textit{NIR})/(\textit{SWIR}+\textit{NIR})$$
(14.9)
$$\textit{NDBI} = \textit{float}\left( {\textit{band}\;6 - \textit{band}\;5} \right)/\textit{float}\left( {\textit{band}\;6 + \textit{band}\;5} \right)$$
(14.10)

where band 6 is a SWIR band and band 5 is a NIR band. Built-up areas were extracted from the built-up density in the form of point features for clear depiction of urbanization.

Interpretation of NDBI and NDVI

Generally, the value of the NDBI and NDVI calculation ranges from −1 to 1. The representation of the values within the range for NDVI is presented in Table 14.2, while Table 14.3 presents the range of index values representing the state of plant health (EOS 2019). For the NDBI, while negative values represent non-urban land areas, urban land areas are represented by positive values.

Table 14.2 NDVI value range and feature represented (Kshteri 2018)
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Table 14.3 NDVI value range and the represented state of plant’s health
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Results and Discussion

For clearer presentation, the results were presented and discussed in two subsections. While the results obtained in the seasonal dynamics of the surface heat intensity of the study area in the dry season were presented in subsection “Surface Heat Intensity of the Area in the Dry Season”, the result obtained for the same analysis in the rainy season was presented in subsection “Surface Heat Intensity of the Area in the Rainy Season”. The relationships and effects of the combined indices (NDVI, NDBI, and LST) on the surface urban heat intensity are also presented.

Surface Heat Intensity of the Area in the Dry Season

Maps depicting the spatial distribution of NDVI, NDBI, and the spatial variation of LST values of the study area for the dry season are presented in Figs. 14.3, 14.4 and 14.5, respectively, while Table 14.4 contains the estimated maximum values of the NDBI, NDVI, and LST obtained for 2015, 2017, 2019, and 2021 for the dry season. From the results, it was observed that in dry season, the highest maximum value of LST was recorded in the year 2017 (51 °C) which also yielded the highest NDVI and NDBI values of 0.388 and 0.577, respectively, while the lowest maximum value of LST was recorded in the year 2015 (33 °C).

Fig. 14.3
Four spatial distribution maps of the N D V I of study area from 2015 to 2021 for the dry season. The four maps exhibit a high distribution of most areas.

Spatial distribution of NDVI in dry season

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Fig. 14.4
Four spatial distribution maps of the N D B I of study area from 2015 to 2021 for the normalized difference in the dry season. The four maps exhibit a high distribution of most areas.

Spatial distribution of NDBI in dry season

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Fig. 14.5
Four spatial distribution maps of the L S T of study area from 2015 to 2021 in dry season. The study area in 2015 exhibits a high distribution, in contrast with 2021 which has a moderate to low distribution.

Spatial variation of LST in dry season

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Table 14.4 Estimated NDVI, NDBI, and LST maximum values for the dry seasons
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Also, the value for LST is lowest in the year 2015, but its value for NDBI (0.425) is higher than the values recorded for the years 2019 and 2021 which are 0.414 and 0.320, respectively, while the vegetative index value for the year 2021 (0.278) is higher than that of the year 2015 (0.257). Therefore, the lowest LST in the dry season over the years understudied is justifiably expected to be reported in the year 2021 since vegetation reduces the effect of LST, and the value of the NDBI recorded for the year 2021 is the least compared to other years. Also, since NDVI value below 0.5 indicates the presence of shrubs, rocks, sand, unhealthy plants, grasses (unhealthy or moderately healthy), etc., and the highest maximum value for NDVI in dry season is 0.388 as recorded in the year 2017, it evidently shows that most plants within the study area have already dried up at this time of the year; therefore, the effect of NDVI in dry season is minimal.

Also, since surface emissivity contributes to surface temperature in an area along with built-up index, the surface emissivity of the study area was examined for each of the years understudied for the dry season, and the obtained result is presented in Fig. 14.6. The maximum surface emissivity recorded in the years 2017 and 2021 illustrates the extent to which various materials, such as bare land or soils, engineering structures, metal, concrete, and tar, absorb all incident radiation completely, and convert it to internal energy. The absorbed energy is then emitted (re-radiated) into the atmosphere and thereby contributing to LST. The recorded surface emissivity in the years 2017 and 2021 is higher than that of the year 2015 for the dry season which explains why the least value of LST for dry season was recorded in the year 2015 and not in 2021. It also explains why LST in the year 2021 is higher than what it was in the year 2019.

Fig. 14.6
A bar graph represents the emissivity of the surface during the dry season. Years 2017 to 2021 have the highest surface emissivity at 0.988.

Surface emissivity in dry season

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Surface Heat Intensity of the Area in the Rainy Season

Figures 14.7, 14.8 and 14.9 present the graphical description of the spatial distribution of NDVI, NDBI, and the spatial variation of LST of the study area for the rainy season, respectively. The estimated maximum values of the NDBI, NDVI, and LST obtained for the years 2015, 2017, 2019, and 2021 for the rainy season are presented in Table 14.5. Similar to what was observed in the results obtained for the dry season, the lowest value of LST was recorded in the year 2021, but its value for built-up index (0.267) is higher than NDBI values for the years 2017 and 2015 which are 0.259 and 0.243, respectively. Also, the year 2019 had the highest maximum value for LST (70 °C), while the year also recorded the lowest NDVI value of 0.544 and the highest NDBI value of 0.421. The implication of this is that in the year 2019, urbanization rate was very high which had a negative effect on vegetation thereby increasing the urban heat intensity of the study area in the rainy season. In contrast, for the year 2021, there was a very low heat intensity judging by the recorded lowest maximum value for LST of 29 °C. This can be attributed to the high NDVI of 0.598 and low NDBI of 0.267, which implies an improvement in vegetation growth and reduced urban growth, respectively.

Fig. 14.7
Four spatial distribution maps of the N D V I of study area from 2015 to 2021 for the rainy season. The four maps exhibit a high distribution of most areas.

Spatial distribution of NDVI in rainy season

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Fig. 14.8
Four spatial distribution maps of the N D B I of study area from 2015 to 2021 for the normalized difference in the rainy season. High distribution is evident in the study area in 2019.

Spatial distribution of NDBI in rainy season

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Fig. 14.9
Four spatial distribution maps of the L S T of study area from 2015 to 2021 in the rainy season. The study area in 2015 exhibits a high distribution, in contrast with 2021 which has a moderate distribution.

Spatial variation of LST in rainy season

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Table 14.5 Estimated NDVI, NDBI, and LST maximum values for the rainy seasons
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Although the vegetation index for the years 2021 and 2015 is very close, the lowest value of LST recorded in the rainy season of the year 2015 is justified by the lowest value for built-up index recorded in the same season and the highest value of the NDVI recorded in the dry season over the studied time epochs. This suggests that while there are low or reduced urbanization activities, there were high vegetated activities which led to the recorded low LST within the period of study.

Also, the surface emissivity of the study area in the rainy season was examined over the studied years and the result is presented in Fig. 14.10. Similar to the surface emissivity of the study area in the dry season, the surface emissivity for the years 2015 and 2017 was observed to be considerably higher than what it was in the years 2019 and 2021 in the rainy season, which explains why the least value of LST for the rainy season was recorded in the year 2021 and not in year 2015.

Fig. 14.10
A bar graph represents the emissivity of the surface during the rainy season. The year 2017 has the highest surface emissivity roughly at 0.9877.

Surface emissivity in rainy season

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Generally, it was observed from the analysis that the surface emissivity values of the study area obtained in the rainy season were significantly higher than the surface emissivity values of the study area obtained for dry season across all the years, except for the results obtained for the year 2019. This is because the strength of wind in the study area is weak at this season of the year.

Conclusion

This research shows the potential of using Geographic Information System (GIS) and remote sensing techniques in assessing the surface heat intensity of any location. The findings of this study affirmed that a rise in surface urban heat intensity is resulted from an increase in urbanization and a decrease in vegetation. The effect of increased built-up density and surface emissivity which resulted to an increase in LST proves that increase in urbanization leads to an increase in surface heat intensity. The study also proved that a rise in vegetation density leads to a decrease in urban heat intensity and that the effect is more significant in the rainy season than dry season as vegetation is usually dry at this season of the year, where soil moisture content is low. In as much as vegetation is more effective in rainy season, wet periods are usually periods of high urban heat intensity because of the high emissivity values associated with this season. This is because wind, which decreases the effect of urban heat intensity, is weak at this season of the year. Further studies will attempt to investigate the effect of other factors such as terrain elevation or topography and presence of water bodies on the urban heat intensity of the study area.