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

Int J Appl  Earth Obs Geoinformation 86 (2020) 102013

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

T

Cici Alexander Aarhus University, Aarhus Institute of Advanced Studies (AIAS), Høegh-Guldbergs Gade 6B, DK-8000 Aarhus C, Denmark

A R T I C LE I N FO

A B S T R A C T

Keywords: Landsat 8 Urban heat island Tree cover NBR NDWI

Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated. The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST.

1. Introduction Global urban population increased from 30 % in 1950 to 55 % in 2018 and is projected to be 68 % by 2050 (United Nations, 2018). Urbanisation contributes disproportionately to global climate change, with ∼97 % of anthropogenic CO2 emissions originating from ∼2 % of total land area in the 1990s (Grubler, 1994; Svirejeva-Hopkins et al., 2004). Extreme heat events (heatwaves), are expected to increase in frequency and severity with global warming, and pose a health threat globally due to the resultant increase in mortality and morbidity. Urbanisation leads to modification of microclimate, which may amplify the effects of global warming. Relevant research on this topic is concentrated in the mid-latitudes while the higher latitudes—above 55°—are under-represented and vulnerable (Campbell et al., 2018). The built environment in the cooler high latitudes is often planned and designed to reduce heat loss and energy consumption during the winter months. Cities with relatively cooler maximum summer temperatures have been shown to have significantly lower temperature threshold for heat-related mortality (Gosling et al., 2007). This could lead to

increased energy use for cooling, with further increase in CO2 emissions and contribution to global warming. Urban vegetation has the potential to reduce air temperature and thereby, energy consumption for cooling (Akbari et al., 1997). Urban areas are characterised by a decrease in vegetation cover and an increase in impervious surfaces—pavements, roads and buildings—compared to rural areas, often resulting in a city being warmer than its surrounding countryside. This phenomenon, referred to as urban heat island (UHI), has been observed in cities around the world since the 1800s (Howard, 1833; Oke, 1982). Meteorological departments in many countries regularly measure air temperature at point locations. However, the complex spatial arrangement of surfaces in urban areas, makes it difficult, or even impossible, to estimate the local variations in surface temperature based on these data alone. Satellite imagery have been used to estimate land surface temperature (LST), the main driver of air temperature, and to map the spatial distribution of LST (Voogt and Oke, 2003). Air temperature can be higher or lower than surface temperature depending on various factors such as the presence and direction of wind, insolation and surface characteristics

https://doi.org/10.1016/j.jag.2019.102013 Received 8 July 2019; Received in revised form 17 October 2019; Accepted 8 November 2019 0303-2434/ © 2019 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Int J Appl  Earth Obs Geoinformation 86 (2020) 102013

C. Alexander

Ziter et al. (2019) quantified the influence of canopy cover and impervious surface cover on urban air temperature using a bicyclemounted measurement system. Studies have also found correlations between LST and landscape metrics, such as patch size and shape complexity (Chen et al., 2014; Masoudi and Tan, 2019). Although it is known that LST generally increases and decreases with impervious surface cover and vegetation cover, respectively, only a few city-wide studies have looked at the influence of tree cover and building cover on LST using geospatial data. Elmes et al. (2017) explored the relationship between urban tree canopy reduction and LST increase in Worcester, Massachusetts, USA using data before and after large scale loss of tree cover within a short period. The aim of this study is to understand the spatial pattern of LST in a city and its surroundings in the high latitudes, and identify indicators of LST in terms of spectral indices and land cover. The first objective is to map the distribution of LST, and delineate areas that are considerably warmer or colder than normal to understand their characteristics. Spectral indices are related to land cover and have been used to sharpen coarse resolution thermal imagery, although many previous studies have been confined to a few widely used indices. The second objective of this study is therefore to identify spectral indices with strong correlations with LST in the summer months that could be used as proxies for LST. The third objective is to understand the relationship of land cover, especially building and tree cover, with LST and the identified spectral indices, to guide urban planning.

(Armson et al., 2012; Prihodko and Goward, 1997). Nevertheless, LST can provide an estimate of the spatial pattern of temperature over large areas. Urban areas can be detected in satellite images by their thermal footprint (Rao, 1972), and thermal sensors have been used to estimate LST, and delineate surface UHI (SUHI), at different spatial and temporal resolutions (Gillespie et al., 1996; Li et al., 2013; Ottlé and VidalMadjar, 1992; Zhou et al., 2018). The spatial resolution of the thermal bands are often lower than that of the visual or infrared bands, due to the requirement for a large instantaneous field of view (IFOV) for thermal sensors to ensure that enough energy reaches the detector to make a reliable measurement. For example, the thermal bands in Landsat 4–6, 7 and 8 have spatial resolutions of 120, 60 and 100 m, respectively, while the other bands have a spatial resolution of 30 m or less. The thermal images captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, and by the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel-3 are coarser, at a spatial resolution of 1 km. Coarse resolutions often limit the use of thermal imagery for various applications (Kustas et al., 2004). The spatial resolution of thermal bands can be enhanced using higher-resolution spectral bands (Rodríguez-Galiano et al., 2011). Gao et al. (2012) used a data mining approach for sharpening the thermal band using higher resolution bands, which could be used for featurelevel mapping of evapotranspiration (Guzinski and Nieto, 2019). Another sharpening approach—TsHARP—is based on the strong correlations of Normalised Difference Vegetation Index (NDVI) with thermal bands (Agam et al., 2007). Although the LST-NDVI relationship is not well-defined over complex heterogeneous landscapes, TsHARP is still widely used for sharpening thermal imagery due to its simplicity and effectiveness (Huryna et al., 2019; Jeganathan et al., 2011). Spectral indices are dimensionless variables derived from the reflectances of two or more bands. NDVI was developed for quantifying vegetation conditions over broad areas and was shown to have a strong correlation with green biomass (Rouse et al., 1974).

NDVI =

ρNIR − ρRed ρNIR + ρRed

2. Study area and datasets Denmark is highly urbanised with 87.7 % of its population living in urban areas compared to 75 % in the whole of Europe. The study area is Aarhus Kommune (Municipality) in Denmark, extending between latitudes 55.99° and 56.33 °N (UTM 32N: Northing 6243528 and 6206102), and between longitudes 9.95° and 10.38 °E (UTM 32N: Easting 558938 and 586012), with a population of approximately 340450 in 2018. The climate of Aarhus is classified as Cfb—temperate oceanic climate—by the Köppen-Geiger system, with an average annual temperature of 7.8 °C and average precipitation of 605 mm. The municipal area covers approximately 470 sq. km (Fig. 1A) and includes a predominantly agricultural rural area (72.25 %), settlements within the rural area (7.01 %) and a core urban area (20.74 %). The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) on the Landsat 8 satellite collect data in eleven bands (Table 1). Landsat 8 datasets for the summer months (June, July and August) from 2015 and 2018, with < 20 % cloud cover, were used for this study. The data were collected around 10:20 AM on 21st August 2015 and 1st June, 26th June, 3rd July and 28th July 2018. Landsat 8 Level-2 Surface Reflectance (SR) products for Bands 1–7, Top-Of-Atmosphere (TOA) Brightness Temperature from Band 10 and Pixel Quality Assessment (QA) data were processed and provided by the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) at a pixel size of 30 m; Band 11 was not used for estimating LST since quantitative analysis of this band is not recommended due to large uncertainties in calibration (USGS, 2019a). Landsat 8 OLI SR products were generated by USGS-ESPA using the Landsat Surface Reflectance Code (LaSRC) (USGS, 2019b; Vermote et al., 2016). A digital surface model (DSM) and a digital terrain model (DTM), representing the surface and terrain elevations, at a grid cell size of 0.4 m generated from airborne LiDAR data by the Danish Geodata Agency (https://download.kortforsyningen.dk/), were used to estimate tree cover. Building footprints were obtained from topographic vector data (KORT10; scale 1:10,000), and were used to estimate the percentage of built-up area. 4-band ortho-rectified aerial imagery collected in the summer of 2018, with a pixel size of 0.2 m, was used to estimate vegetation cover of the urban area.

(1)

where ρNIR and ρRed represent the reflectances of the near-infrared (NIR) and red bands, respectively. The ‘normalised difference’ form has since been used in other spectral indices such as the normalised difference built-up index (NDBI) and the normalised difference water index (NDWI) owing to several advantages (Gao, 1996; Zha et al., 2003). These include simple calculation, easy comparison between datasets since values range from -1 to +1 after normalisation, and partial cancelling out of noise from illumination geometry, atmospheric effects and sensor calibration. Indices developed for specific wavelengths and locations have often been adapted for other sensors and locations. However, similar indices often have multiple names and a single index may have different variants depending on the selected sensors and wavelengths (Ji et al., 2011). Previous studies have looked at a limited number of spectral indices, to analyse the influence of land cover on temperature since vegetation cover, surface soil water content and impervious surface cover are known to be determinants of LST (Chen et al., 2006; Ezimand et al., 2018; Guha et al., 2018; Owen et al., 1998; Weng et al., 2004). Although vegetation cover is known to reduce LST, vegetation types may differ in their ability to reduce surface temperature. The average soil temperature beneath trees and shrubs in green spaces has been found to be lower than that beneath herbaceous cover during the summer months (Edmondson et al., 2016). Trees lower surface and air temperature by shading in addition to evapotranspiration. In a study by Armson et al. (2012), the surface temperature of grass was found to be much lower than asphalt or concrete in sun as well as in shade. Moreover, tree shade had the potential to reduce the temperature of both surfaces by up to 7 °C. 2

Int J Appl  Earth Obs Geoinformation 86 (2020) 102013

C. Alexander

Fig. 1. Location of Aarhus in Denmark (A); Number of days (among 21st August 2015, and 1st June, 26th June, 3rd July and 28th July 2018) on which the land surface temperature (LST) of a 30-m cell, smoothed using a 5 × 5 mean filter, was higher (hot zone) or lower (cold zone) than mean LST by more than two standard deviations (B); Aerial images of a location containing a hot zone (C) and a location containing a cold zone (D) on all the dates, and an agricultural land identified as a hot zone on 21st August 2015 (E) Isotherms generated from estimated LST are also shown [Source (Aerial imagery): DDO®Land 2018, © COWI A/S].

3. Methods

Table 1 Spectral bands of Landsat 8 with their corresponding wavelengths and spatial resolutions (USGS, 2019a) ; * 100 m resolution resampled to 30 m (Barsi et al., 2014). Bands

Wavelength (μm)

Resolution (m)

Band Band Band Band Band Band Band Band Band Band Band

0.435 0.452 0.533 0.636 0.851 1.566 2.107 0.503 1.363 10.60 11.50

30 30 30 30 30 30 30 15 30 100 * (30) 100 * (30)

1 - Ultra Blue (coastal/aerosol) 2 - Blue 3 - Green 4 - Red 5 - Near Infrared (NIR) 6 - Shortwave Infrared 1 (SWIR1) 7 - Shortwave Infrared 2 (SWIR2) 8 - Panchromatic 9 - Cirrus 10 - Thermal Infrared 1 (TIRS1) 11 - Thermal Infrared 2 (TIRS2)

-

0.451 0.512 0.590 0.673 0.879 1.651 2.294 0.676 1.384 11.19 12.51

Landsat 8 SR (Bands 1–7) and TOA Brightness Temperature (BT) data for the five dates were read into MATLAB R2017b, and multiplied by the relevant scale factors (USGS, 2018). Clear pixels (values 322 & 324), not obscured by clouds, within Aarhus Municipality were selected using a mask generated from the Municipal boundary polygon and Landsat 8 QA data for each date (USGS, 2018). LST was estimated from the TOA-BT data, and the warmest and the coolest zones were identified, for each dataset. Simple spectral indices similar to NDVI were calculated for all band combinations in Bands 1-7. These normalised difference spectral indices were smoothed using mean filters to determine the optimum level of smoothing with respect to spatial resolution and correlation with LST. The percentages of three land cover types—trees, buildings and vegetation—within 30-m cells to correspond with Landsat imagery were calculated to estimate their relationship with LST and the spectral indices.

3

Int J Appl  Earth Obs Geoinformation 86 (2020) 102013

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3.1. Spatial pattern of LST

SIBi − Bj (j > i) =

Emissivity (ε), the efficiency of transmitting thermal energy, of a surface varies with surface composition, including moisture, roughness, and particle size, and is an essential factor for an accurate estimation of LST (Avdan and Jovanovska, 2016; Jiménez-Muñoz et al., 2006). The proportion of vegetation (Pv) is an important component of emissivity and can be estimated using NDVI (Sobrino et al., 2004; Wang et al., 2015)

⎜

⎟

(2)

where NDVIs and NDVIv represent NDVI values for soil (0.2) and vegetation (0.5), respectively. Emissivity values have been estimated as 0.991, 0.996 and 0.973 for NDVI values < 0, 0-0.2 and > 0.5, respectively (Avdan and Jovanovska, 2016). The emissivity of surfaces for 0.2 < NDVI < 0.5 was calculated as:

Building footprint polygons were converted to a raster with a grid cell size of 0.4 m to align with those of the DSM and the DTM. A 30-m raster representing the area (%) covered by buildings was generated by calculating the percentage of 0.4 m ‘building’ cells within a 30-m cell, to correspond with the pixel size of Landsat imagery. The DTM was subtracted from the DSM to generate a Height Model (HM), also known as a normalised DSM (nDSM) with a cell size of 0.4 m. Grid cells above 5 m in height that did not overlap with building cells were classified as ‘tree’ cells. A raster representing the percentage of the area covered by trees within the 30-m cells were also generated. Ortho-rectified 4-band aerial imagery of the study area, with a pixel size of 0.2 m, was used to estimate vegetation cover. All cells with NDVI ≥ 0.15 were re-classified as vegetation; the threshold of 0.15 was based on visual analysis. A 30-m raster representing the percentage of vegetation cells was generated. A uniform grid of 30-m cells that were completely within urban areas or within the hot or cold zones in all the five datasets, were selected. Correlations (Pearson's correlation, α: 0.05) between vegetation, tree and building cover and LST, and between each land cover and the spectral indices were determined. Regression models were used to understand the relationships between the land cover variables and LST.

where ε vλ and εsλ are the vegetation and soil emissivities, respectively, and Cλ represents the surface roughness and given a constant value of 0.005 (Sobrino et al., 2004; Sobrino and Raissouni, 2000). The unit of TOA Brightness Temperature was converted from Kelvin to degree Celsius (°C) by subtracting 273.15. The LST was calculated as:

BT

LST =

⎧ ⎡ λBT ⎤⎫ 1 + ⎢ ⎜⎛ c ⎟⎞ lnελ ⎥ h ⎨ ⎬ σ ⎝ ⎠ ⎣ ⎦⎭ ⎩

(5)

3.3. Land cover characteristics, LST and spectral indices

(3)

ελ = ε vλ Pv + εsλ (1 − Pv ) + Cλ

ρj + ρi

where SIBi-Bj is the normalised difference spectral index using bands i and j, and ρi and ρj are surface reflectances of bands i and j, respectively. The mean of the spectral indices and LST within 3 × 3, 5 × 5, 7 × 7 and 9 × 9 windows, corresponding to 90, 150, 210 and 270 m respectively, were calculated using moving average filters, for the five dates. Statistical analyses (Pearson's correlation, α: 0.05) were performed to measure their correlations, and the optimum spatial resolution for further analyses was determined. The differences in the mean spectral indices between the hot and cold spots may have an influence on their correlations with surface temperature. The mean of the spectral indices within the hot and cold spots, and the range of these means were calculated to identify the spectral indices with the largest variations, for all the datasets.

2

NDVI − NDVIs ⎞ Pv = ⎛ NDVI v − NDVIs ⎠ ⎝

ρj − ρi

(4)

where BT is the Brightness Temperature (°C), λ is the wavelength of emitted radiance (taken as 10.895 for Band 10), h is the Planck’s constant (6.626 × 10−34 J s), c is the velocity of light (2.998 × 108 m/s) and σ is the Boltzmann constant (1.38 × 10−23 J/K) (Avdan and Jovanovska, 2016; Markham and Barker, 1985; Weng et al., 2004). The mean temperatures in the urban and the surrounding rural areas, and the differences between them, were calculated for all the datasets. Statistical analyses (t-test) were performed to determine whether there were significant differences between the rural and urban temperatures for the different dates. Urban hot spots have been identified using the mean and standard deviation of temperature (Ma et al., 2010). The means and standard deviations of LST were calculated for the five datasets. In this study, pixels that were more than two standard deviations warmer and cooler than the mean temperature were identified as hot and cold zones, respectively, and pixels that were more than three standard deviations warmer and cooler than the mean temperature were identified as hot and cold spots, respectively.

4. Results 4.1. Spatial pattern of LST The mean (μLST) and standard deviation (σLST) of estimated LST were the highest (28.84 ± 2.81 °C) on 3rd July 2018. The number of clear pixels was also the highest on 3rd July 2018. More than 4 % of the municipal area was obscured by clouds on 28th July 2018 (Table 2). There were 2274, 1566, 1812, 917 and 105 grid cells classified as hot spots (LST > μLST+3σLST) and 14, 9, 411, 2188 and 8501 grid cells classified as cold spots for the five dates (in chronological order). The number of cells in the hot (LST > μLST+2σLST) or cold (LST < μLST2σLST) zones on all the dates were 2481 (2.23 sq.km) and 2401 (2.16

3.2. Spectral indices and LST Normalised difference spectral indices for all combinations of the first seven bands of Landsat 8 were calculated for the five datasets:

Table 2 Minimum, maximum, mean and standard deviation (SD) of land surface temperature (LST) estimated from the TIRS1 (Band 10) of Landsat 8; the number and percentage of clear pixels used for the estimation are also shown. Date

21.08.2015 01.06.2018 26.06.2018 03.07.2018 28.07.2018

Landsat 8

Number of clear pixels

Path

Row

196 197 196 197 196

21 21 21 21 21

518244 519467 523210 526029 505968

(98.27%) (98.50%) (99.21%) (99.74%) (95.94%)

4

LST (°C) Minimum

Maximum

Mean

SD

17.55 17.55 17.45 18.75 15.86

33.45 37.95 44.95 43.06 34.25

22.35 24.50 28.50 28.84 26.90

1.73 2.58 2.78 2.81 1.64

Int J Appl  Earth Obs Geoinformation 86 (2020) 102013

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Fig. 2. Land surface temperature (LST) estimated using Landsat 8 TIRS1 data, and smoothed using a 5 × 5 mean filter, for the five dates: 21st August 2015 (A); 1st June 2018 (B); 26th June 2018 (C); 3rd July 2018 (D) and 28th July 2018 (E). Land use polygons for forests, green urban areas and water bodies, from the Urban Atlas, and building footprints are also shown (F) [Sources: Landsat 8 - USGS-ESPA; Urban Atlas – European Environment Agency; Building Footprints (KORT10) - SDFE (Styrelsen for Dataforsyning og Effektivisering)]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

4.2. Spectral indices and LST

sq.km), respectively (Fig. 1). The differences in temperature between the hot and cold zones for the five dates were 8.20 °C, 11.69 °C, 13.26 °C, 13.17 °C and 7.81 °C. Cells within the urban areas in the municipality seemed to have higher temperatures than the other cells, although this difference was less noticeable for the estimates from 28th July 2018. Areas classified as water bodies, forests or green urban areas seemed, on visual analysis, to have the lowest temperatures while the areas with higher building density had higher temperatures (Fig. 2). The mean urban temperatures were 1.81 °C, 3.92 °C, 3.96 °C, 3.54 °C and 0.99 °C higher than the mean rural temperatures on the five dates (Fig. 3). The estimated urban LST values were also significantly higher than the rural LST on all the days (p < 0.001).

The correlations between the estimated LST and the spectral indices were the highest for the SI using SWIR1 and SWIR2—SISWIR1-SWIR2—on 21st August 2015, for SINIR–SWIR1 on 26th June 2018, and for SINIR–SWIR2 on the other three dates. The correlation was the highest on 1st June 2018, with a correlation coefficient (Pearson’s r) of 0.89 (Fig. 4). The correlations were estimated using a window size of 5 × 5 as a compromise between spatial resolution and correlation with LST. Although SINIR–SWIR2 had a slightly lower correlation with LST than SINIR–SWIR1 on 26th June 2018, the difference was negligible. SINIR–SWIR1 was among the top five on all the dates. SISWIR1-SWIR2 was also among the top five on all the dates, although the ranking was lower than the other two (Table 3). The absolute difference between the mean values of the hot and cold spots was the highest for SINIR-SWIR2 on all the dates. SIRed–NIR had the 5

Int J Appl  Earth Obs Geoinformation 86 (2020) 102013

C. Alexander

Fig. 3. Box plots of land surface temperature (LST) estimated from Landsat 8 TIRS1 (Band 10) data for rural (R) and urban (U) areas for 21st August 2015, and 1st June, 26th June, 3rd July and 28th July 2018.

fifth dataset. SINIR–SWIR2 had the second strongest correlation with LST for all the dates (Table 4). The correlations between tree cover (r: 0.57) and building cover (r: -0.59) with vegetation cover were stronger than the correlation between tree cover and building cover (r: -0.43). Tree cover had a strong negative correlation with the estimated LST, with values for all the dates in 2018 having a correlation coefficient below -0.7. Building cover had a positive correlation (r: 0.53 to 0.71) while vegetation cover had a negative correlation (r: -0.45 to -0.63) with temperature although the correlations were lower than those between tree cover and temperature on all the five dates (Fig. 6). Among the regression models using single variables, tree cover alone could explain more than half of the variation in LST for three datasets, with two having slightly lower values (Table 5). The variation explained by building cover was lower than that explained by tree cover on four dates, and the variation explained by vegetation cover was the lowest on all the dates. The estimated LST could be lowered by up to

second highest difference on four dates with a marginally lower value on the fifth date. SINIR–SWIR1 was among the top three on four of the five dates (Fig. 5). 4.3. Urban land cover, surface temperature and spectral indices There were 5665 cells (30-m) that were within the urban area and 150 m apart so that there were no overlaps between the cells considered for the 5 × 5 mean filters in the smoothed rasters. There were 203 cells that were within the hot or cold zones, out of which 90 were also in the urban area. The remaining 113 cells were added to those in the urban area, and these 5778 cells were used for analysing the correlations between tree, building and vegetation cover and LST, and between spectral indices and temperature in the predominantly urban area. SINIR–SWIR1 had the strongest correlations with the estimated LST in the urban area for four datasets, with the correlation coefficient lower only by 0.01 compared to the one with the strongest correlation, for the

Fig. 4. Scatter plots showing the relationship between estimated land surface temperature (LST) from Landsat 8 TIRS1 (Band 10) and spectral indices with the highest correlation (Pearson’s r) with LST for 21st August 2015 (A), 1st June 2018 (B), 26th June 2018 (C), 3rd July 2018 (D) and 28th July 2018 (E). Band 5 - NIR, Band 6 - SWIR1 and Band 7 - SWIR2. 6

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Table 3 Spectral indices with the highest correlations (Pearson’s r) with estimated land surface temperature (LST); the top five are listed. 21.08.2015

01.06.2018

26.06.2018

03.07.2018

28.07.2018

Spectral Index

Rank

r

Rank

r

Rank

r

Rank

r

Rank

r

SINIR-SWIR2 SINIR-SWIR1 SISWIR1-SWIR2 SIGreen-Red SIRed-NIR SIBlue-Green SIUltraBlue-NIR SIGreen-NIR

2 3 1 – – – 4 5

0.62 0.52 0.75 – – – −0.44 −0.44

1 2 4 3 5 – – –

0.89 0.88 0.82 0.84 −0.78 – – –

2 1 3 5 – 4 – –

0.87 0.87 0.78 0.67 – −0.67 – –

1 2 3 – 5 4 – –

0.87 0.85 0.80 – −0.61 −0.74 – –

1 2 3 5 4 – – –

0.85 0.84 0.75 0.57 −0.57 – – –

Fig. 5. Absolute mean differences of spectral indices between the hot and cold spots, grid cells with estimated land surface temperature more than three standard deviations from the mean, using all band combinations for the five dates; Band4-Band5 (green), Band5-Band6 (yellow) and Band6-Band7 (orange) are highlighted. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Table 4 Spectral indices with the strongest correlations (Pearson’s r) with urban land surface temperature (LST); the top five are listed. 21.08.2015

01.06.2018

26.06.2018

03.07.2018

28.07.2018

Spectral Index

Rank

r

Rank

r

Rank

r

Rank

r

Rank

r

SINIR-SWIR1 SINIR-SWIR2 SIGreen-Red SISWIR1-SWIR2 SIRed-NIR

3 2 5 1 4

0.77 0.77 0.71 0.78 −0.71

1 2 3 4 5

0.85 0.83 0.83 0.81 −0.80

1 2 3 4 5

0.86 0.84 0.80 0.80 −0.71

1 2 3 4 5

0.85 0.82 0.80 0.78 −0.72

1 2 3 4 5

0.82 0.78 0.75 0.71 −0.68

1.3 °C (3rd July 2018) and 0.9 °C (26th June 2018) with a 10 % increase in tree cover and overall vegetation cover, respectively. An increase in building cover of 10 %, on the other hand, could increase the estimated LST by up to 1.7 °C (1st and 26th June 2018; Table 5). The coefficient of determination (R2) increased by a mean value of 0.14 for the datasets when building cover was used in addition to tree cover, for developing the regression models, with R2 increasing from 0.49 to 0.67 and RMSE decreasing from 1.70 °C to 1.38 °C on 1st June 2018. Regression models using tree cover and building cover performed better than models using vegetation cover for all the datasets, with 68 % of the variation in temperature explained for the data from 21st August 2015. The increase in temperature with building cover seemed to be partially or fully (3rd July 2018) reversed by an increase in tree cover on all the dates (Table 6). There is only a marginal, up to 1 %, increase in the ability of the model to explain the variation in temperature when all the three variables—tree cover, building cover and vegetation cover—are used to build the model compared to using the two variables—tree cover and building cover (Table 7). Tree cover had the strongest correlation with SIGreen–Red for three

datasets, and the second highest for the other two. Building cover had the strongest correlation with SISWIR1-SWIR2 for four of the five datasets. Vegetation cover had the strongest correlation with SISWIR1-SWIR2 for all the datasets (Table 8).

5. Discussion The spatial pattern of LST in the study area resembled an ‘archipelago’ rather than an urban heat ‘island’, as observed within an urban area by Ziter et al. (2019). Normalised difference spectral indices using all combinations of the shortwave infrared (SWIR) bands (SINIR–SWIR1, SINIR–SWIR2 and SISWIR1-SWIR2) had stronger correlations with LST than the others in the whole study area, with SINIR–SWIR2 having a slightly stronger correlation than the other two. Previous studies (Chen et al., 2013; Guha et al., 2018; Zhang and Wang, 2008) have shown that NDBI (∼SINIR–SWIR1) had stronger correlations with LST than the widely used NDVI (∼SIRed–NIR). However, SINIR–SWIR2 has seldom been used in the urban context. The influence of land cover on LST could be quantified in terms of the percentages of building, tree and vegetation cover, which could potentially be incorporated in planning guidelines. 7

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Fig. 6. Box plots showing the relationships between tree cover (green), building cover (orange) and vegetation cover (yellow), and land surface temperature (LST) estimated from Landsat 8 TIRS1 (Band 10) on 21st August 2015 (A), 1st June 2018 (B), 26th June 2018 (C), 3rd July 2018 (D) and 28th July 2018 (E); Pearson’s correlation coefficients for tree (rt), building (rb) and vegetation (rv) covers are also noted. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

rural area where temperatures were comparable to built-up areas (Fig. 1E & 2 A), probably due to low soil moisture or vegetation cover. These factors may contribute to increased rural temperature which would lower the estimated intensity of an urban heat island. The difference between hot and cold zones may therefore provide a better estimate of variations in temperature within an area of interest.

5.1. Spatial pattern of LST The difference between the mean LST of the rural and urban areas was up to 3.96 °C (Fig. 3), while the difference between the hot and cold zones in the study area was up to 13.26 °C. Agricultural fields covered more than 70 % of the study area, and there were locations within the 8

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Table 5 Regression models showing the relationships between the percentages of tree cover (t), building cover (b) and vegetation cover (v), and estimated land surface temperature (LST). Models are in the form y = β0 + β1x1 where y = LST and x1 = t, b or v. x1 = t

x1 = b

x1 = v

Date

β0

β1

R2

RMSE (°C)

β0

β1

R2

RMSE (°C)

β0

β1

R2

RMSE (°C)

21.08.2015 01.06.2018 26.06.2018 03.07.2018 28.07.2018

24.36 28.27 32.43 32.57 28.37

−0.08 −0.11 −0.12 −0.13 −0.08

0.46 0.49 0.52 0.56 0.53

1.24 1.70 1.82 1.75 1.05

21.65 24.52 28.49 28.69 26.31

0.12 0.17 0.17 0.16 0.08

0.51 0.46 0.41 0.37 0.28

1.18 1.75 2.00 2.08 1.31

25.17 29.39 33.61 33.55 28.64

−0.06 −0.08 −0.09 −0.08 −0.04

0.39 0.39 0.39 0.35 0.20

1.32 1.86 2.04 2.12 1.37

5.2. Spectral indices and LST

Table 7 Regression models using three variables, showing the relationships between tree cover (t), building cover (b) and vegetation cover (v), and estimated land surface temperature (LST). Models are in the form y = β0 + β1x1 + β2x2 + β3x3 where y = LST, x1 = t, x2 = b, and x3 = v.

SINIR–SWIR2, with the strongest correlation with LST for the whole study area, is the inverse of normalised burn ratio (NBR) used to map burnt areas, and monitor vegetation regeneration after fires (García and Caselles, 1991; Key and Benson, 1999). Normalised difference water index (NDWI), using a similar band combination, was found to have strong positive correlations with leaf water content and vegetation fractional cover (Gu et al., 2007). SINIR–SWIR1 is equivalent to NDBI, which has been used for mapping built-up areas. SINIR–SWIR1 is also the inverse of land surface water index (LSWI), used for representing the effect of water in a vegetation photosynthesis model (Xiao et al., 2004). SISWIR1-SWIR2 is equivalent to normalised burn ratio 2 (NBR2), modified from NBR by replacing the NIR band with SWIR1, to highlight sensitivity to water in vegetation (USGS, 2017). Bands in MODIS similar in wavelengths to SWIR1 and SWIR2 have been used to separate burnt and unburnt areas (Trigg and Flasse, 2001). SWIR reflectance is related to both leaf structure and leaf water content while NIR reflectance is related only to leaf structure and leaf dry matter. Spectral indices using the NIR and SWIR bands therefore cancel out variations in leaf internal structure and improves the accuracy of detecting vegetation water content. Low leaf water content would restrict transpiration, with less water evaporating from the leaf surface, leading to less cooling and an increase in leaf temperature (Ceccato et al., 2001; Jackson, 1986). The strong correlations of the indices using SWIR with LST may be attributed to their ability to sense vegetation water content, and may point to drought-like conditions occurring during heatwaves. It has been noted that sparsely vegetated surfaces suffer from water stress during heatwaves and the reduced water supply for plant transpiration and drying out could stop or reverse the cooling effect of vegetation (Ward et al., 2016). The spatial variation of a spectral index using NIR and SWIR was shown to be higher than that of NDVI, also possibly due to the sensitivity of the former to leaf water content and the saturation of NDVI at higher values (Gao, 1996). This may explain the large variation of SINIR–SWIR2 between the mean values of the hot and cold spots, compared to SIRed–NIR (Fig. 5). The correlations between estimated LST and the 21 (7C2) normalised difference spectral indices (SI) generally increased with increasing window sizes of the mean filter, with a few exceptions. When the correlations decreased with increasing window sizes, it was most often

x1 = t; x2 = b; x3 = v Date

β0

β1

β2

B3

R2

RMSE (°C)

21.08.2015 01.06.2018 26.06.2018 03.07.2018 28.07.2018

23.07 26.75 31.13 31.23 27.28

−0.05 −0.07 −0.08 −0.10 −0.07

0.08 0.10 0.09 0.09 0.05

−0.01 −0.02 −0.02 −0.01 0.01

0.68 0.68 0.66 0.66 0.60

0.95 1.36 1.52 1.52 0.98

after the window sizes of 3 × 3 or 5 × 5. The window size of 5 × 5, corresponding to 150 × 150 m, was therefore used for smoothing the datasets as a reasonable compromise between correlation and window size since the original data for the TIRS bands were collected at a spatial resolution of 100 m. This also roughly agrees with a study where NDVI and LST at 30 m were resampled to five resolutions from 60 to 990 m, and the correlations between the two decreased after 120 m (Weng et al., 2004). The TIRS data were resampled to 30 m using cubic convolution, which makes it difficult to invert the process and work with the original resolution. The composition of the surrounding land cover would have an influence on the estimated LST within a cell, which could be explored in future studies where data from thermal bands are available at their original resolutions.

5.3. Land cover, temperature and spectral indices Tree cover, a subset of vegetation cover, had a stronger correlation with LST than building or overall vegetation cover for all the datasets, and accounted for a large part of the decrease in LST with vegetation cover (Tables 5–7). Grass is known to lose its evaporative cooling function faster than trees in drought like conditions (Gill et al., 2007), which could be a reason for this. The surfaces not covered by vegetation would consist of roads and pavements, exposed soil and buildings, which explains the negative correlation (r: -0.59) between vegetation cover and building cover. The use of three different datasets, airborne LiDAR-based surface models, building footprints and aerial imagery,

Table 6 Regression models using two variables, showing the relationships between tree cover (t), building cover (b) and vegetation cover (v), and estimated land surface temperature (LST). Models are in the form y = β0 + β1x1 + β2x2 where y = LST and x1 and x2 = t, b or v. x1 = t; x2 = b

x1 = t; x2 = v

Date

β0

β1

β2

R

21.08.2015 01.06.2018 26.06.2018 03.07.2018 28.07.2018

22.71 26.17 30.42 30.82 27.58

−0.05 −0.08 −0.09 -0.10 - 0.07

0.09 0.11 0.11 0.10 0.04

0.68 0.67 0.65 0.66 0.59

2

x1 = b; x2 = v

RMSE (°C)

β0

β1

β2

R

0.96 1.38 1.54 1.53 0.98

25.04 29.18 33.37 33.29 28.49

−0.05 −0.08 −0.09 −0.11 −0.08

-

0.54 0.57 0.59 0.60 0.53

9

0.03 0.04 0.04 0.03 0.01

2

RMSE (°C)

β0

β1

β2

R2

RMSE (°C)

1.14 1.57 1.68 1.67 1.05

22.94 26.55 30.90 30.94 27.10

0.09 0.12 0.11 0.11 0.06

−0.03 −0.04 −0.05 −0.05 −0.02

0.57 0.54 0.51 0.45 0.31

1.11 1.62 1.84 1.94 1.28

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Table 8 Spectral indices with the strongest correlations (Pearson’s r) with tree cover, building cover and vegetation cover. 21.08.2015

% Tree Cover % Building Cover % Vegetation Cover

01.06.2018

26.06.2018

03.07.2018

28.07.2018

Spectral Index

Rank

r

Rank

r

Rank

r

Rank

r

Rank

r

SIGreen-Red SISWIR1-SWIR2 SISWIR1-SWIR2

2 1 1

−0.66 0.73 −0.80

1 2 1

−0.74 0.70 −0.84

1 1 1

−0.80 0.69 −0.81

1 1 1

−0.83 0.67 −0.81

2 1 1

−0.77 0.67 −0.78

regulate urban LST.

would have influenced the accuracy of the generated land cover values. The correlations between the percentage covers of trees, other vegetation, buildings, other impervious surfaces and exposed soil with LST could be explored in future studies where accurate high-resolution land cover data are available. The inter-quartile ranges of LST were the highest for vegetation cover, especially for higher values (> 70 %) on the three warmest days (Fig. 6). This is possibly due to the varying moisture and shade conditions, as well as types, of vegetation. The structure and orientation of trees and buildings would influence the location, shape and size of their shadows and the shade and relative cooling provided by them. LST decreased by 1 °C for every 10 % increase in tree cover while it increased by 1 °C for every 10 % increase in building cover on 3rd July 2018, the warmest day (Table 6). Although the correlations between land cover and LST varied with weather conditions on the five dates, determining similar relationships during extreme heat events in other regions could contribute to formulating guidelines for urban development. SIGreen-Red is the inverse of the Green-Red Vegetation Index (GRVI), a less commonly used vegetation index than NDVI, and is an indicator of vegetation phenology, disturbance and ecosystem types, probably due to its sensitivity to changes in green colour (Motohka et al. 2010). This ability could partly be the reason for its strong correlation with tree cover (Table 8) and stronger correlation with LST than NDVI.

Acknowledgement This research was funded by an AIAS-COFUND Fellowship at the Aarhus Institute of Advanced Studies, Aarhus University, under the European Union’s Seventh Framework Programme for Research, Technological development and Demonstration under grant agreement no [609033]. The author is grateful to USGS-ESPA for providing the processed Landsat 8 imagery, to the Danish Geodata Agency for the LiDAR-based surface models and building vector data, to COWI for the aerial imagery, and to the anonymous reviewers for their valuable comments and suggestions. References Agam, N., Kustas, W.P., Anderson, M.C., Li, F., Neale, C.M.U., 2007. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens. Environ. 107, 545–558. Akbari, H., Kurn, D.M., Bretz, S.E., Hanford, J.W., 1997. Peak power and cooling energy savings of shade trees. Energy Build. 25, 139–148. Armson, D., Stringer, P., Ennos, A.R., 2012. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban For. Urban Green. 11, 245–255. Avdan, U., Jovanovska, G., 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. J. Sens. 2016 (8). Barsi, J.A., Lee, K., Kvaran, G., Markham, B.L., Pedelty, J.A., 2014. The spectral response of the Landsat-8 operational land imager. Remote Sens. 6, 10232–10251. Campbell, S., Remenyi, T.A., White, C.J., Johnston, F.H., 2018. Heatwave and health impact research: A global review. Health Place 53, 210–218. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., Grégoire, J.-M., 2001. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ. 77, 22–33. Chen, A., Yao, L., Sun, R., Chen, L., 2014. How many metrics are required to identify the effects of the landscape pattern on land surface temperature? Ecol. Indic. 45, 424–433. Chen, L., Li, M., Huang, F., Xu, S., 2013. Relationships of LST to NDBI and NDVI in Wuhan City based on landsat ETM+ image. 2013 6th International Congress on Image and Signal Processing (CISP) 840–845. Chen, X.-L., Zhao, H.-M., Li, P.-X., Yin, Z.-Y., 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 104, 133–146. Edmondson, J.L., Stott, I., Davies, Z.G., Gaston, K.J., Leake, J.R., 2016. Soil surface temperatures reveal moderation of the urban heat island effect by trees and shrubs. Sci. Rep. 6, 33708. Elmes, A., Rogan, J., Williams, C., Ratick, S., Nowak, D., Martin, D., 2017. Effects of urban tree canopy loss on land surface temperature magnitude and timing. Isprs J. Photogramm. Remote. Sens. 128, 338–353. Emmanuel, R., Loconsole, A., 2015. Green infrastructure as an adaptation approach to tackling urban overheating in the Glasgow Clyde Valley Region, UK. Landsc. Urban Plan. 138, 71–86. Ezimand, K., Kakroodi, A.A., Kiavarz, M., 2018. The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature. Int. J. Remote Sens. 39, 8428–8449. Gao, B.-c., 1996. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266. Gao, F., Kustas, P.W., Anderson, C.M., 2012. A data mining approach for sharpening thermal satellite imagery over land. Remote Sens. 4. García, M.J.L., Caselles, V., 1991. Mapping burns and natural reforestation using thematic Mapper data. Geocarto Int. 6, 31–37. Gill, S.E., Handley, J.F., Ennos, A.R., Pauleit, S., 2007. Adapting cities for climate change: the role of the green infrastructure. Built Environ. 33, 115–133. Gillespie, A.R., Matsunaga, T., Rokugawa, S., Hook, S.J., 1996. Temperature and Emissivity Separation from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Images. SPIE. Gosling, S.N., McGregor, G.R., Páldy, A., 2007. Climate change and heat-related mortality in six cities Part 1: model construction and validation. Int. J. Biometeorol. 51, 525–540. Grubler, A., 1994. Technology. In: William, B.M., Turner IIB.L. (Eds.), Changes in Land

6. Conclusion Localised warming due to heat islands can be similar in magnitude to the predicted warming due to climate change by 2050 (Emmanuel and Loconsole, 2015). With the expected increase in the frequency and severity of heatwaves, cities need to develop strategies to increase the thermal comfort of their inhabitants while reducing the consumption of energy for cooling. Mapping the distribution of LST can help in understanding the factors affecting temperature for guiding future development. In this study, LST estimated from a thermal band, normalised difference spectral indices from non-thermal bands and land cover were analysed to understand their inter-relationship. SINIR–SWIR2 had the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area, while SINIR-SWIR1 had the strongest correlations (r: 0.77 to 0.86) with LST for a predominantly urban sample. Spectral indices using the infrared bands could therefore be used as proxies for LST or for sharpening thermal bands, when thermal imagery is not available or is of low resolution. Although land cover and land use patterns have a strong influence on the variations in microclimate, there has been little focus on developing local strategies for climate change adaptation. Changes in land cover and land use may be easier to implement than global strategies for reducing carbon emissions (Emmanuel and Loconsole, 2015). Tree cover was seen to have a stronger correlation with LST than overall vegetation cover. This is possibly due to dry conditions occurring during heatwaves, and to trees being more resilient than herbaceous vegetation to these conditions. When building cover was used in addition to tree cover, up to 68 % of the variation in LST could be explained. Percentages of tree and building cover, which can be estimated from remotely sensed imagery, had strong negative and positive correlations respectively with LST, and could therefore be modified to 10

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