OPEN Available on line at Directory of Open Access Journals Journal of JOURNAL Journal of Hyperspectral Remote Sensing v.8, n.1 (2018) 23-30 Hyperspectral SYSTEMS www.periodicos.ufpe.br/revistas/jhrs Remote Sensing ISSN:2237-2202 Study of heat islands by orbital data: effects of surface temperature patterns Michelle A. Cerqueira*, Bruna K. de L. Santos**, Diana F. de Freitas*** *Professor, UFAL-CECA, Rio Largo-AL, [email protected] (Corresponding author) **Graduated in Agronomy, UFRPE-UAST, Serra Talhada-PE, [email protected] ***Professor, UFRPE-UAST, Serra Talhada-PE, [email protected] Received 10 July 2018, accepted 27 August 2018 Abstract The dynamics of cities have been changing a lot over time. Population growth and increasing urbanization are examples of this. It is noticeable that natural landscapes are changing into artificial landscapes, replacing green areas by completely asphalted ones. These population dynamics directly interfere with the urban climate, modifying it. One of the phenomena caused by the intense urbanization is the Urban Heat Islands (UHI) that happen in the cities, regardless of their size and location. Therefore, this study aimed to evaluate the land surface temperature (LST) in the effects of UHI in the backlands of Pernambuco, located in northeastern Brazil, using remote sensing techniques. For this, the method used consists of a spatial and temporal analysis of the land surface temperature, for which measuring the orbital data of Landsat satellites 5 and 8 were used. The temporal analysis was performed in 10-year gaps (1985, 1995, 2005 and 2016), and the climatic period of data acquisition was the dry period. The results show that, for this study area, LST increased from 1985 until 2016; in 1985 the lowest measured temperature was <25 °C and the lowest temperature in 2016 was around 25-30 °C. The UHI effect is evidenced when comparing data obtained from the surface temperature of urban areas with surface temperature data of green spaces, from which a difference of just over 6 °C is noticeable. Keywords: remote sensing, surface temperature, orbital images. 1. Introduction UHI is normally evaluated through the difference between the air temperature of urban and The accelerated and disorganized urban rural areas using meteorological data; however, this development has caused a series of environmental method has a limited spatial coverage (Xiaoma et al., impacts, as a response to the occupation and 2013). With the advent of thermal sensors on anthropic activities on the environment, among which satellites, there comes the possibility of determining the modification of the urban climate stands out the UHI in low and medium spatial resolution scale. (Nascimento and Oliveira, 2011). The present work aims to evaluate the surface The growing urbanization and therefore the temperature (LST) on the effects of Heat Islands increase of the urban area implies a change in the (UHI) in the backlands of Pernambuco, by remote atmosphere and in land surface temperature (LST). sensing techniques, highlighting the green spaces and The deforestation and changing of natural landscapes the urban ecosystem. The remote sensing techniques by artificial landscapes, as a result of urbanization, were validated through in situ data. promote a change in the local climate, resulting in temperature variations. 2. Materials and methods The urban heat islands (UHI) are a phenomenon characterized by the increased The studied area was the backlands of temperature in urban areas when compared to Pernambuco, located in the northeast of Brazil, with surrounding rural areas (Howard, 1818; Oke , 1979; an area of approximately 32,450 km² and a Voogt and Oke, 2003; Voogt 2004; Epa, 2008), the population of 1,039,733 inhabitants according to UHI is influenced by reduced vegetation and the IBGE (2017). The caatinga biome is characteristic of properties of urban materials (Voogt, 2004; Epa, the region, featuring a hot and dry climate, with 2008). Cities produce UHIs regardless of their rainfall concentrated in summer and autumn, with location and size, which makes it a problem of global precipitation ranging between 500 and 800 mm and scale. unevenly distributed rain over time. M.A. Cerqueira et al./ Journal of Hyperspectral Remote Sensing 8 (2018) 23-30 23 The proposed method implies a spatial and images. The chosen climate period for the acquisition temporal analysis of surface temperature in the was the dry season, due to low rainfall and low backlands of Pernambuco, using orbital data of the incidence of clouds. satellites Landsat 5 and 8, including the following The temporal analysis was performed in 10- steps: pre-processing, surface temperature years gaps, as a composition of images for each 10- measurement through orbital data and methodology years period, since the used sensors do not cover all validation through meteorological stations. Brazilian semi-arid region in a single image; because The images used in this research were of that, mosaics were generated in order to cover all obtained from the website http://glovis.usgs.gov. the studied area. Table 1 shows the acquisition periods of these Table 1 - Period of acquisition of images, TM and OLI sensor. Dates Sensor Orbit Point 26/10/1985 TM 215 65 26/10/1985 TM 215 66 18/11/1985 TM 216 65 18/11/1985 TM 216 66 24/10/1985 TM 217 65 24/10/1985 TM 217 66 07/11/1995 TM 215 65 07/11/1995 TM 215 66 29/10/1995 TM 216 65 29/10/1995 TM 216 66 05/11/1995 TM 217 65 05/11/1995 TM 217 66 02/11/2005 TM 215 65 02/11/2005 TM 215 66 24/10/2005 TM 216 65 24/10/2005 TM 216 66 15/10/2005 TM 217 65 15/10/2005 TM 217 66 15/10/2016 OLI 215 65 15/10/2016 OLI 215 66 06/10/2016 OLI 216 65 06/10/2016 OLI 216 66 29/10/2016 OLI 217 65 29/10/2016 OLI 217 66 Pre-processing of Landsat 5 – TM Images numbers (DN's) of reflectance images, using the Images from the TM sensor (Thematic Equations 3 and 4. Mapper) were rectified according to images from Landsat 8 – OLI, which are available orthorectified. 휌휆′ = 푀 푄 + 퐴 휌 푐푎푙 휌 (3) Later the digital numbers (DN's) were converted into reflectance, which is presented in Equations 1 and 2. 휌휆′ 휌휆 = 푠푖푛(휃푆퐸) 푏푖−푎푖 (4) 퐿휆,푖 = 푎푖 + 푁퐷 255 (1) Where:ρλ' is the value of the planetary reflectance, 휋퐿휆푖 with solar angular correction; Mρ is the band-specific 휌휆푖 = 푘휆푖푐표푠푍푑푟 (2) multiplicative factor (REFLECTANCE_MULT_BAND_x); x is the number of each band; Qcal is the quantified standard Pre-processing of Landsat 8 – OLI Images product, calibrated for the pixel values; Aρ is the The preprocessing step of images from the additional value of each band OLI sensor constituted in the conversion of digital (REFLECTANCE_ADD_BAND_x); x is the number M.A. Cerqueira et al./ Journal of Hyperspectral Remote Sensing 8 (2018) 23-30 24 of each band, sin(θ_IF) is the solar elevation angle. in the image and it was computed by Equation 8, All the mentioned coefficients were obtained by the obtained by Allen et al. (2002). image metadata. 0,69 SAVI Extraction of Surface Temperature - LST ln 0,59 The extraction of the temperature in the TM IAF sensor was performed using the SEBAL algorithm, 0,91 (8) proposed by Allen et al. (2002) according to Equation Where: SAVI (Soil Adjusted Vegetation Index) is an 5. index that seeks to mitigate the background effects of the soil, given by Equation 09 (Huete, 1987): 퐾₂ 푇 = ɛ 퐾₁ ln( )+1 퐿휆 (5) (1 L)(ρ ρ ) SAVI IV V (L ρ ρ ) IV V (9) Where: K₁ = 607.76 mWcm ˉ²srˉ¹µmˉ¹, K₂=1260.56 Where: the L factor is a function of the type of soil, K are constant thermal band calibration of Landsat - being the most frequent value L= 5 TM (Allen et al., 2002; Silva et al., 2005). Lλ= 0.5 (Huete and Warrick, 1990; Accioly et al., 2002; radiance and ɛ = emissivity (Equation 07) Boegh et al., 2002). For the extraction of OLI temperature, the All the steps proposed in this methodology equation proposed by Carnahan (1990) and Weng et were performed using the software QGIS 2.16. al. (2004), Equation 6. 3. Results and discussion 푇퐵 퐿푆푇(°퐶) = 푇 (6) Land surface temperature (LST) can differ 1+(휆× 퐵)푙푛ε 휌 greatly between geographic locations and time periods, whereas the local background climate can vary greatly with space and time (Zhou et al., 2014). Where: 푇퐵 = Brightness temperature in Band 10; λ = wavelength of emitted radiation (λ = 10.8 μm, the Figure 1 of LST clearly demonstrates an central wavelength of Landsat 8 Band 10); ρ = h × increase in surface temperature between the years of c/σ (1.438 × 10ˉ² mK), σ = Boltzmann constant (1.38 1985 to 2016. In the year 1985 the surface × 10ˉ²³ J/K), h = Planck's constant (6,626 × 10ˉ³⁴Js), temperature in the studied area varied between the and c = speed of light (2,998 × 10⁸m/s); and ε is the cities; for example, in the city of Exu, the emissivity of the Earth's surface, estimated by temperature reached < 25 °C, and in Santa Cruz da Equation 7. Baixa Verde it reached > 40 °C. By observing the According to Allen et al. (2002), the map of 1995 we can notice a difference in the emissivity ɛ can be obtained, for IAF <3, according western area, which in 1995 reached < 25 °C, to Equation 7. differing from 1985, when they reached 25-30 °C. There was a certain homogeneity of temperature in the year 2005, which ranged between 25-35 °C, with ɛ = 0,97 + 0,0033.IAF (7) temperature increases in some cities and decreases in others, when compared with the year 1995.