The relationship between land cover changes and spatial-temporal dynamics of land surface temperature

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1 76 The relationship between land cover changes and spatial-temporal dynamics of land surface temperature Samereh Falahatkar 1, Seyed Mohsen Hosseini 2 and Ali Reza Soffianian 3 1 Academic center for education, cultural research (ACECR), Environmental research institute, Siadati street, Western side of Mohtasham Garden, Rasht, Giulan, Iran, 2 Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, P.O. Box , Noor, Mazandaran, Iran, 3 Dept. of Natural Resources, Isfahan University of Technology, Isfahan, , Iran s7falahatkar@yahoo.com, samereh.falahatkar@modares.ac.ir Abstract Remotely sensed thermal infrared (TIR) data have been widely used to retrieve land surface temperature (LST). The LST is an important parameter in the studies of urban thermal environment and dynamics. Specific objectives are to evaluate land cover change detection in Isfahan and to analyze the impact of these changes on surface temperature using TM and ETM+ thermal bands for 1990 and Hybrid method classification was used for producing land cover maps and post-classification comparison was applied for change detection. The single channel algorithm was used for calculating LST. For investigation the relationship between the kinds of land cover and land surface temperature, the land cover change map for 1990 and 2001 was overlaid with LST map. The technique of image differencing is employed to produce a radiant temperature change image after the surface radiant temperature of each year has been normalized. The results indicate that bare land exhibits the highest surface radiant temperature (44.9 C in 1990 & 48.9 C in 2001), followed by stony body (42.6 C in 1990 & 45.3 C in 2001). After that urban and built up area have temperature less than bare land and stony body. The lowest radiant temperature in 1990and 2001are observed in green cover and river classes. Keywords: Land surface temperature, land cover change detection, Isfahan. Introduction In the recent years, thermal environment has been paid great attention including the greenhouse effect and global warning. It not only refers to the air temperature, but also the land surface temperature (LST) (Zhang et al., 2006). Urbanization and Industrialization improve our material lives and comfort; however, they also induce many problems to human beings, such as global warming, industrial waste and air pollution (Memon et al., 2008). An urban heat island (UHI) is the name given to describe the characteristic warmth of both the atmosphere and surfaces in cities compared to their surroundings (Voogt, 2005; Mirzaei & Haghighat, 2010). Higher urban heat is mainly caused due to the anthropogenic heat released from vehicles, power plants, air conditions and other heat sources and due to the heat stored and re-radiated by massive and complex urban structures (Momen et al., 2008). The change of land use from green area to new built structures results in changes of the natural surface of the earth. The changes of materials that cover the earth s surface affect the absorption of solar energy and the changes of the shapes of the earth s surface, that is man-made uneven ground, affect the air flow (Omija, 1991). Land surface temperature observations acquired by remote sensing technologies have been used to assess the urban heat island to develop models of land surface atmosphere exchange and to analyze the relationship between temperature and land use and land cover in urban area (Voogt & Oke, 2003). The use of remote sensing for mapping and measuring the urban heat island (UHI) is appropriate and requisite for large area urban studies (Gluch et al., 2006). Remotely sensed thermal infrared (TIR) data have been widely used to retrieve land surface temperature (LST) (Quattrochi & Luvall, 1999; Weng et al., 2004). The LST is an important parameter in the studies of urban thermal environment and dynamics (Weng, 2009). The LST of urban surface correspond closely to the distribution of land use and land cover (LULC) characteristics (Lo et al., 1997; Weng, 2001, 2003; Weng et al., 2004). The integration of remote sensing and geographic information systems (GIS) has been widely applied and been recognized as a powerful and effective tool in detecting urban land use and land cover change (Ehlers et al., 1990; Treitz et al., 1992; Harris & Ventura, 1995). The different studies carried out in different contraries about UHI and its relationship with land cover changes. Nichol (1994) carried out a detailed study using TM thermal data to monitor microclimate for housing estates in Singapore. Weng (2001, 2003) examined LST pattern and its relationship whit land cover in Guangzhou and in the urban clusters in the Zhujiang Delta, China. Jusuf et al. (2007) investigated the influence of land use on the urban heat island in Singapore. Nowadays, land cover changes due to changes in surface temperature cause to the urban managers to estimate the urban temperature and its surrounding for management and urban planning. Specific objectives of this research are to evaluate land cover change detection in Isfahan and to analyze the

2 77 impact of these changes on surface temperature using TM and ETM+ thermal bands of Landsat satellite. Material and methods Study area The city of Isfahan, the capital of Isfahan province is in center Iran. Isfahan city is located between and northern latitude and and eastern longitude (Fig. 1). The study area has arid and semi-arid climate and it is in the neighborhood of Zayandehrood River. Since the second half of the 20 th century, increasing migration and industrial development have accelerated population growth, which for 50 year period ( ) was ranked very high in the nation (Iran census center, 2007). In recent years, Isfahan has experienced a high immigration rate from suburbs and other cities, and a significant change in land use and land cover because of industrial and economical growth. Fig.1. Study area. the amount of total variance and correlation between various band combinations. In this study, we used hybrid classification for producing land cover map. The supervised classification was performed using the maximum likelihood method and five land cover classes were identified including stony body, bare land, urban, green cover and river. Unsupervised classification was done with iterative self organizing data analysis (Isodata). NDVI was used to separate the green cover from other classes. The classification accuracy can be assessed by an error matrix. Many measurements have been proposed to improve the interpretation of the error matrix, among which the Kappa coefficient is one of the most popular measures. It is a discrete multivariate technique used in accuracy assessment (Congalton, 1988). The reference data are collected from large-scale aerial photos and topographic maps. Data used & image pre-processing To quantitatively measure land surface temperature and compare urban heat island zones in the study area, Landsat 5 TM image (Sep 17, 1990) and Landsat 7 ETM+ image (Sep 7, 2001) were selected. All images bands 1 5 and 7 have a spatial resolution of 30 m, and the thermal infrared band (band 6) has a spatial resolution of 120 m for Landsat 5 TM images and 60 m for Landsat 7 ETM+ images (Campbell, 2006). These images were suitable for multi-temporal studies because there was little difference in sun elevation and azimuth at the time of image acquisition at different dates. Reflective data from these respectively were used to extract land cover classes. To analyze the changes in temperature and land cover in the study region, multi-temporal images must be co-registered in the same coordinate system (e.g., UTM/WGS84). In this study, the raw images were georeferenced to a common UTM coordinate system based on the 1:50,000 scale topographic maps, and re-sampled using the nearest neighbor algorithm with a pixel size of 30 m by 30 m for all bands, including the thermal band. Its RMSE is less than 1 pixel for each image. Then the study area was cut using the boundary of Isfahan. Image classification & accuracy assessment To make the best false color composite, the optimal index factor (OIF) was used. The OIF value is based on Land cover change detection Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). Post classification comparison is sometimes referred to as delta classification. It involves independently produced spectral classification results from each end of the time interval of interest, followed by a pixel-by-pixel or segment-to-segment comparison to detect changes in

3 cover type. The principal advantage of post classification comparison lies in the fact that the two dates of imagery are separately classified; thereby minimizing the problem of radiometric calibration between dates (Coppin et al., 2004). Different stages of work are shown in Fig. 2. LST retrieval There are 3 methods for LST retrieval from Landsat 5 and 7 TM/ETM+ thermal infrared data based on the comparison of three single channel LST retrieval methods: the radiative transfer equation using in situ radio sounding data; the mono-window algorithm (Qin et al., 2001); the single channel algorithm (Jimenes-Munoz & Sobrino, 2003). Satellite TIR sensors measure radiances at the top of the atmosphere (TOA) from which brightness temperatures (also known as blackbody temperatures) can be derived by using Plank's law (Dash et al., 2002). It is assumed that the water vapor content of the atmosphere is constant for a relatively small region, so that the atmospheric condition could be considered as uniform, and the influence of atmosphere on radiance temperature could be neglected. The procedure described by Weng et al. (2004) was adopted for retrieval of land surface temperature. Convert DN to radiance Before calculating radiance you need to get the bias (or offset) and gain values from the header file. Formula 1 is used for conversion DN to radiance. CV R =G (CV DN ) +B (1) CV R is the cell value as radiance; CV DN is the cell value digital number; G is the gain; B is the bias (or offset) Convert radiance to brightness temperature The next step is to convert the spectral radiance to at satellite brightness temperature (i.e., blackbody temperature, T B ) under the assumption of uniform emissivity (Wukelic et al., 1989; Landsat project science office, 2002). The conversion formula is: T B =K 2 /ln((k 1 /L λ )+1) (2) Where T B is the effective at-satellite temperature in Kelvin (K), L λ =CV R the spectral radiance in W m -2 sr -1 mm -1 ; K 2 and K 1 are the pre-launch calibration constants. For Landsat-5 TM images, K 2 = K, and K 1 = mw cm -2 sr -1 mm -1. For Landsat-7 ETM+ images, K 2 = K, and K 1 = mw cm -2 sr -1 mm -1. Correction of spectral emissivity The temperature values obtained above were referenced to a black body, which is quite different from the properties of real objects. Therefore, correction of spectral emissivity (ε) is a must. Each of the land use types was assigned an emissivity value. Furthermore, the emissivity corrected surface temperature was computed as 2001 follows (Artis & Carnahan, 1982): T s =T B /1+(λ T B /α)lnε (3) Where Ts is the surface radiant temperature in Kelvin (K), T B the black body temperature in Kelvin (K), λ the wavelength of emitted radiance, here in, λ = 11.5 µm (Markham & Barker, 1985), α = hc/k ( mk), h = Planck constant ( Js -1 ) and c = velocity of light ( ms -1 ), K = Boltzman constant ( JK -1 ), ε = surface emissivity. Since the obtained surface radiant temperature is in Kelvin, which is different from the commonly used centigrade. Therefore, the radiant temperature was revised by adding the absolute zero (approx C) (Xu & Chen, 2004). Temperature zone map For each LST map, a base temperature was calculated by averaging the thermal signatures of nonurban land cover types (i.e., Green cover, River (Water pixel)). Next, LST values were classified into a temperature zone map based on the percent increase of each temperature value above the given base temperature. Ten temperature zones were identified, including a base temperature zone, zones of up to 10% temperature increase, up to 20%, up to 30%, and up to 40%, up to 50%, up to 60%, up to 70%, up to 80%, up to 90% and above (Fig. 5). This identification based on relative temperature differences allows an examination of the locations and areal extent of UHIs. Moreover, using an image-differencing technique between two LST maps, the temperature decrease and increase over the time can be studied spatially. Result and discussion Accuracy of land cover map The false color composites were generated from bands 3, 4 and 7 for TM and ETM + by using the OIF index. For accuracy assessment of the 1990 and 2001 land cover maps, updated digital topography maps for 1991 and 2002 were used respectively and then the kappa coefficient and overall accuracy was calculated for them using calculation of error matrix. The results show that overall accuracy of the hybrid method of land cover classification used in this study achieve accuracies of 91.65% and 93.19% and kappa values of 0.88 and 0.91 for the 1990 and 2001 reference maps respectively. We have carried out field survey for the non-change area and used GPS point and for change area we used large scale aerial photos and topography maps (1:25000) for selecting the training data. The accuracy of the postclassification comparison is totally dependent on the accuracy of the initial classifications. Table 1. Post-classification matrix of study area in the period of (ha) Stony body Bare land Urban Green cover River Total Stony body Bare land Urban Green cover River Total

4 79 Land cover change detection Cross tabulation is a mean to determine quantities of conversions from a particular land cover to another land cover category at a later date (Alphan et al., 2008). The change matrices based on post classification comparison were obtained and are shown in Table 1. In this period urban covered ha in 1990 and ha in 2001, while the green cover class covered an area of ha in 1990 and ha in ha of the area which was green cover in 1990 was still green cover in 2001, but ha had been converted to urban use by During this time period, ha of barren land were restored to green cover. The change detection map is shown in Fig. 3. Fig. 3. Land cover change map during no-change. Fig. 4 shows the area graph of different land cover classes. Fig. 4. Changes in land cover composition from 1990 to Analysis of relationship between land cover & urban heat island For investigation the relationship between the kinds of land cover and land surface temperature, the land cover change map for 1990 and 2001 was overlaid with LST map. The technique of image differencing is employed to produce a radiant temperature change image after the surface radiant temperature of each year has been normalized (Fig. 5). In order to understand the impacts of land cover change on surface radiant temperature, Fig. 5. LST image differencing ( ). The result of change detection from 1990 to 2001 shows that ha of barren land were converted to urban class.13.9% of the urban class was changed to green cover class. The result of these changes is described: Streets and highways were generally classified as urban, but when urban tree canopies along the streets grow and expand, the associated pixels may be classified as green cover. We note that the changes from urban to green land occurred almost entirely near highways and streets: for example Chaharbaghe-Bala and Charbaghe-Paein streets. Yuan et al. (2005) obtained the same results in their study. Converting river to urban and inverse change is most likely associated with omission and commission errors in the Landsat classification change map. Geometric correction errors and edge effects can also cause apparent errors in the determination of change vs. The characteristics of the thermal signatures of each land cover class must be studied first. The average values of radiant surface temperatures by land cover type in 1990 and 2001 are summarized in Table 2. In this study, bare land exhibits the highest surface radiant temperature (44.9 C in 1990 & 48.9 C in 2001), followed by stony body (42.6 C in 1990 and 45.3 C in 2001). After that urban and built up area have temperature less than bare land and stony body (39.5 C in 1990 and 43.5 C in 2001). The lowest radiant temperature in 1990 is observed in

5 green cover (32.5 C), followed by river (22.7 C). These temperatures are shown in the Table 2. This part of thermal pattern in 2001 have shown different process related Zayanderoud river had not any water in the most part of its length when Landsat satellite passing there, Due to this case the temperature of green class has been less than river class (river: 38 C, Green cover: 34.1 C). The mentioned thermal pattern for bare land, stony body and urban classes was similar in 1990 and 2001, it accent the similar season for taking images and solar illumination. Weng (2001) has expressed this different pattern is primarily attributed to the differences in solar illumination, the state of vegetation, and atmospheric influences on the remotely sensed TM Table 2. Average surface temperature ( o C) by land cover type. Land cover Average surface temperature( o C) class 1990 Std. dev Std. dev. Stony body Bare land Urban Green cover River dataset. Spatial distribution of land surface temperature The impact of land cover changes on surface radiant temperature can also be examined spatially. Relative temperature changes for the period of were obtained by performing an image differencing over the LST images. The results were regrouped into 6 relative temperature change zones using the classification scheme of equal interval: increase and decrease. Zones Fig. 6. Land surface temperature map of 1990 (left) & 2001 (right) 4, 5 and 6 have a positive value of temperature change, indicating a temperature increase between 1990 and 2001, while others have a negative value. The mapped patterns of temperature change exhibit distinctly different spatial patterns among the 6 temperature zones. Fig. 6 shown that urban area has had the 50% increase related on base temperature in While urban area has had 60% temperature increases in The pixels of basetemperature in Isfahan city had coincided on the green cover and river (water pixel) in land cover maps for 1990 and Spatial occurrences of each zone indicate that spatial pattern of zone 5 has coincided on urban expansion of Isfahan. Areas where vegetated cover was lost to imperviousness from one period to the next displayed a corresponding temperature increase. Increasing in temperature is taken place in Zayanderoud River as another place during 11 years. This river had water in 1990 but it dried in the most of its length during imaged by Landsat satellite at These pixels have positive value in the temperature change map that was produced by differencing LST images. Some pixels of 3 rd zone show decrease in temperature that coincides on expansion of green space in edges of streets, highways and center of boulevard and also margins of Zayanderoud River, especially the area between Siosehpol and khajo bridges. Other pixels of 3 rd zone are located in agricultural land in northern study area. Conclusion In this study, an integrated approach of remote sensing and GIS was developed for evaluation of land cover change and its impact on surface temperature in Isfahan city, Iran. Temporal and spatial dynamics of LST in relation to land cover change was investigated using thermal infrared data of Landsat. However, some of the factors have been negligible for calculating LST but in this study, analysis between land cover change and change of temperature has been investigated completely. The results show that the maximum of temperature exists in the bare land, stony body and urban area, respectively. The minimum of temperature coincides on the green cover and river classes. Urbanization is a main process of land cover change that can modify the effective variables of land surface temperature (Weng et al., 2004). Change of temperature pattern is different for green cover and 80

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