Land surface temperature (LST) is one of the key parameters in the physics
of land surface processes from local to global scales, and it is one of the
indicators of environmental quality. Evaluation of the surface temperature
distribution and its relation to existing land use types are very
important to the investigation of the urban microclimate. In arid and semi-arid
regions, understanding the role of land use changes in the formation of
urban heat islands is necessary for urban planning to control or
reduce surface temperature. The internal factors and environmental conditions of
Yazd city have important roles in the formation of special thermal
conditions in Iran. In this paper, we used the temperature–emissivity separation (TES) algorithm for LST retrieving from the TIRS (Thermal Infrared Sensor) data of the Landsat
Thematic Mapper (TM). The root mean square error (RMSE) and coefficient of determination (
A particular problem in urban areas is the increase in surface temperature due to the conversion of vegetated surfaces into asphalt roads, as well as residential, commercial and industrial areas. Presently, climate change in the cities is occurring through anthropogenic activities and land use changes (Weng et al., 2004). The atmospheric conditions of the urban areas, land surface temperature, warming, evaporation and absorption of solar radiation may be changed through anthropogenic changes. The study of surface temperature in the cities located in the arid and semi-arid areas is necessary (Mallick et al., 2008), because high temperature leads to energy consumption for cooling buildings, which is economically very costly especially in the warm months of year (Santamouris et al., 2001).
The study of soils requires an interdisciplinary approach involving geologists, biologists, physicists, chemists, anthropologists, economists, engineers, medical professionals, military professionals, sociologists and even artists. The most important soil-related critical challenges for research are biodiversity, energy security, climate change, ecosystem services, food security, human health, land degradation and water security (Brevik et al., 2015). Soils provide fundamental ecosystem services, and management to change a soil process in support of one ecosystem service can provide co-benefits to other services. Fundamental research is needed to better understand the relationships between soils and the array of ecosystem services. More knowledge about soil and agricultural systems is required to utilize ecosystem services in order to protect and enhance soils in the long term (Smith et al., 2015). Soil scientists believe that soil knowledge has a key role in major global issues like food production, the loss of biodiversity and the availability of water resources. Agronomic studies show that land management strongly influences pollutant behavior in soils because it influences the filter and buffer functions of soil (Mol and Keesstra, 2012). Using modern soil information can improve the results of studies related to food security, water scarcity, climate change, biodiversity loss and health threats (Keesstra et al., 2016). Management can help to improve the social, economic and environmental sustainability of agricultural and food systems (Decock et al., 2015). Loss of plant species diversity has important effects on the erosion resistance of slopes. The protection and restoration of vegetated covers are necessary to minimize soil erosion, which will not only contribute to greater safety in the most densely populated areas of the world, but will also help maintain soil fertility on pasture lands (Berendse et al., 2015).
Urban planning is often hindered by a lack of knowledge on how land use changes. In wet periods, saturation overland flows occur on urban and agricultural soils. Hydrophilic urban and agricultural soils are characterized by increased infiltration capacity during dry periods. In contrast, urban soils remain mostly hydrophilic, and have relatively high infiltration capacities, whereas on the agricultural sites a rise in soil moisture leads to a decline in infiltration capacity, with soil saturation in areas of shallow soils (Ferreira et al., 2015). The spatial and temporal variability of overland flows and infiltration affect flow connectivity depending on land use and soils. Understanding the mentioned concepts is necessary for land management in order to improve urban planning to minimize flood hazards in territory (Ferreira et al., 2015). Parameters such as water repellence, soil moisture content, vegetation and litter surface soil cover are important for overland flow generation. Urban land use changes affect soil properties and processes. The complexity in hydrological properties and processes of the land use changes as a result of the urban sprawl increases the unpredictability. Urban sprawl has the potential to alter the hydrological response and erosion processes in small catchments. Vegetation induces soil water repellence, promoting discontinuous overland flow processes, which affect soil moisture content. These have major implications for sustainable urban planning (Ferreira et al., 2012). Urban agriculture is a desirable land use for these spaces, but degraded soils are common. Urban agriculture can be productive in vacant urban land and also can improve soil quality at previously degraded sites (Beniston et al., 2016).
Remote sensing can be applied to detect and study various phenomena in Earth
sciences and natural resources. For example, forest fires can be simulated
using thermal remote sensing and normalized difference vegetation index (NDVI) indices derived from satellite
imagery. NDVI data are a suitable substitute for ground-based measurements
in post-fire runoff predictions (Van Eck et al., 2016). Vegetation indices
derived from the satellite data and remote sensing methods can be applied to
investigate dynamic performance of plantations in terms of multitemporal dry
biomass production (Zucca et al., 2015). Remote sensing instruments are key
players to study and map land surface temperature (LST) at temporal and
spatial scales (André et al., 2015). The LST indicator shows effects of
different types of phenomena and features in the electromagnetic energy
dispatch (Bingwei et al., 2015). Remote sensing methodology requires
less time and lower cost than field methods to investigate various phenomena
on the land surface (Niu et al., 2015). The advantages of using remote
sensing methodology are the repetitive and consistent coverage, high
resolution and evaluation of land surface characteristics (Owen et al.,
1998). Thermal infrared (TIR) data in remote sensing can help us obtain
quantitative information of surface temperature. Landsat imagery can be
applied for monitoring different types of land use in arid and semi-arid
regions (Baojuan et al., 2015). The Landsat Thematic Mapper (TM) and ETM
The using of LST values, which vary according to the surface characteristics, is a new method for investigating the effects of different land surface features on the surface temperature especially in urban areas (Guanhua et al., 2015). Derivation of LST from medium to high spatial resolution data of remote sensing is very important to study climate change and environment (Juan et al., 2014). In the several studies the relative warmth of cities was estimated by knowing air temperature and land use changes. The LST index provides important information about climate and the surface physical characteristics. Land use changes and anthropogenic activities affect the environment and land surface temperature (Dehua et al., 2012; Weng and Schubring, 2004). The estimation of the LST from the radiative transfer equation, the mono-window and single-channel algorithms can be used to retrieve the LST from thermal infrared data of the TM sensor (José et al., 2004).
Yazd city location in Iran.
The NDVI is a good indicator for identifying long-term changes in the vegetation covers and their status (Baihua and Isabela, 2015). The NDVI calculation method using surface emissivity can be applied to areas with different soil and vegetation types and where the vegetation cover changes (Valor and Caselles, 1996). Therefore, analysis of spatial variability of NDVI, surface temperature and the relationship between these parameters is essential in environmental studies. Combined study of NDVI, surface temperature and the temporal relation these two parameters with land use changes can be used to investigate climate change and global warming (Schultz and Halpert, 1993). Vegetation cover change is the main factor that causes surface temperature changes. It should be noted that increasing surface temperature may increase vegetation cover density in the area, but only in areas where there are sufficient water resources (Weixin et al., 2011). Different types of vegetation cover have different spatial responses to climate changes (Dehua et al., 2012). In the environmental studies, researchers have investigated land surface temperature using vegetation indices (Wei et al., 2015). Analysis of NDVI and LST of the different times (days, months, seasons and years) can be used to detect land use changes, which were formed because of deforestation, forest fires, mining activities, urban expansion and grassland regeneration (Sandra et al., 2015). Changes in land use and land cover can be evaluated by analysis of the vegetation cover and NDVI trends. The vegetation phenology was detected using Terra MODIS NDVI data by Gong et al. (2015). Land use changes, vegetation cover and soil moisture have strong effects on the land surface temperature; therefore, surface temperature can be applied to study land use changes, urbanization and desertification. Surface emissivity calculation is important to estimate surface temperature. In the several studies, laboratory measurements of the emissivity data were used for estimation of land surface temperature (Salisbury and D'Aria, 1992, 1994).
In the present study, heterogeneous surface temperature and NDVI of Yazd in Iran were calculated using Landsat TM sensor data. Surface temperature variation over different land use types in the Yazd are investigated and the relationship between NDVI and land surface temperature are analyzed. The aims of this study are to apply temperature–emissivity separation (TES) algorithm for LST retrieving from Landsat TM thermal data and to analyze the NDVI, different land use types and their roles in the surface temperature change. The main advantages of this study are TES algorithm (calculating emissivity) for retrieving LST, full statistical analysis for results validation and simultaneous analysis of NDVI, LST and land use. The main difference between this study and previous studies is to investigate the relationship between NDVI, LST and land use changes, and analysis of the effect of the land use changes on land surface temperature.
Yazd has been chosen as the study area, since there is a combination of
different land use categories and it is susceptible to dust storms. Yazd is
located in 31
The Landsat TM sensor data of 8 August 1998 and 6 August 2009 (daytime) were used in the present study. A surface emissivity calculation is the
first step of land surface temperature retrieving by the TES algorithm. The emissivity per pixel was obtained directly
from Landsat TM sensor data. Natural surfaces at the resolution of 30 m
are heterogeneous and they differ from each other in their emissivity. In
addition, the surface emissivity is affected by surface roughness,
vegetation cover and different land use types. In the present study, surface
emissivity was evaluated by analysis of NDVI and the fraction of
vegetation cover per pixel. Emissivity is a quantification of the intrinsic
ability of a surface in converting heat energy into above surface radiation
and depends on the physical properties of the surface and on observation
conditions (Sobrino et al., 2001). Surface emissivity can be extracted using
NDVI values of the bare soil, full vegetation and a mixture of bare soil and
vegetation (Sobrino et al., 2004). In this study, the following equation was
used to extract land surface emissivity for each pixel:
The at-sensor spectral radiance is the amount of energy received by the satellite sensor. Calculation of spectral radiance is the fundamental step in converting satellite image data into a physically radiometric scale. Radiometric calibration of the Landsat TM sensor involves rescaling the raw digital numbers of the satellite image to calibrated digital numbers. The pixel values of unprocessed image data were converted to spectral radiance by radiometric calibration.
TM spectral range and post-calibration dynamic ranges.
Spectral radiance (
Table 1 summarize the TM spectral range, post-calibration dynamic ranges
(LMIN and LMAX scaling parameters, the corresponding rescaling gain
(Grescale) and rescaling bias (Brescale) values). QCALMIN
Thermal band data (band 6 on TM) can be converted from spectral radiance to
an
effective brightness temperature. The brightness temperature assumes that
the Earth's surface is a black body (spectral emissivity of the black body
is 1). Thermal radiance values were converted from spectral radiance to
brightness temperature using the thermal constants with the following equation:
TM thermal band calibration constants.
Since brightness temperature (
Finally, derived land surface temperature in Kelvin was converted to Celsius by subtracting from 273.15.
For processing satellite data and database building, the Gauss–Krueger coordinate system was selected. The false color composite imagery of Landsat TM data (8 August 1998 and 6 August 2009) covering the study area was produced in the ArcGIS environment (Fig. 3). Land use classes were selected in the false color composite imagery for supervised classification of image. Different land use types were classified using maximum likelihood classification in ArcGIS environment (Fig. 4). Following land classification was accepted using the Landsat TM data: asphalt road, park and green spaces, waste land and bare soil, fallow land, residential (urban), commercial and industrial (Mallick et al., 2008). Classification was performed on Landsat TM for spectral reparability of the land use classes existing in the study area. Finally, the relationship between land use classes, surface temperature and NDVI in Yazd was analyzed in detail.
The validation of LST by satellite products is
performed using ground-based measurements of surface temperature. In the
present study, ground-based data of land surface temperature received from
the Yazd Meteorological Bureau were used to validate the LST of the Landsat TM sensors.
Measuring land surface temperature was performed using thermometers with
SMT160 temperature sensor. The thermometers have a temperature range of
Coordinates of the land surface temperature measurement points and their ground-based and satellite temperatures.
Location of the land surface temperature measurement points.
False color composite (bands 2, 3 and 4) image of Landsat TM data
of Yazd city:
Land use classified image of Yazd city by maximum likelihood
classification:
Land use distribution of Yazd city using maximum likelihood classification.
Validation of the obtained temperatures from 8 August 1998 and
6 August 2009 images was performed in the Excel software using the
root mean square error (RMSE) and coefficient of determination (
The Kappa coefficient and overall accuracy were used to validate the land use classification. The error matrix is used as an analytical statistical technique. The simplest descriptive statistical indicator is overall accuracy, which can be calculated using error matrix. Producer's accuracy is the probability that a pixel in the classified image is placed in the same class on land. In the calculation, the correct pixel total in each class is divided by the pixel total of that class as derived from the reference data. The producer's accuracy indicates the probability of a reference pixel being correctly classified. In the user's accuracy the correct pixels total in a land use class is divided by the total number of pixels that were classified in that class. The user's accuracy is indicative of the probability that a pixel classified on the image actually represents that category on the ground (Story and Congalton, 1986). User's accuracy is the probability that a specific class of land was classified in the same class on the classified image.
By classifying land uses of the study area, six land use types for the study area were considered: asphalt roads, parks and green spaces, waste land and bare soil, fallow land, residential, commercial and industrial areas. The land use distribution in Yazd is described in Table 4. By comparing area percentage values of different land use classes between images, it can be concluded that land use types of the study area were significantly converted in the 11-year period. Table 4 shows that the asphalt roads, commercial, industrial and residential (urban) classes of land use were increased. However, parks, green spaces, fallow lands, waste lands and bare soils were decreased during the time. The study results show that residential areas increased most compared to the other classes of land use. The main changes include conversion of parks, green spaces, bare soils and fallow lands to residential areas.
For example in the 1998 image, by comparing the reference and classified data in the Table 5, it was observed that although 100 % of the asphalt roads were being correctly identified as asphalt roads, only 90.9 % of the areas called asphalt roads are actually asphalt roads.
And in the 2009 image by comparing the reference and classified data, it was observed that 95.24 % of the asphalt roads were being correctly identified as asphalt roads, while 86.96 % of the areas called asphalt roads are actually asphalt roads (Table 6). The Kappa coefficient values of the 1998 and 2009 classified images were 0.96 and 0.95, respectively. The error matrixes indicate an overall accuracy of 0.97 and 0.95 for the 1998 and 2009 classified images, respectively. These values were obtained from the analysis of the referenced data (ground-based data) and the classification output (Tables 5 and 6 and Figs. 5 and 6).
Error matrix used to assess the accuracy of a classification of the 8 August 1998 Landsat TM image.
Error matrix used to assess the accuracy of a classification of the 6 August 2009 Landsat TM image.
Land use classes User's and producer's accuracy of the 8 August 1998 Landsat TM image.
Land use classes User's and producer's accuracy of the 6 August 2009 Landsat TM image.
Spatial distribution of NDVI obtained from Landsat TM data:
Land surface temperature retrieved from Landsat TM TIRS data at
urban area of Yazd city:
The spatial distribution of NDVI values from the Landsat TM image can be
seen in Fig. 7. The 1998 NDVI values are in the range of
Linear relationship between ground-based measured temperature and
land surface temperature (LST) of Landsat TM sensor:
Land surface temperature of different land use categories of Yazd city.
Accuracy assessment of land surface temperature derived from
Landsat TM sensor data using calculation RMSE and
In the present study, land surface temperature was retrieved by the TES algorithm from TIRS (Thermal Infrared Sensor) data of the Landsat TM. The spatial distribution of surface temperature of the
1998 and 2009 images is shown in the Fig. 8. Surface temperature of the
1998 LST image ranged from 27.1 to 45.57
In the 1998 classified image, bare soil and waste land (mean value
38.61
Relationship between surface temperatures with NDVI of the different land use categories.
For the investigated area, results show that the land surface temperatures
retrieved from TES algorithm using Landsat TM sensor data have a high
accuracy with a RMSE of 0.9
Linear relationship between NDVI values and land surface
temperature (LST) of Landsat TM sensor:
Variance analysis of relationship between land surface temperature and normalized difference vegetation index (NDVI) retrieved from Landsat TM data.
In the present study, a strong relation was observed between NDVI, surface temperature and land use types changes (Fig. 10 and Tables 9 and 10). The spatial variations of surface temperature are affected by the conversion of land for human-dominated use (land use change) and vegetation cover. There is a linear regression between surface temperature and NDVI (Fig. 10).
The significant differences less than 0.01 between NDVI values and temperatures of land use types for the 1998 and 2009 cases were obtained through statistical analysis of NDVI values and surface temperatures. According to the mentioned significant difference can be concluded that with a probability of 99 % a decrease in the NDVI values caused to increase temperature (Table 10).
This paper proposed the TES algorithm to obtain LST from Landsat TM and change detection in land surface temperature in relation to land use change. The spatial scales of TIRS pixel (100 m), land use and NDVI products (30 m) can reduce accuracy of the results. The classified images show that the agricultural lands were classified as fallow land, and it was also observed that in some places of study fallow lands have been combined with waste land and bare soil because of the similar values between their spectral reflectance. As a result, the main factor of the conversion of land use types of study area in the 11-year period is the human activities for urban growth. Based on the results, the classified images have significantly good accuracy over different land use types. Wheat and barley are mainly grown in the agricultural lands of the study area (seasonal plants) that in the performed classification are placed on the fallow land class because of similar values of spectral reflectance of these products and fallow lands. It should be noted that these agricultural products were harvested in May. As mentioned in Sect. 3.2, the NDVI values decreased over the studied period of time (1998–2009) because of plant-covered surface reduction. It should be noted that the central part of the study area had a slight decrease in the surface temperature. It was probably due to the design and implementation of artificial green spaces and parks by managers in the city. Also, many cooling devices, which are used in residential areas (especially in the summer), were effective in creating this micro-climate. However, reduction of vegetation cover and NDVI values and the consequent rise in temperature were observed in the entire study area. A higher temperature of outlying parts than the central part of city was due to keeping and increase parks and green spaces in the central part and the destruction of vegetation in the outlying parts of the city. Meanwhile, an increase in the overall temperature of Yazd is due to different reasons, including vegetation loss and land use change in the area.
It is generally observed that surface temperature has been increased in
all types of land use, but the greatest increase is registered in the
commercial and industrial sites. It should be noted that residential areas
had a slight decrease in the surface temperature. This is probably due to
many cooling devices, which are used in residential areas of the Yazd,
especially in the summer. The high
The study results show that LST, NDVI and surface emissivity can be
estimated using Landsat TM sensor imagery with high accuracy. The calculation
of
surface temperature and NDVI is important in the earth studies including
global environmental change, urban climate change and urbanization.
Different land use types of urban areas can be studied by estimating NDVI
and land surface temperature values. This paper explored the spatial
and temporal relationship between NDVI, LST and land use types. It was
found, that in the Yazd city combination of vegetation cover decreasing,
residential areas increasing and other changes in land use was directly
causing a surface temperature increase. By comparing two different time
periods
(1998 and 2009), we concluded that the average surface temperature of
Yazd city has risen 1.45
In the present study, were used unprocessed data of the Landsat TM sensor
provided by United States Geological Survey (USGS, 2016) (