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RESEARCH ARTICLE 

 

Land Cover Changes Based on Cellular Automata for Land 

Surface Temperature in Semarang Regency 

  

Fahrudin Hanafi1,* , Dinda Putri Rahmadewi1, Fajar Setiawan2 

1Department of Geography, Faculty of Social Sciences, State University of Semarang, 

Sekaran Gunungpati, Semarang,  50229, Indonesia 
2Limnology Research Center, Indonesian Institute of Sciences (LIPI),  

Bogor, 16911, Indonesia 

 

 Received 7 March 2021/Revised  17 November  2021/Accepted 8 December 2021/Published 20 December 2021 

 

Abstract 

Land cover changes based on cellular automata for surface temperature in Semarang Regency 

has increased significantly due to the continuous rise in its population. Therefore, this study 

aims to identify, analyze and predict multitemporal land cover changes and surface 

temperature distribution in 2028. Data on the land cover map were obtained from Landsat 7 

and 8 based on supervised classification, while Land Surface Temperature (LST) was 

calculated from its thermal bands. The collected data were analyzed for accuracy through 

observation, while Cellular Automata - Markov Chain was used to predict the associated 

changes in 2028. The result showed that there are 4 land cover maps with 5-year intervals 

from 2003 to 2018 at an accuracy of more than 85%. Furthermore, the existing land covers 

were dominated by forest with decreasing trend, while the built-up area continuously 

increased. The existing Land surface temperature range from 20.6°C to 36.6°C, at an average 

of 28.2°C and a yearly increase of 0.07°C. The temperature changes are positively correlated 

with the occurrence of land conversion. Land cover predictions for 2028 show similar forest 

dominance, with a 23,4% built-up area at a surface temperature of 28.9°C. 

 

Keywords: Land cover change; Cellular Automata-Markov Chain; Land Surface 

Temperature  

 

1. Introduction 

According to the United Nations (2018), population increase is a global problem 

experienced in every country, with 55% of humans presently living in urban or regional areas, 

likely increasing by 68% in 2050. These changes tend to affect both local and global climate 

components, such as the land surface temperature (LST). For example, in Nigeria, there was 

an increase of 19,166.13 ha in urban built areas from 2002 to 2013, with a rise in LST by 

6 °C (Igun & Williams., 2018). 

 

                Geosfera Indonesia                              
Vol. 6 No. 3, December 2021, 301-318 

p-ISSN 2598-9723, e-ISSN 2614-8528                                                                                                                                         

https://jurnal.unej.ac.id/index.php/GEOSI          

DOI : 10.19184/geosi.v6i3.23471 

 

*Corresponding author. 

Email address : fahrudin.hanafi@mail.unnes.ac.id (Fahrudin Hanafi) 
  

 

mailto:fahrudin.hanafi@mail.unnes.ac.id


 

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Khandelwal et al. (2018) stated that an increase in LST tends to disrupt the climate-

energy balance, such as the heat wave phenomenon experienced in 7 major European 

countries, namely the United Kingdom (38.1 °C), Germany (41.7 °C), Belgium (41.8 °C), 

France (42.6°C), Luxembourg (40.8 °C), Scotland (31.6 °C), and the Netherlands (40.7 °C) 

recorded in July 2019. In Southeast Asia, several major cities also experienced similar 

conditions. An increase in hotspots was 20% greater than the average LST in Hanoi (Tran et 

al., 2017) and at a temperature of 2.9 ° C in Jakarta which is higher than in Bangkok (Estoque 

et al., 2017). 

Land cover changes also occurred in Central Java Province. Moreover, 128 ha of rice 

fields were converted to settlements or used for other purposes from 2009 to 2010 (BPS, 

2015). On the contrary, the average air temperature in Central Java Province from 2032 to 

2040 was predicted to increase within the range of 0.81 to 0.85 ° C (BMKG, 2019). 

Semarang Regency, Central Java Province, Indonesia, had a high population growth 

rate (8.74%) from 2010 to 2016 (BPS, 2017a). Based on statistical data, in 2016, 1.014 

million people with a density of 1.081 people/km2, was recorded. This figure is higher 

compared to the national average population density of 127 people/km2. However, from 2011 

to 2016, agricultural areas were reduced by 0.94% from 60,439.96 to 59,872.49 ha, while 

land used for other purposes was increased by 1.64%, which is equivalent to 35,148.18 ha 

(BPS, 2017b). This indicates that Semarang Regency is also susceptible to land and climate 

changes problems, specifically areas adjacent to the city, which has experienced rapid 

development and recorded an LST average of 1.32 ° C (Nugraha et al., 2016). 

Analysis carried out using past and present spatial data is considered one of the 

requirements for geographic studies (Dadras et al., 2015). Cellular automata are the 

commonly used spatial model of land cover change. It is dynamic and composed of inter-

related cells with discrete units (Wang et al., 2012). Cellular automata are used mainly to 

generate and predict potential changes (Tran et al., 2017). 

 Fu & Weng (2016) stated that temporal disparities of thermal characteristics due to 

land cover changes and responses need to be carried out comprehensively. One of the 

environmental parameters analyzed in this study is LST, estimated from the Single thermal 

Channel or Split Window Algorithm Method, dependent on the number of bands used (Pu et 

al., 2006). Both have a weakness in respect to the atmospheric profile uncertainty, which 

strongly affects the accuracy of the result (Li et al., 2013). However, this is anticipated by 

inputting the atmospheric profile data into the thermal band spectral radian correction made 

by the USGS (United States of Geological Surveys) (Coll et al., 2010). 



 

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Study carried out on the land cover change in Semarang regency is common for land 

suitability, flood (Susanti et al., 2012), landslide, sedimentation (Apriliyana, 2015), carbon 

stock, and spatial planning review (Pangi et al., 2017). These studies were specifically related 

to land and averaged surface temperatures (Kalinda & Bandi, 2018). Therefore, this study 

aims to model land cover changes based on raster data using cellular automata related to its 

surface temperatures in the future. In addition, it also intends to (1) analyze the surface 

temperature distribution and land cover changes of Semarang Regency in 2003, 2008, 2013, 

and 2018, (2) evaluate the relationship between land cover changes and LST, and (3) 

investigate the distribution of land cover for the following 10 years. 

2. Methods 

This field survey was conducted in Semarang Regency, Central Java Province, from 

April to June 2019. The area was considered due to the record time of the imagery data input. 

Furthermore, simulation data input only requires 2 land cover imageries, the initial and step 

year. However, for the sake of detailed information, this study used those acquired in 2003 

(initial), 2008 (step 1), 2013 (Step 2) and 2018 (Step 3), which was compared using 

population growth and space needed, such as the assumption based on consistent population 

per built area on initial, and each step. The satellite image data used are (1) Landsat 7 

path/row 120/65 imagery recorded on May 20, 2003; (2) Landsat 7 path/row 120/65 imagery 

recorded on June 18, 2008; (3) Landsat 8 path/row 120/65 imagery recorded on June 24, 

2013; (4) Landsat 8 path/row 120/65 imagery recorded on August 25, 2018. 

Unfortunately, Landsat 7 (2008) had some bad qualities due to the sensor stripping. 

However, USGS provided corrections using past imagery with a similar location. Secondary 

data used to support the population growth analysis were obtained from the (1) Population 

growth and built area of Semarang Regency from 2008 to 2018, then (2) Slope from 

AsterGDEM Radar Image data (USGS). Road networks, activity centers, and river patterns 

were obtained from BIG and Spatial Planning of Semarang Regency data and used as 

constraint input on land cover simulation. 

Sampling calculation refers to the Technical Guidelines for collecting and processing 

Spatial Data from the Geospatial Information Agency (BIG). Meanwhile, the number of 

sample points for each land cover type is determined using the proportional stratified 

sampling method, as shown in Figure 1. 

Field surveys are carried out to measure the temperature of the land surface and cover 

the ground check. This analysis was carried out from April to June 2019, synchronized with 



 

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the imagery record period. It was assumed that the weather condition, sun duration, and 

intensity are similar to the imagery and survey data. Also, the duration (on distribution 

sample) is customized from 08.00 to 11.00 AM to adjust the recorded time of the imagery. 

 

Figure 1. Location and Research Sample in Semarang Regency, Central Java 

 

Land cover is classified (Maximum Likelihood) using ENVI 5.1 and differentiated 

according to the method proposed by Danoedoro (2006) concerning water, forest, shrub, 

agricultural, open, and built areas. The classification accuracy threshold used is 85%, thereby 

determining its mapping by comparing the 2018 image with field observations. Data from the 

previous year's image is compared with the temporal interpretation of Google Earth. 

Accuracy is determined using a confusion matrix involving the consideration of omission and 

commission. Overall, it indicates the probability that a pixel belongs to a certain class and its 

representation in the field (Lillesand et al., 2004). 

Land cover prediction in 2028 was made with Selva's version of Idrisi software in 

accordance with the Markov Chain method based on Cellular Automata. Meanwhile, Markov 

Chain is used to analyze 2 land cover data realized in different years, namely past (2008) and 



 

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actual information (2019). The transition matrix is focused on the change of any land cover to 

build area using a 3x3 matrix. Built area conditions control this change from the forest, 

agriculture, or open field, asides the opposite. Agriculture is changed from the forest, or open 

field, besides the opposite including build area, etc. 

LST is estimated by transforming pixel into spectral radian values using USGS 

equations to correct surface reflection errors and earth curvature. Meanwhile, errors due to 

atmospheric disturbances, specifically in terms of processing images of land surface 

temperatures, are determined with the equation proposed by Coll et al. (2010). It contains 

parameters that tend to affect LST, including emissivity, transmission, upwelling and down-

welling (Kalinda & Bandi, 2018). The profile is obtained from the Atmospheric Correction 

Parameter Calculator, modeled according to data input's date, time, and location (Tran et al., 

2017). The corrected spectral radian values are converted to brightness temperatures using 

the USGS formula for Landsat imagery. This is estimated as LST using the equation 

proposed by Artist & Carnahan (1982) and Amiri et al. (2009). It is also used to determine an 

accurate brightness temperature of 8-14 чm wave (Artis & Carnahan, 1982), while this study 

utilized bands 6 (10.4 to 12.5 чm) and 10 (10.60 to11.19 чm). 

The analysis technique is used to determine the effect of each land cover type on LST 

changes by comparing the t-count with the t-table. The t-test is carried out using a simple 

linear regression equation while the t-table size is calculated with the formula t (a/2, n-2) = t 

(0.05/2, 116-2) = t (0.025, 114) = 1.98099, based on the following criteria: (1) assuming the 

significance value is <0.05 and t-count> t-table, it means that there is an impact, and (2) 

assuming the significance value is > 0.05 and t-count <t-table, meaning there is no impact. 

3. Results and Discussion 

3.1 Land Cover Changes 

Image classification performed with the supervised method produces land cover maps 

for 2003, 2008, 2013 and 2018. Confusion matrix analysis on Table 1 shows an accuracy of 

91.38%, 92.24%, 88.79% and 86.21 %. According to Fariz (2016), misclassification is not 

only influenced by differences in recorded time as well as the misidentification of objects 

from residential land to the forest because tall trees cover their appearance. The accuracy test 

results obtained annually exceed 85%, therefore, the data is feasible and needs to be used for 

further analysis (Susanti et al., 2012). 

 

 



 

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Table 1. Producer and Overall Accuracies of Land Cover Matrix Confusion 

Land Cover 
Producer Accuracy 

2003 2008 2013 2018 

Water Body 100.00% 100.00% 100.00% 100.00% 

Forest 98.31% 98.11% 100.00% 93.33% 

Agriculture 61.90% 60.00% 56.25% 80.95% 

Shrubs 100.00% 88.89% 66.67% 66,67% 

Open Field 100.00% 100.00% 100.00% 77.78% 

Built Area 91.67% 100.00% 95.45% 92.00% 

Over All Accuracy 91.38% 92.24% 88.79% 86.21% 

 

The results show that the dominated land covers are forest and agricultural areas 

because the Semarang Regency is near mountains and fertile, as shown in Figure 2. Table 2 

shows the increased number of forests recorded in 2013, possible because “sengon" and teak 

plantations starts with land clearing and an approximately 5 years harvest period (Nuroniah & 

Putri, 2013) and 15 to 20 years for teak (Pudjiono, 2014). This result also corresponds with 

the Semarang Regency data by BPS (2017), which reported a critical land decrease of 26.2% 

in 2013 from the previous year a program on forestry services was held to boost its increase. 

Agricultural areas experienced a significant increase of 75.50% from 2013 to 2018. 

This pattern is common in rural areas, specifically in reduced forest and open fields, thereby 

boosting agriculture. The reverse is the case in urban areas such as agricultural lands in the 

Nanjing region. China was converted to developed or built areas with 2.6% in 2009 (Wang et 

al., 2016).  

Table 2. Land Cover Changes in Semarang Regency in 2003, 2008, 2013 and 2018 

Land Cover 

Years 

2003 2008 2013 2018 

(ha) (%) (ha) (%) (ha) (%) (ha) (%) 

Forest 52,696 51.7 45,742 44.9 49,183 48.3 39,452 38.7 

Built Area 9,903 9.7 18,293 18.0 18,642 18.3 20,012 19.7 

Shrubs 19,188 18.8 16,413 16.1 12,469 12.2 15,522 15.2 
Agricultural  11,415 11.2 9,007 8.8 8,303 8.2 14,572 14.3 

Waters Body 967 0.9 676 0.7 996 1.0 767 0.8 

Open field 7,665 7.5 11,703 11.5 12,240 12.0 11,509 11.3 

Amount 101,834 100 101,834 100 101,834 100 101,834 100 

 

 

 



 

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3.2 Land Cover Simulation 2028 

Land cover simulation processed with Markov Chain on Cellular automata with 

population growth as a control. Furthermore, increase in population and built areas are 

linearly correlated to each step year to average growth. Therefore, the acquired data serves as 

an area of increased control when cellular automata are executed. The cell movement is 

constrained by slope, existent built area, road and river networks. Land cover changes are 

influenced by several attributes, from macro (policy) to micro (regional complex) factors 

(Figure 2). In the regional complex, Semarang Regency develops and changes the influence 

of roads, slopes, demographics, and land availability. 

 
 

 

 

 

 

 

 

 

 

 

 

Figure 2. Land Cover Changes in 2003-2018 

The road as a driving force for mobility affects the pattern of land development, 

which extends along with the northern and southern parts of Ungaran and Tengaran districts, 

respectively. Residents tend to build new settlements at a distance of <250 m from the main 

road.Theslope affects development in the highlands, leading to the clustering of existing built 

areas. 



 

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Population affects human space requirements. The population density in Semarang 

Regency was 889 people per km2 in 2003. However, it increased to 1,081 people per km2 in 

2017. This is presumed to influence the changing of forests and agricultural lands into built 

areas. Land availability along with its clearing increased from 2003 to 2013. This condition is 

also accompanied by a decrease in forests, shrubs and agricultural areas because the 

emergence of open land availability also promotes increased social facilities. 

On the land cover simulation proposed by 2028, a decline in forests, shrubs and 

agricultural fields was recorded in the previous 10 years, except water bodies and built -up 

areas, as shown in Table 3. The spatial distribution shows that the developmental pattern of 

regions built in 2018 is clustered at the regional and economic centers. This includes East, 

and West Ungaran, Bergas, Ambarawa, Bawen, Tengaran, Suruh, and Kaliwungu Districts, 

as indicated in Figure 3. However, a difference of only 284.56 ha was realized in accordance 

with the conditions of built area development in the same location from 1991 to 2001 (Pangi 

et al., 2017). 

Table 3. Land Cover Changes in Semarang Regency, 2018 - 2028 

Land Cover 

Years Percent  

Changes 2003 2028 

(ha) (%) (ha) (%) (%) 

Forest 39,452 38.7 37,010 36.5 -5.68 

Built Area 20,012 19.7 23,727 23.4 18.78 

Shrubs 15,522 15.2 14,295 14.1 -7.24 
Agricultural  14,572 14.3 14,096 13.9 -2.80 

Waters Body 767 0.8 773 0.8 0.00 

Open field 11,509 11.3 11,498 11.3 0.00 

Amount 101,834 100 101,834 100  
 

Land cover simulation results in 2028 show an increase in built areas, a decrease in 

forest (6.19%), shrubs (7.90%), agricultural (3.27%), and open fields (0.09%). Waters bodies 

such as the Rawa Pening Lake increased by 0.88% in 2018 due to the influence of 

agricultural areas and shrubs. Similar conditions also occurred from 2001 to 2011 when an 

increase in agricultural lands (2,905.79 ha)was recorded (Apriliyana, 2015). The population 

and built areain the Semarang regency linearly correlate with the equation y = 0.02x + 32.06, 

where y is the built area and x is the total population, as shown in Figure 4. This is also used 

to determine an estimate of 23,770.62 ha in 2028. Moreover, when compared with the 

predicted area determined by the Cellular Automata method, the 2 estimations are 43.59 ha or 

0.18% deviation. This shows that the results of the Cellular Automata simulation of land 

cover and population linear regression do not contradict each other.  



 

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Figure 3. Land cover in 2018 and 2028 (modeling using the Cellular Automata) 

2018 Existing 

2018 Simulation 



 

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Figure 4. Linear equations for population and built-up area (prediction of built-in area in 

2028)

 

3.3 Imagery Surface Temperature Vs Survey 

The field survey led to the fluctuation of LST data every hour from 09:00 to 15:00 for 

different land covers, as shown in Figure 5. This is used to ascertain the response of each land 

cover type to solar radiation. Besides, each has a different response rate, although they 

generally tend to reach the peak temperature at 13:00. It is clear that the built area has the 

highest LST, reaching 3.6 ° C, and decreases gradually after 13:00. Apart from land cover 

information, satellite imagery is also used to extract LST. This study produced 4 LST 

distributive maps in 2003, 2008, 2013 and 2018 (Figure 6). 

Meanwhile, when compared with the estimated results of the image from the closest 

year, a positive relationship was determined with a correlation coefficient (R) of 0.637, 

coefficient of determination (R2) is 0.45 with a degree of significance <0.005 (significant). 

According to the R-value, the relationship between LST imagery and field survey is 

positively correlated (Fu & Weng, 2016). Figure 6 shows the image temperature is high, as 

well as the field, however, the coefficient is different due to dissimilarities in weather 

conditions. 

y = 0.02x + 32.06

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     Figure 5. Changes in surface temperature on each land cover per hour  

 

 

Figure 6. Linear graph of LST VS Land Cover Changes 

 

 

 

 

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3.4 Surface Temperature Changes 

The results of the LST obtained using satellite imagery shows that the maximum and 

minimum temperatures reached were 37.6 ° C, and 20.6 ° C, respectively. The average LST 

and standard deviation values are 28.2 ° C and 3.39 ° C. Interestingly, the LST temperature 

from 2003 to 2018 tended to increase, while a decrease was detected in the data acquired in 

2008. The decline in land surface temperature is influenced by low solar radiation of 

approximately 425.55 watts/m2, accumulated over the years. This is caused by the position of 

the earth's aphelion to the sun, which usually occurs in June. The Australian Cold Munson 

phenomenon also affects the deviation, which blows cold and dry air from the Indonesian 

coast. It also causes upwelling followed by a decrease of not less than 3° C in the South Java 

Sea, besides this usually occurs from July to August (Sukresno et al., 2018). 

The high-temperature area is regularly experienced in regional centers, as shown in 

Figure 6. This shows that LST is concentrated due to the urbanization influence caused by 

land cover changes (Peng Fu, 2016). However, it is different from the phenomenon that 

occurred in 2008 due to the concentration of newly built areas in Bancak District which has 

varying temperatures, depending on the various types (Pal & Ziaul, 2017). 

3.5 Surface Temperature Vs Land Cover Changes 

To compare continuous data from LST and land cover changes (ordinal), this study 

collected whole coverage and point samples using grid size as the location. The LST was 

generated as positive or negative changes compared to the land cover, which was converted 

to non-changes data. The results of correlation analysis (Figure 7) carried out on the land 

cover and LST from 2003 to 2018 was used to determine the strong relationship between the 

coefficient of determination (R2: 0.430) and the correlation coefficient (R: 0.s655) (Sarwono, 

2006). Meanwhile, from the t-test on each land cover, it is evident that agricultural areas, 

forests, and shrubs have a dominant influence (Table 4).  



 

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 Figure 7. Linear Graph LST VS Land Cover Changes Data 

 

Land cover is associated with temperature changes, it is evident that the dominant one 

is the conversion of forests to agricultural areas with a temperature (averaged area) increase 

of 0.31 ° C/ha, as shown in Figure 8 and Table 5. This led to an increase in the number of 

farmers from 144,369 (2013) to 167,044 (2018) in Semarang Regency (Yuliati, 2018). In 

addition, this condition was also experienced at the national level from 2000 to 2005, where 

plantations and agricultural areas were the dominant land cover types that replaced 44% of 

forests. The increase in average surface temperature of regional LST at the study site was 

raised to relatively 1.06 ° C with an average increase per decade of 0.7 ° C. Lin & Yu (2005) 

classified the LST increase in average surface temperature as being greater than the value 

obtained in Beijing and approximately within a range of 0.25 to 0.31 ° C. Based on this 

approach, it was estimated that the average LST in 2028 is expected to reach 28,9 ° C. 

 
Figure 8. Land Surface Temperature Changes in 2003, 2008, 2013, and 2018  

 

 

 

2003 2008 2013 2018 



 

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Table 4. Regression of Land Cover with Land Surface Temperature 

Land Cover R
2
 t-Calculated Significance 

Forest 0.511 7.912 0.000 

Agriculture 0.566 3.021 0.019 

Open Field 0.331 2.537 0.025 

Shrub 0.433 4.095 0.000 
Water Body 0.078 -0.921 0.379 

Built area 0.285 2.360 0.033 

 

Table 5. Land Cover Area and Average Area LST 

Land Cover 
Area (Ha) 

% 

Area 
Changes (°C) Averaged Area Changes 

2003 2018 

Forest Forest 5,825.07 61.41% -0.09 -0.05 

  Agriculture 734.58 7.74% 4.05 0.31 

  Built Area 97.83 1.03% 2.78 0.03 

  Open Field 123.84 1.31% 2.99 0.04 

  Shrub 279.72 2.95% 2.70 0.08 

Agriculture Agriculture 1.53 0.02% 1.22 0.00 

  Built Area 649.08 6.84% 2.62 0.18 

  Open Field 26.01 0.27% 1.61 0.00 

  Shrub 754.11 7.95% 1.34 0.11 

Shrub Forest 0.54 0.01% -0.31 0.00 

  Agriculture 137.38 1.45% 2.98 0.04 

  Built Area 648.18 6.83% 3.90 0.27 

  Open Field 2.97 0.03% 2.86 0.00 

  Shrub 204.89 2.16% 2.53 0.05 

Amount 9,485.73 100%  1.06 

 

The differences in LST are not only influenced by the land cover type. The results 

obtained from this study differs from other studies due to regional complexes. This bias is 

caused by several factors such as solar radiation, topography, soil and vegetative types. The 

linear regressionshows that the solar radiation value has a positive effect on LST. Every 1 

watt/m2triggers the average LST area by 0.1 ° C from 2003 to 2018. The topography’s 

response was slightly different from that of the LST. In addition, its sample distribution 

shows an inverse comparison. The regression equation is y = -0.001x + 23.77 with R2 of 

0.411. The minus sign (-) in the equation shows a negative relationship, therefore, an increase 

in height causes a decrease in the LST and vice versa. 

Based on the map, the western and southern parts of the study location comprised 

mostly of andosols associated with brown and dark red Mediterranean lithosol, regosol, and 

brown latosol. Furthermore, soil type, texture and color affects the LST distribution. One of 



 

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the main ingredients of andosol soil, which is usually in the form of sand,is its ability to 

absorb high heat as well as to withstand low soil moisture within the range of 4 to 70% 

(Bavel et al., 1972). Meanwhile, hydromorphous alluvial soil types dominate the center, with 

clay as the parent material. It is also able to properly absorb a heat capacity of 0.279 calg-1 

°C-1, although it is lower compared to sand (Lal & Shukla, 2004). 

Vegetation is also considered to influence LST variation due to height, canopy 

character, and location of growth tendency. In accordance with the BPS data, cloves, rubber, 

coffee, sengon, teak, and mostly rice fields and shrubs are cultivated in the eastern part of 

Semarang Regency. Meanwhile, pine trees, fruits, bamboo, rubber, rice fields, and shrubs are 

cultivated in this regency at dense temperature fluctuations with high humidity and wind 

speed of 0.6 m/s.Conversely, the cone canopy has a speed of 0.4 m/s and a higher 

temperature (Fadlurrahman, 2018). 

4. Conclusion  

The dominant land cover in Semarang Regency from 2003 to 2018 was a forest with 

the tendency to be converted into agricultural areas. Land cover predictions for 2028 also 

show a similar change pattern. Furthermore, these changes had a positive relationship with 

LST and a significant effect with a correlation coefficient (R) of 0.655 (strong). Forest, 

agricultural areas, open fields, shrubs and built-up areas affect 51.1%, 56.6%, 33.1%, 43.3%, 

and 28.5% on LST. Land cover simulation shows that the built area is bound to increase by 

18.56%, indicating dense growth in Tengaran, East Ungaran, Bank, and Kaliwungu District s. 

Based on calculations, this caused an average increase of 28.9 ° C at a rate of 0.07 ° C/year. 

 

Conflict of interest 

The authors declare that there is no conflict of interest with any financial, personal, 

other people or organizations related to the material in this study. 

 

Acknowledgements 

 

The authors are grateful to the Department of Geography, Faculty of Social Sciences, 

State University of Semarang for their support in conducting this study and also to all those 

who provided and processed data. 

 

 



 

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