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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

900 950 1000 1050 1100 1150 1200

Thousands people

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sa

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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

09.00 10.00 11.00 12.00 13.00 14.00 15.00

L
a
n
d
S

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T

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Shrub

Built Area

Water Body

Open Field

Forest

1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7

L
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T
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f
S

u
r
v
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(
°

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)

LST of Imagery (° C)

Time

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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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