Proceedings

THE 36th ASIAN CONFERENCE ON REMOTE SENSING 2015

“Fostering Resilient Growth in Asia”

Philippine Geoscience and Remote Sensing Society and

Asian Association on Remote Sensing

HEAT ISLAND DETECTION IN COAL MINING AREAS USING

MULTITEMPORAL REMOTE SENSING

Nurul Ihsan Fawzi and Retnadi Heru Jatmiko

Cartography and Remote Sensing Department, Faculty of Geography

Universitas Gadjah Mada

Yogyakarta, Indonesia

Email: nurul.ihsan.fawzi@live.com

KEY WORDS: heat island, surface temperature, coal mining, Landsat imagery

ABSTRACT:

The changes of land cover due to open coal mining activities have a lot impact to environment. The damage caused

by land cover changes also result in indirect impact, which increase the surface temperature hence cause variations in

surface temperature. Surface temperature variations can generate heat island phenomenon, where the temperatures

are warmer than those of surrounding areas.

The research was conducted in a part of East Kalimantan Province, Indonesia. The data used was Landsat ETM+

imagery for the year 2002 and 2012. Planck equation with emissivity correction and Maximum Likelihood algorithm

were used for the extraction of surface temperature and land cover classification respectively.

The result is that the use of remote sensing technologies provides the estimation with near-real conditions on the earth.

For the land cover extraction from remote sensing, the accuracy of which is owned by 79.06%. Surface temperature

validation have an accuracy of 84.58% for the year 2002 (Δ = ± 5.54°C) and 91.53% for the year 2012 (Δ = ± 1.85°C).

Land cover changes on surface temperature through changes that represent radiant emissivity of the object in the

earth's surface produce R2 = 0.473, which show the effect of changes in the two years, amounted to 47.3%. High

temperatures are fragmented in areas far from urban areas and in the midst of vegetation, which were identified as an

barren land due to mining that led to the heat island with values close to built-up area (like a phenomenon of urban

heat island) with a value of 12.058°C in 2002 and 8.641°C in 2012. In this case, the effect of landscape pattern of the

region did not affect the temperature changes that occurred.

1. INTRODUCTION

Indonesia is an archipelagic country that has many natural resources. From the historical record, it is known that

mineral deposits have been found in several areas (Ishlah, 2008). One of the minerals that becoming the Indonesia's

largest resource is coal, besides petroleum and liquefied natural gas. Coal defined as a solid combustible substance

formed by the partial decomposition of plant material (World Coal Institute, 2005). Currently, Indonesia's coal resource

is more than 105 billion tons of coal reserves and approximately 21 billion tons, equivalent to 80 billion Barrel Oil

Equivalent (Kamandanu, 2011). In 1998, Indonesian coal production only 61.3 million tons, then increased

dramatically to 240 million tons over a period of ten years later. And in 2010, coal production continued to increase to

275 million tons (Kamandanu, 2011). Looking at those prospects, in the future many companies will work in the

exploration and exploitation of coal in Indonesia (Chan, 2012).

Policies provide a gap for coal mining would threaten the existence of vegetation cover such as forest and farm,

considering Indonesia's coal mining using an open pit mining (Marbun, et al., 2013; Adaro Energy, 2013). Damaged

land due to mining may occur during mining and post-mining activities. For example, the process of land clearing

operations as the beginning step of mining has resulted in changes in land cover, the impact with the loss of natural

vegetation will contaminate large areas, groundwater is easily pollute the atmosphere (Han, et al., 2007).

Land cover change is a factor that known as the agent of ecological change and an important factor between human

activities and global environmental change (Wasige, et al., 2013). Land cover changes will affect the function of

ecosystems, biodiversity, and climate (Southworth, 2004). Land cover changes could have influenced the surface

temperature (Chen, et al., 2006). The influence could be a decrease or increase in the surface temperature of the surface

temperature. These changes trigger the occurrence of other natural phenomena, such as changes in local climate

(Landsberg, 1981; Southworth, 2004; Weng, et al., 2004; Leeuwen, et al., 2011; Weng, 2008). Other natural

Proceedings of the 36th Asian Conference on Remote Sensing 2015

Quezon City, Metro Manila, Philippines. October 24 – 28, 2015

phenomena that appear as a result of the influence of land cover change are the surface temperature in an area warmer

than that in the surrounding environment, such as between urban and rural areas, referred to as the urban heat island

(UHI) (United States Environmental Protection Agency, 2008). In this study, the definition of a regional surface

temperature warmer than the surrounding environment modified as heat island, which is not of city and villages, but

due to the heating of an environment as a result of mining activity or post-mining coal with the surrounding

environment. Heat island due to the isolated location (different condition), which has a surface temperature / air is

higher than the surrounding area on in situ measurements.

Analysis of heat island obtained from surface temperature, can be done by measuring in situ, or by using remote sensing

technology using a specific algorithm that also has a close outcome measurement in situ (Sobrino, et al., 2004). The

advantages using remote sensing data are the availability of data with high resolution, consistent, repetition recording,

and the ability to measure/record the condition of the earth’s surface as well (Owen, et al., 1998).

In remote sensing, thermal infrared sensors on satellites that obtain quantitative information about surface temperatures

is associated with the type/category of land cover. The study of the use of remote sensing technology is to give a lot of

information about the phenomenon of land cover changes associated with the surface temperature and the heat island

on differences in the scale and type of data used, such as NOAA-AVHRR with 1.1 km spatial resolution, Landsat

Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensor infrared thermal (Thermal infrared) data

with spatial resolution respectively 120 m and 60 m (Basar, et al., 2008; Cao, et al., 2008; Kindap, et al., 2012; Kumar,

et al., 2012; Laosuwan & Sangpradit, 2012; Sobrino, et al., 2004; Southworth, 2004; Tan, et al., 2009; Rigo, et al.,

2006; Walawender, et al., 2013).

One of the use of many applications of satellites is for Landsat ETM+. The use of these satellites may provide

information when converted into surface temperature; it can be used directly to be associated with other processes

(such as micrometeorological). So far, researches on heat island are more focused on the changes in urban land use

from undeveloped land to vegetative cover. Thus, such research is needed to assess the impact caused by coal mining

- in this case the variation of surface temperature changes on the time difference. This issue is extremely important to

investigate and evaluate the impact of industrialization surrounding impact. The result provides a database environment

to conduct an environmental impact assessment in the regional context and understanding deforestation - land damage

on spatial and temporal domain with remote sensing, which is difficult with conventional methods.

The aim of this study is (1) to estimate the surface temperature using Landsat ETM+ thermal image and to determine

the surface temperature distribution of the study region changes as a result of coal mining, (2) to identify the heat island

phenomenon in mining area; and analyzing the relationship between changes in land cover due to mining with surface

temperature changes that occurred in 2002 and 2012.

2. REMOTE SENSING DATA

Landsat ETM+ path/row 116/60, which was recorded on January 13, 2002 and 30 April 2012 were used in this research.

Landsat ETM+ has 8 bands with different spatial resolution, 3-band visible and infrared band 2 with a resolution of 30

meters, one thermal infrared band with a resolution of 60 meters, and 1 panchromatic band with a resolution of 15

meters. An image in 2012 with the SLC-Off, the picture is not perfect (U.S. Geological Survey, 2013). Thus, it was

necessary that an operating gap-filled conducted so that the image can be used for the analysis. Gap-fill algorithm used

was based on an algorithm developed by US Geological Survey (USGS) Earth Resources Observation Systems (EROS)

Data Center (EDC), which used a multi-scene with path / row the same (U.S. Geological Survey, 2004).

The data used in January 2002 and April 2012. Research location was in the equatorial zone, the climate was also

influenced by Monsoon winds, the wind Monsoon November-April West and East Monsoon winds from May to

October. The rainfall in January was 329.6 mm and 370.6 mm in April, the sunshines were41% and 70%. It can be

said that the two sources of data used had the same characteristics of the season (BPS Provinsi Kalimantan Timur,

2013).

3. METHOD

3.1 Study Area

This research was conducted in Samarinda city and surrounding. This area was selected because it represented the city

with associated urban heat island, and many coal mining area. Development activities during 2002 to 2012 was

assumed to have many changes so that analysis on the parameters of the study was conducted. With the availability of

adequate remote sensing data, in the form of Landsat ETM+ with different record time far enough, in 2002 and 2012,

was also the reason of the selected location.

Proceedings of the 36th Asian Conference on Remote Sensing 2015

Quezon City, Metro Manila, Philippines. October 24 – 28, 2015

Figure 1. This research conducted in Samarinda City, East Borneo (marked as red).

3.2 Data Processing

The processing data included radiometric correction, calibration to equalize the pixel on same object in both images

due to different recording time, and masking cloud and area of interest.

3.3 Image Classification

Image classification produces land cover classification that needed to change detection during the observation time. In

this study, Landsat ETM+ was classified according to the classification scheme of Anderson, et al. (1976) with the

level of classification based on Landsat ETM+ satellite data. Land cover categories used were: (1) high density

vegetation, (2) medium density vegetation, (3) the body of water (including rivers, creeks, ponds, and lakes), (4) built-

up land, and (5) barren land. Classification used the supervised classification using maximum likelihood algorithm.

3.4 Surface Temperature Extraction

To get the estimation of the surface temperature with good quality, the correction process takes four steps, namely

(Weng, et al., 2004; Voogt & Oke, 2003): (1) conversion of pixel values to values Lλ; (2) correction absorption and re-

emission in the atmosphere; (3) surface emissivity correction; and (4) correction of surface roughness. In this study,

we didn’t correct of surface roughness. The correction was conducted only for atmospheric correction and emissivity

correction. However, the horizontal variations can be minimized because this study used imagery acquired on a clear

day and covering a small area. This is the step to produce surface temperature image.

3.4.1 Pixel Value Conversion to 𝐋𝛌

The following equation is used to perform the conversion Qcal do Lλ for Level 1 products (Chander, et al., 2007;

Chander, et al., 2009).

𝐋𝛌 = (Lmax− Lmin

QCALmax− QCALmin) x (BN − QCALmin) + Lmin (1)

where Lλ = spectral radiant sensor (W/(m2 .sr.μm), Qcal = pixel value (DN), Qcalmin = minimum pixel value that

refers to Lmin (DN), Qcalmax = maximum pixel value that refers to lmax (DN), Lmin = minimum value of the spectral

radiant (W/(m2 .sr.μm), and Lmax = maximum value of the spectral radiant (W/(m2 .sr.μm).

3.4.2 Emissivity Correction

Alternative to obtain land surface emissivity is to use vegetation index, such as the NDVI (Valor & Caselles, 1996;

Sobrino, et al., 2001). The use of the method NDVI (Normalized Difference Vegetation Index), the emissivity may

be obtained by reducing the complex atmospheric correction procedure. Advanced methods developed can be used

if the emissivity of the surface of bare ground and vegetation and distribution is known (Valor & Caselles, 1996).

Valor and Caselles (1996) defines emissivity n by the equation:

ε = εvPv + εs(1 – Ps) + 4<dε> Pv(1-Pv) (2)

Proceedings of the 36th Asian Conference on Remote Sensing 2015

Quezon City, Metro Manila, Philippines. October 24 – 28, 2015

Pv is a vegetation fraction, with value various between 0.00 – 1.00 (Carlson & Ripley, 1997). Pv can defined with

equation (Carlson & Ripley, 1997):

𝑃𝑉 = [NDVI− NDVIs

NDVIv−NDVIs]

2

(3)

where NDVIs and NDVIv is a NDVI value for bare soil and surface respectively with 100% vegetation. In the

assessment of this vegetation fraction, NDVIv and NDVIs values are the critical part in determining the value of Pv.

Gutman and Ignatov (1998) as described by Jiménez-Muñoz, et al. (2009), get the value of = 0.04 ± 0.03 NDVIs

and NDVIv = 0.52 ± 0.03, with the minimum and maximum values are deserts and forests. Sobrino, et al. (2004)

using NDVIv and NDVIs = 0.2 and = 0.5. In this case, we used value from Jiménez-Muñoz, et al. (2009), where

NDVIs = 0.15 and NDVIv = 0,801 ± 0,012 is the value that is the most appropriate to be used in the general

conditions.

NDVI as vegetation indices are often used, can be obtained by equation (Carlson & Ripley, 1997):

NDVI = αnir−αvis

αnir+αvis (4)

where αnir and αvis is reflectance at wavelength ~ 0.6 μm (band 3 in Landsat ETM+) and the near infrared

wavelengths ~ 0.8 μm (band 4 in Landsat ETM+) in the image that has been corrected reflectance.

3.4.2 Atmospheric Correction

Along with emissivity correction, we also carry out atmospheric correction. We conducted this correction because

radiant values received by the sensors (at sensor radiances) influenced by profiles of the atmosphere and water

vapor in the atmosphere. NASA developed the correction parameters on a website (http://atmcorr.gsfc.nasa.gov),

which was developed for the correction of Landsat thermal data. This correction is based on the radiative transfer

equation to correct for atmospheric factors that affect the radiation emitted by the object. Radiative transfer equation

is expressed by equation (Sobrino, et al., 2004):

𝐿𝑠𝑒𝑛𝑠𝑜𝑟,𝜆 = [𝜀 𝜆𝐵 𝜆(𝑇𝑠) + (1 − 𝜀 𝜆)Latm↓ ] 𝜏 𝜆 + Latm

↑ (5)

where: 𝐿𝑠𝑒𝑛𝑠𝑜𝑟,𝜆 = the value of the radiant sensor at the Top of Atmosphere (TOA) (W/m2.sr.μm) 𝜀 𝜆 = surface

emissivity, Bλ = reference black body which is obtained from the Planck equation, Ts = surface temperature (K),

Latm↓ = atmospheric downwelling radiance (W/m2.sr.μm), Latm

↑ = atmospheric upwelling radiance (W/m2.sr.μm),

and τλ = atmospheric transmittance.

3.4.2 Corrected Surface Temperature

Corrected surface temperature obtained with equation (Chander, et al., 2007; Chander, et al., 2009):

Tkin = K2

ln(K1

𝐿𝑠𝑒𝑛𝑠𝑜𝑟,𝜆+ 1)

(6)

where Tkin = radiant temperature in Kelvin (K), K1 = constant calibration of spectral radiant (666.09 W/(m2.sr.μm)

and K2 = calibration constant absolute temperature (Kelvin 1282.71).

3.5 Heat Island Analysis

The analysis of heat island using zonal statistical analysis to determine differences in the surface temperature of

each land cover category. The first step is to create a map heat island each year based on the observation equation:

Heat Island = Tkin – (µ + 0,5 α ) (7)

where μ and α are the mean and standard deviation of the surface temperature in the study area respectively. The

value of the generated heat island, can be calculated using equation (Kindap, et al., 2012):

∆Tµ-r = Tµ - Tr (8)

where Tμ is the surface temperature in the city or the forms of land use that is warmer than the surrounding

temperature, Tr is the temperature of the surface around the area being measured Tμ.

Proceedings of the 36th Asian Conference on Remote Sensing 2015

Quezon City, Metro Manila, Philippines. October 24 – 28, 2015

3.5 Relationship Analysis

Surface temperature distribution was known based on the results of surface temperature extractions done in 2002 and

2012 the temperature distribution on the surface of each year were analyzed for distribution in each year and land cover

types. To analyze the spatial relationship between the changes in land cover and surface temperatures are usually done

by statistics only, the analysis used in this study changes in emissivity as a representation of land cover change and the

resulting differences in surface temperature. Their relationship was quantified using Pearson product-moment

correlation coefficient.

In addition, to determine the effect of location of land cover changes that occurred were represented by aggregation of

land cover, then used spatial metrics calculation. In this study, landscape metrics are calculated for each land cover,

with landscape metrics were calculated aggregation index is used to determine the effect of aggregation of land cover

on surface temperature (McGarigal, 2001).

4. RESULT

4.1 Land Cover and Surface Temperature Map and Validation

The result of this research is that we show the land cover classification and surface temperature map. The distribution

of surface temperature follows the distribution of land cover. This means that the difference of surface temperature

because of differences in the thermal capacity of the object. Both observations in 2002 and 2012, showed that there

was overall warmer surface temperatures in the areas of development by human activity, either in the form of settlement,

logging, or mining coal.

Ground checking of land cover map represent by a representative of the same area with the sampling of surface

temperature. The sample is also associated with the characteristic landscape, time, and costs allocated (Stehman &

Czaplewski, 1998). As the result, the accuracy for land cover map is 79.06%. In this case, changes in land cover over

the conversion to other forms of categories that is completely different, like the change from vegetation into built-up

area.

For surface temperature validation, most of satellites have a margin of 3% accuracy with the correct calibration and

correction (Rigo, et al., 2006). An image validation for surface temperature, the accuracy for the year 2002 amounted

to 84.58% (Δ = ± 5,54oC) and for the year 2012 amounted to 91.53% (Δ = ± 1,85oC). In 2012, the margin of accuracy

of 4.22%, where the majority of the temperature with the remote sensing research with average of margin error is 3%.

Therefore, it can be said with the margin of error, that the relatively high accuracy as well as the research is quite good.

4.2 Land cover and Surface temperature change

We used statistical method to analyze changes between years 2002-2012. The average of pixel values of surface

temperatures was counted by type of land cover. Fig.2 shows that the surface temperature each land cover increases

dramatically. It also happens in a barren land from 30oC becomes 33oC.

Figure 2. Charts the relationship

between land cover and surface

temperatures in 2002 and 2012 based on

data from the image.

25

26

27

28

29

30

31

32

33

34

High

Density

Vegetation

Medium

Density

Vegetation

Water Body Built-up

Area

Barren Land

Surf

ace

tem

per

ature

(oC

)

Land cover

2002

2012

Proceedings of the 36th Asian Conference on Remote Sensing 2015

Quezon City, Metro Manila, Philippines. October 24 – 28, 2015

Figure 3. Land cover classification for years 2002 (a) and 2012 (b).

Figure 4. Surface temperature map, for years 2002 (a) and 2012 (b)

4.3 Heat Island Detection

Heat island is a phenomenon that affected by surrounding environment. It can be said, the pixel is not pure representing

its object at that location, the temperature is influenced by the surrounding pixel. So, we had to include this

phenomenon to image that we processed. Kernel analysis with 3 x 3 window was used to produce new temperature

maps to represent near-real condition on earth temperature in the images.

Heat island can be defined as the maximum temperature difference of the threshold value that is applied, instead of the

mean value. Detection of heat island in this study used zonal statistics. The surface temperature was difference on each

land cover category. The surface temperature used as the input was processed from equation (7) and (8).

In this study, the resulting heat island was a surface heat island. The threshold values for years 2002 was 30.58oC, and

for year 2012 was 32.92oC. Increasing the threshold was resulted from the increase in the surface temperature on the

image and the average value. Using equation (8) noticed the surrounding environmental conditions, and without any

limitations mentioned surface temperature heat island or not (limit temperature to define the condition of the heat island

or not).

Proceedings of the 36th Asian Conference on Remote Sensing 2015

Quezon City, Metro Manila, Philippines. October 24 – 28, 2015

Figure 5. heat island map in study area for years 2002 (a) and 2012 (b). Black-strip line is mining border or coal

mining area, and black line is a city border. We try to distinguish a heat island phenomenom in city area and

mining area.

Table 1 listed the result from using threshold value for quantify heat island. We define maximum value as a heat island

value that happens in study area.

Table 1. Statistic result use of threshold value each land cover

Land cover type 2002 2012

Average (o C) Max (o C) Average (o C) Max (o C)

High density vegetation -0.935 3,306 -1.594 2,210

Medium density vegetation -1.386 4,630 -1.119 6,533

Water body -2.269 2,614 -3.277 2,210

Built-up area 0.070 11,629 0.516 9,163

Barren land 0.056 12,058 -0.513 8,641

Table 1 shows that heat island also occurred in barren land as a barren land caused by coal mining. The result tells that

heat island occurred not only in city as an urban heat island, but also in barren land with the same value and the

difference only 0.5oC.

4.4 Relationship analyses

To analyze the spatial relationship, we used changes in emissivity as a representation of land cover change and the

resulting differences in surface temperature. The table 2 shows the results of processing with a significance value

<0.005 and the coefficient of determination R2 = 0.473.

Table 2. Statistic result for relationship analyses land cover change and temperature. Model Summary

Model R R Square Change Statistics

F Change df1 df2 Sig. F Change 1 0.688a 0.473 621484.283 1 691066 0.000

a.Predictor: (Constant), Temperature

This value can be interpreted that the occurred land cover change can lead to changes in surface temperature. In addition,

to determine the effect of location of land cover changes that occur are represented by aggregation of land cover, then

used spatial metrics calculation. Aggregation index analysis showed a very weak relationship, i.e., the value of the

Proceedings of the 36th Asian Conference on Remote Sensing 2015

Quezon City, Metro Manila, Philippines. October 24 – 28, 2015

coefficient of determination R2 = 0.087. This value is interpreted that the aggregation temperature changes do not affect

the value of the surface temperature. This aggregation also showed biophysical factors into the variables that explained

the variation of the surface temperature (Weng, 2008).

5. CONCLUSION

The result told us that heat island occurred not only in city as an urban heat island, but also in barren land in coal

mining areas with the same value and the difference only 0.5oC. High temperatures were fragmented in areas far from

urban areas and in the midst of vegetation, which was identified as barren land due to mining that led to the heat island

with values close to built-up area like a phenomenon urban heat island. In relationship analyzed, land cover changes

on surface temperature through changes that represented radiant emissivity of the object in the earth's surface produced

R2 = 0.473 which showed the effect of changes in the two years amounted to 47.3%. In this case, the effect of landscape

pattern of the region didn’t affect the temperature changes that occurred.

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