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: [email protected]
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.
6. REFERENCE
Adaro Energy. (2013). Laporan Bulanan Aktivitas Eksplorasi PT Adaro Energy Tbk bulan Januari 2013. Jakarta: PT Adaro
Energy Tbk.
Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A Land Use And Land Cover Classification System For
Use With Remote Sensor Data. Washington DC: U.S. Geological Survey.
Basar, U. G., Kaya, S., & Karaka, M. (2008). Evaluation of Urban Heat Island in Istanbul Using Remote Sensing Technique.
The International Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII Part
B7. Beijing, 971-976.
BPS Provinsi Kalimantan Timur. (2013). Kalimantan Timur dalam Angka tahun 2013. Samarinda: Badan Pusat Statistik
Provinsi Kalimantan Timur.
Cao, L., Li, P., Zhang, L., & Chen, T. (2008). Remote Sensing Image-Based Analysis of The Relationhip Between Urban
Heat Island and Vegetation Fraction. The International Archive of the Photogrammetry, Remote Sensing and Spatial
Information Sciences. Vol. XXXVII Part B7. Beijing, 1379-1383.
Carlson, T., & Ripley, D. (1997). On the Relation between NDVI, Fractional Vegetation Cover, and Leat Area Index. Remote
Sensing of Environment, 62, 241 - 252.
Chan, E. (2012). Fitch: Prospek industri batubara masih cerah. Retrieved Januari 8, 2013, from
http://industri.kontan.co.id/news/fitch-prospek-industri-batubara-masih-
Chander, G., L, B., & Barsi, J. A. (2007). Revised Landsat-5 Thematic Mapper Radiometric Calibration. IEEE Geoscience
and Remote Sensing Letter, VOL. 4, NO. 3, 490-494.
Chander, G., Markham, B., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat
MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113, 893-903.
Chen, X.-L., Zhao, H.-M., Li, P.-X., & Yin, Z.-Y. (2006). Remote Sensing Image-Based Analysis of the Relationship
Between Urban Heat Island and Land use/cover Changes. Remote Sensing of Environment, 104, 133-146.
Han, Y., Li, M., & Li, D. (2007). Vegetation Index Analysis of Multi-source Remote Sensing Data in Coal Mining. New
Zealand Journal of Agriculture Research, Vol.50, 1243-1248.
Ishlah, T. (2008). Kajian Pasar Mineral dan Usulan Strategi Eksplorasi Sumberdaya Mineral di Indonesia. Buletin Sumber
Daya Geologi Volume 3, 1-13. Retrieved Juli 2013, from
http://psdg.bgl.esdm.go.id/buletin_2008/Islah_Kajian%20Pasar%20Mineral.pdf
Jiménez-Muñoz, J., Sobrino, J., Plaza, A., Guanter, L., Moreno, J., & Martinez, P. (2009). Comparison Between Fractional
Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS
Data Over an Agricultural Area. Sensor, 9, 768-793.
Kamandanu, B. (2011). Indonesian Coal Mining Outlook. Presented at the IEA workshop “ COAL MARKET’S
OUTLOOK ”, 14 April 2011 Oriental Bay International Hotel, Beijing, P.R. CHINA. Retrieved Desember 2012,
from http://www.iea.org/media/weowebsite/workshops/weocoal/05_02_KAMANDANU.pdf
Kindap, T., Unal, A., Ozdemir, H., Bozkurt, D., Turuncoglu, U. O., Demir, G., . . . Karaca, M. (2012). Quantification of the
Urban Heat Island Under a Changing Climate over Anotalian Peninsula. In N. Chhetri, & N. Chhetri (Ed.), Human
and Social Dimensions of Climate Change (pp. 87-104). Rijeka, Croatia: InTech.
Kumar, K. S., Bhaskar, P. U., & Padmakumari, K. (2012). Estimation of Land Surface Temperature to Study Urban Heat
Island Effect using Landsat ETM+ Image. International Jurnal od Engineering Science and Technology, Vol. 4 No.
2, 771-778.
Landsberg. (1981). The Urban Climate. New York: Academic Press.
Proceedings of the 36th Asian Conference on Remote Sensing 2015
Quezon City, Metro Manila, Philippines. October 24 – 28, 2015
Laosuwan, T., & Sangpradit, S. (2012). Urban Heat Island Monitoring and Analyss by Using Integration of Satellite Data
and Knowledge Based Method. International Journal of Development and Sustainability, Vol. 1 No.2, In Press.
Leeuwen, v. T., Frank, A. J., JIn, Y., Smyth, P., Goulden, M. L., van der Werf, G. R., & Randerson, J. T. (2011). Optimal
use of land surface temperature data to detect changes in tropical forest cover. Journal of Geophysical Research, 116.
doi:10.1029/2010JG001488
Marbun, M., Istilam, & Kurnia, M. (2013). Analisis Yuridis Terhadap Keputusan Sistem Pengawasan Kebijakan Pemda
Provinsi Kalimantan Timur tentang Perizinan Batubara. Thesis, Fakultas Hukum, Universitas Brawijaya. Retrieved
from http://hukum.ub.ac.id/wp-content/uploads/2013/10/380_JURNAL-MANGADAR-MARBUN.pdf
McGarigal, K. (2001). Landscape Metrics for Categorical Map Patterns. Retrieved Desember 2013, from
http://www.umass.edu/landeco/teaching/landscape_ecology/schedule/chapter9_metrics.pdf
Owen, T., Carlson, T., & Gillies, R. (1998). Remotely sensed surface parameters governing urban climate change. Internal
Journal of Remote Sensing, 19, 1663-1681.
Rigo, G., Parlow, E., & Oesch, D. (2006). Validation of satellite observed thermal emission with in-situ measurements over
an urban surface. Remote Sensing of Environment 104, 201 - 210.
Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land Surface Temperature Retrieval from Landsat TM 5. Remote
Sensing of Environment 90, 434–440.
Sobrino, J., Raissouni, N., & Li, Z.-L. (2001). A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data.
Remote Sensing of Environment, 75(2), 256-266.
Southworth, J. (2004). An Assessment of Landsat TM Band 6 Thermal Data For Analysing Land Cover in Tropical Dry
Forest Region. International Journal of Remote Sensing Vol. 25 No.4, 689-706. doi:10.1080/0143116031000139917
Tan, J., Zheng, Y., Tang, X., Guo, C., Li, L., Song, G., . . . Chen, H. (2009). The urban heat island and its impact on heat
waves and human health in Shanghai. International Journal Biometeorol, 54, 75–84.
U.S. Geological Survey. (2004, July 10). SLC-off Gap-Filled Products Gap-Fill Algorithm Methodology. Retrieved
January 2014, from http://landsat.usgs.gov/documents/L7SLCGapFilledMethod.pdf
U.S. Geological Survey. (2013). SLC-off Products: Background. Retrieved January 2014, from
http://landsat.usgs.gov/products_slcoffbackground.php
United States Environmental Protection Agency. (2008, Oktober). Urban Heat Island basics. In Reducing Urban Heat
Islands: Compendium of Strategies; Chapter 1; Draft Report. Retrieved Januari 8, 2013, from US EPA: Washington,
DC, USA: http://www.epa.gov/heatisland/resources/compendium.html
Valor, E., & Caselles, V. (1996). Mapping Land Surface Emissivity from NDVI: Application to European, African, and
South American Areas. Remote Sensing of Environment, 57, 167 - 184.
Voogt, J., & Oke, T. (2003). Thermal remote sensing of urban climates. Remote Sensing of the Environment, 86, 370–84.
Walawender, J., Szymanowski, M., Hajto, M., & Bokwa, A. (2013). Land Surface Temperature Patterns in the Urban
Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM+ Data. Pure and Applied Geophysics,
10.1007/s00024-013-0685-7. doi:10.1007/s00024-013-0685-7
Wasige, J., Groen, T. A., Smaling, E., & Jetten, V. (2013). Monitoring basin-scale land cover changes in Kagera Basin of
Lake Victoria using ancillary data and remote sensing. International Journal of Applied Earth Observation and
Geoinformation, 21, 32-42. doi: http://dx.doi.org/10.1016/j.jag.2012.08.005
Weng, Q. (2008). The Spatial Variations of Urban Land Surface Temperatures: Pertinent Factors, Zoning Effect, and
Seasonal Variability. IEEE Journal of Selected Topics in Applied Erath Observations and Remote Sensing, 1(2), 154-
166.
Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of Land Surface Temperature - Vegetation Abuncance Relationship
for Urban Heat Island. Remote Sensing for Environment, 89, 467-483. doi:10.1016/j.rse.2003.11.005
World Coal Institute. (2005). Sumber Daya Batubara: Tinjauan Lengkap Mengenai Batubara. Retrieved September 12, 2012,
from www.worldcoal.org
Zong-Ci, Z., Yong, L., & Jiang-Bin, H. (2013). Are There Impacts of Urban Heat Island on Future Climate Change? Advances
in Climate Change Research , 4 (2), 133-136. doi:10.3724/SP.J.1248.2013.133