Introduction Materials and Methods

Proceedings of MatricesFor IITTEP – ICoMaNSEd 2015 ISBN: 978-602-74204-0-3 Physics Page 257 APPLICATION OF REMOTE SENSING TECHNIQUES FOR IDENTIFICATION SURFACE TEMPERATURE DISTRIBUTION OF MAHAWU VOLCANO Cyrke A. N. Bujung 1 1 Department of Physics, Faculty of Mathematics and Natural Sciences, State University of Manado, Minahasa, Indonesia cyrkebujungyahoo.com Abstract The presence of geothermal energy resource subsurface associated with active volcano, and indicated on the surface by the appearance of geothermal manifestation. This might to identify the geothermal potentials based on surface temperature distribution recorded on the thermal remote sensing image. This research describes the surface temperature distribution surroundings of Mahawu volcano, temporal analysis to delineate of thermal zone and its laterally change with use thermal infrared channel of Landsat remote sensing image. This research use temporal analysis of thermal infrared remote sensing data of Landsat 7 ETM + recorded in 2010, 2011, and 2014. The result of mapping and analysis shows that thermal anomaly zone located on north section of Mahawu volcano peak, and flow of heat laterally trend to northeast. Keywords : Remote sensing, temperature, and thermal infrared

1. Introduction

The use of satellite remote sensing data in the field of earth has been widely applied in developed countries for the purposes of geological mapping, mineral exploration and energy, natural disasters and so forth use in the field of Earth is basically recognize and map the object and parameters of terrestrial specific, interpret the process of establishing and interpreting relation to other aspects. To do the above two methods are commonly performed by the method of visualmanual that recognize specific objects and geological phenomena that can be seen in the image such as different types of rock, bedding plane, and fault structure. The second way is done through the automatic extraction of objects using certain ways and formulas by using existing software digital processing. Both methods have advantages and disadvantages so that a combination of both would be more effective and optimal. Recording an object in the remote sensing is done by using electromagnetic energy that will interact with every appearance that there is on the surface of the earth Lillesand et al., 2004. Each appearance has spectral characteristics and spectral response specific to interact with the electromagnetic force that will give a certain appearance as well on remote sensing imagery. This research describes the surface temperature distribution surroundings of Mahawu volcano Figure 1, temporal analysis to delineate of thermal zone and its laterally change with the use of thermal infrared channel of Landsat remote sensing image.

2. Materials and Methods

Geothermal systems occur in regions of anomalously high crustal heat flow that may be related to the presence of young bodies or hot igneous rocks located deeper in the crust. Remote sensing can contribute to geothermal studies and exploration by detecting surface thermal anomalies by the use of thermal infrared TIR imagery. TIR remote sensing of data can be used to map and quantify surface temperature anomalies associated with geothermal features such as hot springs, geysers, fumaroles, and heated. Proceedings of MatricesFor IITTEP – ICoMaNSEd 2015 ISBN: 978-602-74204-0-3 Physics Page 258 Figure 1. Location of Research The main character image in remote sensing is the channel range electromagnetic wavelength Table 1. Some of radiation that can be detected by remote sensing system is like the solar radiation that can be detected through the medium of electromagnetic waves. Electromagnetic wavelength region from the visible and near infrared or until the middle of the spatial distribution of thermal energy is reflected from the surface of the earth. Thermal infrared imagery, the images created by the thermal infrared spectrum. Atmospheric window used is a channel with wavelength 3.5 to 5.5 μm, 8-14 μm and about 18 μm. Sensing in this spectrum based on temperature differences of objects and his girlfriend were in the image reflected by different hue or color differences. The research method was Carried out by using thermal infrared remote sensing of data of Landsat ETM+ were recorded in 2010, 2011, and 2014. Landsat ETM + consists of several spectral channels, and one of them is a thermal infrared channel see point 6 in Table 1. Table 1. Bands and main scopes of application of Landsat TM ETM + Band Spectral range μ m Resolution m Main scopes of application 1 0.45-0.52 30 Discriminate water system, shallow-sea mapping, monitoring of chlorophyll content in sea water 2 0.52-0.60 30 Identify the types of plants, evaluate the productivity of plants, water pollution research 3 0.63-0.69 30 Distinguish the types of plants, coverage, identify the plant growth state, health state 4 0.76-0.90 30 Biomass survey, determinate crop growth, soil moisture, looking for groundwater 5 1.55-1.75 30 Reflect the plants and soil moisture, distinguish cloud from and snow 6 10.4-12.5 120 60 ETM Detect the anomaly of the thermal radiation under common temperature 7 2.08-2.35 30 Geologic prospecting Data processing begins with the pre-processing of data in the form of radiometric correction and geometric correction. Digital conversion value of the spectral data in the image into spectral reflectance value, consisting of radiometric conversion and conversion of the reflected appearance. Conversion radiometric aims to calibrate the sensor so that there will be a linear relationship between the number of spectral radiance brightness value. This Proceedings of MatricesFor IITTEP – ICoMaNSEd 2015 ISBN: 978-602-74204-0-3 Physics Page 259 relationship is expressed in the range of parameter values in the image numbers, radiance lowest and highest radiance. Equation 1 states brightness relationship with radiance. min min max 255 L DN L L L     1 with L = radiance Wm -2 sr -1 L max = highest radiance L min = lowest radiance DN = digital value Sensor calibration parameters L max and L min is obtained from image metadata. Conversion appearance of reflections obtained through the equation 2: s H L       cos  2 with π = 3.14 L = radiance Wm -2 sr -1 H = solar radiation in the upper atmosphere θ s = solar zenith angle while recording In addition to recording the reflections, remote sensing record the energy of the earths surface on a thermal channel 3μm - 15μm to collect, display and interpret the thermal elements of the earths surface Calvin et al., 2007.Basically thermal energy emitted by the Earths surface, is not reflected by the earths surface.To estimate the surface temperature of the thermal data, digital image pixel values must first convert to radians using sensor calibration data Bujung, et al., 2011. Figure 2 shows a flow diagram of extraction temperature of the thermal channels Landsat image. Conversion brightness values into radian using equation 3:       min min max min min max L Q Q Q Q L L L cal cal cal cal                3 or can be written   B Q G L cal    4 with: L = spectral radiance at the sensor w m 2 .sr.μm L max = the spectral radiance scaled againstw m 2 .sr.μm L min = the spectral radiance scaled againstw m 2 .sr.μm related to L max, in DN = 255 = Minimum calibrated pixel value associated with the L min, in DN = 1 G = gain w m 2 .sr. M B = bias offset w m 2 .sr.μm Proceedings of MatricesFor IITTEP – ICoMaNSEd 2015 ISBN: 978-602-74204-0-3 Physics Page 260 Figure 2. Flowchart of Data Processing Satellite radiance conversion became effective temperature using equation 5, followed by the emissivity correction to calculate the surface temperature using equation 6.         1 ln 1 2  L K K T 5 T = temperature effective satellite K1, K2 = constant calibration L = spectral radiancethe sensor w m 2 .sr.μm. Emissivity correction to calculate the surface temperature using equation 6.    ln 1 T T T s   6 T s = temperature of the surface = wavelength of the emission radiation = 1.438 x 10 -2 mK Planck constant h = 6.3 x 10 -34 J.detik σ = Stefan Boltzmann constant 1.38 x 10 -23 J K c = speed of light 3 x 10 8 m sec ε = emissivity 0.95

3. Result and Discussion