Riset

1. Tefra Deposition Determination Mt. Kelud 2014 for Lahar Disaster Mitigation Using Synthetic Aperture Radar Data (SAR)

  1. INTRODUCTION

Mt. Kelud located in East Java, Indonesia, is an active stratovolcano. The eruptions of Mt. Kelud are characterized by explosive eruptions at the summit crater, accompanied with pyroclastic flows and lahars. However, the eruptions in February 2014 are more explosive than usual (VEI 4). The eruptions produced tephra and ashes which are deposited to about 600 km toward west. Following this eruption, we assessed the eruption precursor using time series of the Phased Array type L-band Synthetic Aperture Radar (PALSAR) data onboard the Advanced Land Observing Satellite (ALOS). The changes at the summit lava dome prior to the eruption are the target for defining the eruption precursors. Study area and field investigation after the eruption were depicted in Fig 1A-E

Fig. 1: Study area and field investigation after the large eruption of Kelud Volcano in February 2014.

  1. METHOD

The large explosive eruptions of Kelud Volcano occurred in February 13, 2014 with VEI 4. The eruptions produced tephra and ashes which are deposited to about 600 km toward West. The ash clouds and pyroclastic flow deposits were detected using temperature extraction method of Landsat-8 Thermal Infrared (TIR) data for night observation. The surface emissivity separation method was also applied to obtain surface temperature. The changes at the summit lava dome prior to the eruption are the target for defining the eruption precursors. The lava dome roughness on SAR (drSAR) was applied for change detections in backscatter intensity (σ º) and size of the Kelud lava dome. Volume estimation was performed for the lava dome before eruption and volcanic products after the eruption. Estimating the volume of volcanic products is crucial to mitigate secondary hazards such as flooding from mud flows or lahars. Comparison between volume of lava dome and volcanic products can be used to predict the ejected magma beneath the summit.

  1. RESULTS

There are two directions of pyroclastic flow deposits: West and South-West directions. The West direction was also detected by high temperature. However, the South direction is low temperature. The cooling process or different depositional periods is predicted as the cause low ground temperature. The Landsat-8 TIR showed that the main direction of the eruption is toward the west. However, the scoriae deposits from fragmental lava dome are located at the north from the summit. The gravitational collapse was predicted responsible to the deposits. The understanding of volcanic depositional process may lead to better mitigation of the secondary hazards such as lahars. Two parameters should be known: coverage area and thickness of volcanic products. The coverage area was calculated based on ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) image and the thickness  was estimated from ASTER GDEM (Global Digital Elevation Model) 30 m. Based on detected lava dome and volcanic products, the lava dome volume is about 24,000,000 m3 and volcanic products is about 85,000,000 m3. The lahars have flowed about 5 km toward North-West after 7 days of the eruption. There are about 85 million m3 still remaining around the summit. The volume calculations were depicted in Fig. 2.

Fig. 2: (Left) Calculation of lava dome volume before the eruption and (Right) travel distance of lahars after the eruptions.

  1. DISCUSSION AND CONCLUSION

The changes at the summit lava dome prior to the eruption are the target for defining the eruption precursors. The lava dome roughness on SAR (drSAR) was applied for change detections in backscatter intensity (σ º) and size of the Kelud lava dome. The drSAR could be used to detect surface roughness of lava dome as depicted in Fig. 6.  Surface roughness increase according to the increases of the dome size. The roughness is in accordance the increase of fractures at surface of lava dome. This condition is not good for obtaining good coherence of InSAR deformation., but excellent condition for drSAR.

2. Detecting Surface Permeability Based on Polarimetric Synthetic Aperture Radar (POLSAR) Data to Enhance Geothermal Production Field at West Java, Indonesia

  1. Introduction

Remote sensing technology provides ground surface data relatively cheap with large coverage area. However, the application of remote sensing technology for identifying physical properties of ground surface related to geothermal system is still limited. The atmospheric conditions such as cloud and water vapor are always the main problem of the optical sensor as well as the vegetation in tropical countries. On the other hand, the geothermal exploration and exploitation at producing power plant with high success ratio is crucial for Indonesia due to limitation of fossil fuel energy. Overcoming the problem, we proposed a research to identify the ground surface permeability based on surface roughness using Polarimetric SAR (PolSAR) data of The Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard The Advanced Land Observing Satellite (ALOS). The aim of this research is to find the physical properties of surface such as ground permeability which is the most effective to predict geothermal system bellow the ground surface using active remotely sensed data. Further aim is to increase success ratio of geothermal exploration as well as expansion to the existing power plant. Mt. Patuha in West Java Indonesia is selected as study site due to the existence of geotherma powerplant and noticeable surface manifestations.

  1. Data and Methodology

There are two SAR data types used in this study: single and quad (full) polarization types. For single polarization data, we used dual orbit observation in Ascending (satellite orbits toward North) and Descending (satellite orbits toward South). The ALOS PALSAR data level 1.5 in multilook geocoded are used as basis for ground surface characterizations. The SAR sensor observes ground surface at off nadir, thus there is an angle of look between the sensor and target. The angle of look produces ground surface image in the Line of Sight (LOS) direction or slant range. The slant range image caused the geometrical distortion of the ground and lost information especially at mountainous terrain due to foreshortening and shadowing effects. Some SAR sensors such as ALOS PALSAR are utilized by polarization mode. Simply, the electromagnetic signal from transmitter and receiver could be filtered into two components: Vertical and Horizontal termed as V and H, respectively. When the SAR sensor transmit the H component and receive the V component, the HV data type will be produced. Therefore, the sensor produces four combination data types for an acquisition time: HH, HV, VH, and VV. The four data combinations are termed as quad or full-polarimetric data. We used a full-polarimetric of ALOS PALSAR data to characterize surface manifestation following Saepuloh et al., 2015. Figure 1 shows the linear features overlaind on SAR backscattering intensity images of Mt. Patuha for each polarization modes.

Figure 1. Linear features extracted from POLSAR data of ALOS PALSAR at Mt. Wayang Windu.

  1. Surface Permeability on POLSAR Data

Using SAR backscattering intensity data, the Geomorphologic and Structural features (GSF) could be detected in detail (Saepuloh et al., 2012a). The POLSAR data provided visual GSF in opposite point of view satellite. The slope facing toward the sensor will be brighter than backward due to strong backscattering return to the receiver. The back-slope might be dark due to weak signal return to the receiver. The use of polarized data is effective to reduce the weak signal in the SAR imagery. Moreover, the backscattering intensity of high topography or mountainous terrain will be higher than flat terrain. The high contrast between high and low topography is superior for detection purposes of the linear features related to geological structures. The linear features according to the GSF in the SAR imagery are crucial for predicting the fluid path of geothermal system at ground surface. The features are characterized generally by contrast tone of the ridges, valleys, hills, and rivers. Following Saepuloh et al. (2012b), we applied an automatic extraction of linear feature density from Synthetic Aperture Radar (lifedSAR) to quantify linear features as depicted by Figure 2.

Figure 2. The geological structures overaid on the linear feature density (left). The surface manifestation such as fumarole and hot spring (right) are characterized by low anomaly in the linear feature density.

There are two ground surface parameters could be extracted successfully from POLSAR data: linear features density (LFD) and surface roughness (Ro). The two parameters could be used to detect the surface permeability presented by the alteration zones of geothermal system. In order to understand the response of altered rocks to the SAR backscattering intensity, we plotted between the LFD and Ro. Based on this cross-correlation plot, the LFD has a linear correlation to the Ro. It may infer that the alteration zones are located at low LFD from POLSAR data and smooth surfaces. The fractured zones served as fluid path of geothermal system by the means. The interaction between hydrothermal fluids and rocks or formations caused the ground surface smooth. The alteration among the minerals might cause a weakening of molecular bonds. Therefore, the rock surface could be decomposed and altered into clay easily. This process produced smooth ground surface.

  1. Conclusions

The SAR backscattering intensity data from full polarimetric modes proved effective to quantify structural features at ground surface in opposite Line Of Sight (LOS) satellite. The structural features presented in linear features in the SAR backscattering images served as a key to predict the surface permeability at geothermal field. The polarimetric data from ALOS PALSAR data were proved effective to characterize surface manifestation. The surface roughness could be used as parameter to identify surface permeability presented by altered rocks.

3. SPATIOTEMPORAL MODELING OF SURFACE PERMEABILITY BASED ON POLARIMETRIC SAR DATA FOR CONTINUATION OF GEOTHERMAL PRODUCTION IN WEST JAVA, INDONESIA

  1. Introduction

Characterizing physical properties of ground surface based on Synthetic Aperture Radar (SAR) are crucial for geological target detection under Torrid Zone condition (Saepuloh et al., 2010). The backscattering intensity of SAR chiefly a function of two physical quantities of surface material, the surface roughness and dielectric parameters. The two parameters were proved effective to discriminate ground surface materials at volcanic field such as pyroclastic flows, lava, and lahars (Saepuloh et al., 2010). In this research, we focused to obtaining surface parameter related to the permeability based on Polarimetric SAR data. The surface permeability is a crucial parameter for estimating geothermal potential. Furthermore, analyzing spatio- temporal of surface permeability will be important to understand the dynamic of fluid extraction in geothermal power plant.

There were two could be modeled from PolSAR data, termed as the surface roughness and linear features. The two parameters were used as basis of surface permeability assessment according to the field measurement data. The surface roughness means the topographic expression of ground surface at horizontal line with gravel scale and controls the backscattering signals significantly (e.g. Duarte et al., 2008; Saepuloh et al., 2012; Saepuloh et al., 2015). The interaction between hydrothermal fluid and host rocks is aimed to be analyzed by surface roughness parameter. The resistance of the rocks presented by their roughness to the geological processes such as alteration due to geothermal process depends on rock type and/or thermal intensity. The surface roughness was identified for three geothermal manifestation zones, altered surfaces, mud pools, and hot springs in the selected study area of Mt. Wayang Windu, situated in the southern part from Bandung City, West Java, Indonesia (Fig. 1).

Figure 1: Study area located at Mt. Wayang Windu in West Java, Indonesia with sites of explored geothermal prospects in gray arc segments (Hochstein and Sudarman, 2008). Inset showing local topographic of study area presented by SRTM 30 m DEM (Farr et al., 2007).

 

2. FIELD MEASUREMENTS

Obtaining the appropriate model, we measured surface roughness at field in three directions: azimuth, range, and dominant topographic undulation. The azimuth and range directions follow the satellite movement and line of sight, respectively. A fixed pin meter with correlation length 30 cm similar to the L-band frequency was used for measurement. The polarimetric mode of the Phased Array Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite were used in this study. There are 269 points measured surface roughness at field with three different direction of measurement (Fig. 2 upper part). The field roughness measurements permit to establish three qualitative classes of geothermal surface manifestation: alteration zones, mud pools, and hot springs. These classes are depicted by photographs in Figure 3 with measurement methods using a pin meter. In this paper, the discussion is focused on the surface roughness characteristics at the three classes: Zone-1, 2, and 3 (Fig. 2 upper part). Zone-1 located between Bedil and Windu is composed by altered surfaces, Zone-2 between Puncak Besar and Gambung is composed by mud pools, and Zone-3 is composed by hot springs. The selected zones are presenting general geothermal surface manifestation at Wayang Windu geothermal field (Fig. 2 lower part). The ground surface of Zone-1 located at Wayang Crater is composed by vegetation, grasses, and fragmental rocks with size 5 – 30 cm. The rock surface generally has been altered to be soil with bright greyish to yellowish color. The vegetation is rare at alteration areas. Zone-2 located at Burung Crater is composed mainly by dense vegetation, grasses, and farms. The ground surface is composed chiefly by flat soils with mud pools at the crater area. Zone-3 located at Kertamanah is composed by grasses, farms, and ponds. The tea plantations are also existed at hilly terrain. The hot springs deliberate warm water about 55° C with pH 6.3 to the ponds. The ground surface is dominated by the almost flat soils and grasses.

Figure 2: The measurement points covering surface manifestation at Mt. Wayang Windu geothermal field (upper) and The surface roughness measurement at Zone-1 showing (altered surfaces) the rock fragment still remain when the rock matrices were eroded due to strong alteration process, Zone-2 (mud pool) showing flat ground surface due to interaction between hydrothermal and rocks or soils, and Zone-3 (hot springs) showing the ground surface almost flat. The warm water from hot spring are collected in the ponds or small lakes.

         3. Surface permeability Assessment Using Backscattering Polarimetric SAR

Backscattering of SAR data is known to be an effective means of assessing surface roughness and the dielectric property of surface material (e.g., Evans et al., 2012; Saepuloh et al., 2015). However, interaction between surface roughness and polarized backscattering signal is still unclear. The ALOS PALSAR is utilized by capability to transmit and receipt signals in horizontal (H) and vertical (V) propagation. Therefore, once acquisition produced four polarized composition termed as HH, HV, VH, and VV. The co- and cross-polarization were used to simplify explanation for HH-VV and HV-VH, respectively. Following Saepuloh et al. (2015), we quantified the backscattering intensity by taking the logarithmic scale of the multi-look data. The backscattering intensity images in polarized modes are depicted by Figure 3. The dark zone at NE part in the images are blank area due to un-overlapped scene. The range of backscattering intensity values in dB unit for co-polarization is wider than cross-polarization images. For co-polarization mode, the backscattering intensity is about -1 to 7 dB and cross polarization mode is about 0 to 6 dB. More features with low backscattering intensity were detected by cross- than co- polarization mode as presented by black circles in Figure 3. Various physical properties and/or geometry of surface materials are predicted responsible to detected features.

Measurement direction of surface roughness, radar geometry, and polarization mode are the main issue to be discussed in this section on how the surface roughness at field responses to the radar backscattering in range and azimuth direction. The initial surface roughness model was used as basis sensitivity identification to the surface roughness at the field. According to Saepuloh et al. (2015), the model could be calculated for each polarization type as follows:

where  h nE is surface roughness model based on polarized mode, nE is polarized mode either in H and V,  is backscattering coefficient on polarized mode, and θi is local incidence angle.

The linear fitting methods were used to obtain correlation coefficient R2 between surface roughness at field H0 and model h0. Figure 4A shows the linear fitting result regarding polarization modes and measurement directions. The highest R2 were obtained by surface roughness model from cross-polarized modes except in the E-W measurement direction. Regarding the measurement directions, the N-E measurement with cross-polarization is the highest R2 about 0.5. The N-E measurement agreed to the radar flight direction or azimuth provided highest correlation of surface roughness. Therefore, we used the N-E measurement with backscattering of cross-polarization mode to improve the initial h0.

 

Figure 3. Backscattering intensity images of ALOS PALSAR in four polarization modes HH, HV, VH, and VV. The cross-polarized images in HV and VH showed low intensity features from various surface materials as presented by white circles.

Figure 4. The North-South roughness measurements with cross-polarization modes show the higher correlation than other measurement direction and co-polarization mode (A) and scatterplot of roughness model derived from field roughness measurement showing the highest correlation using HV and VH simultaneously.

Improving the h0, we tried to accommodate the HV and VH polarized mode to the initial model as depicted by equation 3. The linear fitting method with R2 was used as the optimum criterion to the h0. The linear fitting for selecting the best fit model was depicted by Figure 4B. The obtained R2 of the h0 with single polarization mode was only less than 0.5. To improve surface roughness model, we used combination of polarization mode HV and VH. The optimum model with R2 about 0.6 is depicted by Figure 7B and written as follows:

where h0 (HV,VH) is surface roughness model using  HV and VH polarized mode, h0(HV) and h0(VH) are surface roughness function based on eq. 3 for  and , respectively.

The spatial surface roughness model was calculated using a Triangulated Irregular Network (TIN) gridding method and depicted by Figure 5. The smooth and rough surface model are presented by blue and red portion, respectively. We identified and simplified explanation for the rough, medium, and smooth surfaces with RMS height more than 10, about 8, and less than 8 cm, respectively. According to the model, the rough surfaces are mainly located the summits such Malabar, Puncak Besar, Gambung, Bedil, Wayang, Windu, and hilly at Southwest and East part. The smooth surfaces covered wide area in the middle part of the image and among the summits. The RMS height of surface roughness between the model and field data agreed in general (see Figure 8 on the right subset). According to the field data, the gradational surface roughness from rough to smooth surface is located at south to northwest part. The maximum surface roughness located at WW16 to WW20.  According to the model, the rough surfaces are distributed at southeast to the west. The medium and smooth surfaces are distributed at the east to the west.

 

Figure 5. The roughness model map obtained from cross-polarization modes. The surface manifestation related with alteration surfaces located at high roughness, hot spring and mud pool are located at low surface roughness.

The linear features were detected automatically using modified of Segment Tracing Algorithm (mSTA) under MATLAB script. The advantage of this method is to extract linear features in the area with a low gray level. The backscattering intensities of ALOS PALSAR in ascending and descending orbits were used as basis of detection process. Following the linear features detection, the detected linear features were evaluated using vector summations to remove noises. The vector summations were aimed to eliminate the double-detected linear features in ascending or descending images due to hill-shade effect in high topography. Fig. 6 shows an example of linear features before and after noise removal using vector summation from the ALOS PALSAR descending image. The linear features segment are distributed chiefly in the hill-shade (Fig. 6A). Some of the features probably originated from the geomorphologic pattern from erosional process. After the noise removal process, then number of linear features were reduced and the remaining are predicted as the detection targets (Fig. 6B). Finally, the linear features obtained from ascending and descending images were combined. The similar direction and location of linear features from the two images were removed to avoid the ambiguity.

Figure 6. Detected linear features before [A] and after [B] noise removal using vector summations.

Linear features derived from Ascending and Descending image will be merged to get an overview of linear features in the field. Merging the results of linear features possible overlap will occur, where linear features are detected in Ascending and Descending image. To avoid overlap, then made another correction of linear features of the combination. The average length of linear features is 1.11 km. Although the total length of the linear features becomes a little, but longer than the linear features before combination results (Table 1).

Figure 7. Detected linear features in ascending (red lines), descending (blue lines) and combination (green lines) using modified Segment Tracing Algorithm (mSTA).

After noise removal, the general direction of linear features in the Ascending image is N280ºE, N30ºE and N340ºE, while the Descending image is N275ºE, N340ºE, and N30ºE. The general direction of liner features from combination is N290ºE, N340ºE and N30ºE (Figure 8). Generally, the directions of linear features shows the same direction is WNW-ESE, NNW-SSE and NNE-SSW.

Figure 8. The direction of linear features after noise removal in Ascending [A], Descending [B], and Combination [C].

4. CONCLUDING REMARKS

The physical properties high permeability at surface manifestation including acidity and surface roughness at geothermal field could be identified successfully. The acidity shown by lower pH than 7 at geothermal surface manifestations revealed a constant pattern in general that the lowest pH is located at and around surface manifestations. The separated plots between high and low pH were originated from thermal features in that the surface manifestation is local and the thermal effect from geothermal fluids affect the ground surface locally. Based on surface roughness, the high surface permeability related to surface manifestations were classified into altered surfaces, mud pools, and hot springs. The highest correlation between surface roughness and acidity was provided at the altered surfaces with volcanic products such as lava and pyroclastics, and mud pools with tuff and lahars. The discrepancy was shown by hot springs surfaces with laharic deposits containing fine to coarse old volcanic products. The rock types and thermal activities were significant to the surface roughness condition. The surface roughness model could be estimated based on Polarimetric Synthetic Aperture Radar (PolSAR) data from the Phased Array Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite (ALOS). The N-S field roughness measurements at altered surfaces provided the best correlation with the backscattering of cross-polarization, HV and VH modes. The azimuth or flight direction of the ascending satellite was influenced to the detected roughness in the cross-polarization mode. According to the field data and model, the low pH coincided to the rough surfaces at altered surfaces and the smooth surfaces at mud pools. The linear features that appear on the satellite images and may represent fractures permeability that exist in the field. The linear features extraction using STA method was applied successfully. The linear features detection method using modified Segment Tracing Algorithm (mSTA) for ALOS PALSAR in study area shows that there are three general directions NNE-SSW, WNW-ESE and NNW-SSE. The directions were interpreted as the high surface permeability zones. Detail analysis and validation should be performed to obtain surface permeability