As a microwave remote sensing, an airborne high-resolution multiparameter synthetic aperture radar (Pi-SAR) has two types of frequencies, L-band and X-band.  In this paper, the authors used L-band frequencies of Pi-SAR data due to the wavelength which has possibility to penetrate vegetations. The aim of this study is to demonstrate the effectiveness of Pi-SAR data in analyzing volcanic surface condition. Some image processing methods were used to extract surface terrain features such as unsupervised classification and false color composition. The results were compared with an optical sensor image such as Landsat ETM+ for the same area. Mt. Sakurajima, a typical active volcano in southwest Japan, was chosen as a study site due to its high activity. The results showed that the Pi-SAR data could generate geomorphologic units such as volcanic cone, volcanic-terrace, and volcanic foot which were validated by the polarimetric signatures. On the other hand, lava flows structure appears clear and easy to be distinguished from the other products such as debris pumice or pyroclastic deposits. However, geomorphologic features and lava flows structure could not be detected by the optical remote sensing. Consequently, the Pi-SAR polarimetric data was proved had to have high capability to detect roughness of the volcanic terrain rather than optical remote sensing.

Fig. 1. Study area overlaid on a Landsat ETM+ image

Fig. 2. Color composite of L-band Pi-SAR magnitude data (R=HH; G=HV; B=VV)

Fig. 3. Image classification of Pi-SAR (left) clarifies typical feature for the flat area on the centre of the image, contrary with image classification of Landsat ETM+ image (right) which shows feature continues from the crater to the foot

(A) Volcanic Cone

(B) Volcanic Terrace

(C) Volcanic Foot

Fig. 4. Polarimetric signatures for each geomorphologic feature

Fig. 5. Lava edge detection by using synthetic color image of Pi-SAR data (R=HH; G=HV; B=VV)

Source:

Saepuloh A., Koike K., Omura M., Iguchi M., The application of Pi-SAR polarimetric data to detect surface condition of an active volcano, Proceeding of the 9th International Symposium on Mineral Exploration (ISME09), Bandung, Indonesia, pp. 236-240, September 2006.

Analyzing the volcanic product mainly for pyroclastic deposits soon after eruption time is difficult because the gases and ashes usually cover over half more the volcanic field. However the microwave remote sensed system can solve the difficulties. In this paper we demonstrate how the radar system using microwave wavelengths can detect the volcanic products soon after eruption. The main aim of this detection is to estimate the geothermal system, especially for fast assessment purpose.
The existence of pyroclastic deposits implies that explosive eruptions have occurred. Calculation of the volume of the deposits can be used to estimate the size of their parental magma chamber. Our approach for geothermal system in an active volcano is based on primarily understanding the volume and characteristic of pyroclastic rocks (tephra). The study site is located at Mt. Merapi, Indonesia which has been active during the last 5 years.

Fig. 1.   Backscattering intensity images before (A) and after (B) eruption; ratio image of TAC which shows some features flowing from the volcanic vent (C).

Fig. 2. New volcanic products detected contain pyroclastics and lava flow generated from binarization of ratio image (A) and　pyroclastics dispersal model by elliptical distribution model (B).

Fig. 3. Draping image of volcanic products to DEM 10-m mesh.

Fig. 4. General topographical relief from the north (left) to the south (right) in the west flank of Mt. Merapi.

Source:

Saepuloh A., Koike K., Omura M., Detecting pyroclastic flow deposits and aerial dispersal models by microwave remote sensing to evaluate geothermal system, Abstract of the MMIJ spring regular meeting, Graduate School of Engineering Kyushu University, Japan, pp. 64-66, May 2007.

Various techniques and methods have been proposed to detect spatially the surface manifestation of a geothermal system using remotely sensed data especially optical sensors. However, the sensors are usually hampered by atmospheric condition and time acquisition problems. Meanwhile, the superiority of the microwave sensors with capability to operate in any weather condition and regardless time acquisition provides accurate surface observation especially at the Torrid Zone such as Indonesia. This paper presents a multi-sensor remote sensing data and applications to calculate spatially the rank of the geothermal potential using Weighted Fuzzy Logic (WFL) method.

The Bacan Island in North Maluku Province, Indonesia was used as study area. The free world-wide Landsat ETM+ combined with ASTER TIR data were used to detect the surface condition in visible to thermal infrared region. Two back-scattering intensity data of ALOS/PALSAR in ascending and descending mode were also used to delineate the geomorphologics and structural features in a microwave region. An integrated application of both data types to assess spatially a geothermal potential will be benefit for a geothermal exploration.

Fig. 1. Study area at Bacan Island, North Maluku, Indonesia presented by black rectangle on the right figure.

Fig. 2. Detected altered minerals as shown by illite, montmorillonite, and alunite (a) and the reference spectra as input of SAM classification (b).

Fig. 3. Surface thermal radiance of ASTER data during daytime (a) and nighttime (b) observation.

Fig. 4. The interpreted-geomorphologic and structural features overlaid on the backscattering intensity of ALOS/PALSAR data.

Fig. 5. The histogram of eight fuzzy membership functions shows a normal distribution in general.

Fig. 6. The map of eight fuzzy membership functions as input of the WFL method.

Fig. 7. The map of WFL presents the rank of geothermal potential from 0 (=no plausible) to 1(=the most plausible). The red circles are the location of the hot springs.

Fig. 8. The rank of geothermal potential in dash curves based on WFL method overlaid on the geological map of Bacan Island modified partly from Yasin, 1980.

Source:

Saepuloh A., Urai M., Sumintadireja P., Suryantini, Spatial priority assessment of geothermal potentials using multi-sensor remote sensing data and applications, proceeding of the 1st ITB Geothermal Workshop 2012, Bandung, Indonesia, March 6, 2012.

Mt. Merapi located in the Central Java-Indonesia is the most active volcano over the world. The disaster usually occurs when the hot pyroclastic flows reached to the dense populated area around the volcano. The latest eruption in November 2010 caused fatalities to about 150 people died and 280.000 people sent to flee. The pyroclastic flow deposits reached about 15 km from the summit to the southern flank and devastated everything on their path. The eruption also caused significant change to the land cover around the volcano. This paper presents the effect of the hot pyroclastic flows and ashes to the soil layer condition soon after eruption. The purpose of this study is to quantify the damage level of the soil layer in accordance with soil moisture condition. The two scenes of Phased Array L-band type Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite (ALOS) were used in this study. The advantage of ALOS/PALSAR is that the sensor can penetrate vegetation canopy. Therefore, the soil layer could be identified clearly. The acquisition dates of the both data are before and after the eruption. Change detection analyses are applied to the two backscatter intensities of ALOS/PALSAR data. The hot pyroclastic flows decreased the backscatter intensity of soil layer about -15 dB. On the contrary, the ashes increased the backscatter intensity of soil layer about 12 dB. The damage levels are calculated by taking the cosine angle of the square root of the two backscatter intensities. The highest damage level was located at the main path of pyroclastic flow deposits. The medium damage level was located at the ashes deposits. The both damage levels might to be caused by the change of soil moisture and texture. This result could be used for delineating farming possibility area and/or disaster recovery after the eruption.

Fig. 1. Color composite of backscattering intensity image of ALOS/PALSAR before and after eruptions.

Fig. 2. The histogram of pyroclastic flows and ashes in β image of after eruption.

Fig. 3. The damage level map of soil layer calculated from the cosine angle of the ratio of the two β data.

Source:

Saepuloh A., Wijaya K., Sumintadireja P., Detecting soil layer condition soon after Merapi eruption 2010 using ALOS/PALSAR data, Proceeding of the Annual Meeting of Science and Technology Studies 2011 (AMSTECS-2011), Tokyo, Japan, in PDF format, ISSN: 2088-2041, pp. 13-16, June 2011.

During the last century, Merapi eruptions characterized by effusive dome growth and collapsed producing “Merapi Type” pyroclastic flows. The eruption of Mt. Merapi in November 2010 was more explosive, a VEI 4 eruption, involving large size dome and fountain collapse pyroclastic flows as well as ash falls. To obtain the deformation precursor to the eruption, we applied a Differential Interferometric Synthetic Aperture Radar (D-InSAR) with short-continuous baseline method using ALOS PALSAR data. We collected 38 scenes single and dual polarization modes in total. Among them, there are only 25 scenes plausible for D-InSAR analysis due to low coherency and data quality. To reduce the atmospheric disturbance in the interferograms, we combined the Pair-wise Logic (PWL) with Referenced Linear Correlation (RLC) method. The Electronic Distance Measurement (EDM) and Seismicity statistics prior to the eruption were used to know the correction performance. This proposed method was proved effective to reduce the atmospheric phase twice from deformation phase.

Fig.1. Original interferogram containing deformation, atmospheric delay, and noise.

Fig.2. Interferogram after atmospheric phase delay removal.

Source:

Saepuloh A., Urai M., Evaluating the Deformation and Atmospheric Signals in the InSAR of ALOS PALSAR data at Mt. Merapi, Abstract of Workshop on Renovation of Observation of Natural Disaster 2012, DPRI-Kyoto University, Japan, pp. 3-7, September 2012.

Understanding precursory signal leading to a large and explosive eruption, such as Merapi eruption in 2010, is the key to a successful hazard assessment in the future. Towards resolution of this problem, time series of Differential Interferometric SAR (D-InSAR) of ALOS/PALSAR data together with thermal radiance at summit area were analyzed to characterize magmatic process. The D-InSAR could detect deformation changes in between two eruption episodes of Merapi in 2006 and 2010. The maximum uplifting rate ~0.7 mm/day is observed twice: two years and one month before eruption in October 26, 2011. The first uplift is related to magma ascent and the later is precursory to an imminent eruption. Thermal radiance of ASTER data not only served as indicator on the arrival of fresh magma near the surface, but also to confirm whether or not the deformation signal is related to the imminent eruption.

Fig. 1. Location of Mt. Merapi in Central Java, Indonesia.

Fig. 2. The interferograms of ALOS/PALSAR shows the uplifting and subsidence phenomena prior to the eruption.

Fig. 3. ASTER TIR images of Mt. Merapi from 2006 to 2010. The hot spots indicated the beginning and the ending of one periodical of eruption.

Fig. 4. The estimated-deformation rate at Mt. Merapi.

Source:

Saepuloh A., Urai M., Widiwijayanti C., Aisyah N., Observing 2006-2010 ground deformations of Merapi volcano (Indonesia) using ALOS/PALSAR and ASTER TIR data, Proceeding of the IEEE International Geoscience and Remote Sensing Symposium 2011 (IGARSS-2011), Vancouver, Canada, July 2011.

Surface volcanic rocks identification in active volcano is crucial not only to mitigate volcanic hazards, but also to characterize eruption, urban rehabilitation, and reconstruction especially after eruption. Remote sensing technology provides ground surface data relatively cheap and large coverage area. However, the application of remote sensing technology for identifying volcanic rocks distribution is still limited. The cloud is always the main problem of the optical sensor as well as the vegetation and geometric distortion for microwave sensor. Overcoming the problem, we tried to identify the volcanic rocks distribution using Polarimetric SAR data of The Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard The Advanced Land Observing Satellite (ALOS). The aim of this study is to evaluate the possibility using polarimetric SAR data for delineating volcanic rocks. The spatial comparison using optical sensor data was used to delineate the Geomorphologic and Structural Features (GSF) in the Polarimetric SAR data. Then, a seed fill method with pixel growth criterion was applied to identify volcanic rocks distribution based on the GSF automatically. The geological map was used to validate this approach. The Advanced Land Imager (ALI) instrument onboard EO-1 satellite and ASTER GDEM 30-m were used as benchmark to predict the effect of vegetation canopy and gradient slope of topography to the SAR backscattering data. Mt. Tangkuban Parahu located in a dense populated area in Bandung City, West Java, Indonesia was selected as study area. The small phreatic historical eruptions at this volcano have been recorded dominantly since the 19^th century. The distribution of volcanic rocks at Mt. Tangkuban Parahu followed mainly the GSF of the Polarimetric SAR data. Therefore, delineating the GSF of the Polarimetric SAR data is the key to interpret the volcanic rocks distribution. This approach is supposed to be applicable for other regions which have similar geological setting.

Figure 1. Onset of Mt. Tangkuban Parahu in West Java, Indonesia overlaid on the elevation map.

Figure 2. The illustration of backscatter signal in X-, C-, and L-bands respects to the clouds, vegetation canopy, and ground surface.

Figure 3. The color composite of backscattering intensity image for R=HH, G=HV, and B=VV (A), the local incidence angle in radian unit (B), the ASTER GDEM 30-m (C), and the NDVI originated from EO-1 ALI data (D) show the contribution of the surface condition to the polarimetric SAR data. The Red triangle is the summit of MTP.

Figure 4. The geological map of Mt. Tangkuban Parahu (A), the selected seed locations (B), the color composite of the P image for R=σ_HH, G=σ_HV, and B=σ_VV (C)_, and the seed fill map overlaid on the P image (D).

 Source:

Saepuloh A., Urai M., Bayuaji L., Sumintadireja P., Suparka E., Identifying volcanic rocks using geomorphologic and structural features of polarimetric SAR data, Proceeding of the 5th Indonesia Japan Joint Scientific Symposium (IJJSS-2012), October 2012.