ORCID Profile
0000-0002-1962-7511
Current Organisation
University of Twente
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Publisher: MDPI AG
Date: 20-01-2018
DOI: 10.3390/RS10010151
Publisher: IOP Publishing
Date: 08-2019
DOI: 10.1088/1755-1315/311/1/012016
Abstract: In the beginning of December 2017, Mt. Agung eruption powered down to minor ash emissions and on the middle of December, aerial photographs of the crater were taken by Indonesia Centre of Volcanology and Geological Hazard Mitigation (PVMBG) showing a steadily growing lava occupying approximately one third of the crater. 3D digital elevation model (DEM) of crater were created by PVMBG during and before the eruption, corresponded to lava volume around 2 x 10 −2 km 3 has been filled the crater. Here we present a method for deriving thermal model within the lava during eruption on 8 and 9 December 2017 using observations from multi infrared satellite SENTINEL 2A and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We use Thermal Eruption Index (TEI) based on the Shortwave infrared (SWIR) on SENTINEL 2A and Thermal Infrared (TIR) on ASTER, allowing us to differentiate thermal domain within the lava. This study has successfully produced model of sub-pixel temperature (T h ), radiant flux (Φrad) and crust thickness model of lava (Δh). The subpixel temperature and radiant flux during the eruption is in the range 655 to 975 °C and 179 MW respectively. The crust thickness model of the lava in the range of 9 to 14 m and the total volume of lava crust during this period is estimated at 3 x 10 −3 km 3 . The combination of infrared satellite remote sensing data shows a potential for fast and efficient classification of difference thermal domains and derive thermal model of lava.
Publisher: MDPI AG
Date: 31-03-2020
DOI: 10.3390/GEOSCIENCES10040125
Abstract: Roughness can be used to characterize the morphologies of a lava flow. It can be used to identify lava flow features, provide insight into eruption conditions, and link roughness pattern across a lava flow to emplacement conditions. In this study, we use both the topographic position index (TPI) and the one-dimensional Hurst exponent (H) to derive lava flow unit roughness on the 2014–2015 lava field at Holuhraun using both airborne LiDAR and photogrammetric datasets. The roughness assessment was acquired from four lava flow features: (1) spiny lava, (2) lava pond, (3) blocky surface, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small surficial slope indicating a flat and smooth surface. Lava pond is characterized by low to high TPI values and forms a wave-like pattern. Meanwhile, irregular transitions patterns from low to high TPI values indicate a rough surface that is found in blocky surface and flow margins. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The roughest surface is the blocky, and inflated lava flows appear to be the smoothest surface among these four lava units. In general, the Hurst exponent values in the 2014–2015 lava field at Holuhraun has a strong tendency in 0.5, both TPI and Hurst exponent successfully derive quantitative flow roughness.
Publisher: MDPI AG
Date: 26-02-2019
DOI: 10.3390/RS11050476
Abstract: The Holuhraun lava flow was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 and covering an area of ~84 km2. The six month long eruption at Holuhraun 2014–2015 generated a erse surface environment. Therefore, the abundant data of airborne hyperspectral imagery above the lava field, calls for the use of time-efficient and accurate methods to unravel them. The hyperspectral data acquisition was acquired five months after the eruption finished, using an airborne FENIX-Hyperspectral sensor that was operated by the Natural Environment Research Council Airborne Research Facility (NERC-ARF). The data were atmospherically corrected using the Quick Atmospheric Correction (QUAC) algorithm. Here we used the Sequential Maximum Angle Convex Cone (SMACC) method to find spectral endmembers and their abundances throughout the airborne hyperspectral image. In total we estimated 15 endmembers, and we grouped these endmembers into six groups (1) basalt (2) hot material (3) oxidized surface (4) sulfate mineral (5) water and (6) noise. These groups were based on the similar shape of the endmembers however, the litude varies due to illumination conditions, spectral variability, and topography. We, thus, obtained the respective abundances from each endmember group using fully constrained linear spectral mixture analysis (LSMA). The methods offer an optimum and a fast selection for volcanic products segregation. However, ground truth spectra are needed for further analysis.
Publisher: MDPI AG
Date: 24-01-2022
DOI: 10.3390/RS14030543
Abstract: Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.
Location: Netherlands
No related grants have been discovered for Muhammad Aufaristama.