The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface

Publisher's version (útgefin grein)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 diverse 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 amplitude 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.The first author was supported by the Indonesia Endowment Fund for Education (LPDP) Grant No. 20160222025516, European Network of Observatories and Research Infrastructures for Volcanology (EUROVOLC), The European Facility for Airborne Research (EUFAR) and Vinir Vatnajökuls during his Ph.D. project.Peer Reviewe

Simultaneous and constrained calibration of multiple hyperspectral images through a new generalized empirical line model

The empirical line (EL) calibration method is commonly used for atmospheric correction of remotely sensed spectral images and recovery of surface reflectance. The current EL-based methods are applicable to calibrate only single images. Therefore, the use of the EL calibration is impractical for imaging campaigns, where many (partially overlapped) images are acquired to cover a large area. In addition, the EL results are unconstrained and an undesired reflectance with negative values or larger than 100% can be obtained. In this paper, we use the standard EL model to formulate a new generalized empirical line (GEL) model. Based on the GEL, we present a novel method for simultaneous and constrained calibration of multiple images. This new method allows for calibration through multiple image constrained empirical line (MIcEL) and three additional calibration modes. Given a set of images, we use the available ground targets and automatically extracted tie points between overlapping images to calibrate all the images in the set simultaneously. Quantitative and visual assessments of the proposed method were carried out relatively to the off-the-shelf method quick atmospheric correction (QUAC), using real hyperspectral images and field measurements. The results clearly show the superiority of MIcEL with respect to the minimization of the difference between the reflectance values of the same object in different overlapping images. An assessment of the absolute accuracy, with respect to 11 field measurement points, shows that the accuracy of MIcEL, with an average mean absolute error (MAE) of ∼11%, is comparable with respect to the QUAC

The 2014-2015 lava flow field at Holuhraun: Deriving physical properties of the lava using multi remote sensing techniques and datasets

The purpose of this thesis is to employ remote sensing to study lava flow products during the 2014-2015 eruption at Holuhraun, Iceland. Multimodal remote sensing techniques and datasets were applied and developed for three study themes (1) deriving thermal properties from satellite infrared remote sensing, (2) differentiating lava surface using airborne hyperspectral remote sensing, and (3) quantifying lava surface roughness from elevation model acquired by airborne LiDAR. In the first study, we present a new approach based on infrared satellite images to derive thermal properties within the lava field during eruption and then compare the results with field measurement during the 2014-2015 eruption at Holuhraun. We develop a new spectral index for Landsat 8, named the thermal eruption index (TEI), based on the SWIR and TIR bands (bands 6 and 10). The purpose of the TEI consists mainly of two parts: (i) as a threshold for differentiating between different thermal domains; and (ii) to apply dualband technique to determine the maximum subpixel temperature (Th) of the lava. Lava surface roughness effects are accounted for by using the Hurst exponent (H), which is estimated from radar backscattering profiles. A higher H (smooth surface) generates thinner crust and high thermal flux meanwhile a lower H (rough surface) generates thicker crust and lower thermal flux. The total thermal flux peak is underestimated compared to other studies, although the trend shows good agreement with both field observation and other studies. In the second study, we focus on retrieving the lava surface types contributing to the signal recorded by airborne hyperspectral at the very top surface of the 2014-2015 lava flow field at Holuhraun. For this purpose, an airborne hyperspectral image acquired at Holuhraun with an AisaFENIX sensor onboard a NERC (Natural Environment Research Council Airborne Research Facility) campaign. For sub-pixel analysis, we used the sequential maximum angle convex cone (SMACC) algorithm to identify the spectral image endmembers and the linear spectral mixture analysis (LSMA) method was employed to retrieve the abundances. SMACC and LSMA methods offer a fast selection for volcanic product segregation. However, ground-truthing of spectra is recommended for future work. In the third study, we perform both the topographic position index (TPI) and onedimensional Hurst Exponent to derived lava flow unit roughness on the 2014-2015 lava flow field at Holuhraun using both airborne LiDAR and photogrammetry topography datasets. The roughness assessment was acquired from four lava flow features: (1) spiny pāhoehoe, (2) lava pond, (3) rubbly pāhoehoe lava, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small 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 characterize lava with rough blocky surfaces, i.e. rubbly pāhoehoe to 'ā'a flows and lobes and their margins. These lobes and margins may give the impression of having similar roughness as the ”rough” surface on meters scale since this is an “apparent” roughness. On centimeters scale these multitudes of lobes feature coherent and smooth surfaces because they are pāhoehoe. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The rubbly pāhoehoe / 'ā'a has the roughest surface and the inflated lava channel along with pāhoehoe feature the smoothest surfaces among these four surface types. In general, the Hurst exponent values in the 2014-2015 lava field at Holuhraun has a strong tendency in 0.5, which is compatible with results from other study of geological surface roughness. Overall, this project provides an important insights into the application of remote sensing for monitoring and studying active lava flow fields and the techniques developed here will benefit such work in future events.Tilgangurinn með verkefninu var að rannsaka hraunrennsli og landform er urðu til í eldgosinu norðan Vatnajökuls 2014-2015 og kennt við Holuhraun. Fjölþátta fjarkönnunartækni og gögn úr gervitunglum og flugvélum voru nýtt við úrvinnslu verkefnisins. Rannsóknin sneri að þremur megin þáttum: (1) greiningu á eðli varmaútstreymis frá Holuhrauni, út frá innrauðri varmageislun sem mæld er með gervitunglagögnum (2) aðgreining á mismunandi hraunyfirborði, út frá ofur-fjölrófs mælingum úr lofti, og (3) greiningu og flokkun á yfirborðshrjúfleika Holuhrauns út frá hæðarlíkani er aflað var með LiDAR settur upp í flugvél. Fyrsti þáttur beindist að eðli varmaútstreymis á meðan á eldgosi stóð. Stuðst var við gervitunglagögn og mælingar með FLIR tækni á meðan eldgosið stóð yfir. Afraksturinn er nýr hitastuðull fyrir Landsat 8 og greiningu á eldgosum, (TEI). Hitastuðullinn TEI er unninn út frá SWIR og TIR böndum Landsat 8 (bönd 6 og 10). Með TEI næst fram tvennt: (i) að greina þröskuld milli tveggja hitasviða; og (ii) að beita tvíbanda tækni til að greina hitastig innan hverrar myndeiningar (Th) af hrauninu. Hrjúfleiki hraunsins hefur áhrif á varmaútstreymi, og er gert ráð fyrir honum með því að reikna Hurst veldisstuðulinn (H) og eru reiknuð út frá radar endurkasti hraunyfirborðs. Hátt H einkennir flatt og mjúkt yfirborð og þunna skorpu á hrauninu, á meðan að lágt H einkennir úfið yfirborð, þykka skorpu og lága varmaútgeislun. Heildar varmaútgeislun með þessari aðferð er heldur vanmetin en ofmetin í samanburði við aðrar aðferðir. Hinsvegar er góð fylgni með mælingum í mörkinni og samanburðar aðferðum. Annar hluti rannsóknarinnar sneri að túlkun ofur-fjölrófsgreininga á yfirborði Holuhrauns. Flogið var yfir Holuhraun sumarið 2015 með ofur-fjölrófsmæli (AisaFENIX) um borð í flugvél frá NERC (Natural Environment Research Council Airborne Research Facility). Við greiningu á yfirborði innan hverrar myndeiningar var, (i) notast við aðferð runubundins hámarkshorns kúptrar keilu (SMACC) til að finna útmörk ofurrófs mælinganna, (ii) blönduð línulega rófgreining (LSMA) var nýtt til að greina styrk eða gnægð innan myndeiningar. SMACC og LSMA aðferðirnar bjóða upp á mjög hraða greiningu á yfirborði og útfellingum efna á yfirborðið. Hins vegar þarf að gera fleiri rófmælingar á staðnum, til þess að auka notkunnargetu aðferðarinnar í hraungosum framtíðarinnar. Þriðji þáttur rannsóknarinnar sneri að því að greina landfræðilega stöðuvísitölu (TPI) og einvíðan Hurst veldisvísi til að meta hrjúfleika á hinu endanlega yfirborði Holuhrauns. Við þessa greiningu var notast við LiDAR mælingu af hrauninu og hæðagrunn unninn út frá ljósmyndum. Hrjúfleikinn var metinn fyrir fjögur yfirborð sem einkenna hraunið: (1) broddahraun „spiny pāhoehoe lava“, (2) hrauntjörn „lava pond“, (3) klumpahraun „rubbly pāhoehoe lava“ og (4) upptjakkaða hrauntröð „inflated lava channel“. TPI fyrir yfirborð (1) og (4) gefur meðalgildi sem einkennist af litlum halla og flötu yfirborði. Hrauntjörnin einkennist af lágum og háum TPI gildum sem endurspegla bylgjukennt mynstur. Hinsvegar einkennast hrjúfustu yfirborðin (3) og hraunjaðrar af óreglulegu mynstri lágra og hárra TPI gilda. Hrjúfleika stuðull þessara yfirborða H, er á bilinu 0.30 ± 0.05 til 0.76 ± 0.04. Mestur er hrjúfleiki kubbahrauna og minnstur er hrjúfleiki þandar hrauntraðar. Hurts veldisvísir Holuhrauns er nærri 0.5, en það er í mjög góðu samræmi við niðurstöður fyrri rannsókna á jarðfræðilegum yfirborðum. Í heild gefur verkefnið mikilvæga sýn á notagildi fjarkönnunaraðferða við rauntímaeftirlit með hraungosum, m.a. með þróun stuðla sem munu nýtast við atburði framtíðar. Þá voru tengsl hraunmyndana við ýmsa eiginleika eldgosa skýrð, sem aftur getur gefið vísbendingar um eðli fyrri atburða