12 research outputs found

    Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors

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    Publiher's version (útgefin grein)We propose an unmixing framework for enhancing endmember fraction maps using a combination of spectral and visible images. The new method, data fusion through spatial information-aided learning (DFuSIAL), is based on a learning process for the fusion of a multispectral image of low spatial resolution and a visible RGB image of high spatial resolution. Unlike commonly used methods, DFuSIAL allows for fusing data from different sensors. To achieve this objective, we apply a learning process using automatically extracted invariant points, which are assumed to have the same land cover type in both images. First, we estimate the fraction maps of a set of endmembers for the spectral image. Then, we train a spatial-features aided neural network (SFFAN) to learn the relationship between the fractions, the visible bands, and rotation-invariant spatial features for learning (RISFLs) that we extract from the RGB image. Our experiments show that the proposed DFuSIAL method obtains fraction maps with significantly enhanced spatial resolution and an average mean absolute error between 2% and 4% compared to the reference ground truth. Furthermore, it is shown that the proposed method is preferable to other examined state-of-the-art methods, especially when data is obtained from different instruments and in cases with missing-data pixels.This research was partially funded by the Icelandic Research Fund through the EMMIRS project, and bythe Israel Science Ministry and Space Agency through the Venus project.Peer Reviewe

    Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data

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    Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data. Acquiring high-resolution ground truth (GT) by human interpretation is both cost-ineffective and inconsistent. We propose, instead, to utilize high-resolution, hyperspectral images for solving this problem, by unmixing these images to obtain reliable GT for training a deep network. Specifically, we simulate GT from high-resolution, hyperspectral FENIX images, and use it for training a convolutional neural network (CNN) for pixel-based classification. We show how the model can be transferred successfully to classify new mid-resolution VENuS imagery

    A Stepwise Analytical Projected Gradient Descent Search for Hyperspectral Unmixing and Its Code Vectorization

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    We present, in this paper, a new methodology for spectral unmixing, where a vector of fractions, corresponding to a set of endmembers (EMs), is estimated for each pixel in the image. The process first provides an initial estimate of the fraction vector, followed by an iterative procedure that converges to an optimal solution. Specifically, projected gradient descent (PGD) optimization is applied to (a variant of) the spectral angle mapper objective function, so as to significantly reduce the estimation error due to amplitude (i.e., magnitude) variations in EM spectra, caused by the illumination change effect. To improve the computational efficiency of our method over a commonly used gradient descent technique, we have analytically derived the objective function's gradient and the optimal step size (used in each iteration). To gain further improvement, we have implemented our unmixing module via code vectorization, where the entire process is ''folded'' into a single loop, and the fractions for all of the pixels are solved simultaneously. We call this new parallel scheme vectorized code PGD unmixing (VPGDU). VPGDU has the advantage of solving (simultaneously) an independent optimization problem per image pixel, exactly as other pixelwise algorithms, but significantly faster. Its performance was compared with the commonly used fully constrained least squares unmixing (FCLSU), the generalized bilinear model (GBM) method for hyperspectral unmixng, and the fast state-of-the-art methods, sparse unmixing by variable splitting and augmented Lagrangian (SUnSAL) and collaborative SUnSAL (CLSUnSAL) based on the alternating direction method of multipliers. Considering all of the prospective EMs of a scene at each pixel (i.e., without a priori knowledge which/how many EMs are actually present in a given pixel), we demonstrate that the accuracy due to VPGDU is considerably higher than that obtained by FCLSU, GBM, SUnSAL, and CLSUnSAL under varying illumination, and is, otherwise, comparable with respect to these methods. However, while our method is significantly faster than FCLSU and GBM, it is slower than SUnSAL and CLSUnSAL by roughly an order of magnitude.Israel Science Ministry Scientific Infrastructure Research Grant Scheme, Helen Norman Asher Space Research Grant Scheme, Technion PhD Scholarship, new England fund Technion, Environmental Mapping and Monitoring of Iceland by Remote Sensing EMMIRS projectPeer Reviewe

    Hekla Volcano, Iceland, in the 20th Century: Lava Volumes, Production Rates, and Effusion Rates

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    Publisher's version (útgefin grein)Lava flow thicknesses, volumes, and effusion rates provide essential information for understanding the behavior of eruptions and their associated deformation signals. Preeruption and posteruption elevation models were generated from historical stereo photographs to produce the lava flow thickness maps for the last five eruptions at Hekla volcano, Iceland. These results provide precise estimation of lava bulk volumes: V1947–1948 = 0.742 ± 0.138 km3, V1970 = 0.205 ± 0.012 km3, V1980–1981 = 0.169 ± 0.016 km3, V1991 = 0.241 ± 0.019 km3, and V2000 = 0.095 ± 0.005 km3 and reveal variable production rate through the 20th century. These new volumes improve the linear correlation between erupted volume and coeruption tilt change, indicating that tilt may be used to determine eruption volume. During eruptions the active vents migrate 325–480 m downhill, suggesting rough excess pressures of 8–12 MPa and that the gradient of this excess pressure increases from 0.4 to 11 Pa s−1 during the 20th century. We suggest that this is related to increased resistance along the eruptive conduit.Icelandic Research Fund. Grant Number: 152266‐052Peer Reviewe

    Of mosses and men: Plant succession, soil development and soil carbon accretion in the sub-Arctic volcanic landscape of Hekla, Iceland

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    Post-print (lokagerð höfundar)Lava flows pose a hazard in volcanic environments and reset ecosystem development. A succession of dated lava flows provides the possibility to estimate the direction and rates of ecosystem development and can be used to predict future development. We examine plant succession, soil development and soil carbon (C) accretion on the historical (post 874 AD) lava flows formed by the Hekla volcano in south Iceland. Vegetation and soil measurements were conducted all around the volcano reflecting the diverse vegetation communities on the lavas, climatic conditions around Hekla mountain and various intensities in deposition of loose material. Multivariate analysis was used to identify groups with similar vegetation composition and patterns in the vegetation. The association of vegetation and soil parameters with lava age, mean annual temperature, mean annual precipitation and soil accumulation rate (SAR) was analysed. Soil carbon concentration increased with increasing lava age becoming comparable to concentrations found on the prehistoric lavas. The combination of a sub-Arctic climate, gradual soil thickening due to input of loose material and the specific properties of volcanic soils allow for continuing accumulation of soil carbon in the soil profile. Four successional stages were identified: initial colonization and cover coalescence (ICC) of Racomitrium lanuginosum and Stereocaulon spp. (lavas 600 years); and highland conditions/retrogression (H/R) by tephra deposition (70−860 years). The long time span of the SC stage indicates arrested development by the thick R. lanuginosum moss mat. The progression from SC into VPD was linked to age of the lava flows and soil depth, which was significantly deeper within the VPD stage. Birch was growing on lavas over 600 years old indicating the development towards birch woodland, the climax ecosystem in Iceland.The Icelandic Research Fund, Rannís, Grant of Excellence no. 152266-052 (Project: EMMIRS).Peer Reviewe

    Historical lava flow fields at Hekla volcano, South Iceland

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    Publisher's version (útgefin grein)Hekla volcano is known to have erupted at least 23 times in historical time (last 1100 years); often producing mixed eruptions of tephra and lava. The lava flow volumes from the 20th century have amounted 80% to almost 100% of the entire erupted volume. Therefore, evaluating the extent and volume of individual lava flows is very important when assessing the historical productivity of Hekla volcano. Here we present new maps of the historical lava flow fields at Hekla in a digital format. The maps were produced at a scale of 1:2000–10000 using a catalogue of orthophotos since 1945, acquired before and after each of the last five eruptions, combined with field observation of stratigraphy, soil profiles, tephra layers and vegetation cover. The new lava flow maps significantly improve the historical eruptive history of Hekla, prior to the 1947 eruption. The historical lava flow fields from Hekla cover 233 km2 and the lavas reach up to 16 km from Hekla volcano. Flow lengths up to 20 km are known, though lava flows only travelled up to 8–9 km from Hekla in the last 250 years. Identified historical vents are distributed between 0 and 16 km from Hekla volcano and vents are known to have migrated up to 5 km away from Hekla during eruptions. We have remapped the lava flow fields around Hekla and assigned the identified flow fields to 16 eruptions. In addition, ca. 60 unidentified lava units, which may be of historical age, have been mapped. It is expected that some of these units are from known historical Hekla eruptions such as the 1222, 1341, 1510, 1597, 1636 and potentially even from the previously excluded eruptions such as 1436/1439.Hekla hefur gosið 23 sinnum svo vitað sé síðan land byggðist. Oftast hafa gosin verið blandgos og framleitt bæði gjósku og hraun. Í gosum 20. aldar var hlutfall hrauns á milli 80–100% af gosefnunum svo þau skipta verulegu máli þegar framleiðni eldstöðvarkerfisins er metin. Í þessari grein eru birtar niðurstöður stafrænnar kortlagningar á Hekluhraunum frá sögulegum tíma eins langt aftur í tímann og unnt er. Þetta er engan veginn auðvelt viðfangsefni á svo virku eldfjalli sem Hekla er, því ný hraun hylja þau sem fyrir eru. Hraunakortin eru gerð í mælikvarða 1:2000–10000 og styðjast við uppréttaðar loftmyndir sem teknar hafa verið síðan 1945, bæði fyrir og eftir síðustu fimm eldgos. Einnig er stuðst við innbyrðis afstöðu hraunanna, landslagsform, jarðvegssnið, gjóskulög og gróðurþekju. Tekist hefur að bæta talsvert hraunakort Heklu og gert hefur verið kort af hraunum sem runnu fyrir gosið mikla 1947. Hraun frá eldstöðvarkerfi Heklu á sögulegum tíma þekja u.þ.b. 233 km2 lands. Hraun hafa runnið allt að 16 km vegalengd frá megineldstöðinni og hraunstraumar hafa náð 20 km lengd. Á síðustu 250 árum hafa hraun þó aðeins runnið 8–9 km frá megineldstöðinni. Eldvörp á sögulegum tíma dreifast allt að 16 km út frá megineldstöðinni. Í sumum gosum hefur eldvirknin færst um allt að 5 km út frá eldstöðinni þega leið á gosið. Borin hafa verið kennsl á hraun frá 16 gosum og að auki hafa um 60 hraunflákar verið kortlagðir sem gætu verið frá gosum á sögulegum tíma. Þessi hraun eru líklega frá þekktum gosum, s.s. 1222, 1341, 1510, 1597 og 1636 en þau gætu líka verið að einhverju leyti frá gosum sem þótt hafa vafasöm, á árunum 1436–1439.Icelandic Research fund, Grant of Excellence No. 152266-052 (Project EMMIRS)Peer Reviewe

    Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions

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    This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health

    Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions

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    This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health
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