The AVTOD (ASTER Volcanic Thermal Output Database) Latin
America archive
K. Reath
a,⁎
, M.E. Pritchard
a, S. Moruzzi
a, A. Alcott
a,b, D. Coppola
c, D. Pieri
da
Earth and Atmospheric Sciences, Cornell University, 112 Hollister Dr., Ithaca, NY 14853, United States of America
bGeological Sciences, University of Missouri, 400 S 6th St., Columbia, MO 65211, United States of America c
Department of Earth Sciences, Universita Degli Studi di Torino, Via Valperga Caluso 35, 10139 Torino, Italy
d
Jet Propulsion Laboratory (JPL), California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, United States of America
a b s t r a c t
a r t i c l e i n f o
Article history:
Received 28 September 2018 Received in revised form 28 March 2019 Accepted 29 March 2019
Available online 1 April 2019
Thermal spaceborne remote sensing has been used to study, monitor, and forecast volcanic activity for decades. But these data have not been used systematically at high spatial resolution to study changes in volcano temper-atures across an entire region spanning multiple decades to understand background thermal activity and its re-lation to unrest and eruption. We have developed afirst-of-a-kind database that uses manual analysis to identify and collect data for volcanic thermal output with 90 m/pixel spatial resolution for 330 potentially active volca-noes found in Latin America between the years 2000–2017. This database is reliant on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor due to its high spatial resolution data, capability of detecting low-level thermal features, its accessibility and reliability compared to similar data types, and the long time series available at multiple volcanoes.
A total of 88 Latin American volcanoes were found to have some type of volcanic thermal feature detected by ASTER, and here we document thermal features at 16 of these volcanoes detected from space for thefirst time. We have recorded these thermal features, including the temperature above background, area above background, and the timing and location of detection in the ASTER Volcanic Thermal Output Database (AVTOD). By compre-hensively analyzing such a large dataset, we are able to quantitatively analyze some of the issues with these data, including 24% of all volcanoes in this study failing to meet the acquisition rates proposed by the current acquisi-tion plan and 44.5% of all acquisiacquisi-tions being unusable due to interference from clouds. We provide recommenda-tions of how to update future acquisition plans that would focus on night-time and cloud-free acquisirecommenda-tions. In order to confirm the validity of AVTOD it was tested against the existing Moderate Resolution Imaging Spectroradiometer (MODIS)-based MIROVA database. In some cases, we found a high degree of correlation be-tween the two datasets (r2= 0.87). In other cases, however, correlation was limited due to the difference in
spa-tial resolution of these two data types. By examining the maximum temperature detected by every volcano in the database we found 46 that never reach a temperature high enough to be detected by MODIS-class thermal sen-sors. The information in this database provides new insights about volcanic activity both on its own, and in com-bination with other data types, as well as a data-driven approach to improve key features in future sensors.
© 2019 Elsevier B.V. All rights reserved.
1. Introduction
Volcanic eruptions create hazards that pose a risk to the health and livelihood of local communities and can have global implications by disrupting businesses and air travel (Brown et al., 2015a, 2015b;
Loughlin et al., 2016). Currently ~1400 subaerial Holocene volcanoes pose a risk to the estimated 800 million people that live within a 100 km radius (Brown et al., 2016). Past studies have demonstrated that many, but not all, volcanic eruptions are preceded by some form of volcanic unrest (e.g.Barberi et al., 1984;Oppenheimer et al., 1993).
Properly identifying this pre-eruptive unrest enables a forecast to be made of the likelihood of an upcoming eruption (e.g.Phillipson et al., 2013). For example, before the 2010 Merapi explosion in Indonesia, proper monitoring on volcanic unrest has aided in evacuation and been credited with saving thousands of lives (Pallister et al., 2012).
In order to determine if the volcanic activity is above base-line levels, and therefore may be leading to an eruption, consistent and long-term monitoring is required. Currently, a comprehensive ground-based mon-itoring network is the most effective method to monitor any type of vol-canic unrest (e.g.,Kauahikaua and Poland, 2012;Mothes et al., 2015;
Winson et al., 2014). However, less than half of the potentially eruptive volcanoes have any ground based monitoring (Brown et al., 2015a) and far less have the necessary array of instruments needed to effectively
⁎ Corresponding author.
E-mail address:kar287@cornell.edu(K. Reath).
https://doi.org/10.1016/j.jvolgeores.2019.03.019 0377-0273/© 2019 Elsevier B.V. All rights reserved.
Contents lists available atScienceDirect
Journal of Volcanology and Geothermal Research
j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / j v o l g e o r e smonitor the volcano. Spaceborne remote sensing instruments can at least partly accommodate for the monitoring gaps left by insufficient ground-based networks (Reath et al., 2019). A variety of techniques have been applied to spaceborne data to monitor different types of vol-canic activity, such as volvol-canic gas emissions (e.g.,Carn et al., 2017), de-formation (e.g.,Biggs et al., 2014), and thermal output (e.g.,Wright, 2016).
Thermal InfraRed (TIR) volcanic monitoring focuses on the increased surficial temperatures produced as a result of volcanic activity, or volca-nic thermal features (VTFs), and measuring the changes of these VTFs over time (i.e. thermalflux) (e.g.,Coppola et al., 2016;Vaughan et al., 2012;Wright et al., 2004). Retrospective studies of TIR remote sensing data have positively identified pre-eruptive activity that can be linked to eruptions (e.g.,Dehn et al., 2002;Pieri and Abrams, 2005;Reath et al., 2016). However, these studies focus on a single volcano and the amount of variability in each volcanic system limits their ability to be reproduced on a regional or global scale. Further,National Academies of Sciences (2018)noted the necessity of“high quality” (i.e. high-spatial resolution) TIR data for detecting the subtle temperature changes on the surface that can lead up to an eruption (e.g.,Dehn and Harris, 2015;Ramsey et al., 2015;Vaughan et al., 2005). Data acquired from the Advanced Spaceborne Thermal Emission and Reflection Radi-ometer (ASTER) sensor is currently the most accessible and accurate data type that thatfits these constraints. ASTER TIR data, however, are currently under-utilized for monitoring VTFs at a large number of volca-noes. There are currently two sources for volcano specific ASTER data: the Image Database for Volcanoes (https://gbank.gsj.jp/vsidb/image/ index-E.html) developed by M. Urai hosts all ASTER images captured over 964 volcanoes, however no analyses are performed on these im-ages (Urai, 2011). The ASTER Volcano Archive (AVA) is the only publicly available ASTER database that attempts to determine what ASTER scenes contain VTFs (Pieri et al., 2007). However, these data are not op-timized for VTFs with lower temperatures and therefore do not provide adequate coverage of pre-eruptive VTFs (Reath et al., 2017), although improved coverage is planned. We seek to resolve this discrepancy by developing a new database, the ASTER Volcanic Thermal Output Data-base (AVTOD). This dataData-base is populated by manually analyzing all ASTER data acquired over Holocene volcanoes, providing a more accu-rate measure of subtle VTFs. This tool can then be used to analyze pre-eruptive behavior and to determine the level of thermal activity at vol-canoes where relevant ASTER data are available, including volvol-canoes that have never before been analyzed.
In order to test the accuracy of AVTOD data, we compare it against an independent dataset. Due to the lack of ground-based measurements of temperature, comparisons are made against thermal detections from the Moderate Resolution Imaging Spectrometer (MODIS) instrument, as analyzed by the Middle InfraRed (MIR) Observation of Volcanic Activ-ity (MIROVA) algorithm (Coppola et al., 2016). The MODVOLC algo-rithm (Wright et al., 2004) was also considered, however MIROVA was determined to be a more suitable analog due to its increased sensi-tivity to more subtle thermal features. MIROVA is a complex system that integrates both spatial and spectralfiltering when analyzing MODIS data for VTFs with a level of sensitivity previously unseen (Coppola et al., 2016).
Compiling the data used in this database has multiple benefits be-yond the identification of new thermal features. We will be able to quantitatively identify the issues related to the cloud-free availabil-ity of these data and determine how this varies on a sub-regional scale. Such a wide range and increased quantity of data also enables us to experiment with and evaluate methods to best measure ther-mal output. Finally, we can relate the type of therther-mal output ob-served to the types of volcanic activity that are being recorded in an attempt to better understand how thermal features relate to vol-canic unrest. Our goal is to identify the importance, and increase the effectivity, of satellite volcanic monitoring both for current and future TIR instruments.
1.1. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor
Data acquired from the ASTER instrument are the sole source for populating this database. The ASTER sensor was launched in December 1999 as one offive instruments on the National Aeronautics and Space Administration (NASA) Terra satellite. This instrument hasfive channels in the TIR subsystem with a 90 m per pixel spatial resolution and a tem-poral resolution limited to 1–5 days at higher latitudes and 16 days at the equator (Ramsey and Dehn, 2004;Yamaguchi et al., 1998) with a pointing mirror that can reduce this repeat time where necessary (Fujisada, 1994). ASTER data have the highest TIR spatial resolution of any satellite TIR data openly available to the scientific community. These data are the most effective data type to both remotely determine subtle variations in the temperature and area of VTFs and be accessible enough to build a database. Additionally, ASTER data have multiple data products, but because of the surface temperature focus of this study, the atmospherically corrected AST_08 (kinetic surface temperature) (Gillespie et al., 1998) data product is used exclusively.
Although there are several other spaceborne TIR systems (e.g., EO-1 (Lencioni et al., 2005), etc.), ASTER is the focus of this database due to its freely available high spatial resolution TIR Spaceborne data. Landsat-8 is another freely available high spatial resolution TIR sensor that was con-sidered for this database, however these data come with their own set of complications. First, Landsat night time acquisitions are only made by special request as per the long-term acquisition plan (Arvidson et al., 2001), therefore Landsat does not regularly acquire night observa-tions for many of the volcanoes included in this database. Additionally, Landsat-8 TIR data has been resampled from 100 m to 30 m pixels (Johnson, 2015), introducing an unknown amount of error when deter-mining kinetic surface temperature. Further, the TIR bands of Landsat-8 suffer from calibration errors due to stray light contamination, making some acquisitions unreliable for quantitative work (e.g.,Barsi et al., 2014;Schott et al., 2014). Therefore, these data do not provide reliable quantitative results and were not used.
1.2. Latin America
Thefirst archive of this database, in what is expected to be a global initiative, is based in Latin America. This was motivated by the Commit-tee on Earth Observation Satellites (COES) volcano pilot project to use remote sensing to monitor all of the volcanoes in Latin America (Pritchard et al., 2018). Including the total number of known Holocene volcanoes, and Pleistocene volcanoes with previously established unrest (Reath et al., 2019), there are 330 volcanoes in this region (Fig. 1). Pre-vious studies have identified that 42 of these volcanoes have had erup-tive activity since 2000, when the ASTER sensor started functioning, and 80 have remotely detectable volcanic activity (Furtney et al., 2018;Jay et al., 2013;GVP, 2013). However, it is estimated that 64% of the 330 vol-canoes are lacking the array of ground-based instrumentation necessary for adequate monitoring (Brown et al., 2015a, 2015b). The abundant levels of volcanic activity in the area and the significant risk to local pop-ulation (Pritchard et al., 2018), combined with the gaps in volcanic monitoring in this region, make this a good starting location for this database.
Additionally, Latin America provides a wide range of environments and volcanic systems that allow us to determine where this database is most effective, and where additional types of monitoring are neces-sary. The region includes many volcanoes that are considered both “open” and “closed” systems (e.g.,Chaussard et al., 2013), eruptive be-havior ranging from small strombolian (e.g. Llaima, Fuego) to plinian (e.g. Láscar, Chaiten), and environments that can be extremely cloudy around the equator and clearer in drier areas like the central Andes. By collecting data before different types of eruptive activity we can at-tempt to relate different patterns in pre-eruptive activity to different eruption types. Finally, 250 Holocene volcanoes have no history of
prior remotely detected activity, we can determine how well the in-creased sensitivity of these methods can identify thermal activity at pre-viously undetected systems.
2. Methods
All data collection and processing in this database was performed manually. Whereas this helps to identify subtle VTFs, it also introduces an element of human error. We have developed specific methods in order to increase the reproducibility of measurements from volcano to volcano and to keep human error to a minimum.
2.1. Data collection
All data used in this study were acquired using the ASTER sensor and initially inspected using the US Geological Survey (USGS) Global visual-izer viewer (Glovis), the NASA Reverb Echo web tool (now deprecated), or the NASA EarthData search web tool. Data are limited to nighttime acquisitions due to the higher signal-to-noise ratio and the decreased amount of solar heating effects, topography effects, and clouds present in these data (Pieri and Abrams, 2004). All of these data were then man-ually inspected for cloud cover over the target volcanoes using the
available web tools. Initial identification of VTFs was made using the web tools (that do not include information on quantitative values) to examine all cloud-free nighttime images acquired between 1 January 2000 and 1 January 2018 to determine which data would be downloaded. Investigators searched for pixels in the general location of the volcano of interest that were brighter than surrounding pixels. In the cases where no VTFs at all are identified via web tools, one scene was downloaded for every two-year interval where data are available to search for subtle thermal features the initial investigation may have missed. If a VTF is identified in any scene, all cloud-free night ASTER scenes acquired 1 year before and after that scene (tofill the two-year interval should every scene have a VTF) were acquired and investigated.
In order to develop a uniform database, the same information is col-lected and displayed for every volcano stored in the database. This infor-mation includes: the volcano name and country, the Smithsonian Global Volcanism Program (GVP) identification number, the latitude and longi-tude of the central crater, the start and end date of any eruptive periods since 2000 recorded in GVP, the Volcanic Explosivity Index (VEI) value and GVP identification number for each eruption, the number of cloudy/unusable ASTER scenes, the number of cloud-free scenes, and fi-nally the percentage of cloud-free scenes. Further information is
included for acquisitions with VTFs and null confirmation acquisitions including: the acquisition granule number, the date and time of acquisi-tion, the presence of a VTF, the presence of clouds, the VTF temperature above background and area, and the lead time before eruption.
This comprehensive database has been included as part of the ASTER Volcano Archive (AVA) (https://ava.jpl.nasa.gov/) and temperature above background time series graphs can be found within the World Or-ganization of Volcano Observatories database of volcanic unrest (WOVOdat) (http://www.wovodat.org/).
2.2. Data processing
Data downloaded to assess thermal features use the standard level 2 kinetic surface temperature (AST_08) data product. These data are de-rived by applying the ASTER temperature and emissivity separation al-gorithm to the atmospherically corrected ASTER surface radiance data product (AST_09T) and are accurate to ±1–2 °C (Abrams, 2000;
Gillespie et al., 1998). The data were then processed using the Environ-ment for Visualizing Images (ENVI) software suite. A detailed visual in-spection was performed to confirm the location and intensity of each thermal feature believed to be volcanogenic. In cases where the visual inspection leads the authors to believe the surrounding clouds may have interfered with the thermal readings, no temperature measure-ments of the VTF are included in the database and the image is consid-ered“cloudy”. To determine conditions where clouds may interfere with VTFs, investigators considered the image to be cloudy if any clouds were detected within 2 km. In these cases, water vapor that may not be readily apparent could still have an influence on the VTF measurements. Thefirst step in processing these data involves calculating a back-ground temperature from the average temperature of a 10 × 10 pixel (8.1 × 105m2) region of interest. To be considered, this region of
inter-est must be (a) void of clouds or any non-volcanic thermal features (e.g., surface water features, compositional variations, etc.), (b) adjacent to the VTF, at a similar elevation, and (c) exhibit a similar composition to the material surrounding the VTF (similar to methods by, e.g.:Harris et al., 1997;Jay et al., 2013;Reath et al., 2016). This back-ground temperature was then subtracted from the maximum pixel temperature of the VTF in order to determine the temperature above background. If this maximum pixel temperature did not exceed 2 °C above background, it was not determined to be above the noise level of this data product and was therefore not considered thermally dis-tinct. Additionally, if a thermally distinct pixel, or group of pixels, did not occur in a persistent location in at least three scenes, it was not clas-sified as a VTF. All newly identified VTFs were examined using high spa-tial resolution Google Earth visible imagery to ensure these features were volcanogenic and not the result of surface water or compositional variations. By measuring temperature above background, rather than maximum pixel temperature, we are reducing any variations in temper-ature occurring as a result of seasonal or diurnal effects (Reath et al., 2016). In some cases, due to an excess of surficial heat, ASTER thermal data were saturated. Where this occurred, a value of 120 °C was assigned to the temperature above background. This corresponds with the approximate saturation temperature for the ASTER brightness tem-perature algorithm (Abrams et al., 2002).
Acquisitions with a positive identification of a VTF were then ana-lyzed to determine the area of the VTF. Every pixel believed to be related to volcanogenic heating surrounding the original VTF was included in a region of interest (ROI), which determined the number and location of every pixel that isN2 °C above background. The total number of pixels found to be thermally distinct using this method was recorded in the database. Finally, we determined the lead time before eruption for each detectable VTF. This time was calculated in days before the eruption that is closest in time after the VTF, as categorized by the Smithsonian Institution Global Volcanism Program (GVP) (GVP, 2013).
2.3. Summary table
The summary table (Table S1) for AVTOD was generated as a refer-ence guide for this paper. This table contains many features included in every AVTOD entry (e.g., volcano name, GVP number, county, and sub-region) and well as some new categories (e.g., max temp above background, % cloud free, number of acquisitions) meant to condense information found in the database.
Three groupings of volcanic activity can be found within the “classi-fication” category: active, quiescent, and inactive. A total of 42 active volcanoes have had an eruption since the launch of ASTER, 38 quiescent volcanoes have had any of a variety of remotely detected unrest includ-ing degassinclud-ing, deformation, and previously noted thermal detections (e.g.,Jay et al., 2013;Wright, 2016), and 250 inactive volcanoes have no satellite detectable unrest prior to AVTOD. An“apparent VTF” is noted if a VTF can be detected at this volcano in any of the observations, and“max temperature above background” is the maximum pixel tem-perature the VTF reaches among all the acquisitions included in the time series. We note that the pixel temperature is an average over the full area covered by the pixel, and sub-regions within the pixel likely reach higher temperatures (e.g.Francis and Rothery, 2000;Rothery et al., 1988;Wright et al., 2000). Finally, the columns labeled“number of acquisitions” and “% cloud-free” provide the total number of night time acquisitions collected over a given volcano and the percentage of these observations where clouds had no observable influence on the VTF surface temperature value.
3. Results
330 volcanoes are included in the Latin American archive. We found 88 volcanoes with thermal activity detectable by ASTER. 72 of the volca-noes with AVTOD detected VTFs are include in the 80 volcavolca-noes with some form of previously detected unrest (e.g.,Carn et al., 2016;Jay et al., 2013;Pritchard et al., 2018;Wright, 2016). This leaves 16 volca-noes that were previously considered inactive with newly identified VTFs. By combining these new detections with the 80 volcanoes with previously detectable unrest, the total number of volcanoes with re-motely detectable volcanic activity in Latin America rises to 96, a 20% in-crease. These 16 new volcanoes have subtle VTFs that were below the detection threshold for automated detection algorithms, or were not considered in the algorithms due to a lack of previously recorded unrest. In the following sections, we use this database to reveal some of the shortcomings with the data availability, and to test new methods in interpreting ASTER TIR data.
3.1. Data availability
Although ASTER has global coverage, the amount of data available for any given volcano is constrained by two main factors: the number of scene acquisitions and the percentage of those scenes that are free of clouds surrounding the volcano. Whereas the possibility of having a cloud-free scene is dependent on environmental factors, the number of scenes acquired at a volcano is dependent upon the monitoring goals of the ASTER sensor (Yamaguchi et al., 1998) and more specifically the ASTER science team acquisition requests (STARs). There are numer-ous STARs for different objectives (e.g.,Ramsey and Lancaster, 1998;
Raup et al., 2000;Stefanov et al., 2001). The STAR designed to make rou-tine observations of the world's volcanoes (i.e., Volcano STAR) was con-ceived byUrai et al. (1999)and is routinely updated every few years by adding and subtracting volcanoes and adjusting how frequently they are observed (e.g.,Ramsey, 2016;Urai, 2011;Urai and Pieri, 2010). As part of the Volcano STARs plan, volcanoes with recent eruptions would have ~19 acquisitions per year (11 of which should be night time acquisitions) and volcanoes believed to be inactive would have 2 acquisitions per year (e.g.,Urai et al., 1999;Urai and Pieri, 2010). In a re-cent analysis, volcanoes with a rere-cent eruption received, on average,
85% of the planned coverage (Ramsey, 2016). In late 2006 the ASTER Ur-gent Request Protocol (URP) was implemented, which triggers an ac-quisition from every available overpass for up to 25 high-profile volcanoes (e.g.,Duda et al., 2009;Ramsey and Dehn, 2004;Ramsey, 2016). The URP provided a significant increase in data available at high-profile and erupting volcanoes, but a majority of the volcanoes in AVTOD have not been included in the URP.
Fig. 2demonstrates the large variation in the number of available ac-quisitions for Latin American volcanoes. 75 volcanoes, or 23% of the Ho-locene volcanoes in Latin America, hadb33 acquisitions over the ~18-year study period. In most cases, even this limited amount of data can determine the presence of persistent volcanic thermal activity. How-ever, with fewer than two acquisitions per year, meaningful interpreta-tions of the characteristics of short-term activity are difficult to impossible.
Even with adequate planned sampling frequency, the presence of clouds can prevent that data from being usable. In total, 44.5% of the scenes we inspected were cloudy.Fig. 3demonstrates the distribution of cloud-free scenes broken down by sub-regions. It may appear that there is a relatively uniform distribution of cloud-free scenes from 0 to 80%, however, there are large variations on a sub-regional scale. In more arid locations, such as the Central Andes and Mexico, 66.3 and 54.8% of all acquisitions were cloud-free, whereas in more humid loca-tions, such as Central America and the Caribbean, only 29.9 and 9.5% of acquisitions are cloud-free. Percentages this low can have a signi fi-cant effect on the amount of interpretable data available at volcanoes in humid locations. Three volcanoes, Chiles-Cerro Negro (Colombia/ Ecuador), Isla Isabel (Mexico), and Cayutue-La Vigueria (Chile), have no interpretable scenes available due to both a lack of data and cloudy conditions in the data available.
3.2. New thermal detections
Even with manual analysis, detecting new VTFs can be a difficult task. Thermal features (TFs) manifest in varied ways, making it difficult to distinguish VTFs from other TFs.Figs. 4and S1–S10 are examples of positive identifications of new VTF detections. Many of the previous
ASTER VTF detections have been documented byJay et al. (2013). As demonstrated inFig. 4A and E, water bodies may exhibit thermal signa-tures very similar to volcanogenic sources in night time observations. This is due to the large thermal inertia value of water, relative to its sur-roundings (Price, 1985). Crater lakes, or other surface water that could potentially lead to a false identification of volcanogenic thermal activity were identified at: Aliso (Ecuador), Antisana (Ecuador), Antofagasta (Argentina), Arcar (Argentina), Cerro de Azufre (Chile), Chacana (Ecuador), Easter Island (Chile), Huambo (Peru), Ilopango (El Salvador), Laguna Jayu Khota (Bolivia), Maipo (Chile), Nevado del To-luca (Mexico), Payun Matru (Argentina), Purico Complex (Chile), Quilotoa (Ecuador), Quimsachata (Peru), Sara Sara (Peru), Taapca (Chile), Tralihue (Argentina), Trolon (Argentina), Tromen (Argentina), and Tupungatito (Chile) volcanoes. Additionally, Genovesa (Ecuador), Isla San Cristóbal (Ecuador), and Irazu (Costa Rica) volcanoes (Figs. S2, S4, S5) had crater lakes that were both unevenly heated and warmer than other non-volcanic local water bodies, thus were determined to be volcanically heated. All other new VTFs correspond with hydrother-mal deposits or glacial melt pits (Fig. S1), as identified in VIS/NVIR data. Although we have identified a 20% increase in volcanoes with thermally detectable unrest, this number would have been larger without the per-vious manual analysis of ASTER data performed on some volcanoes in this region byJay et al. (2013).
3.3. Recording temperature and area
Both the change in relative surface temperature (e.g. highest tem-perature above background) and area act as important indicators of vol-canic activity. Not all changes in thermal output are expressed solely as a change in intensity (e.g.,Murphy et al., 2010;Wessels et al., 2013). As described inSection 1, past research has been primarily focused on changes in intensity, however changes in area or as a combination of these two components may be important. To demonstrate how both changes in surface temperature and area are used to identify pre-eruptive unrest, we present a case study of the pre-pre-eruptive period be-fore the recent eruptive cycle at Sabancaya volcano (Peru). It is impor-tant to note that Sabancaya is an uncommonly effective example of
Fig. 2. Data availability as constrained by the number of nighttime acquisitions within each sub-region. Number of acquisitions represents the total number of acquisitions over the lifetime of the ASTER sensor up to 1 January 2018, including cloudy scenes. Values are tabulated in Table S1.
Fig. 3. Data availability as constrained by the percentage of nighttime observations that are cloud-free surrounding the volcanic summit. Total cloud-free percentage values for each region are: Mexico-54.8%, Central America-29.9%, Caribbean-9.5%, Galapagos-34.8%, Northern Andes-31.6%, Central Andes-66.3%, Pacific Ocean-14.3%, and Southern Andes-44.6%. The total num-ber of acquisitions represents the numnum-ber of acquisitions over the lifetime of the ASTER sensor up to 1 January 2018. Values are tabulated in Table S1.
Fig. 4. New VTF detections, all images use the same distance scale and temperature range, all images have been rotated so north is up. Two types of TFs have been identified, VTFs and crater lakes. Each TF has been labeled to designate its type. Each scene corresponds to: A) Calabozos (Chile), acquired 25 May 2017, crater lake can be found in the central crater, VTF identified on the SEflank (34.564°S, 70.489°W); B) Crater Basalt Volcanic Field (Argentina), acquired 14 February 2015, a series of VTFs are identified in the SE portion of the volcanic field. The VTFs are located adjacent to a group of playa lakes that occasionallyfill with water (42.110°S, 20.003°W), however the VTFs are persistent and hotter than surrounding surface water, and are there-fore likely volcanogenic; C) Izalco (El Salvador), acquired 11 December 2014, VTF identified in summit crater (13.814°N, 89.633°W); D) Nevado Cachani (Peru), acquired 2 August 2017, VTF identified on the N flank shield volcano (16.034°S, 71.532°W); E) Apaneca Range (El Salvador), acquired 14 January 2017, a series of VTFs are identified N of Laguna Las Ninfas (13.841°N, 89.787°W); F) El Hoyo (Nicaragua), acquired 17 November 2000, VTF identified on SW edge of summit crater (12.488°N, −86.668°W).
how the pre-eruptive area of VTFs can vary, providing unique indepen-dent information. In many other cases, the VTF area remains relatively static or simply mirrors increases in intensity.
3.3.1. Case study: Sabancaya Volcano
Sabancaya Volcano is an active stratovolcano with several eruptive cycles occurring subsequent to the launch of the ASTER sensor. From April 2000 until late 2003 small ash explosions at the summit were re-motely observed. This eruptive cycle was followed by nearly a decade of quiescence where thermal readings and some limited degassing mea-surements were apparent from the main vent (GVP, 2017). On 9 August 2014 an ash cloud originating from Sabancaya was followed by a signif-icant (1500 m above the summit) ash cloud produced on 25 August. This eruptive event lasted only a short period, with the last confirmed ash cloud occurring on 29 August 2014. Quiescence continued at Sabancaya with observable vapor and SO2gas plumes continuing until
several minor ash producing explosions were recorded on 10 July 2015, 11 December 2015, and 27 August 2016 (GVP, 2017). On 8 No-vember 2016 Sabancaya began producing regular intermittent eruptive activity (approximately 2 km high ash plumes) until 21–26 December when 6.4–7.6 km ash clouds were observed, indicating the onset of the current eruptive event, which has become its largest eruptive cycle since 1990 (GVP, 2017).
We have seen phases of Sabancaya expressed by both changes in in-tensity and area before different eruptive periods. As early as 5 May 2011 (Fig. 5A) an increase in intensity can be observed, potentially re-lated to seismic unrest starting in February 2013 (Jay et al., 2015), that culminated in the 2014 eruption. Prior to the ongoing eruptive event, which began November 2016, new fumarolic areas were observed sur-rounding Sabancaya's summit. The ASTER sensorfirst observed the for-mation of these new thermally elevated areas on 23 October 2015. The thermal energy produced by these new fumarolic areas grew in inten-sity (Fig. 5A) and area (Fig. 5B) as Sabancaya began its current eruptive period (Kern et al., 2017). However, in this case, the increase in intensity can be difficult to interpret, as there is no persistent increase in temper-ature preceding the eruption. There is a clear increase in thermal vari-ability, which has been related to pre-eruptive unrest at other volcanoes byReath et al. (2016), but this variability is similar to the un-rest exhibited before other, smaller eruptions that occurred on 10 July 2015 and thus would not have been a useful diagnostic for forecasting the size of the eruptions starting in November 2016. Measuring the change in area of the VTF presents a more definitive deviation from pre-vious activity before the November 2016 eruption. Starting on 19 July 2015 a consistent trend of increasing area is measured leading up to eruption. This trend is significant, as both the size and rate of increase in area of the VTF had never previously been determined by using ASTER data at Sabancaya.
3.4. Data correlation: ASTER and MIROVA
In order to test the validity of AVTOD, we compare our results against those of the independent MODIS-based MIROVA algorithm.
Fig. 6demonstrates the correlation between AVTOD and MIROVA
datasets for the nine Latin America volcanoes with the most complete sample sizes: Chaitén (Chile), Colima (Mexico), Copahue (Chile), Fuego (Guatemala), Láscar (Chile), Llaima (Chile), Popocatépetl (Mexico), Santa María (Guatemala), and Villarrica (Chile) (Figs. S11– S19). Both ASTER and one of the two MODIS sensors operate as part of the Terra satellite enabling data acquisitions to occur concurrently. MODIS, however, has a higher temporal resolution (i.e., more frequent observations) than ASTER due to its increased swath width. In order to generateFig. 6, MIROVA data were sub-sampled so only acquisitions captured concurrently with ASTER data and only the maximum MIROVA volcanic radiative power (VRP) values on a given date are com-pared against their counter-part. Each of these plots has the same scale, allowing for direct comparison. In order to use a uniform scale, MIROVA
data points with particularly large VRP values (usually associated with eruptions) are not included. However, this effect is minimal on most plots, as the majority of these points correspond with thermally satu-rated ASTER data. Trend lines are calculated for each of these plots based upon all data points that are below 120 °C in AVTOD and above 0 W VRP in MIROVA (i.e., no detections were made). The standard devi-ation (SD) of these data is measured in r2. Data points not included in
the trend line or SD calculation (because of null MIROVA detections or saturated ASTER values) have been presented as red dots inFig. 6.
The AVTOD maximum temperature above background data were determined to have the best correlation to MIROVA VRP values. The change in the thermally elevated area was considered as another ana-log. However, due to the variation in pixel size between ASTER (90 m) and MODIS (1 km), changes in area do not affect each of these data types uniformly. Additionally, the MIROVA algorithm calculates VRP on a pixel by pixel basis, meaning that area is constant (1 km), this is similar to the AVTOD maximum temperature above background, which is also calculated on a pixel by pixel basis. Further, it is likely that the total area of the thermal feature identified in AVTOD extends beyond the boundaries of a single MODIS pixel, whereas the hottest AVTOD pixel will be contained within the hottest MODIS pixel. The MIR-method (Wooster et al., 2003), which is used to determine VRP, is more sensitive to hot surfaces having integrated temperatures above 326 (600 K). This may lead to some inconsistences between the two data types, due to the different spatial resolutions and wavelengths used to estimate the radiative power.
Although the ASTER dataset used in this study is manually analyzed and the MIROVA dataset is based on a spatial and spectral algorithm used on MODIS data (Coppola et al., 2016), the two are correlated for most volcanoes. The highest correlation occurs at Láscar volcano (Fig. 5E) which has an r2of 0.87. This is hypothesized to occur because
of the relatively uniform area of its VTF (Francis and Rothery, 1987;
Oppenheimer et al., 1993).
Thermal output is a measurement of both intensity and area of the VTF, therefore changes in either of these variables will have an effect on the temperature value of the pixel. Typically, a thermally elevated pixel is non-isothermal and made up of a“hot” feature that, in this case, is influenced by volcanogenic heating, and a “cold” feature that is not and maintains background temperatures (e.g., Oppenheimer, 1991;Rothery et al., 1988;Wright and Flynn, 2003). Non-isothermal pixel temperatures will be an average of the“hot” and “cold” tempera-tures dependent upon the percentage of the pixel area that contains each of these features. When a volcano with a VTF smaller than 90 m2
(e.g., Fuego and Láscar (Fig. 6D and E)) expands, it covers a larger per-centage of the pixel and raises its temperature. In many cases this is also accompanied by an increase in the VTF temperature as well. Here, the increase we see in pixel temperature is a result of both the increased temperature and area of the VTF.
The impact of varying VTF areas is best demonstrated by comparing the correlation trend line of volcanoes with smaller VTFs, like Fuego and Láscar (Fig. 5D and E) against those with a larger surface area VTFs, like Villarrica and Chaitén (Fig. 5A and I). This is because in these cases the VTF is larger than 90 m2,filling an ASTER pixel but not a MODIS pixel.
Here, when the VTF expands in area its percentage within the ASTER pixel remains consistent (100%) whereas within the larger MODIS pixel, the percentage of“hot” area increases. Therefore, by tracking the hottest pixel, our analysis of ASTER data is limited to measuring changes in temperature alone, whereas MODIS tracks both changes in area and temperature, hence the thermal output measured by MIROVA data in-creases at a larger rate, resulting in a steeper sloped trend line for these volcanoes.
Changes in VTF area plays a large role in how well these datasets cor-relate. Volcanoes with persistent eruptions during the study period, Co-lima, Fuego, Santa María, and Popocatépetl (Fig. 5B, D, H, and G) have more scatter in the correlation plots. These regular eruptions cause an inconsistent VTF area which affects ASTER and MIROVA values
differently. Copahue (Fig. 4C) also has a low r2, this is once again the
re-sult of a change in area of the VTF as its crater lake cycles between full and empty (e.g.,Agusto et al., 2013;Caselli et al., 2016).
Due to its smaller pixel size, ASTER can detect subtler thermal fea-tures then MIROVA. The general trend of MIROVA detections seems to be consistent from volcano to volcano, no detections are made below ~20 °C above background in AVTOD. However, these values vary when examining the data on a scene to scene basis and there is no consistent ASTER temperature threshold above which MIROVA data are detectable. The x-intercept of the trend line for seven of the nine volcanoes occurs between 20 and 30 °C. Exceptions occur at Popocatépetl and Santa María (Fig. 5G and H) where a low degree of correlation results in a skewed intercept. At Láscar (Fig. 5E) MIROVA detections were made for ASTER temperatures as low as 18 °C, however there are also null MIROVA detections that span to temperatures as high as 37 °C due to variations in VTF area. It is also worth noting that MIROVA data were not available after 1 January 2017, resulting in missed detections with concurrent ASTER measurements over 60 °C at Colima, Fuego, and Lás-car (Fig. 4B, D, and E). Also, several high intensity VTFs at Popocatépetl (Fig. 5G) and a single detection of 57 °C on 11 June 2009 at Láscar (Fig. 5E) were thrown out of the MIROVA algorithm due to distalfire de-tections in the same scene.
The general correlation between AVTOD and MIROVA data suggest that the results from these databases are reliable, although further work is needed. For instance, the comparison revealed that there are no detections by MIROVA that were missed by AVTOD. There are, how-ever, some unexpected results in this comparison, where AVTOD found high values that were not echoed by MIROVA. This is likely due to small area, high intensity VTFs that would have a greater effect on ASTER data than MIROVA, nearby clouds having a larger effect on MODIS pixels than
ASTER pixels, orfiltering issues such as the distal fire detections men-tioned in the previous paragraph.
3.5. Types of thermal features
To relate the thermal output measurements to types of activity, we have grouped volcanoes based on the maximum temperature detected during the full time series of AVTOD data. The groupings are divided into pixel temperature ranges of 2 to 10, 10 to 20, 20 to 35, andN35 °C above background. Volcanoes with no thermal detection have also been in-cluded in thefinal count. These ranges were determined based on the types of VTFs observed and the range of existing thermal detection algo-rithms. As demonstrated in the previous section, even MIROVA, with its increased sensitivity compared to other MODIS algorithms, can only detect VTFs producing an ASTER thermal signature of approximately N20 °C above background — 42 of the 88 volcanoes with detectable ther-mal unrest reach this threshold. Any detections of cooler VTFs are lim-ited to manual detections performed on ASTER or other suitable high spatial TIR data.
Wefirst grouped volcanoes based on the unrest classification (i.e., Active, Quiescent, Inactive) inFig. 7. As anticipated, active noes dominate the higher temperature ranges, whereas 14 of 16 volca-noes with no previous detectable unrest (i.e., inactive) fall in to the lowest temperature range. There are also 14 quiescent volcanoes in the lowest temperature range, however all previously detected unrest is either non-thermal (e.g., SO2degassing, ground deformation) or the
result of the previous ASTER manual survey (Jay et al., 2013). The single active volcano in this range is Concepción (Nicaragua), which only pro-duces a cloud-free scene in 4.7% of its night acquisitions. Ground instru-ments recorded temperatures as high as 100 °C at the surface during
Fig. 5. Thermal time-series of all available cloud-free night time ASTER data up to 1 January 2018 at Sabancaya, Perú, measured in A) the temperature above background of the hottest pixel in the VTF B) the total area of the VTF (sum of anomalous pixel areas). A red line is used to signify the start of the current eruptive period in November 2016.
eruptive periods at Concepción (GVP, 2013). However due to the low number of usable scenes these detections were missed by ASTER data. In the next temperature range, from 10 to 20 °C, the majority of vol-canoes are quiescent. This is the result of the detection of accompanying non-thermal unrest increasing as the thermal output escalates. There are alsofive active volcanoes in this category, two of these volcanoes have very few cloud-free scenes, similar to Concepción. The other three, Callaqui (Chile), Calbuco (Chile), and Cerro Azul (Ecuador) all had very short eruption periods that received limited to no ASTER coverage.
In the next temperature range, 20 to 35 °C, there is an even split be-tween quiescent and active volcanoes. Much like in the previous ranges, the active volcanoes here have limited coverage, with an average of 21% of scenes being cloud-free. However, volcanoes that have short-lived yet persistent eruptions, such as San Cristóbal (Nicaragua), which had 21 separate eruptive events since 2000, can be found in this category.
All volcanoes in thefinal category are active, this includes small eruptions with proper coverage up to VTFs that consistently produce a large thermal output, such as the lava lake on Villarrica.
Our next focus was to apply these temperature ranges to the dif-ferent sub-regions to search for regional trends in unrest (Fig. 8). If there were no variability in sub-regional unrest all categories would be evenly dispersed in the histogram. However,Fig. 8 demon-strates a fair amount of variability, with the Central Andes and Northern Andes acting as end-members. In the Central Andes, volca-noes with detectable VTFs are disproportionately prevalent toward low temperatures with 14 of 28 volcanoes in the lowest temperature range and 7 in the second to lowest temperature range. Conversely, 5 of 7 volcanoes with VTFs in the Northern Andes are in the highest temperature range. As mentioned in the previous paragraph, poor temporal coverage of a volcano can result in missed high thermal output events (e.g., eruptions), skewing data to lower temperature ranges. However, the Central Andes has a higher cloud-free percent-age (66.3%) relative to the Northern Andes (31.6%), which should have the opposite effect than what is being observed. Therefore, we can conclude these sub-regional variabilities are not related to tem-poral coverage variations.Reath et al. (2019)discussed some of the geological reasons why this sub-regional variation may occur, such
Fig. 6. Plot of“MIROVA VRP” against nighttime “ASTER °C Above Background” values to demonstrate the correlation between these two data types at nine volcanoes where sufficient data exist for comparison. Data points not included in the trend line and r2
as tectonic setting, crustal structure, and magma composition, but a more in depth analysis of these driving forces will be left for future publications.
4. Discussion
4.1. Comparison with automated detection algorithms
An issue with AVTOD is that all results have been manually gener-ated, which is labor intensive and invites human error. An automated al-gorithm would be a more attractive alternative, as it would generate new results in near real time and would apply the same exact standards from scene to scene. However, current numerical algorithms do not have the sensitivity necessary to detect low intensity VTFs in ASTER data. For example, where AVTOD identified VTFs in 84 acquisitions at Sabancaya (Peru), AVA only found 13 (acquired 8 November 2003, 15 November 2003, 8 November 2009, 30 June 2011, 5 November 2011, 28 August 2015, 11 November 2016, 3 April 2017, 20 April 2017, 28 April 2017, 2 August 2017, 10 August 2017, and 5 October 2017) for a ~15% accuracy comparison to AVTOD. Further, the AVA algorithm only provides a pixel count and surface temperature of the corresponding pixel whereas AVTOD provides values corrected for background and more directly related to volcanic thermal output.
To improve the AVA database, we have incorporated AVTOD data into AVA. AVTOD values are directly linked to the existing AVA detec-tions, enabling volcanic thermal output values to be associated with those existing in AVA and providing new values for acquisitions with VTFs missed by AVA. Further, AVTOD results are being used as a sample set for developing new algorithms with increased sensitivity. Our hope is that in the future, by combining the progress made in developing an automated algorithm by AVA with the more sensitive and comprehen-sive data collection of AVTOD, we will have an automated algorithm that can produce the same results as AVTOD.
Other automated TIR volcanic activity detection algorithms are available, although they tend to focus on MODIS based detections due to its high temporal resolution (4 acquisitions/day). MODVOLC (Wright et al., 2004) and MIROVA (Coppola et al., 2016) are the two most utilized algorithms of this type in use. Although, as discussed in
Section 3.4the low spatial resolution of MODIS restricts detection of lower intensity and smaller sized VTFs. Additionally, MODIS-based algo-rithms are based on analysis of MIR data, not TIR data. This variation in wavelengths can cause a change in sensitivity to the temperature distri-bution inside the VTF (Dennison and Matheson, 2011;Li et al., 2013). 4.2. Expanding coverage
Whereas AVTOD successfully identified thermal activity at 16 new volcanoes in Latin America, most of these volcanoes still lack the data necessary to make meaningful interpretations of temporal variations in thermal output. About 24% of all volcanoes in this study fail to meet the minimal acquisition rate proposed by the ASTER Volcano STAR mon-itoring plan (e.g.,Urai et al., 1999;Urai, 2011) of two acquisitions per year. There are several reasons for this lack of data. First is that AVTOD only includes night time data. With the inclusion of daytime data, the number of acquisitions will increase. The Volcano STARs plan is also based on the ~1500 volcanoes included in the Japan Meteorological Agency Catalogue (Kato et al., 2011). The number is very similar to the 1437 Holocene volcanoes included in the GVP list, howeverRamsey (2016)suggest the number of volcanoes currently included in this plan is closer to 1000 andUrai (2011)points out that there are only 964 volcanoes included in ASTER image database for volcanoes. Based on the numbers generated in this study, it is likely that the Volcano STAR plan is no longer applied to the full ~1500 Holocene volcanoes originally included in this plan.
Further, cloudy conditions prevent many scenes from providing use-ful information and nighttime acquisitions provide more reliable data (Section 2.2). Rather than having an acquisition strategy where the goal is to reach a certain number of acquisitions per year, perhaps a bet-ter strategy would be to reach a certain number of nighttime cloud-free scenes per year. Based on the average percentage of clear scenes in each
Fig. 7. Three levels of unrest grouped by the maximum VTF pixel temperature above background as determined by AVTOD. A total of 29 volcanoes fall in the range of [2–10 °C], 17 in [10–20 °C], 12 in [20–35 °C], 30 in [N35 °C] and 242 with no detections. Values can be found in Table S1.
Fig. 8. Sub-regional unrest group by the maximum VTF temperature above background as determined by AVTOD. The single volcano in the Pacific island sub-region has no detectable thermal output and is not shown. Values can be found in Table S1.
sub-region determined inSection 3.1, an estimate of the number of ac-quisitions needed per year to reach this goal can be calculated. For ex-ample, in Central America, an average of 29.9% nighttime acquisitions are cloud-free. Therefore, a minimum of 6 night acquisitions would need to be made per year in order to have the greatest likelihood of ac-quiring 2 usable scenes.
4.3. Relating VTFs to unrest
Based on the temperature ranges used inSection 3.5along with the automated algorithm detection limits established inSections 3.4 and 4.1, and the in situ observations available in the GVP database (GVP, 2013), we can make some connections between the VTFs that are being observed and the unrest that is being represented. VTFs in the 2 to 10 °C temperature range represent a very low level of thermal output, possibly the result of a single hot fumarole or a low temperature fuma-rolefield. These VTFs may be undetectable in situ, and would not be de-tected by any automated thermal detection algorithm currently in operation. The 10 to 20 °C temperature range includes VTFs with a slightly larger thermal output. This category would include regular fu-marolefields that produce a noticeable amount of heat and would likely be detected in situ. These VTFs may cross the threshold for detection by highly sensitive thermal detection algorithms such as MIROVA, how-ever this would be an infrequent occurrence and likely ignored without prior evidence of volcanic unrest. VTFs included in the 20–35 °C temper-ature range are high intensity fumaroles that can be detected using the more sensitive automated thermal algorithms (e.g. MIROVA, AVA). This unrest is likely the result of a large and active fumarolefield; in many cases, such a large output of heat is accompanied by volcanic gasses that are detectable by the Ozone Monitoring Instrument (OMI) sensor (Carn et al., 2016), or by ASTER. This level of unrest indicates that the magma has possibly reached very shallow levels, producing high-temperature thermal anomalies detectable by MODIS. Finally, VTFs with a thermal outputN35 °C correspond with energy levels produced during an eruptive event, it would also include extremely hot thermal features such as lava lakes. This unrest is detectable by all satellite ther-mal detection algorithms.
The particular duty cycles of orbital platforms, including the pointing requirements for ASTER, have led to data acquisitions that are non-uniform in sampling frequency. That, combined with the variability of volcanic systems, precludes the precise determination of a quantitative statistical estimate of the minimum amount of data needed to observe pre-eruptive activity in the volcanoes we surveyed. However, at some volcanoes (e.g. Láscar, Sabancaya, Copahue, Hudson (Delgado et al., 2014)) AVTOD provides evidence of pre-eruptive activity, in most cases an increase in thermal output, before eruption. At Láscar (Fig. S15), a cyclical pre-eruptive period with radiance increase followed by decrease before eruption has been noted from 1986 to 1995 (Matthews et al., 1997;Oppenheimer et al., 1993;Wooster and Rothery, 1997). However, since 2000, we observe a different pattern in AVTOD of a long-term trend in decreased thermal output that abruptly shifts to increased output 1.5 months to days before eruption —the causes for this change will be explored in future work. Other vol-canoes like Copahue (Fig. S13) and Sabancaya (Fig. 5) have pre-eruptive unrest that can be detected months to years before eruption (Reath et al., 2019).
Increased ASTER cloud-free observations at every volcano with a thermal feature would provide similar results to the productive time se-ries observations available for Láscar, Sabancaya, and Copahue, where coverage has been generous. Without the increased coverage at these three volcanoes, it would have been difficult, if not impossible, to ob-serve pre-eruptive thermal activity. For example, no pre-eruptive mea-surements of elevated unrest exist for Chaitén (Fig. S11), before its 2 May 2008 VEI level 4 eruption (Wunderman, 2008). The most recent cloud-free ASTER night observation before this eruption was acquired on 29 July 2006, over two years before the eruption. Had ASTER data
been acquired following the standard we proposed inSection 4.2, at least 4 cloud-free night time acquisitions may have been made during this period. This would have greatly increased the possibility of detect-ing a thermal increase preceddetect-ing the eruption, possibly providdetect-ing timely evidence of the growing instability at Chaitén.
5. Conclusions
We have demonstrated the utility of manual analysis of ASTER data and the utility of the AVTOD database, which increased the number of volcanoes with satellite detectable unrest by 20%, and expanded the available thermal dataset for all volcanoes with unrest in Latin America. Due to the volume of ASTER's global coverage, there are many scenes of Holocene volcanoes that have never been analyzed manually. The Latin America archive is meant to act as a starting point for AVTOD, with the intention of obtaining global coverage of all ~1500 Holocene volcanoes. This database is unique in that it has both a level of sensitivity to subtle VTFs unseen in similar databases and that it expands its analysis to all potentially active volcanoes, not just volcanoes with known unrest. The downside of this database is that it is labor intensive, time consuming, and introduces human error in ana-lyzing and recoding data. However, we are working toward improving the sensitivity of the existing AVA thermal detection algorithm and our plan is to automate this process in the future.
Although ASTER Volcano STARs have established an observation schedule with the set purpose of observing every volcano at least twice per year, not all volcanoes have received this planned level of cov-erage because of the complicated on-demand pointing, scheduling and duty cycle requirements, and a 16-day nadir repeat observation cycle. Volcanoes that met the observation requirements were limited by cloud cover and, unless they had been additionally included in the ASTER URP list, typically lack the amount of usable data necessary to de-termine short-term variations in thermal output. Augmenting ASTER scheduling and targeting a subset of its image budget for volcanology, should provide valuable lessons-learned for future TIR missions that in-clude volcano observations.
By including area of VTFs in the dataset, we provide an additional critical component of TIR volcanic remote sensing. Changes in volcanic thermal output can be expressed as variations in both intensity and area. Whereas past studies have had success in identifying pre-eruptive behavior by relying solely on measurements of the changes in thermal intensity eruptions (e.g., Dehn et al., 2002; Pieri and Abrams, 2005; Reath et al., 2016), Section 3.3 demonstrates how changes in area can be an equally important measurement to record. For example,Section 3.3.1demonstrates how changes in the expressed area of thermal features on Sabancaya provide a more distinct pre-eruptive trend before its 2016 eruption compared to changes in intensity.
We have found a degree of correlation between the newly devel-oped AVTOD and MIROVA thermal datasets (Fig. 5), which are based on two entirely different sensors (i.e. ASTER and MODIS), and have dif-fering methods for data analysis (i.e. manual and automatic). This com-parison enables a cross-check of their consistency., and an ability to check their mutual accuracy against ground truth. Where data are avail-able for both approaches, they track similar general trends in thermal output. However, the increased temporal resolution of MIROVA pro-vides a more precise measurement of thermal trends at volcanoes with high thermal output and the increased sensitivity and spatial reso-lution of ASTER enables detection of low output VTFs missed by MIROVA.
Although the remaining lifespan of the ASTER sensor is limited (“Terra Spacecraft Bus Status”, 2014), this database will prove useful in-formation after ASTER has been retired. The most recent Decadal Survey (National Academies of Sciences, 2018) calls for the next generation of high spatial TIR sensors, along the lines of the Hyperspectral Infrared Imager study already accomplished by NASA (e.g.,Lee et al., 2015)
and potential TIR capabilities on contemplated future Landsat missions (e.g.,Crawford et al., 2017). Further, we plan on expanding this level of coverage, whether through continued manual analysis or by increasing the sensitivity of detection algorithms, to include other regions with the hope of having a global database of every Holocene volcano in the next five years. This database demonstrates the utility of applying these data on a global scale, and lays the ground-work for how to effectively iden-tify, analyze, and classify volcanic thermal output measurements for all high-spatial TIR datasets used in the future.
Acknowledgements
This work was conducted as a part of the Volcano Remote Sensing Working Group supported by the John Wesley Powell Center for Analy-sis and SyntheAnaly-sis, funded in part by the U.S. Geological Survey. K.R., A.A., S.M., and M.E.P. were partly supported by NNX16AK87G issued through NASA's Science Mission Directorates Earth Science Division. A.A. and S.M. were also supported by a grant from the Cornell University Engi-neering Learning Initiatives. We acknowledge NASA and LPDAAC for the ASTER and MODIS data. MIROVA is a collaborative project between the Universities of Turin and Florence (Italy) based on MODIS data pro-vided by the LANCE-MODIS system. We thank Rowena Lohman, Francisco Delgado, Kyle Murray, Paula Burgi, Alex Styler, and Justin Linick for discussions. Work performed here, in part, was conducted un-der contract to NASA, as part of the ASTER Volcano Archive activity and ASTER Science Team Member activity (DP) at the Jet Propulsion Labora-tory of the California Institute of Technology. Thank you to Karen Papazain, Harrison Tran, as well as an anonymous reviewer for improv-ing the quality of this paper. The data included in this database are avail-able through AVA (https://ava.jpl.nasa.gov/avtod.php).
Appendix A. Supplementary data
Supplementary data to this article can be found online athttps://doi. org/10.1016/j.jvolgeores.2019.03.019.
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