Automated tracking of lava lake level using thermal images at Kīlauea Volcano, Hawai’i
© Patrick et al. 2016
Received: 8 July 2015
Accepted: 8 March 2016
Published: 16 March 2016
Tracking the level of the lava lake in Halema‘uma‘u Crater, at the summit of Kīlauea Volcano, Hawai’i, is an essential part of monitoring the ongoing eruption and forecasting potentially hazardous changes in activity. We describe a simple automated image processing routine that analyzes continuously-acquired thermal images of the lava lake and measures lava level. The method uses three image segmentation approaches, based on edge detection, short-term change analysis, and composite temperature thresholding, to identify and track the lake margin in the images. These relative measurements from the images are periodically calibrated with laser rangefinder measurements to produce real-time estimates of lake elevation. Continuous, automated tracking of the lava level has been an important tool used by the U.S. Geological Survey’s Hawaiian Volcano Observatory since 2012 in real-time operational monitoring of the volcano and its hazard potential.
KeywordsKilauea Lava lake Thermal camera Image processing Volcano monitoring
Thermal cameras at Halema‘uma‘u have proven effective for continuous monitoring of the lava lake because thermal imagery can “see” through thick volcanic fume in the Overlook crater which often obscures the view of the lake to normal webcams and the naked eye (Patrick et al. 2014). In this paper we present a simple image processing routine that performs customized analysis of incoming thermal images of the lava lake, producing automated measurements of lava level. This function runs in real-time in an operational volcano-monitoring environment at the Hawaiian Volcano Observatory (HVO) and aids in tracking activity levels at the summit vent.
The image processing techniques we present are applied to images from a stationary, continuously operating thermal camera on the Halema‘uma‘u Crater rim (Patrick et al. 2014). The camera is a Mikron Infrared (now Lumasense Technologies) M7500 8–14 micron camera using a lens with a horizontal field of view of 53°, operating at about 85 m above the Overlook crater rim (and typically 120–200 m line-of-sight distance from the lava lake surface). Images are 320 × 240 pixels in size. Although the images show calibrated temperatures (Fig. 1b), fluctuating volcanic fume between the camera and lava surface may produce large errors in the apparent temperature values (Patrick et al. 2014). For our purposes, approximate temperatures are adequate. The maximum measureable temperature of this camera model is 500 °C, which is adequate for monitoring the lava lake as the vast majority of the lake surface – consisting of large crustal plates – has apparent temperatures below that value.
Images are acquired every 5 s and transmitted to the observatory in real-time. Windows Scheduler runs the described function hourly, analyzing the preceding hour’s images. We used Matlab version 2012b with the Image Processing toolbox. The function takes about 6 min on the computer that is also running the image acquisition (a dual-core 2.6 GHz processor with 4 Gb RAM). This script to track lava level is one of a suite of Matlab scripts that comprise the thermal camera image acquisition scheme described by Patrick et al. (2014).
- 1)Edge detection: The margin of the lava lake has an abrupt temperature boundary with the back wall of the crater (Fig. 2a), so an edge detection routine is effective at identifying the lake outline. The function applies Matlab’s default edge detection function (Gonzalez et al. 2004) to each image in the two minute block. The script then builds a composite image (Patrick et al. 2010) of these individual edge detection images, in which the composite image shows the total extent of edge detection pixels through the two minute block, producing a binary image (Fig. 2b).
Maximum temperature thresholding: From each two minute block of data the function produces a single composite image of maximum temperature, which is constructed from the maximum value at each pixel position through that time span (Patrick et al. 2010). The composite image effectively shows the full spatial distribution of hot cracks on the lake surface during the time span, which provides higher contrast than a simple averaging of images. Simple thresholding at a high temperature (300 °C) is then applied to this composite image, producing another binary image (Fig. 2c).
Temporal standard deviation: The dynamic nature of the lava-lake surface is another aspect that can be leveraged to distinguish it from the surrounding static crater walls. The standard deviation of temperature values at each pixel position through the two minute block is recorded, and used to construct a new “change” image. The lava-lake surface is constantly migrating, with hot incandescent cracks commonly passing through any given point on the lake surface. The temporal standard deviation is very high over the lake surface relative to the crater walls. The image of temporal standard deviation is thresholded at 40 °C to produce a third binary image (Fig. 2d).
We combine the three binary images above to better isolate the lake from the remainder of the image, because the composited data are more consistently effective than any one criterion at detecting the lake surface owing to changing activity and shifting viewing conditions. The three binary images are summed to create a new composited image (Fig. 2e). A swath of image columns is extracted from the center of the combined image (Fig. 2f), which covers the northern margin of the lava lake. The pixel values in each row from this swath are summed to produce a single profile from the image. An empirical threshold is used to then distinguish the lava portion of this profile from the crater wall portion.
Comparison with manual image measurements and laser rangefinder data
The automated measurements of lava level in the images compare well with manual image measurements over longer periods as well. Over the course of a year (June 2013 to June 2014), we compared each hourly manual measurement with the automated measurement closest in time (Fig. 3b). The automated measurement error is less than or equal to one pixel (or roughly 1.1 m in elevation) 52 % of the time, and less than or equal to two pixels (roughly 2.2 m) 95 % of the time. The overall RMS error is 1.4 pixels (roughly 1.5 m).
Not all lava level changes, however, are due to pressure fluctuations. In general, short-term changes (e.g. seconds to hours) are normally attributable to shallow gas-related processes, such as rockfall-triggered spattering (Orr et al. 2013) and “gas pistoning” (Swanson et al. 1979; Patrick et al. 2011, 2014, 2016; Nadeau et al. 2015). Two episodes of intense spattering occurred on April 10–11, 2014, during deflation and lava level drop (Fig. 6), and at least one of these episodes was related to a small rockfall into the lake. The spattering episodes are visible as spikes in lava level, followed by a rapid drop in lava level. The spike itself is an artifact, in which spattering along the north margin of the lake causes the algorithm to incorrectly detect a rise in the entire lake. However, the drop in lava level following the spike is real, and presumably due to release of a large volume of gas during the intense spattering phase (Patrick et al. 2016). This characteristic lava level signal – a spike followed by sharp drop – is diagnostic of intense spattering episodes, which are often triggered by small rockfalls into the lake.
The automated measurement of lava level is an effective tool for operational monitoring at HVO, allowing HVO staff to quickly assess recent changes in lava level and compare these changes with other datasets. This approach uses several arbitrary thresholds (e.g. 300 °C for the maximum temperature composite image, Fig. 2c) based on rough trial and error, and these thresholds could be improved with a more rigorous optimization. We expect that other segmentation approaches could also be effective at isolating the lava lake from its surroundings for level measurement. Furthermore, this image processing routine could likely be performed with free, open-source software such as Python. Future work tracking the lava level with thermal imagery could include an additional camera and development of an automated photogrammetric routine to create a surface model of the Overlook crater, thereby measuring not only lava level but also changing vent crater geometry.
As webcams, thermal cameras and other types of imaging systems increase in number at volcano observatories worldwide (Spampinato et al. 2011; Kern et al. 2014), the need for automated analyses using the incoming images will likewise become greater. Automated image analysis not only provides a more efficient use of human resources, but also allows a continuous, uninterrupted watch on activity levels.
HVO staff members Kevan Kamibayashi, Loren Antolik and Lopaka Lee were vital to installing and maintaining the camera and acquisition system. We thank C. Kern, J. Major and an anonymous reviewer for comments which improved the manuscript. Funding for the thermal cameras was provided by the American Reinvestment and Recovery Act. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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