Assessing Performance of the RSTVOLC Multi-Temporal Algorithm in Detecting Subtle Hot Spots at Oldoinyo Lengai (Tanzania, Africa) for Comparison with MODLEN

Abstract

Abstract:The identification of subtle thermal anomalies (i.e., of low-temperature and/or spatial extent) at volcanoes by satellite is of great interest for scientists, especially because minor changes in surface temperature might reveal an unrest phase or impending activity. A good test case for assessing the sensitivity level of satellite-based methods is to study the thermal activity of Oldoinyo Lengai (OL) (Africa, Tanzania), which is the only volcano on Earth emitting natrocarbonatite lavas at a lower temperature (i.e., in the range 500-600 • C) than usual magmatic surfaces. In this work, we assess the potential of the RST VOLC multi-temporal algorithm in detecting subtle hot spots at OL for comparison with MODLEN: A thermal anomaly detection method tailored to OL local conditions, by using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Our results investigating the eruptive events of 2000-2008 using RST VOLC reveal the occurrence of several undocumented thermal activities of OL, and may successfully integrate MODLEN observations. In spite of some known limitations strongly affecting the identification of volcanic thermal anomalies from space (e.g., cloud cover; occurrence of short-lived events), this work demonstrates that RST VOLC may provide a very important contribution for monitoring the OL, identifying subtle hot spots showing values of the radiant flux even around 1 MW.

The Contribution of Multi-Sensor Infrared Satellite Observations to Monitor Mt. Etna (Italy) Activity during May to August 2016

“…The MIR−TIR (x, y, t) index is used jointly with the previous one for minimizing spurious effects associated with non-volcanological signal fluctuations [28]. RST VOLC , combining those indices, is capable of guaranteeing an efficient identification of volcanic thermal anomalies (a cloud-masking procedure is used in the daytime before running the algorithm) under different observational conditions (e.g., [12,18,[41][42][43]).…”

Implementation of Robust Satellite Techniques for Volcanoes on ASTER Data under the Google Earth Engine Platform

“…In addition, RASTer could be profitably used to assess information from systems monitoring volcanoes in near real time (using satellite data at lower spatial resolution), apart from areas (e.g., equatorial zones) where the scarcity and the bad quality of ASTER images do not enable the full RASTer implementation. Among those systems, the ones sharing the same RST-based approach, applied to data from polar (e.g., [2,14,30]) and geostationary (e.g., [53]) meteorological satellite sensors, will benefit from a RASTer-based validation and training. The RASTer contribution to detect and map lava flows and other thermal features related to volcanic activity is particularly relevant, considering that AVA products are no longer available (since 2017), the NHI usage on ASTER data is limited to 2000-2008 [54] and that other recent systems such as the ASTER Volcanic Thermal Output Database (AVTOD; [55]) operate over specific regions of interest and not globally.…”

“…The AVA database, whose data and products are disseminated in a common format (e.g., HDF, TIFF, KML) through the website http://ava.jpl.nasa.gov was continuously updated until late 2017 ( [18]). Information from this ASTER-based system was also used to assess thermal anomalies flagged by algorithms running on high temporal resolution satellite data (e.g., [30]). In this paper, we compare RASTer to AVA detections by quantifying differences in detecting and mapping thermal anomalies through ASTER TIR data over three different volcanic areas (see next section).…”

A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data

“…Figure 15 shows the NHI map from Sentinel 2 MSI data of that day, showing the identification of several thermal anomalies on both flanks and slopes of this high-risk volcano, which were generated by a number of well-documented anthropogenic fires burning several hectares of vegetation (e.g., [77]). The use of a spatial filter analyzing distance of detected hotspot pixels from summit craters could then enable a better discrimination of volcanic thermal anomalies from bush fires, especially in remote areas where validation sources usually lack (e.g., [90]). On the other hand, Figure 15 confirms that NHI might potentially be used for mapping thermal anomalies from different sources (e.g., fires; gas flares), although further and more accurate investigations have to be performed to assess this potential, which is out of the scope of this study.…”