Introduction to this website

This website has, as its chief theme, the study of natural hazards.

As a senior lecturer in physical geography and natural hazards at Coventry University, my chief area of research (when not teaching) is using remote sensing techniques to monitor natural hazard phenomena (mainly volcanoes and earthquakes). Key natural hazards occurring globally will be automatically posted daily (via RSS feed from NASA’s Earth Observatory).

I hope you find the material herein useful and interesting. If you have any comments then please contact me.

Unfortunately, I can only make the teaching section open to relevant students. You’re only missing lecture material and reading lists but if you’re really interested, why not come and take a Geography degree at Coventry Uni!?

Read current post concerning earthquake clouds and also details concerning my recent research trip to the China University of Ming and Technology.

Visit to China University of Mining and Technology, Xuzhou, China

I was awarded funding from the Universities China Committee in London to visit Dr Qin Kai for research collaboration discussions. The visit went ahead over March and April 2014 and during the visit, I presented on my research into the monitoring of natural hazards via remote sensing. We were also able to discuss many potential research collaborations for the future, including with Professor Lixin Wu.



The official link from the university in China can be found here.

The first Landsat-8 thermal imagery of volcanic activity

Abstract of recently published paper

The Landsat-8 satellite of the Landsat Data Continuity Mission was launched by the National Aeronautics and Space Administration (NASA) in April 2013. Just weeks after it entered active service, its sensors observed activity at Paluweh Volcano, Indonesia.


Given that the image acquired was in the daytime, its shortwave infrared observations were contaminated with reflected solar radiation; however, those of the satellite’s Thermal Infrared Sensor (TIRS) show thermal emission from the volcano’s summit and flanks. These emissions detected in sensor’s band 10 (10.60–11.19 μm) have here been quantified in terms of radiant power, to confirm reports of the actual volcanic processes operating at the time of image acquisition, and to form an initial assessment of the TIRS in its volcanic observation capabilities. Data from band 11 have been neglected as its data have been shown to be unreliable at the time of writing. At the instant of image acquisition, the thermal emission of the volcano was found to be 345 MW. This value is shown to be on the same order of magnitude as similarly timed NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer thermal observations. Given its unique characteristics, the TIRS shows much potential for providing useful, detailed and accurate volcanic observations in the future.

Available at:

Earthquakes changing the character of the ionosphere?

Review of: He, L.M., Wu, L.X., De Santis, A., Liu, S.J. and Yang, Y. (2014). Is there a one-to-one correspondence between ionsopheric anomalies and large earthquakes along Longmenshan faults? Annales Geophysicae (accepted).

This is a very interesting and timely paper. Interesting as it both shows evidence of one potential precursor to a particular earthquake (Wenchuan, China, May 2008) and, in relation to another earthquake event, shows evidence against the presence of such precursors (Ya’an, China, April 2013). This suggests the paper will be completely balanced. The paper is timely as, despite having been around for a long time, wavelets are now increasingly being used to quantify geophysical phenomena that provide a non-stationary signal (one case in point: Murphy et al. 2013, concerning periodic episodes of thermal emissions from various volcanoes).

The authors propose precursors which relate to phenomena in the ionosphere. This part of the atmosphere is influenced by many factors including solar and geomagnetic phenomena and as such, these ‘normal’ signals need to be removed from those which might be pre-seismic and to do this, a method of removing the background signal is presented (first discussed in He et al. 2012). The dataset analysed is from the global navigation satellite system and consists of the total electron content (TEC) of the ionosphere, which reflects irregularities of the ionosphere. The TEC signal was initially smoothed, so as to remove diurnal and seasonal patterns. Two wavelet methods were then applied to the dataset: the analytic wavelet transform (AWT), to detect variability in the TEC, and the cross-wavelet transform (XWT), to determine whether the changes were associated with the impending earthquake. The XWT is most pertinent here as it is applied to determine any ‘mutual relationship of two time series in the time-frequency domain’ (pg. 8); these two timeseries here are the ionospheric signal and any geophysical indices.

In relation to the Wenchuan earthquake, the solar activity was found to be low and as such, did not pollute any potentially seismic signals. What was found were signals over the region of the subsequent earthquake that were unusual, not happening in previous years or anywhere else at the same latitude; indeed they appear to be over the location of the impending earthquake (see below).


In relation the Ya’an earthquake however, the period was associated with high solar activity which had to be removed from the signal. Even applying these methods, no ionospheric anomalies could be found in the month prior to the earthquake. Interestingly, the authors note that had they not applied their own method of noise removal, they might well have concluded that these large signals were associated with the impending earthquake.

So evidently, the authors acknowledge that they do not know everything about these observations and that they cannot predict earthquakes on their basis, but what they have shown is a line of enquiry which should be explored more thoroughly. What you may be wondering however, is: how can an earthquake possibly cause changes in the ionosphere? Well the theory postulated is that radon emanations appear to increase prior to an earthquake (this is a widely proven observations see e.g. Choubey et al. 2004). This gas could ionise the lower atmosphere and as such, affect the electrical characteristics of the entire atmospheric column. Electromagnetic energy generated on rock compression (noted in Ouzounov and Freund, 2004) is also claimed as having a potential influence. So why then do some earthquakes seemingly produce such effects and other don’t? Well it might all be down to the size of the forthcoming earthquake (a greater magnitude earthquake releasing more energy), or the formation of the lithosphere which could potentially augment, or shield, some of the effects from the atmosphere.

Overall, this is an interesting study and it is great to see the presentation of both positive and negative results.  I guess like most things in this field, more data is required to prove, categorically, the presence (or otherwise) of such phenomena. This work is a good step in that direction. In terms of methods applied too, this work could potentially form a reference piece for the application of wavelets. It explains their function much more comprehensively than most other studies do and, given that large proportions of scientific research focus on attempting to find correlations between datasets, the presentation of the methods that can be applied to do that  is very useful. Let’s hope more research continues in the field, using techniques such as these, to confirm or refute the presence of earthquake precursors convincingly and/or, like this paper, to explain apparent anomalies.


Choubey, V.M., Mukherjee, P.K. and Ramola, R.C. (2004). Radon variation in spring water before and after Chamoli earthquake, Garhwal Himalaya, India. In: Proceeding of 11th International Congress of the International Radiation Protection Association. Madrid, Spain, 23-28 May.

He, L., Wu, L., Pulinets, S., Liu, S. and Yang, F. (2012). A nonlinear background removal method for seismo-ionospheric anomaly analysis under a complex solar activity scenario: A case study of the M9.0 Tohoku earthquake. Advances in Space Research, 50, 211–220.

Murphy, S.W., Wright, R., Oppenheimer, C. and Souza Filho, C.R., 2013, MODIS and ASTER synergy for characterizing thermal volcanic activity, Remote Sensing of Environment, 131, 195–205.

Ouzounov, D. and Freund, F. (2004). Mid-infrared emission prior to strong earthquakes analyzed by remote sensing data. Adv Space Res, 33, 268–273.

Do unusual clouds precede earthquakes…?

EQCloud source_Guanmeng and Jie (2013)

As I’ve stated before, I am no advocate of the existence of precursory earthquake signals which might allow for prediction – particularly signals which apply globally. However, I remain open to the idea that given their continental scale, earthquakes are quite likely associated with other phenomena which might potentially be reflected in certain observations (be it surface temperature and/or latent heat fluxes [e.g. Ouzounov and Freund, 2004; Qin et al. 2009; Qin et al. 2012], unusual gas emissions [Liperovsky et al. 2008] or changes in the electronic characteristics of the upper atmosphere/ionosphere [Pulinets and Ouzounov, 2011]). In many cases however, the analyses conducted in an attempt to show such relations have been inadequate (see: Blackett et al. 2011).

One recent piece of research published in Natural Hazards and Earth System Sciences is that by Guangmeng and Jie (2013): Three attempts of earthquake prediction with satellite cloud images (v.13, 91-95). This paper claims three prediction attempts which later came true, based on geostationary satellite data (although nowhere am I able to find precisely which satellites were used). In the first case, a stationary (for 8 hours) linear cloud is noted over the central-eastern regions of Italy. I concur that it is unusual for a cloud not to move in 8 hours when all others around are, but I don’t claim to be a meteorologist so this might not be all that unusual (comments would be welcome!) Based on this observation, the authors sent a ‘prediction’ to a couple of reputable researchers on 23 April 2012, suggesting that a magnitude 5-6 earthquake would occur in the region within 30 days. On 20 March 2012, a magnitude 6 earthquake struck. The second and third cases relate to a cloud which appeared over Iranian fault lines. In the first case, it remained linear, and stationary, for nearly a day in February 2012. On this basis, a prediction was sent to another reputable source with a rough estimation of 10-30 days; seven days later an earthquake struck, although a little weaker and further from the predicted locale. In the second case, a similar cloud pattern was isolated and a warning for a magnitude 5-6 earthquake was issued; an earthquake subsequently occurred.

On the face of it, there seems to be some weight in what the authors are claiming, and the provision of 3 cases is certainly compelling. However, there are a few things which I am concerned about. Firstly, no justification is offered as to how possible locations and magnitudes are derived purely from the cloud observations. Precise magnitudes and locations are suggested in the predictions (and in the main, these are only out by a small amount), but their derivation is not explained. I want more detail discussing this! Secondly, although some attempt is made to show the likelihood of the earthquakes occurring by chance following the observations, I think more robust examinations of this are required. I could predict and earthquake tomorrow on the basis of some random phenomenon and there is a chance it could happen and be completely unrelated. Finally, I can’t stop wondering how many predictions might have been made that did not come true. This issue is entirely missing from the paper.

These authors could well be onto something big but more work is needed to show a greater association between the cloud observations and the subsequent earthquakes. Suggestions I can think of include: getting a meteorologist on-board to confirm the clouds, as observed, are unusual; examining the regions of interest over a much longer time scale (perhaps these clouds are a regular occurrence in this region due to topographical, or other, factors), and providing a concrete, scientifically scrutinised mechanism (a mechanism is discussed but this is arguably just as controversial at the moment).


Blackett, M., Wooster, M. J and Malamud, B. (2011). Exploring Claims of Land Surface Temperature Precursors to the 2001 Gujarat Earthquake. Geophysical Research Letters38, L15303, 7 PP., 2011, doi:10.1029/2011GL048282.

Guangmeng, G. and Jie, Y. (2013). Three attempts of earthquake prediction with satellite cloud images. Natural Hazards Earth System Science, 13, 91-95.

Liperovsky, V. A., Meister, C. V., Liperovskaya, E. V. and Bogdanov, V. V. (2008). On the generation of electric field and infrared radiation in aerosol clouds due to radon emanation in the atmosphere before earthquakes. Natural Hazards Earth System Science, 8, 1199-1205.

Ouzounov, D. and Freund, F. (2004). Mid-infrared emission prior to strong earthquakes analyzed by remote sensing data. Advances in Space Research, 33, 268–273.

Pulinets, S. and Ouzounov, F. (2011). Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model – An unified concept for earthquake precursors validation. Journal of Asian Earth Sciences, 41, 371–382.

Qin, K., Wu,L., De Santis,A. and Cianchini, G. (2012). Preliminary analysis of surface temperature anomalies that preceded the two major Emilia 2012 earthquakes (Italy). Annals of Geophysics, 55, DOI: 10.4401/ag-6123

Qin, K., Guo, G. and Wu, L. (2009). Surface latent heat flux anomalies preceding inland earthquakes in China. Earthquake Science, 22, 555-562.

Daily Global Natural Hazards Update

UK’s current flood warnings


NASA’s Earth Observatory

ScienceDaily: Natural Disaster News

  • Predicting drift of floating pumice 'islands' can benefit shipping 23/04/2014
    A new technique will aid in predicting the dispersal and drift patterns of large floating ‘islands’ of pumice created by volcanic eruptions at sea. Known as pumice rafts, these large mobile accumulations of pumice fragments can spread to affect a considerable area of the ocean, damaging vessels and disrupting shipping routes for months or even years. The abi […]

The Philippine Typhoon (Haiyan). Updates.

“Earthquake lights” – some interesting findings re-presented in ‘Nature’


A new study has been published which has cataloged the occurrence of earthquake lights (Thériault et al. 2014). These workers obtained details of the most reliable accounts of such phenomena extending back to 1600. Of 65 of the phenomena documented, 56 occurred along rift zones (ancient or active) and 63 occurred on faultlines where the rupture was near vertical (as opposed to of a shallower angle) (Witze, 2014).

Explanations offered to explain such phenomena are diverse and, let’s face it, controversial, but have yet to be completely ruled out. Indeed, in the words of John Ebel, a geophysicist at Boston College in Massachusetts, the mechanisms posed for their presence “makes enough sense, but that doesn’t mean that it’s right” (Witze, 2014).

So it’s early days before we can categorically prove or disprove their existence, but work continues and hopefully one day, categorical evidence either way will be presented.

Thériault, St‐Laurent, Freund and Derr (2014). Prevalence of Earthquake Lights Associated with Rift Environments. Sesimological Research Letters, doi: 10.1785/0220130059.

Witze (2014). Earthquake lights linked to rift zones. Nature, doi: 10.1038/nature.2014.14455

Remotely sensing the characteristics of erupting lava surfaces

So okay, this paper is not particularly up to date (Wright et al., 2011) but its findings are very important in the field of volcanic remote sensing. What Wright et al. (2011) show is that it is possible to determine some important aspects of volcanic activity (i.e. the eruption style and lava composition) by statistically examining the temperature distributions evident at the surface. Importantly, they show that individual pixel temperature values are not necessarily a reliable indication of lava temperature or composition, although temperature maxima are. Temperature maxima for mafic lavas are found to be around 200 K higher than for intermediate or felsic


In terms of statistical examination, what these workers did was to take EO-1 Hyperion imagery of 13 volcanoes and determine the proportion of the visualised surface that fell into discrete temperature bins (following atmospheric and surface emissivity corrections). They found that a bimodal distribution of pixel temperatures is characteristic of persistent effusions i.e. lava fountains and/or channel fed a’a lava flows. In contrast, a log-normal distribution is displayed by eruption styles which are either spatially limited and/or episodic (i.e. lava domes, lava lakes and pahoehoe flows).

The implications of these findings are two-fold. Firstly, it allows the remote assessment of the conditions of the surface at active volcanoes and secondly, it shows the potential for determining the conditions of extra-terrestrial volcanoes, the prospect of visiting which is currently remote.

For more details, see the paper:

Wright, R., Glaze, L. and Baloga, S. (2011). Constraints on determining the eruption style and composition of terrestrial lavas from space. Geology, 39, 1127–1130. doi:10.1130/G32341.1.

Research review: An update on earthquake precursors?

From Qin et al 2013

From Qin et al 2013

Article: Qin, Wu, Zheng and Liu (2013). A Deviation-Time-Space-Thermal (DTS-T) Method for Global Earth Observation System of Systems (GEOSS)-Based Earthquake Anomaly Recognition: Criterions and Quantify Indices, Remote Sensing5, 5143-5151; doi:10.3390/rs5105143

It will not be unknown that I have been skeptical regarding the presence (or otherwise) of precursors to earthquakes (i.e. Blackett et al. 2012). However, this recent work (Qin et al., 2013) shows some compelling evidence for their presence, based on application to a number of earthquakes globally. In this work, the researchers have developed a Deviation-Time-Space-Thermal algorithm which should isolate thermally-related anomalies from within a time series of remotely sensed data. The time series of data they use is the NCEP/NCAR Reanalysis dataset, with data analysed from 1979 to present. For confirmation purposes, they also use FY-VISSR and AVHRR thermal data.

The basis of their anomaly detection is to determine the ‘normal’ surface temperature conditions over the period of interest. They then are able to subtract this from data for a particular day to determine whether or not it is anomalous. They provide evidence, for example, of a clear anomaly, a thermal enhancement, one week prior to the Wenchuan earthquake of 2008 (see image above from the paper). However, they do not just examine the land surface temperature data for patterns but also parameters such as the outgoing long wave radiation, the diurnal temperature range and the surface latent heat flux. For a potential anomaly to actually be anomalous, they use the criterion that the parameter must be 1.5 times the standard deviation. The anomaly noted must also bear some resemblance to the structural geology of the region in question. The intensity of the anomaly, as compared with the average conditions and in relation to the location of the earthquake and associated faultlines, is ingeniously quantified using a derived index value.

By examining their index value, they identify what appear to be thermal anomalies associated with a number of earthquake – particularly focusing on a 2010 magnitude 7 Chinese earthquake, for which anomalies in temperature, outgoing longwave radiation and diurnal temperature range are isolated.

It is suggested that by automating such calculations, these sorts of methods might be applied in the search for earthquake precursors.




NEW: Research reviews

On a (fairly) regular basis, I plan to publicise cutting edge research in the fields of natural hazards and/or thermal remote sensing. Keep posted for reviews of new papers in these fields. Some papers reviewed will be actual research, others will be papers perhaps summarising trends or developments in the field. These will be listed below:


Wright, R., Glaze, L. and Baloga, S. (2011). Constraints on determining the eruption style and composition of terrestrial lavas from space. Geology39, 1127–1130. doi:10.1130/G32341.1.

Qin, Wu, Zheng and Liu (2013). A Deviation-Time-Space-Thermal (DTS-T) Method for Global Earth Observation System of Systems (GEOSS)-Based Earthquake Anomaly Recognition: Criterions and Quantify Indices, Remote Sensing5, 5143-5151; doi:10.3390/rs5105143.