Klemen Zakšek

Sky-View Factor as a Relief Visualization Technique

Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.

Application of sky-view factor for the visualisation of historic landscape features in lidar-derived relief models

Aerial mapping and remote sensing takes another step forward with this method of modelling lidar data. The usual form of presentation, hill shade, uses a point source to show up surface features. Sky-view factor simulates diffuse light by computing how much of the sky is visible from each point. The result is a greatly improved visibility — as shown here by its use on a test site of known topography in Slovenia.

Solar radiation modelling

The Sun is the main energy source of the life on the Earth. Thus, solar radiation energy data and models are important for many areas of research and applications. Many parameters influence the amount of solar energy at a particular standing point of the Earths surface; therefore, many solar radiation models were produced in the last few years. Solar radiation energy depends mostly on incidence angle, which is defined by astronomical and surface parameters. Our solar radiation model is based on defining incidence angle by computing normal-to-the-surface tangent plane and direction of the Sun. If a part of the surface is in the shadow, it receives lesser energy than sunny areas. That is why shadow determination is an important part of the model. The sky is usually not completely clear, so meteorological parameters had to be integrated into the model. Meteorological model distinguishes among direct and diffuse Sun radiation. The model was tested and implemented for the whole Slovenia and it was also compared with previous studies. Case study surface data were calculated from the DEM with a 25m resolution. The astronomical data, which were required for virtual Sun motion simulation around the Earth, were derived from the astronomical almanac. Meteorological data were acquired from observed mean values on 24 meteorological stations between 1961 and 1990. All calculations were made for hours and decades and finally, the annual quasiglobal radiation energy, which is the energy received by inclined plane from the Sun in one year, was calculated from the sum of all the energies of all the decades.

Estimation of Dense Time Series of Urban Air Temperatures from Multitemporal Geostationary Satellite Data

Monitoring and nowcasting of urban air temperatures are of high interest for prediction of heat stress in cities. Routine observation is so far limited by the complex coupling between atmosphere and land surface in urban areas, which makes estimation more difficult. In this study, we have investigated the capability of multitemporal land surface temperatures (LSTs) from the geostationary Spinning Enhanced Visible Infra-Red Imager instrument for estimation of urban air temperatures. The results are very promising with root-mean-square errors (RMSEs) of 1.5-1.8 K for six stations in Hamburg and explained variances of 97%-98%. Both the annual and diurnal cycles were well represented by the empirical models and the use of multitemporal data substantially increased the model performance. Further, the model was run in a forecast mode without actual LST information. Here, the best predictors reached RMSEs of 1.9-2.4 K and R2 of 95%-97% for a 2-h forecast.

Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano

As specifically designed platforms are still unavailable at this point in time, lava flows are usually monitored remotely with the use of meteorological satellites. Generally, meteorological satellites have a low spatial resolution, which leads to uncertain results. This paper presents the first long term satellite monitoring of active lava flows on Stromboli volcano (August–November 2014) at high spatial resolution (160 m) and relatively high temporal resolution (~3 days). These data were retrieved by the small satellite Technology Experiment Carrier-1 (TET-1), which was developed and built by the German Aerospace Center (DLR). The satellite instrument is dedicated to high temperature event monitoring. The satellite observations were accompanied by field observations conducted by thermal cameras. These provided short time lava flow dynamics and validation for satellite data. TET-1 retrieved 27 datasets over Stromboli during its effusive activity. Using the radiant density approach, TET-1 data were used to calibrate the MODVOLC data and estimate the time averaged lava discharge rate. With a mean output rate of 0.87 m3/s during the three-month-long eruption, we estimate the total erupted volume to be 7.4 × 106 m3.

Constraining the uncertainties of volcano thermal anomaly monitoring using a Kalman filter technique

Abstract The activity status of a volcano can be evaluated from the remotely measured radiant power (RP). The measured RP contains noise due to reasons such as atmospheric effects and instrument characteristics. Here we first show how to estimate the uncertainty of each single RP measurement. To additionally reduce the temporal noise of the RP time series we apply a Kalman filter. The Kalman filter is able to recursively analyse an unevenly sampled time series. To validate our filtering scheme, we applied it to an eruption of Etna in 2002, as well as to the eruption of Nyamuragira in 2010. In the case of the Etna eruption, the denoised time series agrees well with in situ observations of a waxing and waning flow. For the case of Nyamuragira, the enhanced time series of RP shows more fluctuation probably due to cloud coverage. Thus, we propose a multiple instrument approach that increases the temporal resolution of the RP time series and reduces the associated noise.

An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas

The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities and impacts human health. The study of this effect for monitoring and mitigation purposes is crucial, but it is hampered by the lack of high spatiotemporal temperature data. This article presents the work undertaken for the implementation of an operational real-time module for monitoring 2 m air temperature (TA) at a spatial resolution of 1 km based on the Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This new module has been developed in the context of an operational system for monitoring the urban thermal environment. The initial evaluation of TA products against meteorological in situ data from 15 cities in Europe and North Africa yields that its accuracy in terms of Root Mean Square Error (RMSE) is 2.3 °C and Pearson’s correlation coefficient (Rho) is 0.95. The temperature information made available at and around cities can facilitate the assessment of the UHIs in real time but also the timely generation of relevant higher value products and services for energy demand and human health studies. The service is available at http://snf-652558.vm.okeanos.grnet.gr/treasure/portal/info.html.

Conclusion: recommendations and findings of the RED SEED working group

Abstract RED SEED stands for Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters, and combines stakeholders from the remote sensing, modelling and response communities with experience in tracking volcanic effusive events. The group first met during a three day-long workshop held in Clermont Ferrand (France) between 28 and 30 May 2013. During each day, presentations were given reviewing the state of the art in terms of (a) volcano hot spot detection and parameterization, (b) operational satellite-based hot spot detection systems, (c) lava flow modelling and (d) response protocols during effusive crises. At the end of each presentation set, the four groups retreated to discuss and report on requirements for a truly integrated and operational response that satisfactorily combines remote sensors, modellers and responders during an effusive crisis. The results of collating the final reports, and follow-up discussions that have been on-going since the workshop, are given here. We can reduce our discussions to four main findings. (1) Hot spot detection tools are operational and capable of providing effusive eruption onset notice within 15 min. (2) Spectral radiance metrics can also be provided with high degrees of confidence. However, if we are to achieve a truly global system, more local receiving stations need to be installed with hot spot detection and data processing modules running on-site and in real time. (3) Models are operational, but need real-time input of reliable time-averaged discharge rate data and regular updates of digital elevation models if they are to be effective; the latter can be provided by the radar/photogrammetry community. (4) Information needs to be provided in an agreed and standard format following an ensemble approach and using models that have been validated and recognized as trustworthy by the responding authorities. All of this requires a sophisticated and centralized data collection, distribution and reporting hub that is based on a philosophy of joint ownership and mutual trust. While the next chapter carries out an exercise to explore the viability of the last point, the detailed recommendations behind these findings are detailed here.

Stereoscopic Estimation of Volcanic Ash Cloud-Top Height from Two Geostationary Satellites

The characterization of volcanic ash clouds released into the atmosphere during explosive eruptions includes cloud height as a fundamental physical parameter. A novel application is proposed of a method based on parallax data acquired from two geostationary instruments for estimating ash cloud-top height (ACTH). An improved version of the method with a detailed discussion of height retrieval accuracy was applied to estimate ACTH from two datasets acquired by two satellites in favorable positions to fully exploit the parallax effect. A combination of MSG SEVIRI (HRV band; 1000 m nadir spatial resolution, 5 min temporal resolution) and Meteosat-7 MVIRI (VIS band, 2500 m nadir spatial resolution, 30 min temporal resolution) was implemented. Since MVIRI does not acquire data at exactly the same time as SEVIRI, a correction procedure enables compensation for wind advection in the atmosphere. The method was applied to the Mt. Etna, Sicily, Italy, eruption of 23 November 2013. The height of the volcanic cloud was tracked with a top height of ~8.5 km. The ash cloud estimate was applied to the visible channels to show the potential accuracy that will soon be achievable also in the infrared range using the next generation of multispectral imagers. The new constellation of geostationary meteorological satellites will enable full exploitation of this technique for continuous global ACTH monitoring.

Thermal monitoring of volcanic effusive activity: the uncertainties and outlier detection

Abstract Thermal observations of volcanic activity when the volcano is partially covered by clouds or observed under a wide-scan angle are often removed from further analyses. In the event of a volcanic crisis, such a reduced set of data is not adequate. Even when the observation conditions are favourable, the full observation set is still required to provide decision-makers with quality information about the data. Automatic quality estimation and outlier detection was not estimated in the past. We propose to analytically define the uncertainty for individual observations based on the measurement circumstances. To additionally reduce the temporal noise of the radiant power (RP) time series we apply a Kalman Filter (KF). The KF is able to recursively analyse an unevenly sampled time series. Based on some proposed rules, it can also detect outliers. We apply the proposed methodology to the 2008–09 Etna eruption monitored by MODIS (Moderate Resolution Imaging Spectroradiometer). The analysis of the results shows that the topography has a greater influence on RP than previously considered.