Coal fire quantification using ASTER, ETM and BIRD satellite instrument data

Coal fires cause severe environmental and economic problems. Although satellite remote sensing has been used successfully to detect coal fires, a satellite data based concept that can quantify the majority of the detected coal fires is still missing. Recently, the determination of fire radiative energy (FRE) has been introduced as a new remote sensing tool to quantify forest and grassland fires. This thesis tests the concept of remotely measured FRE, with a view to ascertaining its potential applicability to coal fires. It contains an investigation of a new generation of satellite instruments, including the operational Enhanced Thematic Mapper (ETM) instrument, the experimental Bi-spectral InfraRed Detection (BIRD) satellite sensor and the experimental Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which explores the potential of these sensors to determine coal fire radiative energy (CFRE). Additionally, based on the results of this analysis, the thesis presents a new, automated ETM and ASTER data based algorithm, adapted to quantify coal fires in semi-arid to arid regions in northern China. Field observations carried out in September 2002 and 2003 in three coalfields in northern China (the Wuda, Gulaben and Ruqigou coalfields) demonstrate that coal fire related, surface anomalies are significantly cooler than forest and grassland fires. The theoretical investigation of the ASTER, ETM and BIRD instruments outlines the fact that the thermal infrared (TIR) or mid infrared (MIR) spectral channels of the ASTER, ETM and BIRD instrument are particularly effective in registering these ‘warm spots’, whilst the short wave infrared (SWIR) spectral range is, however, insufficiently sensitive to be able to register spectral coal fire radiances. The commonly used bi-spectral fire quantification method (Dozier, 1981) can be applied to BIRD data in order to quantify relatively large and / or hot coal fires. However, existing FRE retrieval approaches fail to quantify coal fires via ASTER and ETM instrument data. In this thesis, a new CFRE retrieval method is presented, which links the fire and background TIR spectral radiances to the CFRE through an empirical relationship. This newly developed TIR method is applied to visually detected fire clusters from night-time ASTER data, and from both day- and night-time ETM data, taken from the three study coalfields in northern China. The ASTER and ETM CFRE values, calculated via the TIR method, are compared to CFRE estimates from BIRD data, calculated via the existing bi-spectral method. Despite the different spatial resolution and spectral properties of the ETM, ASTER and BIRD instruments, CFRE computed from ASTER, ETM and BIRD data show good correlations with one another. However, CFRE retrievals from daytime data appear to be very undependable to background temperature variations, while CFRE, estimated from night-time data, appears to be relatively stable. A comparison between night-time ETM-derived CFRE and the figures given by local mining authorities for total coal fire induced, coal loss estimates in the Wuda coalfield gives a clear indication that the overall dimension of the coal fire problematic can in fact be approximated via satellite data CFRE retrievals. It is thus expected that CFRE derived from night-time satellite data will become a crucial tool in obtaining reliable, quantitative information for coal fires. A multi-temporal comparison of CFRE retrievals from night-time BIRD and ETM data, covering the Ruqigou and Wuda coalfields, indicates that only major shifts or activity changes in coal fire induced, surface anomalies can be observed by means of these data. These results, which could only partially be verified by field observations, indicate that ETM or BIRD data can be used to monitor major changes in coal fire related, surface anomalies. These data however cannot entirely replace detailed field observations, especially in case of smaller and / or cooler coal fire related, surface anomalies

A review of geothermal mapping techniques using remotely sensed data

Exploiting geothermal (GT) resources requires first and foremost locating suitable areas for its development. Remote sensing offers a synoptic capability of covering large areas in real time and can cost effectively explore prospective geothermal sites not easily detectable using conventional survey methods, thus can aid in the prefeasibility stages of geothermal exploration. In this paper, we evaluate the techniques and approaches used in literature for the detection of prospective geothermal sites. Observations have indicated that, while thermal temperature anomalies detection have been applicable in areas of magmatic episodes and volcanic activity, poor resolution especially from space borne data is still a challenge. Consequently, thermal anomalies have been detected with some degree of success using airborne data, however, this is mostly in locations of known surface manifestations such as hot springs and fumaroles. The indirect identification of indicator minerals related to geothermal systems have been applied using multispectral and hyperspectral data in many studies. However, the effectiveness of the techniques relies on the sophistication and innovative digital image processing methods employed to sieve out relevant spectral information. The use of algorithms to estimate land surface temperature and heat fluxes are also applied to aid thermal anomaly detection, nevertheless, remote sensing techniques are still complementary to geologic, geophysical and geochemical survey methods. While not the first of its kind, this review is aimed at identifying new developments, with a focus on the trends and limitations intrinsic to the techniques and a look at current gaps and prospects for the future.Keywords: Geothermal, remote sensing, thermal anomalies, indicator minerals, multispectral, hyperspectra

Lineament mapping for groundwater exploration using remotely sensed imagery in a karst terrain : Rio Tanama and Rio de Arecibo basins in the northern karst of Puerto Rico

Remote sensing and advanced digital image processing techniques were developed and tested for delineating karst features important for the subterranean hydrology in the Tanamá River and Rio Grande de Arecibo catchments located in the North Coast Tertiary Basin of Puerto Rico, where groundwater contributes to base flow for surface water bodies which in itself is the main supply of drinking water. This aquifer region is a karst platform of carbonate rocks and clastic beds, thought to comprise a confined aquifer beneath and an unconfined aquifer. Products derived from ASTER, Landsat (ETM+ and TM), a NED DEM (30 m), and a LiDAR DEM (2 m) were analyzed in the interpretations of the karst flow system. In addition, field verification, VLF-EM, and previously published hydrologic data were analyzed to characterize fracturing and dissolution features on groundwater hydrology in the region. Remote sensing assessments show that Landsat PCA (incorporating thermal band,) ASTER PCA, LiDAR Hillshade were best at detecting “true” lineaments in this type of terrain. NDMI proved to be helpful in detecting moisture changes attributed to lineaments influencing the shallow hydrology in the karst. Geomorphic data agrees with lineaments as faulting and fracturing in addition to linear bedding control features. Sinkholes, springs and geotectonic evidence locations occur along and at the end of lineaments. Lineaments interpreted from LiDAR DEM data (Aspect, Hillshade) show regional geomorphotectonic evidence correlated to sharp river bends, hill alignment, and aspect trends

An Overview of Infrared Remote Sensing of Volcanic Activity

Volcanic activity consists of the transfer of heat from the interior of the Earth to the surface. The characteristics of the heat emitted relate directly to the geological processes underway and can be observed from space, using the thermal sensors present on many Earth-orbiting satellites. For over 50 years, scientists have utilised such sensors and are now able to determine the sort of volcanic activity being displayed without hazardous and costly field expeditions. This review will describe the theoretical basis of the discipline and then discuss the sensors available and the history of their use. Challenges and opportunities for future developments are then discussed

SPATIO-TEMPORAL ANOMALIES IN SURFACE BRIGHTNESS TEMPERATURE PRECEDING VOLCANO ERUPTIONS DETECTED BY THE LANDSAT-8 THERMAL INFRARED SENSOR (CASE STUDY: KARANGETANG VOLCANO)

Indonesia's geological as part of the “ring of fire” includes the consequence that community life could be affected by volcanic activity. The catastrophic incidence of volcanic eruptions in the last ten years has had a disastrous impact on human life. To overcome this problem, it is necessary to conduct research on the strengthening of the early warning system for volcanic eruptions utilising remote sensing technology.  This study analyses spatial and temporal anomalies of surface brightness temperature in the peak area of Karangetang volcano during the 2018-2019 eruption. Karangetang volcano is an active volcano located in North Sulawesi, with a magmatic eruption type that releases lava flow. We analyse the anomalies in the brightness temperature from channel-10 of the Landsat-8 TIRS (Thermal Infrared Scanner) time series during the period in question. The results of the research demonstrate that in the case of Karangetang Volcano the eruptions of 2018-2019 indicate increases in the surface brightness temperature of the crater region. As this volcano has many craters, the method is also very useful to establish in which crater the center of the eruption occurred

Role of Emissivity in Lava Flow ‘Distance-to-Run’ Estimates from Satellite-Based Volcano Monitoring

Remote sensing is an established technological solution for bridging critical gaps in volcanic hazard assessment and risk mitigation. The enormous amount of remote sensing data available today at a range of temporal and spatial resolutions can aid emergency management in volcanic crises by detecting and measuring high-temperature thermal anomalies and providing lava flow propagation forecasts. In such thermal estimates, an important role is played by emissivity—the efficiency with which a surface radiates its thermal energy at various wavelengths. Emissivity has a close relationship with land surface temperatures and radiant fluxes, and it impacts directly on the prediction of lava flow behavior, as mass flux estimates depend on measured radiant fluxes. Since emissivity is seldom measured and mostly assumed, we aimed to fill this gap in knowledge by carrying out a multi-stage experiment, combining laboratory-based Fourier transform infrared (FTIR) analyses, remote sensing data, and numerical modeling. We tested the capacity for reproducing emissivity from spaceborne observations using ASTER Global Emissivity Database (GED) while assessing the spatial heterogeneity of emissivity. Our laboratory-satellite emissivity values were used to establish a realistic land surface temperature from a high-resolution spaceborne payload (ETM+) to obtain an instant temperature⁻radiant flux and eruption rate results for the 2001 Mount Etna (Italy) eruption. Forward-modeling tests conducted on the 2001 ‘aa’ lava flow by means of the MAGFLOW Cellular Automata code produced differences of up to ~600 m in the simulated lava flow ‘distance-to-run’ for a range of emissivity values. Given the density and proximity of urban settlements on and around Mount Etna, these results may have significant implications for civil protection and urban planning applications

Satellite remote sensing for hydrothermal alteration minerals mapping of subtle geothermal system in unexplored aseismic environment

Mapping prospective geothermal (GT) resources and monitoring associated surface manifestations can be challenging and prohibitively expensive in subtle systems especially when using conventional survey methods. Remote sensing offers a synoptic and costeffective capability for identification of GT systems. The objective of this research is to refine and develop methods of identifying unconventional GT systems by evaluating the applicability of the ASTER, Landsat 8 and Hyperion satellite data for mapping hydrothermal alteration indicator minerals as proxy for detecting subtle GT targets in unexplored aseismic settings. The study area is Yankari Park in North Eastern Nigeria, characterized by the thermal springs; Wikki, Mawulgo, Gwana and Dimmil. Spectral Angle Mapper (SAM), Linear spectral Unmixing (LSU) and Mixture Tuned Matched Filtering (MTMF) were comparatively evaluated by using image derived spectra and corresponding library spectra for mapping pixel abundance of GT indicator minerals in a novel and efficient manner. The results indicated that employing image derived spectra from field validated and laboratory verified regions of interest as reference, gives more accurate results than using library spectra around known alteration zones remotely detectable on the imagery. The MTMF provided high performance subpixel target detection with an accuracy of 50-100% and 70-100% subpixel abundance for argillicphyllic- silicic and propylitic alteration mineral assemblages respectively, as compared to less than 10% for the same endmembers when using library spectra. The MTMF is thus best suited for mapping alterations associated with subtle GT systems than the less selective LSU. The per-pixel SAM was unsuitable for target detection of alteration indicators of interest with poor overall accuracy of 33.81% and 0.24 Kappa coefficient at 0.02 radian angle. Results of mapping thermally anomalous pixels do not conform to known locations of the thermal springs signifying the limitations of the current thermal sensors in mapping low temperature GT systems even at 60m spatial resolution. However, examining the spatial correlation of the anomaly areas with the major geologic structure systems from geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat insulating sedimentary formations. This study establishes the integrative applicability of Multispectral and Hyperspectral data for mapping subtle GT targets in unexplored regions using in-situ validated alteration mineral mapping and thermal anomaly detection. This has significant implication for the GT green energy industry as the developed methods and GT prospect map could aid the prefeasibility stage narrowing of targets for in-depth geophysical, geochemical, geothermometric and related surveys

Geothermal Reconnaissance: Remote Sensing and GIS Applications in the Investigation of Thermal Anomalies Near Trinidad, Colorado

The focus of this thesis is to explore remote sensing and GIS techniques used in the exploration of geothermal energy resources. A literature review investigated various methodologies, workflows, and algorithms used in geothermal reconnaissance. This knowledge was then implemented in a case study of regions in Colorado. Using GIS data and maps, a region showing high potential of geothermal resource presence was determined. Located near Trinidad, Colorado, this region of interest was then investigated through image analysis incorporating methods researched in the literature review. Anomalies were found and confirmed complications found in current practices in remote sensing geothermal resources. Thermal infrared (TIR) analysis is complicated with introduced thermal noise from insolation interactions with both surface and atmosphere, resulting in a masking of subsurface thermal anomalous readings. Further research is required to remove the effects of insolation to create an efficient geothermal reconnaissance workflow. This workflow would be improved with the incorporation of in situ spectroscopic analysis used in tandem with hyperspectral and high resolution shortwave infrared (SWIR) image analysis methodologies accounting for insolation and thermal inertia effects. Field work obtaining thermal and mineralogical spectroscopy of regions with discovered anomalies using GIS data and interpolated map surfaces will improve workflow. Incorporating field data covering a region of interest over time, observing seasonal changes in solar radiation and precipitation can improve landcover classification techniques and improve the location of geothermal resources