Modern methods of geomonitoring in post-mining processes

The long and often undefined period in the post-mining phase challenges geomonitoring with its various responsibilities, like the detection of environmental changes, ground movements and water management. Modern methods of geo- and environmental monitoring observe numerous aspects of these using active and passive sensors with multispectral, hyperspectral and microwave sensors. These offer diverse spatial dimensions and spatiotemporal resolutions from satellites, unmanned aerial vehicles (UAVs) and in situ sensors. Data integration, together with on-site knowledge and experience, provides the basis for comprehensive modeling, which improves the understanding of the environmental changes and their related causes, and is an essential step toward public acceptance. In this article, we aim to give a brief overview of various geo- and environmental monitoring methods. Geomonitoring in post mining Mining is subject to a specific life cycle. It begins with the granting of rights and concessions, followed by the exploration and exploitation stage, and ends with mine closure and post-mining processes (Goerke-Mallet et al., 2016). Mining processes have a strong impact on the surrounding environment. Therefore, continuous monitoring is important to detect changes and deterioration. Monitoring and protection of the environment in mining areas is a complex issue that touches many environmental factors. First, there is water management: mine drainage affects water quality in the area. Soil removal or deposit may lead to ground instabilities, subsidence or uplift, gas emissions, or changes in vegetation (Goerke-Mallet et al., 2016). Geomonitoring in post-mining areas requires an integrated spatial monitoring concept, which is based on project data and common research techniques, which should be collected, evaluated and implemented (Rudolph, 2020; Rudolph et al., 2020).The data used for geomonitoring in postmining processes come from a variety of sources; starting from data collected during mining exploitation, for example, mining or geological maps, data coming from space missions, both including multispectral and hyperspectral sensors, photo documentation coming from UAV or plane flights, and data coming from in situ sensors. Techniques such as geophysics, monitoring wells and geodetic networks above and below the ground are also valuable sources of data. Each of these provide the basis for understanding post-mining processes. This article is intended to provide an overview of modern geomonitoring possibilities, which provide observations of important postmining objects from space, air, and down to the surface and subsurface. Relating to observations from multisensors and multiscales, we review the data integration in a comprehensive framework. Satellite remote sensing Remote sensing is a method of obtaining data on processes, phenomena and objects from a long distance (remotely) by means of high-tech sensors (Wiggenhagen and Steensen, 2021). These sensors are mounted on terrestrial stations (terrestrial hyperspectral cameras), drones, aircraft and satellites. In space, satellites of various space missions orbit the Earth and regularly scan the Earth’s surface with the help of sensors. Developments in space technology have led to better resolution of data from space missions, and data are increasingly being made available through web portals such as those of the U.S. National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The ESA’s Copernicus program relies on a fleet of Sentinel satellites, each with its own mission. For the observation of post-mining processes, two missions are worth mentioning: Sentinel 1, which monitors Earth’s movements, and Sentinel 2, which monitors changes in the use of Earth, and allows observation of vegetation. Optical remote sensing Optical remote sensing covers the characteristics of electromagnetic waves such as visible range, near infrared and thermal infrared. This method relies on the reflection of radiation emitted by the Sun from objects. In this method, sensors on the satellite record the radiation returned by objects on the Earth’s surface (such as vegetation, buildings or water surfaces). Due to cloud cover, not all images taken are used. An important advantage in the use of satellite data is the large observation area. The obtained data is converted into information that is used to analyze and interpret phenomena on the Earth’s surface. Figure 1 An NDVI calculated from the visible and nearinfrared light reflected by vegetation. Healthy vegetation (left) and unhealthy vegetation (right). The numbers represent NDVI values. (Source: Simmon, 2000) Figure 2 Satellite images from 1991 through 2021 of the closed coal mine Prosper-Haniel. (Data from project Digital Twin, THGA) Monitoring of post-mining activities is possible using remote sensing methods. This allows the initiation of preventive measures in the future (Kretschmann and Nguyen, 2020). One of the aspects of geomonitoring of post-mining processes is the observation of vegetation changes through the calculation of vegetation indices. One of them is the normalized difference vegetation index (NDVI) (Rouse et al., 1973), which obtains information on the health of vegetation by applying calculations using surface reflections in the red and near-infrared channels. This index takes advantage of the property that the spectrum of vegetation vitality reflections differs significantly in the red and near-infrared wavelength ranges. There is a property that healthy vegetation absorbs almost completely the red part of sunlight (it is needed for photosynthesis) and reflects a very large part of incident solar energy in the near infrared. In contrast, nonliving vegetation shows a different reflective behavior (Fig. 1). This characteristic of healthy vegetation has been mathematically formulated using the NDVI index (Simmon, 2000): where means near-infrared wavelength (from about 800 nm to 900 nm) and means red wavelength (from about 600 nm to 700 nm). The individual wavelengths depend on the satellite in question and the sensor used. NDVI takes values from −1 to 1. The waters show values close to −1, the value of 0 means that there is no vegetation. The healthier the vegetation, the higher the value of NDVI becomes. There are examples in the world literature of the use of these vegetation indices to monitor vegetation changes in post-mining areas in: China (Ma et al., 2017), the Czech Republic (Halounová et al., 2005; Matejicek and Kopackova, 2010), Germany (Padmanaban et al., 2017), Poland (Buczynska and Blachowski, 2021), Turkey (Erener, 2011; Koruyan et al., 2012) and the United States (Raval et al., 2014). The above case studies using individual vegetation indices in the calculations indicate the validity of using vegetation indices in the geomonitoring of post-mining processes. An important aspect is the spatiotemporal analysis of satellite images (Fig. 2) and selected vegetation indicators, thanks to which it is possible to observe changes in a given area. Collecting data from a given time interval and then presenting satellite date as well as calculated vegetation indicates allows for the detection of areas where a change in vegetation has been made, as well as the detection of wetlands in mining areas. An example of a spatiotemporal analysis in the Ruhr area is an article by Padmanaban et al. (2017) who, using NDVI, identified two areas where adverse vegetation changes and wetland formation occurred in the Kirchheller Heide area. Observation of vegetation condition using remote sensing methods is one of the aspects of post-mining geomonitoring. Remote sensing methods allow for compiling data for large areas, which is an undoubted advantage of this method. Before starting the study, it was necessary to get acquainted with the characteristics of the output data, in particular: spatial and spectral resolution. Satellite data from NASA’s Landsat 7, Landsat 8 and ESA’s Sentinel 2 space missions have the best characteristics in terms of spatial resolution and spectral ranges, which are used to calculate vegetation indices. Figure 3 Differential interferogram for a mining-induced subsidence from Sentinel-1 data (2015-09.04-2015.11.15). Radar remote sensing Radar is the abbreviation for “radio detection and ranging” and uses the principle of echolocation, in which the echoes of a transmitted signal are measured. The signal carrier is electromagnetic waves in the 300-1 GHz microwave range, the frequency range of which is divided into different radar bands. An active, imaging radar system is mounted on an aircraft or satellite, which moves along a straight flight path at a height above a reference surface. Optical satellite remote sensing collects Earth observation data typically from reflected sunlight, in the relatively short wavelength range from visible light to the thermal infrared. Therefore, traditional optical remote sensing is severely dependent on atmospheric effects such as cloud cover, which limits the ability to acquire information from the Earth’s surface. Radar systems, in comparison with optical remote sensing systems, are independent of the time of day because they have their own radiation source. Radar remote sensing uses the microwave range, which makes it possible to penetrate the atmosphere. This enables radar recordings to be made even when the sky is cloudy. The radar remote sensing method is a synthetic aperture radar (SAR) system. Such radar systems mainly provide two pieces of information: the backscatter intensity and the phase position. The intensity of the backscattered radar radiation depends on the physical features of the object surfaces, such as roughness and geometry, and the material properties, such as water content and dielectric properties. This is used to detect water reservoirs (Hahmann et al., 2008) and retrieve soil moisture data (Esch et al., 2018). Figure 4 Water mask extracted from Sentinel-1 data. The phase information enables analysis about the length of the propagation paths of the radar signals, which is used for the detection of ground movements or extraction of the digital elevation model. These can be done by using SAR interferometry (InSAR), which calculates phase differences, the so-called interferograms, from two SAR images. The phase differences result from the recording geometry and the slightly different orbit positions of the satellite at the recording times, and on the other from the object heights of the terrain and object surface and their deformation. Furthermore, atmospheric effects trigger additional signal delays. The remaining part, the measurement noise, is described by term noise. Methods like differential SAR-interferometry are used to calculate the deformation, which uses at least two SAR data sets acquired at different times over the same area, and the deformationrelated phase component is calculated from the interferogram. Figure 3 shows an example of a (wrapped) differential interferogram of a mining-induced subsidence. The map of interferogram (phase shift), which is depicted with a repeating color scale, shows displacement in line of sight between the two used acquisitions. Figure 5 A thermal photo (left) and photo in natural colors (right) in the Kircheller Heide. (Data from project Digital Twin; made by Benjamin Haske and Bodo Bernsdorf, both of THGA) Toward the better solution of the decorrelations (noisy area in Fig. 3) time series-based methods, such as persistent scatter interferometry (PSI) and small baseline subset (SBAS) are increasingly used for monitoring ground movement. While PSI determines displacement time series only from the “highquality” backscatters with long-term stable and strong signals, which are usually only available in urban areas due to the double-bounce effect of buildings, SBAS uses the network of highly coherent interferograms to overcome the influences of decorrelation, also in rural areas. In such areas, just like most of the mining and postmining areas, the dominate so-called distributed scatters, are used in the new developments combined with PSI in order to determine highquality results for ground movement in large areas. An abstract explanation of the measuring principle of these methods can be found in Crosetto et al. (2016). A comprehensive investigation on opportunities and limits of radar interferometry for ground movements due to mine water rise in an abandoned coal mining areas is provided in John (2021). Radar polarization refers to the polarization state of the waves emitted by the radar and the echoes received. In radar remote sensing, the polarization state of the wave changes because the waves interact with the medium and target during the propagation process. These changes therefore indicate the information of the medium and the target. Polarization interferometric SAR (PolSAR) uses the characteristics of coherence and interferometric phase observations that vary with polarization, enabling interferometric SAR observations to achieve target elevation acquisition. It also has the ability to distinguish between different scattering mechanisms by polarimetric SAR to identify targets such as forest, rocks, water bodies (Fig.4) and bare land (Wang et al., 2019). Such methods are also used for wide-area monitoring in agriculture and land use, which are also monitoring objects in post-mining regions. All in all, radar remote sensing plays a major role during the post-mining phase with its capabilities in environmental monitoring and ground movement detection. Unmanned aerial vehicles UAVs have become an increasingly popular type of data acquisition. First, they are characterized by high accuracy of the obtained results. Second, data collection time is shorter compared to satellite imagery, where acquisition time takes several days. UAVs are increasingly used in monitoring and surveying mining areas. Data collection by UAV is a fast but equally safe method. However, it is worth remembering that apart from its advantages, this method of monitoring also has its drawbacks. First of all, it depends on the prevailing weather conditions (precipitation, temperature or wind). In addition, national and European Union regulations limit the areas in which UAVs can be used. Similar with aircraft and satellites, it is possible to mount sensors on UAVs. Passive sensors based on electromagnetic radiation are digital cameras. Active sensors, which emit electromagnetic waves toward a ground target and then collect information from the reflected target are, for example, LiDAR (light detection and ranging). Various types of sensors are used to monitor post-mining processes. These include digital cameras, multispectral cameras, hyperspectral cameras, thermal imaging cameras, gas decoders and LiDAR sensors (Ren et al., 2019). Depending on the sensor equipment, they enable the preparation of images (an orthophoto map) and thermal analyses (Fig. 5) (Bernsdorf et al., 2020; Bernsdorf, Formaniuk et al., 2021; Bernsdorf, Rudolph et al., 2021). Figure 6 TELID sensors for soil moisture measurements in Project C2M2, THGA. UAVs show better accuracy than satellite images. Multispectral cameras placed on UAVs also make it possible to calculate vegetation indices. Thanks to the use of UAV flights, it is possible to visualize data in a two-dimensional as well as three-dimensional space, which allows the presentation of the cinematography area in the form of a digital terrain model and the integration into computer-aided design (CAD) and building information model (BIM) environments. Software for airstrip preparation is under continuous development. On the market, the most commonly used commercial programs are Agisoft Metashape, Pix4d and Drone2Map. Therefore, UAVs are used to verify and better understand phenomena in a limited area. In situ measurements During monitoring, in situ measurements are integral to describe ongoing natural processes, since they are conducted at discrete locations at a given time. Depending on the type and purpose of the in situ sensor used and, for example, its spacing, various observations can be taken into account to evaluate phenomena in the environment. By contrast, data are gathered remotely, typically in nondiscrete ways based on indirect observations. For optical satellite remote sensing and UAVs, images are recorded during the day from reflected sunlight. From the collected information in various frequencies, the data of interest are calculated as derived quantity. Instead of measuring area-wide information, the in situ measurements mostly provide point-related measurement results, which are very important for geomonitoring of environmental changes, including during postmining. In order to derive information on the vegetation vitality and stress, through indices like NDVI, local sampling is a vital part in the validation process. Furthermore, it needs to be taken into consideration that only the top layer of vegetation can be detected remotely. Sublayers down to the ground level are hidden to the Nadir imaging and require additional local input from site inspection to complement the dataset. Terrestrial measurements from surveying results of geodesy and mine surveying are of particular importance for radar interferometric determination of ground movements. The InSAR methods only determine the movement in the line of sight of the satellite, which is not important for most applications that require a detection of gravity-oriented, three-dimensional ground movement components. The radar interferometric results can be improved by validation with terrestrial height changes, for example, from leveling and the threedimensional movements measured by GNSS. Measurements with soil moisture sensors are required for many approaches to calculating soil moisture from radar remote sensing. With this kind of soil sensors, the continuous recording of soil moisture on the sensor point at different depths is possible, for example, in project C2M2 of THGA (Fig. 6), this was also used to derive the seasonal change of the soil moisture. Due to the numerous influences on the propagation path of the radar waves, the complete separation of the signal caused by soil moisture from other factors is only possible to a limited extent. The connection between the SAR acquisitions and the suitable model for deriving the soil moisture is only possible by considering them together with the measurements for the same period. During the implementation of the model, validation is an ongoing process. In addition to the use in the verification as the ground truth of the results of the image processing and adaption of the calculation model of the various indicators, the observation of objects that are located in the ground or that are not visible for pictorial recordings can only be carried out through in situ measurements. These include, for example, the groundwater measuring points and the soil analysis and mapping through sampling. Since the observation of the contamination through pollutants is one of the most important aspects in post-mining, the establishment of a network of regular in situ measurements is necessary. Closely related to the theme of in situ measurements is the concept of mobile GIS, which allows users to enter, display, update and above all analyze the data and geospatial information using applications on smartphones and tablets in the field. During the field work, it is possible to verify the places that are not visible on the map or satellite image, thus increasing the accuracy of the study. Data can be collected in a form as descriptive data of a place. It is also possible to add photos taken in the field as attachments (Fig. 7). This method is strictly dependent on the GNSS signal, which is a disadvantage, especially for forest and mountain areas. The data collected using mobile GIS can then be made available in the form of web applications. Figure 7 The design of the mobile GIS application including an example of road damage. The red lines indicate tectonic faults and the orange lines indicate lineaments that have been vectorized using data from mining and geological maps. (Data from project Digital Twin, THGA) Integration of monitoring in multiscale By use of methods based on in situ measurements with UAVs and satellites it is possible to provide monitoring results with different temporal and spatial resolution. Geomonitoring of post-mining processes is the analysis and integration of data from many independent sources, which enables an understanding of post-mining phenomena as a complex system. For mapping and quantifying in geomonitoring, satellite remote sensing provides the information in a wide area with mostly low spatial resolution of a few meters. Cloud coverage can lead to a total loss of information by optical satellite remote sensing, while the use of UAVs can greatly improve the local spatial resolution (in centimeters). The low flight elevation also provides compatibility in cloudy weather conditions. For this purpose, the recording of the UAVs in comparable spectral range and bands to a similar period for local observations. It is also possible to record with UAVs during the necessary period in which the recording from satellites cannot be used, for example, due to high cloud coverage. In the time domain, the fusion of different monitoring data with different temporal resolutions is also a great advantage. The repetition cycle for the optical satellites is a few days, while UAVs can be operated on demand. Continuous measurements are possible selectively using in situ sensors. Validations and additions through an in situ measurement system allow the derived indicators for monitoring objects to be confirmed with high-quality, objectrelated information. The positional accuracy of different data and the comparability of data with different resolutions and quality is one of the greatest challenges. It might be necessary to develop local solutions to ensure the correct data fusion from the various scale. Numerous topics are or may be of interest for geomonitoring in the context of post-mining landscapes. An additional complexity is given by the interconnected nature of compartments in geomonitoring; one cannot be fully understood without identifying originating from the entire system. Geomonitoring in the post-mining landscape can only be successfully managed when the system is recognized in its entirety. This includes not only the collected data, but also the knowledge and experience of local experts. Table 1 Characteristics of multiscale geomonitoring methods. Conclusions The combination and integration of different geomonitoring and observation methods is key to a successful management of post-mining areas. In a complex system, the fusion of available resources with different types of data from satellites, UAVs and in situ sensors is necessary to provide data and information for disclosure and public discourse. As each method provides specific data, their combination makes it possible to analyze and correlate a comprehensive set of data, and to develop conclusive models that provide useful information about the post-mining area. This analysis yields an interpreted, modern understanding and provides transparency for the public. Therefore, geo- and environmental monitoring methods are important for the public acceptance of mining operations. References Goerke-Mallet, P., Melchers, C. and Müterthies, A. (2016) “Innovative monitoring measures in the phase of postmining,” IMWA 2016, Freiberg/Germany, pp. 570-577. Rudolph, T. (2020) “Digital Twin – Integrated Geomonitoring Further Developed” (in) Kretschmann, J., Goerke-Mallet, P. and Melchers, C. (2020) “Done for Good 2.0, Results in Post-Mining Research, A Compilation of Research Papers by the Research Center of Post-Mining,” Technische Hochschule Georg Agricola, University, pp. 273-278. Rudolph, T., Goerke-Mallet, P. and Melchers, C. (2020) “Geomonitoring in Alt- und Nachbergbau,” zfv 3/2020, pp.168-173. Wiggenhagen, M. and Steensen, T. (2021) “Guide for Photogrammetry and Remote Sensing,” 6th revised and extended edition, Wichmann. Kretschmann, J. and Nguyen, N. (2020) “Research Areas in Post- Mining – Experiences from German Hard Coal Mining,” July-December – Journal of the Polish Mineral Engineering Society, pp. 255-262. Rouse, J.W., Haas, R.H., Schell, J.A. and Deering D.W. (1973) “Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation,” Prog. Rep. RSC 1978-1, Remote Sensing Center, Texas A&M Univ., College Station. Simmon, R. (2000) Illustration NDVI, in: Weier, J. and Herring, D. (2000) “Measuring Vegetation (NDVI&EVI),” https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php (Online: 18.10.2021). Ma, B., Pu, R., Wu, L. and Zhang, S. (2017) “Vegetation Index Differencing for Estimating Foliar Dust in an Ultra-Low-Grade Magnetite Area Using Landsat Imagery,” IEEE Access, pp. 8825-8834. Halounová, L., Petruchová, J. and Junek, P. (2005) “Analysis of reclaimed areas in the Northern Bohemia brown coal open cast mining areas monitored by remote sensing data using cartography tools and GIS,” WSEAS Int. Conf. on Remote Sensing, Venice, Italy, November 2-4, pp. 49-54. Matejicek, L. and Kopackova, V. (2010) “Changes in croplands as a result of large scale mining and the associated impact on food security studied using time-series Landsat images,” Remote Sensing 2, pp. 1463-1480. Padmanaban, R., Bhownik, A.K. and Cabral, P. (2017) “A Remote Sensing Approach to Environmental Monitoring in a Reclaimed Mine Area,” ISPRS International Journal of Geo-Information 6(12) 401, pp. 1-14. Buczyska, A. and Blachowski, J. 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(2008) “Extraction of water and flood areas from SAR data.” In: The EUSAR 2008, 7th European Conference on Synthetic Aperture Radar, 2-5 June 2008, Friedrichshafen, Germany, pp.1-4. Esch, S., Korres, W., Reichenau, T.G. and Schneider, K. (2018) “Soil moisture index from ERS-SAR and its application to the analysis of spatial patterns in agricultural areas,” Journal of Applied Remote Sensing 12(2), 022206. Crosetto, M., Monserrat, O., Cuevas-Gonzàles, M., Devanthéry, N. and Crippa, B. (2016) “Persistent Scatterer Interferometry: A review,” ISPRS Journal of Photogrammetry and Remote Sensing Band 115, pp. 78-89. John, A. (2021) “Monitoring of Ground Movements Due to Mine Water Rise Using Satellite-Based Radar Interferometry – A Comprehensive Case Study for Low Movement Rates in the German Mining Area Lugau/Oelsnitz,” Mining 1, no.1, pp. 35-58. Wang, H., Xu, F. and Jin, Y. (2019) “A Review of PolSAR Image Classification: from Polarimetry to Deep Learning,” IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, 28 July-2 August 2019, Yokohama, Japan, pp. 3189-3192. Ren, H., Zhao, Y., Xiao, W. and Hu, Z. (2019) “A review UAV monitoring in mining areas: current status and future perspectives,” Int. J Coal Sci Technol 6(3), pp. 320-333. Bernsdorf, B., Formaniuk, A. and Rudolph, T. (2020) “Possibilities of a method for copter-supported gas leak detection with thermal imaging cameras in industry and hazards prevention,” Oil Gas – European Magazine, 46th Edition, Issue 4/2020, December, pp. 13-20. Bernsdorf, B., Formaniuk, A. and Rudolph, T. (2021) “Ein Ansatz zur Detektion von Gaslecks mit Hilfe UAV-gestützter Wärmebildkameras,” vfdb – Zeitschrift für Forschung, Technik und Management im Brandschutz, 1/2021, pp. 22-31. Bernsdorf, B., Rudolph, T. and Goerke-Mallet, P. 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Modern methods of geomonitoring in post-mining processes

Authored by:
J. Benndorf, J. F. Brune, P. Goerke-Mallet, M. Pawlik, T. Rudolph, X. Yin

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