GI6.1

Indices are designed to differentiate land use and land cover classes to avoid misinterpretation of landscape features. The resemblances of spectral reflectance of mines with urban built-up and barren land cause difficulties in identification of objects. Open pit mines of Rampura-Agucha for Zn and Pb were selected for this study. The freely available data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was selected from the year of 2001 and 2003. It is observed that b1-b5/b1+b5 equation of ASTER imagery significantly differentiate Zn-Pb mine from urban settlement and other features. The reflected range (µm) for b1 and b5 is 0.52-0.60 (Visible and Near-Infrared) and 2.145-2.185 (Shortwave Infrared) respectively. The pixel values indicate higher reflectance of open pit suggesting feasibility of equation for differentiating it from barren and built-up area. The mine is rich in sphalerite followed by galena, pyrite and pyrrhotite in different proportions of abundance. Spectral reflectance depends on type of minerals hence need further studies to develop the index according to specific minerals and mines. In the mining regions, the role of temperature, moisture content, vegetation covers and high concentration of pollutants in variation of spectral reflectance are highly important. The developed index would be beneficial for tracing the extent of overburden dumps, tailings and mines at faster rate.

How to cite: Punia, A., Bharti, R., and Joshi, P. K.: Satellite imagery band ratio for mapping the open pit mines: A preliminary study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8296, https://doi.org/10.5194/egusphere-egu22-8296, 2022.

The lignite mine called 'Friendship of Nations - Babina Shaft', located on the border between Poland and Germany, was closed almost 50 years ago. Despite the cessation of mining works (carried out by opencast and underground methods) and carrying out reclamation process, the negative effects of the former mineral exploitation are still observed in this region (e.g. sinkholes, local flooding, subsidence). It should be emphasized that the area of ​​the currently closed mine is also characterized by a complicated glaciotectonic structure, which is the result of successive glacial periods in the past. Both factors, i.e., the past mining activity and geological conditions, may affect the condition of soils and vegetation of the analysed area. The aim of this study was to determine, whether and to what extent the former lignite mining and the complicated glaciotectonic structure had an impact on the changes in the state of plant cover and soils, noted in the period of 1989-2019. A new index, Mining and Geology Impact Factor (MaGIF), was developed to describe the strength and the nature of the relationship between the aforementioned factors within four test fields, based on coefficients’ values and variables of six Ordinary Least Squares (OLS) models. In the research 12 independent variables, representing geological and mining conditions of the area, were prepared. The dependent variables, statistics of selected spectral indices obtained for 1989-2019, were determined in the GIS environment, within individual pixels of the research area. In this study, two vegetation indices (NDVI and NDII) and four soil indices (DSI, SMI, Ferrous Minerals and SI3), calculated on the basis of Landsat TM/ETM +/OLI images, were used. The values of the obtained MaGIF index were ​​in the range of -9.99 - 0.62, and their distribution in the test fields proved that the former mining and geological conditions had the strongest impact on the vegetation and soils of the central part of field no. 1, as well as on north-western and south-eastern parts of field no. 4. The nature of the influence of explanatory factors on the indicated components of the environment was negative (an increase or decrease in the value of the independent variable correlated with a decrease or increase in the value of a given spectral index, respectively). In the western and southern parts of field no. 1, eastern part of field no. 3, central and eastern parts of field no. 4, as well as in a major part of field no. 2, the influence of explanatory factors was the smallest. Only in fields no. 2 and 4, the small zones of positive impact of the independent variables were observed. The results indicate that the former mining and geological conditions have a significant influence on the condition of the vegetation and soils of post-mining areas. Therefore, it is extremely important to monitor the changes taking place in these regions in order to undertake appropriate preventive works and eliminate the resulting damage.

How to cite: Buczyńska, A. and Blachowski, J.: New index for assessment of environment in post-mining area – Mining and Geology Impact Factor (MaGIF), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1107, https://doi.org/10.5194/egusphere-egu22-1107, 2022.

Underground mining, regardless of the excavation method used, has an impact on the terrain surface. For this reason, continuous monitoring of the ground surface above the excavations is necessary. Deformations on the ground surface occur with a time delay in relation to the mining works, which poses a risk of significant deformations in built-up areas, leading to building disasters. In addition to monitoring, it is therefore necessary to forecast displacements, which at present is usually done using the empirical integral models, which describes the shape of a fully formed subsidence basin and require detailed knowledge of the geological situation and parameters of the deposit. However, insufficiently precise determination of coefficients may lead to significant errors in calculations. Machine learning can be an interesting alternative to predict ground displacement in mining areas. Machine learning algorithms fit a model to a set of input data so that it best represents all the correlations and trends detected in the set. However, the fitting process must be controlled to avoid overfitting. The validated model can then be used to detect new deformations on the ground surface, categorize the resulting displacements, or predict the value of subsidence. In this case ARIMA model (Auto-Regressive Integrated Moving Average) was used to predict deformation values for single points placed in the centers of the subsidence basins in the LGCB (Legnica-Głogów Copper Belt) area. The InSAR time series calculated using the SBAS method for the years 2016-2021 was used as input data. The results were compared with the persistence model, against which there was an improvement in accuracy of several percentage points.

How to cite: Sompolski, M., Tympalski, M., Kopeć, A., and Milczarek, W.: Application of the autoregressive integrated moving average (ARIMA) model in prediction of mining ground surface displacement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12697, https://doi.org/10.5194/egusphere-egu22-12697, 2022.

Identification of relatively stable ground control points is always difficult in satellite-based remote sensing microwave technology. In our case, we have analyzed the amplitude and phase of backscattered signal of artificial objects in the resolution cell. In 2020, we have temporarily installed a compact active transponder (CAT) to the top of the Satellite Geodetic Observatory (SGO). During this probation period we had tested the operation of this electronic corner reflector (ECR).

In November, 2021 we have deployed, adjusted and precisely aligned the CAT and also mounted a 90 cm inner leg of passive double-backflip triangular corner reflector pair (part of the Integrated Geodetic Reference Station) to serve as Persistent Scatterers. Hence, we have observed the behaviour of the complex microwave signal using interferometric synthetic aperture radar technique (InSAR), utilizing Sentinel-1 SAR high resolution images. We have concentrated to demonstrate the effect of the corner reflector (CR) installation: estimate the Signal-to-Clutter Ratio (SCR), calculate the Radar Cross Section (RCS), define the phase center in sub-pixel dimension over well-specified stack of time-series.

We are expecting and focusing to integrate the CRs as benchmarks, into our developing processing algorithm system to achieve more accurate results of surface displacement using ground control points. In addition, the function of this project is to contribute and ensure the extension of our passive corner reflector reference network (SENGA). In this paper, we present the interpretation of the recent outcomes.

How to cite: Horváth, R., Magyar, B., and Tóth, S.: Impact of different corner reflectors installation on InSAR time-series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8417, https://doi.org/10.5194/egusphere-egu22-8417, 2022.

Remote sensing techniques are an ever-growing reliable means for monitoring, detecting and analysing the spatial and temporal changes of solid waste and landfill sites. In this paper, different UAV and satellite sensors are used to detect, characterize and monitor dumpsites in Sicily (Italy). In particular, data acquired and processed are (i) high-density point clouds detected from LIDAR sensor; (ii) optical photograms with a resolution of 3 cm; (iii) thermal photograms with a resolution of 5 cm/pixel and (iv) multispectral photograms with 5 cm/pixel. High spatial resolution UAV multispectral and thermal remote sensing allowed for the extraction of indicators, such as the Normalized Difference Vegetation Index (NDVI) and the Land Surface Temperature (LST), useful to characterize the changes in the vegetation and the skin temperature increase due to organic waste decomposition, respectively. On the other hand, the processing of UAV optical images to extract high-resolution orthophotos and their integration with high-density point clouds obtained from LIDAR, were used to provide the identification of the effective perimeter of the landfill body and the extraction of waste volumes. These products were integrated and compared with those obtained from different kinds of medium-to-high spatial resolution satellite images, such as from Landsat, Aster, Sentinel-2 and Planetscope sensors. Results show that UAV data represents an excellent opportunity for detecting and characterizing dumpsites with an extremely high detail, and that the joint use with satellite data is recommended for having a comparison on different scales, allowing continuous monitoring. Additional SAR data methodologies will be investigated for evaluating the landfill body landslides over the years that could be integrated with high resolution satellite multispectral and hyperspectral images for monitoring dumpsites environmental impact.

How to cite: Celauro, A., Cagnizi, M., Cappello, A., D'Amato, E., D'Aranno, P. J. V., Ganci, G., Lodato, L., Marini, I., Marsella, M., and Moriero, I.: Large and small-scale multi-sensors remote sensing for dumpsites characterization and monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10455, https://doi.org/10.5194/egusphere-egu22-10455, 2022.

Although the C–H chains of petroleum derivatives display unique absorption features in the short-wave infrared (SWIR), it is a challenge to identify plastics on terrestrial surfaces. The diverse reflectance spectra caused by chemically varying polymer types and their different kinds of brightness and transparencies, which are, moreover, influenced further by the respective surface backgrounds. This paper investigates the capability of WorldView-3 (WV-3) satellite data, characterized by a high spatial resolution and equipped with eight distinct and relatively narrow SWIR bands suitable for global monitoring of different types of plastic materials. To meet the objective, hyperspectral measurements and simulations were conducted in the laboratory and by aircraft campaigns, based on the JPL-ECOSTRESS, USGS, and inhouse hyperspectral libraries, all of which are convolved to the spectral response functions of the WV-3 system. Experiments further supported the analyses wherein different plastic materials were placed on different backgrounds, and scaled percentages of plastics per pixel were modeled to determine the minimum detectable fractions. To determine the detectability of plastics with various chemical and physical properties and different fractions against diverse backgrounds, a knowledge-based classifier was developed, the routines of which are based on diagnostic spectral features in the SWIR range. The classifier shows outstanding results on various background scenarios for lab experimental imagery as well as for airborne data and it is further able to mask non-plastic materials. Three clusters of plastic materials can clearly be identified, based on spectra and imagery: The first cluster identifies aliphatic compounds, comprising polyethylene (PE), polyvinylchloride (PVC), ethylene vinyl acetate copolymer (EVAC), polypropylene (PP), polyoxymethylene (POM), polymethyl methacrylate (PMMA), and polyamide (PA). The second and third clusters are diagnostic for aromatic hydrocarbons, including polyethylene terephthalate (PET), polystyrene (PS), polycarbonate (PC), and styrene-acrylonitrile (SAN), respectively separated from polybutylene adipate terephthalate (PBAT), acrylonitrile butadiene styrene (ABS), and polyurethane (PU). The robustness of the classifier is examined on the basis of simulated spectra derived from our HySimCaR model, which has been developed inhouse. The model simulates radiation transfer by using virtual 3D scenarios and ray tracing, hence, enables the analysis of the influence of various factors, such as material brightness, transparency, and fractional coverage as well as different background materials. We validated our results by laboratory and simulated datasets and by tests using airborne data recorded at four distinct sites with different surface characteristics. The results of the classifier were further compared to results produced by another signature-based method, the spectral angle mapper (SAM) and a commonly used technique, the maximum likelihood estimation (MLE). Finally, we applied and successfully tested the classifier on WV-3 imagery of sites known for a high abundance of plastics in Almeria (Spain), Cairo (Egypt), and Accra, (Ghana, West Africa). Both airborne and WV-3 data were atmospherically corrected and transferred to “at-surface reflectances”. The results prove the combination of WV-3 data and the newly designed classifier to be an efficient and reliable approach to globally monitor and identify three clusters of plastic materials at various fractions on different backgrounds.

How to cite: Zhou, S.: A knowledge-based, validated classifier for the identification of aliphaticand aromatic plastics by WorldView-3 satellite data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3130, https://doi.org/10.5194/egusphere-egu22-3130, 2022.

Many hydromorphological restoration measures have been applied on German water courses since 2000 the European water framework directive has been induced. The measures aim to improve the diversity of habitat alteration. Often a positive effect on aquatic biota can’t be detected, therefore implementation and the hydromorphological development of such measures can be questioned. But also the common monitoring and assessment methods for physical river habitat mapping can be questioned as they are limited in spatial scale and objectiveness of the mapper itself.

In the last decade, Unmaned Areal Vehicle (UAV) in combination with high-resolution sensors open new opportunities in a spatial and temporal scales. This research shows a case study of the river Lippe for the detection of hydromorphological habitat structures using Structure from Motion (SfM) and Deep learning based classification methods. In detail, this work discusses the difficulties of creating digital surface and orthomosaics from field survey data, but also shows results from a case study using a deep learning classification approach to identify physical river habitat structures.

How to cite: Dacheneder, F., Schulz, K., and Niemann, A.: AI-based hydromorphological assessment of river restoration using UAV-remote sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6995, https://doi.org/10.5194/egusphere-egu22-6995, 2022.

Airborne Laser Scanning (ALS) is a widely used method in Earth science, Agriculture or Forestry. This method could provide high resolution and accurate spatial data for the better understanding of surface structures, moreover, based on the laser pulses, it can even show important features of the ground below dense vegetation. However, these ALS surveys requires specially designed aircrafts, pilots and operators, detailed flight planning, which leads to an expensive way of data analysis. The application of laser scanners for Unmanned Aerial Systems (UAS) has started in the last few years. These sensor payloads provide less weight and size and decreased accuracy compared to the traditional ALS surveys but still serve as more reliable mapping technology contrary to the photogrammetric methods in many cases. However, several new UAS laser scanners are being developed but their accuracy conditions and applicability for agricultural monitoring must be studied in many ways.

In our study we applied the novel Zenmuse L1 LiDAR sensor mounted on a DJI Matrice M300 RTK UAS. We surveyed a ~50 ha area of corn field near Berettyóújfalu, Hungary in the summer of 2021. Our aim was to reveal the applicability of UAS laser scanning for the precise ground surface reconstruction. In this period, the corn was under irrigated condition, therefore, extensive weed patches were observed between the paths. The laser scanner ground filtering data was compared to a photogrammetry-based aerial survey that we have carried out at the beginning of the vegetation cycle at the same parcel. Our results showed both the potentials and limitations of this sensor for precision agriculture. The laser beams produced significant amount of noise between the paths that had to be cleaned to extract the ground surface below the corn canopy. Based on our data processing methods we were able to delineate similar drainage networks within the parcel that was also processed from the initial aerial survey. However, the UAS LiDAR gained the most accurate surface reconstruction at the more clear grassland patches around the parcel. 

L. Bertalan was supported by the INKP2022-13 grant of the Hungarian Academy of Sciences. This research was funded by the Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary. This research was also influenced by the COST Action CA16219 “HARMONIOUS - Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.

How to cite: Bertalan, L., Riczu, P., Bors, R., Szabó, S., and Eltner, A.: Application of UAS laser scanning for precision crop monitoring in Hungary, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1185, https://doi.org/10.5194/egusphere-egu22-1185, 2022.

Land cover dynamics play a vital role in many scientific fields, such as natural resources management, environmental research, climate modeling, and soil biogeochemistry studies; thus, understanding the spatio-temporal land cover status is important to design and implement conservation measures. Remote sensing products provide relevant information regarding spatial and temporal changes on the earth’s surface, and recently, time series analyses based on satellite images, and spectral indices have become a new tool for accurate monitoring of the spatial trend, and land cover changes over large areas. This work aims to determine the trends of vegetation spectral response expressed as the Normalized Difference Vegetation Index (NDVI) over the period 2005 and 2018 and compare these trends with the land-use and cover changes between 2005 and 2018 in wetland areas across Denmark. Change detection methods between two years based on bi-temporal information may lead up to the detection of pseudo-changes, which hinders the land-use and cover monitoring process at different scales. We studied the potentiality of including NDVI temporal curves derived from a yearly time-series Landsat TM images (30-m spatial resolution) to obtain more accurate change detection results. We computed the NDVI temporal trends using pixel-wise Theil-Sen and Man-Kendall tests, then we explored the relationship between NDVI trends and the different land-use and cover change classes. We found a significant relationship between NDVI trends and changes in land use and cover. Changes from cropland to wetland and cropland to forest coincided with statistically significant (p≤0.05) negative NDVI, and positive NDVI trends, respectively. Changes from grasslands to permanent wetlands corresponded with statistically significant negative NDVI trends. The difference in vegetation productivity trends could be indicative of the combined effect of human activity and climate. We show that this combined analysis provides a more complete picture of the land use and cover changes in wetland areas over Denmark. This analysis could be improved if the NDVI time series is seasonally aggregated.

How to cite: Gutierrez Diaz, J. S., Humlekrog Greve, M., and Wollesen de Jonge, L.: Trends in vegetation changes over wetland areas in Denmark using remote sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2545, https://doi.org/10.5194/egusphere-egu22-2545, 2022.

The “La Fossa” summit crater of Vulcano island (Sicily, Italy) showed increasing volcanic activities, characterized by strong gases emissions and high soil temperatures, during July 2021 (https://cme.ingv.it/stato-di-attivita-dei-vulcani-eoliani/crisi-idrotermale-vulcano-2021). The National Civil Protection Department declared the “yellow alert” level and the Mayor of the island issued an order to prohibit citizens to stay in areas surrounding the harbor due to large amounts of gases emitted; an alternative accommodation was sought for about 250 persons. In this work, we report and analyze the surface temperature estimated by using satellite data (ASTER and Landsat-8) from 2000 to 2022. These analyses extend the study described in “Silvestri et al., 2018” which reports a time series of thermal anomalies from 2000 to 2018, with a focus on two specific sites of the Vulcano island: “La Fossa” and “Fangaia”. So, we updated the dataset up to 2022 and analyzed space-borne remotely sensed data of the surface temperature on the whole island. We applied the Pixel Purity Index technique to ASTER and Landsat-8 satellite data (GSD=90 m) in order to detect pixels that are most relevant from the thermal point of view; thus, we used these pixels as significant points for the time series analysis. Moreover, strong carbon dioxide emissions could be detected from satellite data acquired by the new Italian space mission PRISMA (GSD=30 m) carrying onboard a hyperspectral sensor operating in the range 0.4-2.5 µm; this possibility will be explored by analyzing data on active fumaroles in the island. The goal of the analysis is also to verify if volcanic activity variations (in terms of thermal anomalies and gases emissions), in the Vulcano island, can be detected by satellite data.

How to cite: Silvestri, M., Rabuffi, F., Romaniello, V., Musacchio, M., and Buongiorno, M. F.: The use of satellite data to support the volcanic monitoring during the last Vulcano island crisis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3532, https://doi.org/10.5194/egusphere-egu22-3532, 2022.

Forecasting volcanic and limnic eruption for improving early warning systems is crucial to prevent severe impact on human lives. One of the main triggers of explosive eruptions is volcanic gases which, contrary to the atmosphere, are easily detected in water column, particularly using hydro-acoustic methods [1]. Two pioneering studies have monitored gas venting into Kelud Crater Lake (Indonesia) from a hydroacoustic station shortly before a Plinian eruption in 1990 [1] and, nearly two decades later, by empirically quantifying CO2 fluxes by acoustic measurements in the same lake just before a non-explosive eruption [2]. However, despite hydroacoustic detection capabilities, fundamental advances are limited by technology performances. Overall acoustic detection of a bubble field is easy, while its quantification remains complex due to the 3D structure of clouds, heterogeneous bubbles sizes and acoustic interactions between them. It is thus necessary to accurately map the different bubble clouds, to monitor their evolution through time to reduce the volcanic risk, which is major in aqueous environments. Here, we present preliminary results of water column gas distributions and quantification from an Eifel crater lake (Germany), using iXblue Seapix 3D multi splitbeam echosounder. SeapiX acoustic array is based on very special geometry, a dual/steerable multibeam echosounder with a Mills Cross configuration. It allows a 120° x120° coverage (quasi realtime coverage) with 1.6° resolution, made by 128 single elements. All beams in all steering direction process Split Beam TS measurement to provide true acccurate volumic TS from all single target in the volume. Backscatter profiles of elements in the water column allowed to distinguish fish and gas bubbles, which demonstrates a potential for the development of an automatic gas detection module using the Seapix software. Ongoing research on the Target Strengh (TS) of bubbles suggest they are of very small size (35 μm), much smaller than observed elsewhere using single beam echosounders, which might also explain why, in the same spot, we did not observe gas bubbles using camera mounted on ROV. Using the steerable capability of the system, a recent mission performed a 4D monitoring of gas bubbling of a single gas plume, in a static position placed on a USV and anchored, raising new perspectives to anticipate the tipping point of a critical enhancement of gas release and to mitigate the volcanic risk.

[1] Vandemeulebrouck et al (2000) J. Volcanol. Geotherm. Res 97, 1-4: 443-456

[2] Caudron et al (2012) JGR: Solid Earth 117, B5

How to cite: Jouve, G., Caudron, C., Matte, G., and Mosca, F.: Monitoring gas dynamics in underwater volcanic environments using iXblue SeapiX multi split beam echosounder: an example from the Laacher See (Eifel, Germany), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3583, https://doi.org/10.5194/egusphere-egu22-3583, 2022.

Recently, numerous agencies and administrations in their latest reports show how it’s impossible to overlook the negative impact of atmospheric air pollution on human health. In this regard, it’s essential to be able to understand the spatial and temporal distribution of the concentration of main pollutants, and its ways to change. Among the numerous strategies proposed to tackle this problem, from the ’70s the study of satellite data assumed a key role, extending the analyzes carried out only with ground tools.

In this work we analyzed the data acquired by TROPOMI (TROPOspheric Monitoring Instrument), a multispectral imaging spectrometer mounted onboard the ESA Copernicus Sentinel-5P satellite (orbiting since October 2017) and specifically focused on mapping atmospheric composition. In particular, we processed the TROPOMI NO₂ products acquired over Piedmont Region (Italy) between 2018 and 2021.  We obtain preliminary results by comparing the satellite-derived tropospheric NO₂ columns data with ground-based NO₂ concentration acquired by the ARPA-Piemonte network in different urban and geomorphological contexts. In particular, we compared the TROPOMI-derived time series with the acquisitions of ground stations located in urban and suburban areas (e.g. in the city of Turin), identified as “traffic stations”, and in rural areas (low population density and countryside areas) identified as “background stations”. The results allow us to investigate the correlation and coherence between the two datasets and discuss the added values and limits of satellite data in different environmental contexts, with the prospective of providing NO₂ concentration maps of the Piedmont Region.

How to cite: Campus, A., Acquaotta, F., and Coppola, D.: Mapping NO2 pollution in Piedmont Region (Italy) using TROPOMI: preliminary results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9328, https://doi.org/10.5194/egusphere-egu22-9328, 2022.