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Remote sensing for environmental monitoring

Remote sensing measurements, acquired using different platforms - ground, UAV, aircraft and satellite - have increasingly become rapidly developing technologies to study and monitor Earth surface, to perform comprehensive analysis and modeling, with the final goal of supporting decision systems for ecosystem management. The spectral, spatial and temporal resolutions of remote sensors have been continuously improving, making environmental remote sensing more accurate and comprehensive than ever before. Such progress enables understanding of multiscale aspects of high-risk natural phenomena and development of multi-platform and inter-disciplinary surveillance monitoring tools. The session welcomes contributions focusing on present and future perspectives in environmental remote sensing, from multispectral/hyperspectral optical and thermal sensors. Applications are encouraged to cover, but not limited to, the monitoring and characterization of environmental changes and natural hazards from volcanic and seismic processes, landslides, and soil science. Specifically, we are looking for novel solutions and approaches including the topics as follows: (i) state-of-the-art techniques focusing on novel quantitative methods; (ii) new applications for state-of-the-art sensors, including UAVs and other close-range systems; (iii) techniques for multiplatform data fusion.

Co-organized by ESSI4/GMPV1/NH6
Convener: Annalisa CappelloECSECS | Co-conveners: Sabine Chabrillat, Gaetana Ganci, Gabor KereszturiECSECS, Veronika Kopackova
| Tue, 24 May, 08:30–11:44 (CEST)
Room 0.51

Tue, 24 May, 08:30–10:00

Chairperson: Gaetana Ganci

Maria Marsella et al.


Remote sensing measurements have benefited from a great technological improvement, which has allowed a higher degree of automation while increasing spatial and temporal resolution of the collected data. Multi-     scale and multi-frequency optical and radar satellite sensors, often adopted in an integrated manner, are starting to provide efficient solutions for controlling and monitoring rapidly evolving urban and natural areas. On the other hand, close range remote-sensing techniques, such as operated by UAV platforms, and innovative ground-based instruments offer, respectively, the chance to downscale the observation performing site-specific analysis at an enhanced resolution and to collect in-situ dataset for calibration and data quality. By improving the quantity and quality of the collected data, a better understanding of the in-going processes is possible and the setting up of a numerical forecast model for future scenarios.


Therefore, implementation of integrated techniques for environmental monitoring turns out to be a strategic solution to analyze hazardous areas at different spatial and temporal resolution. Research devoted to the optimization of data processing tools by means of AI algorithms has evolved with the aim of improving the level of information and its reliability. In this context, a great impact is linked to the availability of open data and open-source processing tools distributed after the Copernicus Program.


A review of the available technologies for environmental monitoring is provided including examples on experimental cases in areas affected by natural hazards (volcanic eruptions, landslides, coastal erosion, flooding, etc.) and human activities that can produce incidental damages on the environment (urbanization, agriculture, infrastructures, landfills, dumpsites, pollutions, etc.). In addition, the same approach is useful for monitoring the degradation of the cultural heritage sites. Finally, the capability of collecting fat at a global level contributed to the analysis of environmental and economic impacts consequent the Covid-19 pandemic.


How to cite: Marsella, M., Celauro, A., and Moriero, I.: An integrated approach for environmental multi-source remote sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6153, https://doi.org/10.5194/egusphere-egu22-6153, 2022.

María Cuevas-González et al.

In sub-Saharan Africa, artisanal and small-scale mining (ASM) represents a source of subsistence for a significant number of individuals. While 40 million people officially work in ASM across 80 countries, more than 150 million rely indirectly upon ASM. However, because ASM is often illegal, and uncontrolled, the materials employed in the excavation process are highly dangerous for the environment, as well as for the people involved in the mining activities. One of the most important aspects regarding ASM is their localization, which currently is missing in most of the African regions. ASM inventories are crucial for the planning of safety and environmental remediation interventions. Furthermore, the past location of ASM could be used to predict the spatial probability of the creation of newborn mines. To this end, we propose a Deep Learning (DL) based approach able to exploit Sentinel-2 open-source data and a non-complete small-size mine inventory to accomplish this task. The area chosen for this study lies in northern Burkina Faso, Africa. The area is chosen for its peculiar semi-desert environment which, in addition to being a per se challenging mapping environment, presents a wide spatial variability. Moreover, given the high level of danger involved in field mapping, it is fundamental to develop reliable remote sensing-based methods able to detect ASM. Performance comparison of two convolutional neural networks (CNNs) architectures is provided, along with an in-depth analysis of the predictions when dealing both with dry and rainy seasons. Models’ predictions are compared against an inventory obtained by manual mapping of Sentinel-2 tiles, with the help of multitemporal interpretation of Google Earth imagery. The findings show that this approach can detect ASM in semi-desertic areas starting with a few samples at a low cost in terms of both human and financial resources.

How to cite: Cuevas-González, M., Nava, L., Monserrat, O., Catani, F., and Meena, S. R.: Deep Learning and Sentinel-2 data for artisanal mine detection in a semi-desert area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11409, https://doi.org/10.5194/egusphere-egu22-11409, 2022.

Anita Punia et al.

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.

Anna Buczyńska and Jan Blachowski

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.

Marek Sompolski et al.

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.

Roland Horváth et al.

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.

Angela Celauro et al.

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.

Shanyu Zhou

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.

Jalissa Pirro et al.

Harmful algal blooms (HABs) are a threat to freshwater quality, public health, and aquatic ecosystems. The economic losses suffered by the agricultural, fishing, and tourism industries as a result of HABs exceed billions of dollars worldwide annually, with cleanup costs from local and national governments reaching a similar price. Current manual field-based sampling methods followed by laboratory analysis to detect and monitor HABs in expensive, labor-intensive, and slow, delaying critical management decisions. Moreover, current detection methods have limited success documenting HABs in freshwater bodies and such attempts employ satellite-based multispectral remote sensing; however, satellite-based methods are limited by cost, low spatial and spectral resolution, and restricting temporal windows for on-demand revisits. Our study used relatively low-cost unpiloted aerial systems (UAS) and hyperspectral sensors to detect HABs with higher resolution while having the capacity to conduct near real-time detection. Additionally, our hyperspectral remote sensing can detect and differentiate between HABs that produce cyanobacteria and other chlorophyll-producing plants. We detected a spectral peak of 710 nm that is characteristic of cyanobacteria producing HABs. Principal components analysis (PCA) was useful to spatially highlight HABs over wide areas. By utilizing hyperspectral remote sensing with UAS, HABs can be monitored and detected more efficiently. This new state-of-the-art research methodology will allow for targeted assessment, monitoring, and design of HABs management plans that can be adapted for other impacted inland freshwater bodies. 

How to cite: Pirro, J., Thomas, C., Wallace, C., Alpert, Z., Tuohy, M., de Smet, T., Yokota, K., Jackson, P., Cleckner, L., Wigdahl-Perry, C., Young, K., Amejecor, K., and Scheer, A.: Utilizing Hyperspectral Remote Sensing to Detect Concentration of Cyanobacteria in Freshwater Ecosystems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-629, https://doi.org/10.5194/egusphere-egu22-629, 2022.

Felix Dacheneder et al.

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.

Lukas Dörwald et al.

Remote sensing is being used widely to detect, map, and monitor environmental changes and remains a rapidly developing field. The detection of dune movement rates is carried out in field since the 20th century and through remote sensing, once the technical requirements were met in the 1970th (Hugenholtz et al. 2011). A wide variety of imagery from the last four decades is freely available in the archives of Sentinel-2 and Landsat 5 to 8 satellite images with spatial resolutions ranging between 10 and 25 meters. Complementing these data sources, in this study, we additionally used CORONA KH-4B images from the 1960s and 1970s. Despite its age, the KH-4B satellite delivered a considerably high spatial resolution of up to 1.8 m, thus bridging a considerable time gap of high resolution imagery and enabling the detection and mapping of singular dunes and dune fields. These satellites were originally used to record military intelligence images before being declassified for scientific use in 1995. After georeferencing, these images were utilized to detect and quantify the rates and directions of sand dune movement as well as for the estimation of dune height through a simple trigonometric approach.

We focus on single dunes and their movement rates in the high-altitude intramontane Gonghe Basin in Central Asia. The location of the study area at the north-eastern edge of the Asian summer monsoon and the mid-latitude Westerlies makes it especially sensitive to climatic variability (Vimpere et al. 2020). The dominant south easterly dune migration directions are in good agreement with the prevailing wind patterns. Dune heights of ~8–28 meters and ~3-31 meters for the late 1960s and 2020s, respectively, were calculated. Also, movement rates of under one meter up to ~24 meters per year were assessed for the time range of the late 1960s and 2020s.References:

Hugenholtz, C., H., Levin, N., Barchyn, T.E., Baddock, M., C. (2012): Remote sensing and spatial analysis of Aeolian sand dunes: A review and outlook. Earth-Science Reviews 111, 319334, https://doi.org/10.1016/j.earscirev.2011.11.006

Vimpere, L., Watkins, S., E., Castelltort, S. (2021): Continental interior parabolic dunes as a potential proxy for past climates. Global and Planetary Change, 206: 103622, https://doi.org/10.1016/j.gloplacha.2021.103622

How to cite: Dörwald, L., Walk, J., Lehmkuhl, F., and Stauch, G.: Remotely sensed dune movement rates in desert margins of Central Asia over five decades using satellite imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4460, https://doi.org/10.5194/egusphere-egu22-4460, 2022.

Tasneem Ahmed et al.

Coastal areas are socially, economically, and environmentally intensive zones. Their risk to various natural coastal hazards like coastal flooding, erosion, and storm surges has increased due to climate-induced changes in their forcing agents or hazard drivers (e.g. sea-level rise). The increased exposure (e.g. dense population living near the coast) and vulnerability (e.g. insufficient adaptation) to these hazards in the coastal areas have complicated the adaptation challenges.

Thus, monitoring coastal hazards is essential to inform suitable adaptation to increase the climate resilience of the coastal areas. In monitoring coastal climate hazards to develop coastal climate resilience, both the forcing agents and the coastal responses should be observed.

As coastal monitoring is often expensive and challenging, creating a database through a systematic analysis of low-cost sensing technologies, like UAV photogrammetry for monitoring the hazards and their drivers would be beneficial to the stakeholders. Real-time information from these low-cost sensors in complement to the existing institutional sensors will facilitate better adaptation policies including the development of early warning support for building coastal resilience. In addition, it would also provide a valuable dataset for validating coastal numerical models and providing insights into the relationship between these hazards and forcing agents. Additionally, such low-cost sensors would also create opportunities for engaging citizens in the data collection process, for efficient data collection, and increasing scientific literacy amongst the general public. For instance, in the Sensing Storm Surge Project (SSSP), citizen science was used to collect technical data to characterise estuarine storm surges, generating data useable in peer-reviewed Oceanography journals. Coastal areas show complex morphological changes in response to the forcing agents over a wide range of temporal and spatial scales. Thus, monitoring the hazards with a sufficient temporal and spatial resolution is imperative to distinguish the changes in these hazards/drivers due to climate change from natural variability. This will not only help address the response strategies to these hazards but also adjust these response strategies according to the changing vulnerability of a particular region.

The database of the low-lost sensors thus created is in no way exhaustive since those have been retrieved through a certain combination of keywords in databases like Sciencedirect, Web of Science, and Scopus, nonetheless it is useful as these are the latest low-cost sensors available to monitor the major coastal hazards in the vulnerable coastal regions.

How to cite: Ahmed, T., Creedon, L., Anton, I., and Gharbia, S.: The use of low-cost sensors for monitoring coastal climate hazards and developing early warning support against extreme events. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8825, https://doi.org/10.5194/egusphere-egu22-8825, 2022.

Tue, 24 May, 10:20–11:50

Chairperson: Gaetana Ganci

Gian Marco Salani et al.

Geochemical investigations of agricultural soils are fundamental to characterize pedosphere dynamics that sustain ecosystem services linked with agriculture. Parameters like soil moisture, soil organic matter (SOM), and soil organic carbon (SOC) are strong instruments to evaluate carbon sink potential.

Satellite Earth Observation is a significant source of free data that can be linked to soil characteristics and dynamics and employed to produce temporal series. Access to these data is nowadays facilitated by platforms such as ADAM (https://adamplatform.eu), which allow users to quickly search for, visualize and subset data products, greatly reducing the volume of data that end users must handle.

In this work we demonstrate the usefulness of such systems by carrying out a geochemical investigation of 100 superficial (0-15 cm) soil samples collected in the province of Ferrara (North-Eastern Italy) and using the ADAM platform to associate to each a time series of Sentinel 2 data. The samples were collected in October 2021 in fields that were ploughed or mono-cultivated at maize, soybean, rice, and winter vegetables. To obtain the average soil properties over a spatial scale larger than the satellite sensor resolution, we adopted a composite sampling strategy, merging 5 sub-samples collected at the vertexes and at the center of a 30x30 m2 area. Soil granulometry was recognized from clay to medium sand, with exception of peat deposits. Soil moisture, and SOM, contents were estimated by loss on ignition (LOI), respectively at 105°C (values from 0.3 to 7.4 wt%), and 550°C (values from 2.1 to 21.0 wt%). SOC contents (values from 0.7 to 9.3 wt%) were determined through DIN19539 analysis performed with an Elementar soliTOC Cube. Using the ADAM platform, we associated a temporal series from 2016 to 2021 of the Sentinel 2 NDVI data product to each sampling location, using a cloud coverage mask to eliminate values taken on cloudy days. Localized phenological cycles for each year are recognizable in the remotely-sensed data. Hence, our database describes for each parcel, geochemical parameters and vegetative temporal series.

In a separate study, we also attempted to train a neural network to predict geochemical properties from the soil spectrum measured by the hyperspectral satellite PRISMA. We used the geochemical properties of our 100 samples as training data, associated with the PRISMA spectra of the sampling locations measured on April 7 2020, when, according to our NDVI data, none was covered in vegetation.

How to cite: Salani, G. M., Lissoni, M., Natali, S., and Bianchini, G.: Geochemical investigations of 100 superficial soils observed by Sentinel 2 and PRISMA, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6983, https://doi.org/10.5194/egusphere-egu22-6983, 2022.

László Bertalan et al.

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.

Joan Sebastian Gutierrez Diaz et al.

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.

Miina Rautiainen et al.

Spectral libraries of different components forming forests – such as leaves, bark and forest floor – are needed in the development of remote sensing methods and land surface models, and for understanding the shortwave radiation regime and ecophysiological processes of forest canopies. This poster summarizes spectral libraries of boreal forest vegetation and lichens collected in several projects led by Aalto University. The spectral libraries comprise reflectance and transmittance spectra of leaves (or needles) of 25 tree species, reflectance spectra of tree bark, and reflectance spectra of different types of forest floor vegetation and lichens. The spectral libraries have been published as open data and are now readily available for the community to use. 

How to cite: Rautiainen, M., Hovi, A., Forsström, P., Juola, J., Kuusinen, N., and Schraik, D.: Open data sets on spectral properties of boreal forest components , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2711, https://doi.org/10.5194/egusphere-egu22-2711, 2022.

Matteo Rolle et al.

Information of crop sowing dates is important to enhance the accuracy of crop models and for the assessments of crop requirements during the growing seasons. The sowing calendars of densely harvested areas are often driven by heterogeneous factors like annual crop rotations, crop switches and the alternation of winter and summer products over the same fields. Remote sensing is widely used for agricultural applications, especially to maximize crop yields through precision farming tools. Indices combining optical and infrared bands are particularly suitable for the crop classification algorithms and the plant health monitoring. Synthetic Aperture Radar (SAR) is often used in agriculture to classify irrigated and rainfed fields, due to its high sensitivity to soil water content. Despite SAR data are also used to identify changes in the ground roughness, this information has been rarely combined with optical data to identify crop sowing dates at the field scale.

In this study, SAR data from Sentinel-1 and NDVI derived from multispectral (MSI) acquisitions of Sentinal-2 have been used to identify the sowing dates of maize over a densely harvested pilot area in South Piedmont (Italy). NDVI data have been used to identify maize fields together with the agricultural geodatabase provided by the Piedmont public authority. The moisture-induced noise of SAR data has been filtered to avoid the impact of precipitation on the radar signal during the bare soil phase. Combining the VH and VV bands acquired by Sentinel-1 it was possible to identify the moment when maize plants break through the soil in each field.

Results show a good alignment with the information of sowing periods acquired from local farmers, also in terms of multiple growing seasons due to the presence of different maize types. The distribution of sowing dates points out that most of the maize is sown during the second half of May, while the other fields are sown even a month later after the harvesting of winter crops. The method proposed in this study may lead to significant applications in the agriculture monitoring, providing useful information for crop-related management policies. The combined use of SAR and NDVI data has the potential to improve the crop models for the benefit of yields and food security.

How to cite: Rolle, M., Zribi, M., Tamea, S., and Claps, P.: Estimation of maize sowing dates from Sentinel 1&2 data, over South Piedmont, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10490, https://doi.org/10.5194/egusphere-egu22-10490, 2022.

Jacob Jesús Nieto Butrón et al.

Tenosique is a small town located on the border between Mexico and Guatemala, on the banks of the Usumacinta River. The area is considered a tropical climate with swampy and jungle areas. Previous studies had exposed the changes in vegetation cover related to the public policies applied at the site. Some examples of these policies are: the 1917 agrarian reform of land distribution to the peasants for cultivation, in 1938 concessions were made to national and foreign companies to exploit forest resources; in 1958 the agrarian reform for cultivation made the agricultural zone advance towards the jungle forest; in 1965 the food crisis promoted livestock; in 1976 it opted for the extraction of oil, and with the economic crisis in 1982 the financial support to the peasants and their ejidos is withdrawn, and finally in 2008 this area becomes a flora and fauna protection area. Past studies have been developed from a social and artistic point of view as well as quantifiable with the use of Landsat satellite images, covering large temporalities as well as a regional coverage scale, however, the results resolutions have made their interpretation difficult, reporting only the 20% plant loss over time. The objective of this project is to update the pre-existing study using high-resolution images, on a smaller surface. For this, 5-meter resolution Rapideye satellite images were downloaded from the Planet platform (Planet Application Program Interface: In Space for Life on Earth) with the help of an educational license obtained from an artistic quality project. The temporality of the images ranges from 2010 to 2020. The methodology includes corresponding atmospheric corrections, the supervised classification, and the coverage analysis obtained from the application of the Normalized Difference Vegetation Index (NDVI).  Conclusions show the impact of the inputs resolution improvement in the study.

How to cite: Nieto Butrón, J. J., Ramírez Serrato, N. L., Jácome Paz, M. P., Ruiz Santos, T. X., and Manuel Núñez, J.: Use of Rapideye images from the planet platform to update vegetation cover studies in Tenosique, Tabasco, Mexico., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10607, https://doi.org/10.5194/egusphere-egu22-10607, 2022.

François Toussaint et al.

Palo Verde National Park, located in the northwest of Costa Rica, contains a wetland plain of international ecological importance in Central America. It is home of a rich biodiversity and provides vital shelter for over 60 species of migratory and resident birds.

From the 1980’s onward, the wetland landscape has shifted from diverse vegetation and large open water areas to a near monotypic stand of cattail (Typha domingensis). This resulted into a sharp reduction in the number of birds in the area, as many bird species prefer other native plants and open water for feeding, nesting and for shelter. The Fangueo technique, which consists in crushing the plant under water using a tractor equipped with angle-iron paddle wheels has been adopted to reduce the spread of Typha.

This plant management technique typically results in a significant decrease in Typha population in the first year after its implementation, as well as an increase in plant diversity and open water area.

In this study, we used historical Landsat and Sentinel imagery to investigate the medium to long-term impact of Fangueo on vegetation and open water. We found that invasive vegetation regrowth happened faster than previous studies had indicated. The increase in open water areas was therefore short-lived. This result questions the adequacy of this technique for invasive plant management.

This work highlights how crucial simple remote sensing methods can be for assessing the adequacy of supposedly effective environmental management practices, and for informing stakeholders.

How to cite: Toussaint, F., Alonso, A., and Javaux, M.: Questioning the adequacy of an invasive plant management technique through remote sensing observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11908, https://doi.org/10.5194/egusphere-egu22-11908, 2022.

Malvina Silvestri et al.

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.

Boglarka Kis et al.

In our study, we tested a UAV-based IRT and Structure from Motion (SfM) for the identification of CO2 rich gas emission areas at Ciomadul dormant volcanic area, Eastern Carpathians. Our aim is to demonstrate the efficiency of the identification method providing example from a case-study in the Eastern Carpathians.

The gas emissions from Ciomadul come with high flux and are of magmatic origin, associated with the volcanic activity in the past. We had the following assumptions before performing the measurements with the drone: the temperature of the gas vents is constant, as well as their flux, variability is represented only by the changes in ambient temperature. We had previous knowledge on the temperature of the gas emissions (6 °C), so we chose periods when the ambient temperature is either lower or higher than the temperature of the gas. We performed several field observations with the camera both at daytime and in the evening.

The acquisition of UAV photography was made using a DJI Mavic 2 Enterprise Dual drone. This device is equipped with a 12 MP visual camera (RGB) with a 1/2.3" CMOS sensor. The visual camera has a lens with field of view of approx. 85°, 24 mm (35 mm format equivalent) lens with an aperture of f/2.8. It was also equipped with an Integrated Radiometric FLIR® Thermal Sensor. It is an Uncooled VOx Microbolometer with a horizontal field of view of 57° and f/1.1 aperture, sensor resolution is 160x120 (640x480 image size) and a spectral band of 8-14 μm.

The gas vents were clearly visible on the thermal images, and we discovered additional seeps that were not identified before. Later we confirmed the presence of the gas emissions with in situ measurements on the concentrations of CO2. The visibility of the gas emissions was influenced by parameters like temperature, the orientation of the gas vent, the influence of sunlight, the flux of the gas vent, etc.


How to cite: Kis, B., Tămaș, D. M., Tămaș, A., and Szalay, R.: Using UAV-based Infrared Thermometry in the identification of gas seeps: a case study from Ciomadul dormant volcano (Eastern Carpathians, Romania), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12774, https://doi.org/10.5194/egusphere-egu22-12774, 2022.

Guillaume Jouve et al.

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.

Adele Campus et al.

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.