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 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.
vPICO presentations: Thu, 29 Apr
In the last decade, a range of new remote-sensing techniques has led to a dramatic increase in terrain information, providing new opportunities to understand better Earth surface processes based on geomorphic signatures. Light detection and ranging (LiDAR) technology and, more recently, Structure from Motion (SfM) photogrammetry have the capability to produce sub-meter resolution digital elevation models (DEM) over large areas. LiDAR high-resolution topographic surveying is traditionally associated with high capital and logistical costs. Remotely Piloted Aircraft Systems (RPAS) on the other hand, offer a remote sensing tool capable of acquiring high-resolution spatial data at an unprecedented spatial and temporal resolution at an affordable cost, thus making multi-temporal surveys more flexible and easy to conduct. The scientific community is now providing a significantly increased amount of analyses on the Earth’s surface using RPAS in different environmental contexts and purpose. The goal of this talk is to provide a few useful examples of surveys through airborne LiDAR and RPAS monitoring of anthropogenic landscapes with a specific focus on mining (e.g., open-pit) and agriculture (e.g., terraces). In details, multi-temporal surveys and geomorphometric indexes (including novel landscape metrics) have been carried out and tested in key study areas in order to (i) map the extension of the investigated features, (ii) track any anthropogenic change through time, (iii) analyze the effects of the change related to changes in erosion. The proposed analysis can provide a basis for a large-scale and low-cost topographic survey for sustainable environmental planning and, for example, for the mitigation of anthropogenic environmental impacts.
- Chen J, Li K, Chang K-J, Sofia G, Tarolli P (2015). Open-pit mining geomorphic feature characterization. International Journal of Applied Earth Observation and Geoinformation, 42, 76-86, doi:10.1016/j.jag.2015.05.001.
- Cucchiaro S, Fallu DJ, Zhang H, Walsh K, Van Oost K, Brown AG, Tarolli P (2020). Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions. Remote Sensing, 12, 1946, doi:10.3390/rs12121946.
How to cite: Tarolli, P.: Advanced remote sensing techniques for monitoring anthropogenic landscapes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3574, https://doi.org/10.5194/egusphere-egu21-3574, 2021.
Soil is one of the world’s most important natural resources for human livelihood as it provides food and clean water. Therefore, its preservation is of huge importance. For this purpose, a proficient regional database on soil properties is needed. The project “ReCharBo” (Regional Characterisation of Soil Properties) has the objective to combine remote sensing, geophysical and pedological methods to determine soil characteristics on a regional scale. Its aim is to characterise soils non-invasive, time and cost efficient and with a minimal number of soil samples to calibrate the measurements. Konen et al. (2021) give detailed information on the research concept and first field results in a presentation in the session “SSS10.3 Digital Soil Mapping and Assessment”. Hyperspectral remote sensing is a powerful and well known technique to characterise near surface soil properties. Depending on the sensor technology and the data quality, a wide variety of soil properties can be derived with remotely sensed data (Chabrillat et al. 2019, Stenberg et al. 2010). The project aims to investigate the effects of up and downscaling, namely which detail of information is preserved on a regional scale and how a change in scales affects the analysis algorithms and the possibility to retrieve valid soil parameter information. Thus, e.g. laboratory and field spectroscopy are applied to gain information of samples and fieldspots, respectively. Various UAV-based sensors, e.g. thermal & hyperspectral sensors, are applied to study soil properties of arable land in different study areas at field scale. Finally, airborne (helicopter) hyperspectral data will cover the regional scale. Additionally forthcoming spaceborne hyperspectral satellite data (e.g. Prisma, EnMAP, Sentinel-CHIME) are a promising outlook to gain detailed regional soil information. In this context it will be discussed how the multisensor data acquisition is best managed to optimise soil parameter retrieval. Sensor specific properties regarding time and date of acquisition as well as weather/atmospheric conditions are outlined. The presentation addresses and discusses the impact of a multisensor and multiscale remote sensing data collection regarding the results on soil parameter retrieval.
Chabrillat, S., Ben-Dor, E. Cierniewski, J., Gomez, C., Schmid, T. & van Wesemael, B. (2019): Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics 40:361–399. https://doi.org/10.1007/s10712-019-09524-0
Stenberg, B., Viscarra Rossel, R. A., Mounem Mouazen, A. & Wetterlind, J. (2010): Visible and Near Infrared Spectroscopy in Soil Science. In: Donald L. Sparks (editor): Advances in Agronomy. Vol. 107. Academic Press:163-215. http://dx.doi.org/10.1016/S0065-2113(10)07005-7
How to cite: Mommertz, R., Konen, L., and Schodlok, M.: Regional characterisation of soil properties by combining soil science and hyperspectral and thermal remote sensing: A technical overview of lab and field remote sensing methods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4818, https://doi.org/10.5194/egusphere-egu21-4818, 2021.
The integrity of a sea dike, especially its surface soil and biological revetment, is indispensable for coastal protection, as a dike breach would result in damages and economic losses. Estimates of the condition of a sea dike are typically established by on-site inspections and expert judgement at regular intervals. These status assessments of the protection level of the sea dike evaluate grass coverage and animal burrows, since structural inconsistencies deter the overall safety levels on coastal protection. In laboratory settings, erosion resistance of a sea dike is often determined by means of assessing critical shear stress induced by wave-run up and overtopping. Whereby the grain size distribution and soil aggregate formation on the one hand and the root penetration of the sample on the other are significant factors influencing critical shear stress and therefore erosion resistance.
Drone-/UAV-based remote sensing can be used to easily determine the degree of coverage of the dike revetment via green value detection. Thermal spectroscopy is also already used in agriculture to detect the state of health of plants at an early stage, for example due to a shortage of water. In addition, plants can be classified using hyperspectral imaging data.
We aim to derive transfer functions correlating ground truthing data, drawn from coastal real world- and a full scale laboratory dike, with plant species, its characteristic taxonomic traits and assessed top soil parameters. This approach bears the advantage of yielding an erosion-resistance estimate of the dike cover based on the plant classification using UAV-derived hyperspectral information. Furthermore, taxonomic species are sought to be paired with their respective, site specific, root architecture. Soil parameters such as nutrient availability and humidity will be observed and integrated into the approach, as they bear an impact on subterranean vegetation growth in that plants with lower nutrient availability develop a higher root network (high root length density [cm/cm³]). Finally, grazing livestock on the dike impacts the root system and soil structure as well and both aspects will be investigated comparing mowed against grazed areas as preliminary results show a dike cover void of grazing livestock exhibits a higher root shoot ratio than one with grazing. We hypothesize that classifying plants based on optical, hyperspectral UAV-derived data and the knowledge about the composition of the subsoil, the correlation of plant-specific root architecture and root growth with nutrient availability and agricultural maintenance could provide valuable information about erosion resistance of the dike cover to support dike inspection on an objective basis.
How to cite: Schönebeck, J.-M., Paul, M., Lojek, O., Schröder, B., Visscher, J., and Schlurmann, T.: Measuring soil erosion resistance on coastal dikes linking hyperspectral UAV-data, plant traits and soil information, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7116, https://doi.org/10.5194/egusphere-egu21-7116, 2021.
The characterization of the Earth’s surface cover based on predefined classes is among the fundamental activities in the domain of satellite image analysis image since the early 70s. It was the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites that start to continuously acquired images of the Earth's land surface, providing uninterrupted data to help land managers and policymakers make informed decisions about natural resources and the environment. However, in 2020, the collected data even if are of continuous flow in terms volume of terrabytes per day from various optical and radar systems, are limited in terms of spectral resolution since almost all sensors are limited to a maximum of 25 spectral channels in the visible, near-and-shortwave-and-thermal infrared spectrum. The need of denser spectral information has been highlighted in early 80s and the first satellite-based hyperspectral sensor, AVIRIS, start to provide data allowing the extraction information on material composition and precise surface cover information. Since then few attempt appear but more are undergoing for launching. In 2019, the Italian Space Agency launch the PRISMA hyperspectral satellite which collect spectral data in the 400-2500nm spectrum; in total 250 spectral channels with a spectral width of ~ 12nm, at 30m pixel size. Here we present first results of the use of Level 2D PRISMA hyperspectral data in mapping the surface characteristics of the urban and periurban area of Heraklion city along with the coastal zone of the urban front aiming at the simultaneous creation of a land-and-coastal cover map along with the extraction of coastal bathymetry information using artificial intelligence approaches within open access platforms. The use of hyperspectral information allow the separation of urban surfaces based on material signatures, while the availability of dense spectral information in the blue-green spectrum allow the more accurate retrieval of coastal seascape characteristics. It is envisaged that hyperspectral missions soon to be the normal in Earth Observation, allowing the accurate creation of geospatial information for further use in several applications.
How to cite: Poursanidis, D. and Chrysoulakis, N.: PRISMA Hyperspectral – First insights in the performance in urban surface cover and coastal seascape analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-593, https://doi.org/10.5194/egusphere-egu21-593, 2021.
Geochemical mineral prospecting approaches are mostly point-based surveys which then rely on statistical spatial extrapolation methods to cover larger areas of interest. This leads to a trade-off between increasing sampling density and associated attributes (e.g., elemental distribution). Airborne hyperspectral data is typically high-resolution data, whilst being spatially continuous, and spectrally contiguous, providing a versatile baseline to complement ground-based prospecting approaches and monitoring. In this study, we benchmark various shallow and deep feature extraction algorithms, on airborne hyperspectral data at three different spatial resolutions, 0.8 m, 2 m and 3 m. Spatial resolution is a key factor to detailed scale-dependent mineral prospecting and geological mapping. Airborne hyperspectral data has potential to advance our understanding for delineating new mineral deposits. This approach can be further extended to large areas using forthcoming spaceborne hyperspectral platforms, where procuring finer spatial resolution data is highly challenging. The study area is located along the Rise and Shine Shear Zone (RSSZ) within the Otago schist, in the South Island (New Zealand). The RSSZ contains gold and associated hydrothermal sulphides and carbonate minerals that are disseminated through sheared upper green schist facies rocks on the 10-metre scale, as well as localized (metre-scale) quartz-rich zones. Soil and rock samples from 63 locations were collected, scattered around known mineralised and unmineralized zones, providing ground truth data for benchmarking. The separability between the mineralized and the non-mineralised samples through laboratory based spectral datasets was analysed by applying Partial least squares discriminant analysis (PLS-DA) on the XRF spectra and laboratory based hyperspectral data separately. The preliminary results indicate that even in partially vegetated zones mineralised regions can be mapped out relatively accurately from airborne hyperspectral images using orthogonal total variation component analysis (OTVCA). This focuses on feature extraction by optimising a cost function that best fits the hyperspectral data in a lower dimensional feature space while monitoring the spatial smoothness of the features by applying total variation regularization.
How to cite: Chakraborty, R., Kereszturi, G., Pullanagari, R., Durance, P., Ashraf, S., and Craw, D.: Feature Extraction Techniques for Airborne Hyperspectral Images – Implication for Mineral Exploration, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3551, https://doi.org/10.5194/egusphere-egu21-3551, 2021.
Remote sensing techniques are used to explore geothermal areas. They can offer spatial, temporal and spectral information to map lithological boundaries and hydrothermal alteration in a fast and cheap manner. However, some geothermal areas are densely covered by vegetation, which can hamper remote sensing monitor efforts for geothermal areas.
Vegetation cover in geothermal areas can reflect the subsurface activity, reacting to interactions between soil’s chemical conditions, heat and gas emissions. An example of such is kanuka (i.e. kunzea ericoides), an endemic shrub of geothermal areas in the Taupo Volcanic Zone (TVZ), New Zealand, which has been used as an indicator species for ground-based geothermal studies. This study assesses the use of airborne hyperspectral and thermal data over the Waiotapu Geothermal Field, TVZ, New Zealand, analysing kanuka shrub surface cover and its spectral response to geothermal activity. To explore the capability in hyperspectral remote sensing for geothermal site mapping and exploration, a series of vegetation indices, including; Anthocyanin Reflectance Index, Atmospherically Resistant Vegetation Index, Moisture Stress Index, Normalised Difference Vegetation Index, Simple Ratio Index, Vogelmann Index and Water Band Index were calculated from narrow bandwidth high-resolution hyperspectral.
The spectral response of vegetation was then analysed to explore the effects of geothermal heat, offering surrogate information on vegetation health. Vegetation indices results were compared against the thermal infrared data by visual interpretation and quantitative analyses, which shows strong spatial correlation among the vegetation cover type and heat distribution. Furthermore, exponential trendlines produced the best fit between vegetation indices and thermal infrared data. This correlation indicates soil temperatures affect the vegetation health (e.g. chlorophyll concentrations, newly forming leaves, water content). This relationship can highlight that there is valuable information in airborne hyperspectral data to complement exploration efforts, such as heat flux mapping. We conclude kanuka shrub has the potential to be employed as a proxy in exploration and monitoring of geothermal areas in New Zealand from remote sensing platforms.
How to cite: Rodriguez-Gomez, C., Kereszturi, G., Reeves, R., Rae, A., Pullanagari, R., Jeyakumar, P., and Procter, J.: Airborne Hyperspectral Imaging for Monitoring Geothermal Activity Through Vegetation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10346, https://doi.org/10.5194/egusphere-egu21-10346, 2021.
Mining generates a number of significant environmental impacts, such as increased acidity of the soil/water environment, called mineral Acid Mine Drainage (AMD) being produced when sulphide-bearing material is exposed to oxygen and water. Similar problem represent acid sulphate soils which are naturally occurring soils containing iron sulphide minerals (predominantly pyrite) or their oxidation products. Once these soils are drained, excavated or exposed to air by a lowering of the water table, the sulphides react with oxygen to form sulfuric acid. For both AMD and acid sulphate soils, there is a lack of historical and update records and, consequently, there is a need for new monitoring techniques allowing systematic analysis. A systematic study on how to map mineral patterns that characterize these acid environments using proximal remote sensing and imaging spectroscopy is presented. Furthermore, the upscaling to the spectral and spatial resolution of the satellite data such as WorldView2/3 and Sentinel-2 is discussed as well as an issue of transferability of the developed methods between the test sites which are characterized by different geographical conditions and environments.
How to cite: Kopackova-Strnadova, V.: Acid Mine Drainage (AMD) and Acid Sulphate Soil monitoring using mineral and image spectroscopy: hyperspectral and multispectral approaches, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11864, https://doi.org/10.5194/egusphere-egu21-11864, 2021.
Across Europe there are 2.5 million potentially contaminated sites, approximately one third have already been identified and around 15% have been sanitized. Phytoremediation is a well-established technique to tackle this problem and to rehabilitate soil. However, remediation methods, such as biological treatments with microorganisms or phytoremediation with trees, are still relatively time consuming. A fast monitoring of changes in heavy metal content over time in contaminated soils with hyperspectral spectroscopy is one of the first key factors to improve and control existing bioremediation methods.
At former sewage farms near Ragow (Brandenburg, Germany), 110 soil samples with different contamination levels were taken at a depth between 15-20 cm. These samples were prepared for hyperspectral measurements using the HySpex system under laboratory conditions, combing a VNIR (400-1000 nm) and a SWIR (1000-2500 nm) line-scan detector. Different spectral pre-processing methods, including continuum removal, first and second derivatives, standard normal variate, normalisation and multiplicative scatter correction, with two established estimation models such as Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR), were applied to predict the heavy metal concentration (Ba, Ni, Cr, Cu) of this specific Technosol. The coefficient of determination (R2) shows for Ba and Ni values between 0.50 (RMSE: 9%) and 0.61 (RMSE: 6%) for the PLSR and between 0.84 (RMSE: 0.03%) and 0.91 (RMSE: 0.02%) for the RFR model. The results for Cu and Cr show values between 0.57 (RMSE: 17.9%) and 0.69 (RMSE: 15%) for the PLSR and 0.86 (0.12%) and 0.93 (0.01%) for the RFR model. The pre-processing method, which improve the robustness and performance of both models best, is multiplicative scatter correction followed by the standard normal variate for the first and second derivatives. Random Forest in a first approach seems to deliver better modeling performances. Still, the pronounced differences between PLSR and RFR fits indicate a strong dependence of the results on the respective modelling technique. This effect is subject to further investigation and will be addressed in the upcoming analysis steps.
How to cite: Kaestner, F., Sut-Lohmann, M., Raab, T., Feilhauer, H., and Chabrillat, S.: Remediation-related monitoring of heavy metal concentration of contaminated Technosol using hyperspectral measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11479, https://doi.org/10.5194/egusphere-egu21-11479, 2021.
A deeper understanding of the agricultural sector is needed to provide the informed and transparent framework required to meet increasing resource demands and pressures, without compromising sustainability. In this regard, an integrated management of the ecosystems is critical to address the priorities laid out by global policies and, achieve land degradation neutrality and resource efficient regions. Soils are an essential component of the ecosystem, they function as an important carbon storage, and provide the basis of agricultural activity. For the sustainable management of soil resources, and to prevent land degradation the regular assessments of spatially referenced soil conditions is essential. Critical soil properties, such as texture and organic and inorganic carbon content, provides farmers with the information to detect soil vulnerable to soil erosion and land degradation in its early stages in order to locally intervene and to assess soil fertility. Hyperspectral remote sensing been proven to be an effective method for the quantitative prediction of topsoil properties. However, remote sensing observations of the traditionally used visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelength regions (0.4-2.5 µm) can be limited for the estimation of coarse texture soils due to the lack of distinct spectral characteristics of these properties in the VNIR-SWIR (e.g., sand content, quartz and feldspar mineralogy). Spectral information from the longwave infrared region (LWIR, 8-12 μm) has the potential to improve the determination of these properties, due to the presence of fundamental vibration modes of silicate and carbonate minerals, as well carbon-hydrogen bonds in this spectral range.
The main objective of this study is to evaluate the increased analytical potential of combined VNIR-SWIR and LWIR hyperspectral remote sensing for the estimation of soil properties with the focus on soil organic matter, texture and mineralogical composition. In the frame of EnMAP GFZ/FU airborne campaign in Northern Greece in September 2019, an airborne survey with the HySpex VNIR-SWIR and Hyper-Cam LWIR cameras mounted on a Cessna airplane. A simultaneous ground sampling campaign took place at the agricultural landscape of the Amyntaio region including fields spectroscopy for calibration and validation porpoise, as well as soil sampling of bare soil fields. Fields in the study area have highly variable topsoil composition ranging from silicate to carbonate rich mineralogy, loamy to clay texture and to organic carbon rich fields around a lignite mine in the south-east of the area. Different statistical and machine learning methods such as Partial Least Squares (PLS) and Random Forest (RF) regression are applied to derive soil properties and the variable importance of the spectral dataset is discussed. A further goal of this study is the simulation and validation of the soil products with recent relevant satellite sensors (e.g., EnMAP, PRISMA, ECOSTRESS), as well as upcoming next generation of hyperspectral optical and thermal multispectral satellite missions (ESA CHIME and LSTM, NASA/JPL SBG) to evaluate their potential for quantitative soil properties mapping.
How to cite: Milewski, R., Chabrillat, S., Loy, C., Brell, M., Tziolas, N., Angelopoulou, T., Zalidis, G., and Ben Dor, E.: Advantages using combined VNIR-SWIR and LWIR hyperspectral remote sensing for estimation of soil properties in the Amyntaio agricultural region, Northern Greece, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12612, https://doi.org/10.5194/egusphere-egu21-12612, 2021.
Snow and ice melt processes are key variables in Earth energy-balance and hydrological modeling. Their quantification facilitates predictions of meltwater runoff and distribution and availability of fresh water. Furthermore, they are indicators of climate change and control the balance of the Earth's ice sheets. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), making imaging spectroscopy a powerful tool to measure and map this phenomenon. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and space borne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in the inversion and also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. An additional validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet yields promising results. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.
How to cite: Bohn, N., Thompson, D., Carmon, N., Susiluoto, J., Turmon, M., Helmlinger, M., Green, R., Cook, J., and Guanter, L.: Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14359, https://doi.org/10.5194/egusphere-egu21-14359, 2021.
Throughout Mexico, there is an alarming situation due to the contamination generated by solid waste, because many of the final disposal sites do not have adequate measures to avoid contamination, and that waste when decomposing or burning releases a large number of toxins that severely affect soil and air pollution. Also, not all the waste that is generated ends up in the final disposal sites. In Puebla city, 300 of the 1,700 tons of municipal solid waste generated per day are deposited daily in streets, vacant lots, rivers, and ravines, severely affecting the environment and the health of the nearest population. The study area is part of the metropolitan area of the Puebla city, southeast of Mexico City, capital of the Mexican Republic. At an altitude of 2,137 meters above sea level, where most of the year it has a temperate subhumid climate and with rains from June to October. Around 2,322,686 people live within the study area, which represents 37.6% of the total population of Puebla state. In the study area, cases have been observed where solid waste, such as plastic, cardboard, etc. accumulates near kilns for the manufacture of bricks, which means the use of this waste as fuel for the kilns. Besides, a large number of old quarries have been found where construction material was extracted, which have become clandestine landfills, which accumulate a great diversity of waste, especially waste from construction. These cases were mostly found in the northern part of the city, where the main industrial zones are located. Therefore, the inadequate disposal of solid waste enhances the environmental impact, increases the vulnerability to present greater environmental pollution of the air and soil. Map the environmental impact factors and the location of misplaced waste and evaluated the correlation between them. This mapping will also serve to make zoning of places of priority attention towards management policies of the sites. For this, some environmental factors affected by the presence of solid waste in Puebla city were evaluated. Using remote sensing and geographic information systems, the water stress of the vegetation, change in land use, air pollution, soil temperature was evaluated, and the results were correlated with the location of the residues through an analysis of principal components. The result is a zoning map of priority attention areas. Where the highest areas correspond to a severe environmental impact, containing poorly located solid waste and coinciding with socioeconomic factors of high vulnerability.
How to cite: García Cruzado, S., Delgado Ayala, D., and Ramirez Serrato, N. L.: Mapping of the environmental impact linked to clandestine sites for the final disposal of solid waste using remote sensors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9022, https://doi.org/10.5194/egusphere-egu21-9022, 2021.
The application of remote sensing for monitoring, detecting and analysing the spatial and extents and temporal changes of waste dumping sites and landfills could become a cost-effective and powerful solution. Multi-spectral satellite images, especially in the thermal infrared, can be exploited to characterize the state of activity of a landfill. Indeed, waste disposal sites, during the period of activity, can show differences in surface temperature (LST, Land Surface Temperature), state of vegetation (estimated through NDVI, Normalized Difference Vegetation Index) or soil moisture (estimated through NDWI, Normalized Difference Water Index) compared to neighboring areas. Landfills with organic waste typically show higher temperatures than surrounding areas due to exothermic decomposition activities. In fact, the biogas, in the absence or in case of inefficiency of the conveying plants, rises through the layers of organic matter and earth (landfill body) until it reaches the surface at a temperature of over 40 ° C. Moreover, in some cases, leachate contamination of the aquifers can be identified by analyzing the soil moisture, through the estimate of the NDWI, and the state of suffering of the vegetation surrounding the site, through the estimate of the NDVI. This latter can also be an indicator of soil contamination due to the presence of toxic and potentially dangerous waste when buried or present nearby. To take into account these facts, we combine the LST, NDVI and NDWI indices of the dump site and surrounding areas in order to characterize waste disposal sites. Preliminary results show how this approach can bring out the area and level of activity of known landfill sites. This could prove particularly useful for the definition of intervention priorities in landfill remediation works.
How to cite: Ganci, G., Cappello, A., Bilotta, G., Pollicino, G., and Lodato, L.: Characterizing waste disposal sites by using multi-spectral satellite imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12495, https://doi.org/10.5194/egusphere-egu21-12495, 2021.
Remote sensing technologies, such as satellite and drone imagery, have been proven over the years- due to their constant development- to be extremely useful for environmental monitoring. They may collect and provide data pertaining to natural disasters, state of oceans, atmosphere, land, vegetation, food, public health etc, which are further essential for the effective decision making of public authorities. At the same time such data may facilitate the right for access to environmental information to the public. They also consist valuable tools for environmental law enforcement by allowing to detect for example planning breaches, illegal dumping of waste, illegal logging or illegal oil spills, on which inspections could then focus.
The article briefly presents the legal framework regarding the application of Remote Sensing Technologies in environmental monitoring in the European Union. It also outlines certain limitations of such technologies, such as the need for data verification and the need for data procession according to privacy and personal data law requirements. Important ECtHR and CJEU case law on the issue is approached, while it is examined under what legal circumstances a wider application of Remote Sensing Technologies in environmental monitoring could be envisaged. Finally, Greek legislation on the subject is as a “case study” analyzed.
This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014-2020” in the context of the project “Legal issues derived from the use of monitoring and earth observation technologies to ensure environmental compliance in the Hellenic legal order- HELLASNOMOSAT” (MIS 5047355).
How to cite: Maniadaki, M., Papathanasopoulos, A., Mitrou, L., and Maria, E.-A.: Application of Remote Sensing Technologies in environmental monitoring: legal framework, limitations and potential in the European Union, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12400, https://doi.org/10.5194/egusphere-egu21-12400, 2021.
Currently, natural areas are being devastated by anthropogenic activity. Activities such as agriculture, illegal logging, non-organic farms, and livestock exploitation, disrupt an ecosystem that has been in balance for many years. Therefore, regulations implemented by governments are required for their preservation. However, these regulations are not always the most used in terms of conservation. Such is the case of the town "Tenosique", in this area is one of the most important rivers in Mesoamerica, the Usumacinta River, which is a great regulator of ecological processes and is connected to Mexico with Guatemala. This site has been under the influence of regulations applied to the economic impulse of the area, whether for agricultural and livestock activities, which has affected the apparent vegetation cover, unlike Guatemala that has opted for regulations with a forest conservation approach. These policies sought to boost the agricultural sector, but many deforested areas to carry out this activity turned out not to be suitable due to the type of soil. With the change of regime, financing ends and with it economic activity decreases, leaving the area quite affected and the communities with financial problems. Recently, conservation and protection actions were implemented in the area together with support for these communities. The proximity between Mexico and Guatemala visually shows the results of the application of different public policies. The objective of this study is to quantify the loss and gain of vegetation over time from satellite images of the area, in order to compare this statistic with the different government programs of each era. For this, at least 10 multispectral satellite images of free access will be used, from the Landsat 7 satellite, which has 30 meters of resolution but visually adjustable to 15 meters with the union of its panchromatic channel, and that cover a time range from 1999 to 2020. On these, two processes will be carried out: 1) a normalized vegetation index calculation and 2) a supervised classification. With which it is intended to measure the area and the greenness of a mask of the vegetation cover. The results will serve to update the projects carried out on the site and detect areas of priority interest resolution for larger projects, as well as the future estimation of the critical state of the site regarding the loss of vegetation cover and quantify the conservation efforts that have been carried out. carried out from 2008 to the present.
How to cite: Nieto, J., Vidal García, G., Jácome Paz, M. P., Ruiz Santos, T. X., Nuñez, J. M., and Ramírez Serrato, N. L.: Analysis of change in vegetation cover linked to public policies, case study: Tenosique, Tabasco, Mexico., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6708, https://doi.org/10.5194/egusphere-egu21-6708, 2021.
Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or “tanks”) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over pedological contexts characterized by Vertisol, Ferralsols and Chromic Luvisols in sub-humid and semi-arid Tropical climate, this practice is characterized by an application of black-colored tank silt providing from Vertisol, to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application.
We used data collected in a cultivated watershed (Berambadi, Karnataka state, South India) including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February 2017 and April 2017. The surface soil color over each Sentinel-2 image was classified into two-class (“Black” and “Red” soils). A change of soil color from “Red” in February 2017 to “Black” in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from “Red” to “Black”, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image.
The results showed that 1) the CART method allowed to classify the “Red” and “Black” soil with overall accuracy around 0.81 and 0.76 from the Sentinel-2 image acquired on February and April 2017, respectively, 2) a tank silt application was identified over 97 fields with high confidence and over 107 fields with medium confidence, based on the bootstrap results and 3) the identified soil color changes are not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.
How to cite: Gomez, C., Subramanian, D., Lagacherie, P., Riotte, J., Ferrant, S., Sekhar, M., and Ruiz, L.: Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India)., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8125, https://doi.org/10.5194/egusphere-egu21-8125, 2021.
Global climate change has a major impact on the availability of water in agriculture. Sustainable agricultural productivity to ensure food security requires good agricultural water management.
Soil moisture is one of the important variables in irrigation management, and there are many different techniques for estimating it at different scales, from point to landscape scales.
Cosmic-Ray Neutron Sensor (CRNS) technology has the capability to estimate field-scale soil moisture (SM) in large areas of up to 20 to 30 ha and has demonstrated its ability to support agricultural water management and hydrology studies. However, measurement of soil moisture on a global or regional scale can only be achieved from satellite remote sensing.
Recently, active microwave remote sensing Synthetic Aperture Radar (SAR) imaging from Sentinel-1 shows great potential for high spatial resolution soil moisture monitoring and can be the basis for producing soil moisture maps. However, these maps can be only used after calibration. Such calibration can be done through traditional, point soil moisture sampling or measurement, which is time-consuming and costly. CRNS technology can be used for calibration and validation remote sensing imagery predictions at field and area-wide level.
In this study a conversion model to retrieve soil moisture from Sentinel-1 (SAR) was developed using the VV (vertical-vertical) polarization, which is highly sensitive to soil moisture, and then calibrated and validated using CRNS data from temperate (Austria) and semi-arid (Kuwait) Environments. This study is a major step in the monitoring of soil moisture at high spatial and temporal resolution by combining remote sensing and the CRNS based nuclear technology. The preliminary results show the great potential of using nuclear technology such as CRNS for remote sensing calibration of Sentinel-1 (SAR).
How to cite: Said, H., Mbaye, M., Heng, L. K., Fulajtar, E., Weltin, G., Franz, T., Dercon, G., Strauss, P., Rab, G., Saud Al-Menaia, H., and Ndiaye, M.: High-resolution soil moisture mapping through the use of Cosmic-Ray Neutron Sensor and Sentinel-1 data for temperate and semi-arid environments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9688, https://doi.org/10.5194/egusphere-egu21-9688, 2021.
Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (e.g. Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable “ground truth” labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be used for weakly supervised training of deep-learning models that have a potential to improve predictions on higher resolution data nowadays available. The term weakly supervised learning was originally coined by (Zhou 2017) and refers to the attempt of constructing predictive models from incomplete, inexact and/or inaccurate labels as is often the case in remote sensing. To this end, we investigate advanced deep-learning strategies on Sentinel-1 timeseries and Sentinel-2 optical data to improve large-scale automatic mapping and monitoring of landcover changes in the Amazon area. Sentinel-1 data has the advantage to be resistant to cloud cover that often hinders optical remote sensing in the tropics.
We propose new architectures that are adapted to the particularities of remote sensing data (S1 timeseries and multispectral S2 data) and compare the performance to state-of-the-art models. Results using only spectral data were very promising with overall test accuracies of 77.9% for Unet and 74.7% for a DeepLab implementation with ResNet50 backbone and F1 measures of 43.2% and 44.2% respectively. On the other hand, preliminary results for new architectures leveraging the multi-temporal aspect of SAR data have improved the quality of mapping, particularly for agricultural classes. For instance, our new designed network AtrousDeepForestM2 has a similar quantitative performances as DeepLab (F1 of 58.1% vs 62.1%), however it produces better qualitative land cover maps.
To make our approach scalable and feasible for others, we integrate the trained models in a geoprocessing tool in ArcGIS that can also be deployed in a cloud environment and offers a variety of post-processing options to the user.
Souza, J., Carlos M., et al. (2013). "Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon." Remote Sensing 5(11): 5493-5513.
Zhou, Z.-H. (2017). "A brief introduction to weakly supervised learning." National Science Review 5(1): 44-53.
"Project MapBiomas - Collection 4.1 of Brazilian Land Cover & Use Map Series, accessed on January 2020 through the link: https://mapbiomas.org/colecoes-mapbiomas?cama_set_language=en"
How to cite: Brandmeier, M. and Cherif, E.: Taking the pulse of the Amazon rainforest by fusing multitemporal Sentinel 1 and 2 data for advanced deep-learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3749, https://doi.org/10.5194/egusphere-egu21-3749, 2021.
UAVs (Unmanned Aerial Vehicles) are increasingly used for monitoring river networks with a broad range of purposes. In this contribution, we focus on the use of multispectral sensors, either in the thermal infrared band LWIR (Long-wavelength infrared, 8-15 µm) or in the infrared band NIR (Near-infrared, 0.75-1.4 µm) to map network dynamics in temporary streams. Specifically, we discuss the first results of a set of surveys carried out in 2020 within a small river catchment located in northern Calabria (southern Italy), as part of the research activities of the ERC-funded DyNET project. Preliminary, a rigorous methodology was identified to perform on-site surveys and to process and analyse the acquired images. Experimental results show that the combined use of LWIR and NIR sensors is a suitable solution for detecting water presence in channels characterized by different hydraulic and morphologic conditions. LWIR sensors alone allow one to discriminate water presence only when the thermal contrast with the surrounding environment is high. On the other hand, NIR sensors permit to detect the presence of water in most of the analyzed settings through the estimate of the Normalized Difference Water Index (NDWI). However, NIR sensors can be misled in case of shallow water depth, due to the NIR radiation emitted by the riverbed merging with that of the water. Overall, the study demonstrates that a combined LWIR/NIR approach allows addressing a broader range of conditions. Moreover, the information provided can be further enhanced by combining it with geomorphologic information and basic hydraulic concepts.
How to cite: Micieli, M., Botter, G., Mendicino, G., and Senatore, A.: Thermal and multispectral images from Unmanned Aerial Vehicles (UAVs) for water presence detection in temporary streams: first results, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9546, https://doi.org/10.5194/egusphere-egu21-9546, 2021.
One of the best preconditions for sufficient monitoring of peat bog ecosystems requires a unique collection, processing, and analysis of spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL), and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs), RGB-, multispectral-, and thermal orthoimages, reflecting topo-morphometry, vegetation, and surface temperature information, generated from drone mapping. We applied 34 predictors to feed the Random forest (RF) algorithm. Predictors selection, hyperparameter tuning, and performance assessment were accompanied using Leave-Location-Out (LLO) spatial Cross-Validation (CV) joined with the forward feature selection (FFS) to overcome overfitting. The spatial CV performance statistics unveiled low (R2 = 0.12) to high (R2 = 0.78) model predictions. Predictor importance was used for model interpretation, where the temperature has proved the be a powerful impact on GWL and SM and significant other predictors' contributions such as Normalized Difference Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model was certainly applied and where the predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data and having no knowledge about these environments. The AOA method is perfectly suited and unique for decision-making about the best sampling strategy, notably for limited data to circumvent this issue.
How to cite: Lendzioch, T., Langhammer, J., Vlcek, L., and Minarik, R.: Mapping the groundwater level and soil moisture of a montane peat bog using UAV monitoring and machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6687, https://doi.org/10.5194/egusphere-egu21-6687, 2021.
Urbanization has a major impact on the spatio-temporal variation of near-surface temperature for world cities. Recent studies indicate that the understanding of changes in temperature with urbanization has provided greater insights into the effects of Urban Heat Islands (UHI) on various issues such as excessive energy consumption, health hazard and climate change. In this study, spatio-temporal variations of near-surface temperature for India’s three major cities with different climatic conditions are evaluated. In addition, an attempt is made to establish a quantitative relation between land surface temperature (LST) and various geographical indices indicating vegetation cover (NDVI, Normalized Difference Vegetation Index), water surfaces (NDWI, Normalized Difference Water Index), and impervious land (NDBI, Normalized Difference Built-up Index). The dataset is selected for years 2014 to 2020 for three major cities: (i) Chennai (coastal); (ii) Hyderabad (inland); and (ii) Mumbai (coastal). The study uses Landsat – 8 OLI / TIRS images to derive land use/cover types, land surface temperature datasets, NDVI, NDBI, and NDWI. The preliminary evaluations indicate that the maximum contribution towards the UHI is impervious land, and the effect is more prominent in the areas of rapid urbanization. Urban areas relatively have a high temperature compared to the surrounding rural areas, and the effect is more prominent during night times. The analysis derived from the study will be useful for decision-makers or stakeholders to take necessary actions for reducing the effects of UHI and planning for urban sprawl.
How to cite: Jallu, S., Bommineni, P. C. R., and Srivastav, R.: Remote Sensing-based Spatiotemporal Analysis of Near-Surface Temperatures for Cities Located in Different Climatic Zones, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8788, https://doi.org/10.5194/egusphere-egu21-8788, 2021.
The motion of clouds at a given location can be detected using ground-based all-sky imagers that frequently acquire images of the sky dome. Motion flow is used for minute-scale forecasting of cloud cover and solar irradiance, for example in the case of forecasting photovoltaic power production. While visible-range sky cameras are often applied for this purpose, they neither allow to detect the altitude of clouds, nor accurately detect clouds at night time. However, thermal-infrared all-sky imagers, such as Reuniwatt’s Sky InSight, retrieve brightness temperatures with constant accuracy at day and night time. This allows for the retrieval of diverse cloud parameters such as cloud base height. Atmospheric wind vectors can be derived and geolocalised by combining cloud motion detection and cloud-base height retrieval. In this study, we evaluate the accuracy of atmospheric wind vector retrievals by the means of the Sky InSight. Radiosoundings and wind profiler observations are used as a reference.
How to cite: Kurzrock, F., Boudreault, L.-E., Reinhardt, M., Schoger, S. Y., Potthast, R., Millerioux, Q., and Schmutz, N.: Remote sensing of atmospheric winds using an infrared all-sky imager, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8751, https://doi.org/10.5194/egusphere-egu21-8751, 2021.
Illegal landfills are unfortunately a plague in some areas of Southern Italy and cause significant direct and indirect environmental issues, including contamination of aquifers and dioxin release in the atmosphere due to arson attacks, potentially dangerous for local populations. The Italian government, at both central and local scale, is by long time enforcing these crimes, but the surveillance of wide areas is difficult with traditional on-site methodologies, even because it is possible that landfills are located within private properties not accessible without formal authorization. Therefore, remote sensing technologies are a key to improve and make more efficient the and frequent the monitoring activities.
Crowd for the Environment (C4E) is a project funded by the Italian Ministry of University and Research, aiming at the development of an innovative framework for the identification of illegal landfills using satellite and drone remote sensing in order to support decision makers in the organization of subsequent on-site actions. To this end, a new algorithm to detect possible polluted sites or sources of pollution has been developed. Specifically, the work has been focused on two particularly challenging and inter-related targets like micro-landfills and greenhouses.
Micro-landfills are often the result of waste disposal processes from industrial or agricultural activities partially or totally clandestine. The corresponding unregistered industrial or agricultural plants are potential sources of pollution. The comparison of satellite detections with the database of legal activities allowed to determine whether or not a plant is registered and therefore potentially harmful. In the study area, located in the nearby of the city of Caserta, greenhouses are a typical example of unregistered agricultural infrastructures which could illegally dispose micro-dumps of their plastic cover after the use.
Among the tested algorithms, those working on the spatial characterization of targets based on Scattering Transform were of particular interest. Such algorithms were used to extract textural features from images and their effectiveness was tested in comparison and in conjunction with spectral features within multi-class classifiers. The results obtained on very-high resolution Pleiades images with 50 cm spatial resolution showed that these features can significantly improve the detectors of both the identified targets. In the case of greenhouses, which are targets without significant spectral characteristics, due to their transparency and reflectivity, the features based on Scattering Transform, alone, allow to build very competitive detectors. In the case of micro-dumps, which are targets very difficult to detect from satellite, both for their size and for the heterogeneous spectral characteristics, the use of the Scattering Transform seems the most effective tool, while the combined use of spectral features does not provide particular added value.
Ultimately, the use of the Scattering Transform seems to find an interesting application in the detection of environmental criticalities, also in relation to targets which are particularly difficult to be detected due to their high spectral ambiguity.
How to cite: Cicala, L., Parrilli, S., Angelino, C. V., and Amitrano, D.: Micro-dumps detection in satellite images with Scattering Transform, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8485, https://doi.org/10.5194/egusphere-egu21-8485, 2021.
Variations in volcanic trace gas composition and fluxes are a valuable indicator for changes in magmatic systems and therefore allow monitoring of the volcanic activity. An established method to measure trace gas emissions is to use remote sensing techniques like, for example, Differential Optical Absorption Spectroscopy (DOAS) and more recently SO2-cameras, that can quantify volcanic sulphur dioxide (SO2) emissions during quiescent degassing and eruptive phases, making it possible to correlate fluxes with volcanic activity.
We present flux measurements of volcanic SO2 emissions based on the novel remote sensing technique of Imaging Fabry-Pérot Interferometer Correlation Spectroscopy (IFPICS) in the UV spectral range. The basic principle of IFPICS lies in the application of an Fabry-Pérot Interferometer (FPI) as wavelength selective element. The FPIs periodic transmission profile is matched to the periodic spectral absorption features of SO2, resulting in high spectral information for its detection. This technique yields a higher trace gas selectivity and sensitivity than imaging approaches based on interference filters, e.g. SO2-cameras and an increased spatio-temporal resolution over spectroscopic imaging techniques, e.g. imaging DOAS. Hence, IFPICS shows reduced cross sensitivities to broadband absorption (e.g. to ozone, aerosols), which allows the application to weaker volcanic SO2 emitters and increases the range of possible atmospheric conditions. It further raises the possibility to apply IFPICS to other trace gas species like, for example, bromine monoxide, that still can be characterized with a high spatial and temporal resolution (< 1 HZ).
In October 2020, we acquired SO2 column density distribution images of Mt Etna volcanic plume with a detection limit of 2x1017 molec cm-2, 1 s integration time, 400x400 pixel spatial, and 0.3 Hz temporal resolution. We compare the SO2 fluxes retrieved by IFPICS with simultaneous flux measurements using the mutli-axis DOAS technique.
How to cite: Fuchs, C., Kuhn, J., Bobrowski, N., and Platt, U.: Technological advances to improve the quantification of volcanic emissions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1308, https://doi.org/10.5194/egusphere-egu21-1308, 2021.
Stable isotopes have several applications in geosciences and specifically in volcanology, fluids vs earthquakes studies, environmental surveying, and atmospheric sciences. Both geological and human-related gas sources emit carbon dioxide promoting its molar fraction increase in the lower levels of the atmosphere. The strong dependence of global warming from the carbon dioxide (CO2) concentration in the air promoted the detailed investigation of the sources of CO2. Land use inspection and the correlated increase of air CO2 concentration proved often the potential identification of the gas sources. Both the precise identification of the gas source and the specific contribution are still open challenges in environmental surveying. Isotopic signature allows both source identification and tracking fate of carbon dioxide (i.e. natural degassing in volcanic and active tectonic regions, photosynthetic fractionation in tree forests, and human-related emissions in urban zones). The isotopic signature allows evaluating the environmental impact of specific actions and better addressing the mitigation efforts by tracking fate of CO2.
This study aims to identify the CO2 sources in different ecosystems by using a laser spectrometer that allowed to determine rapidly and with high precision the isotope composition of CO2 in the space and/or at high frequency (up to 1Hz). Various environments include both volcanic, seismic and urban zones because of their strong effects on the low levels of the atmosphere were considered, showing how this kind of instruments can disclose new horizons, in many different applications and especially in the time domain. In the considered zones, both the anthropogenic and geological sources caused the increases of CO2 molar fraction in the last few centuries. Suitable case studies were: i) the air CO2 surveying at Palermo; ii) the soil CO2 emissions at Vulcano (Aeolian Islands - Italy), and iii) the punctual vent CO2 emissions at Umbertide (Perugia - Italy).
The results of this study show detailed investigation of both sources and fate of the CO2 in various environments. The results of the isotope surveying in Palermo show that air CO2 correlated with human activities (i.e. house heating, urban mobility, and landfill gas emissions). Comparison with air CO2 at Umbertide shows the greater contribution of the geogenic reservoir near the active fault of Alto Tiberina Valley. Volcanic CO2 distinguished from biological CO2 by different isotopic signature in the soil gases of Vulcano. The soil CO2 partitioning at the settled zone of Vulcano Porto occurred through both gas source identification and data interpretation through a specifically designed isotopic mixing model.
This study provides several innovative experimental solutions that are suitable to understand the complexity of carbon cycle and unexplored so far environmental scenarios.
How to cite: Capasso, G., Di Martino, R. M. R., Caracausi, A., and Favara, R.: Distinguishing human related, biological, and geological carbon dioxide in the air through isotopic surveying, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9620, https://doi.org/10.5194/egusphere-egu21-9620, 2021.
Understanding of wild animal behaviour and habitat preference are important factors for further assessment of their living space capacity. Red deer Cervus elaphus is an important game species which population has increased from 54 thousand in 2016 to 66 thousand in 2020 (22% increase in last four years) in Latvia. Meanwhile, the number of hunted animals has increased from 12 thousand in 2016 to 20 thousand in 2020 (a 67% increase in the last four years). The increasing number of red deer and other ungulate species results in increased damage to new forest stands and crops. Traditional methods for population abundance estimation and monitoring, such as grazing damage observation, pellet or snow track counts are time and resource consuming and require trained experts. Technological approaches (trail cameras, microphones and drones) have the potential to support and improve the monitoring of wildlife.
In this study, we present results based on the location data of four red deer individuals. Red deers were cached and collared in Mar-Apr 2020, their location has been recorded every 30 minutes since then. The data is used for mapping of red deer migration routes, analysis of living and feeding places as well as movement behaviour. Available geospatial data products are terrain and canopy cover information obtained from LiDAR data, land cover and vegetation density information obtained from Sentinel-2 satellite data as well as proximity to feeding places, natural resources and human settlements. Hedonic regression approach is used for preference evaluation of different factors.
Detection of wild animals is also performed using a drone equipped with thermal and RGB cameras, networks of camera traps and microphones. The data from GPS collars allow validating the detection accuracy of other technological approaches.
How to cite: Jakovels, D., Filipovs, J., Brauns, A., Vecvanags, A., Kocina, I., and Ozolins, J.: Analysis of red deer Cervus elaphus behaviour and habitat modelling using data from GPS collars and available geospatial data products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15436, https://doi.org/10.5194/egusphere-egu21-15436, 2021.
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