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 ap