GI6.3 | Remote sensing for environmental monitoring
EDI
Remote sensing for environmental monitoring
Co-organized by ESSI4/GMPV9
Convener: Annalisa CappelloECSECS | Co-conveners: Sabine Chabrillat, Gaetana Ganci, Gabor Kereszturi, Veronika Kopackova
Orals
| Wed, 26 Apr, 08:30–10:15 (CEST), 10:45–11:55 (CEST), 14:00–14:50 (CEST)
 
Room G2
Posters on site
| Attendance Wed, 26 Apr, 16:15–18:00 (CEST)
 
Hall X4
Posters virtual
| Attendance Wed, 26 Apr, 16:15–18:00 (CEST)
 
vHall ESSI/GI/NP
Orals |
Wed, 08:30
Wed, 16:15
Wed, 16:15
Remote sensing measurements, acquired using different platforms - ground, UAV, aircraft and satellite - have increasingly become rapidly developing technologies to study and monitor Earth surface, to perform comprehensive analysis and modeling, with the final goal of supporting decision systems for ecosystem management. The spectral, spatial and temporal resolutions of remote sensors have been continuously improving, making environmental remote sensing more accurate and comprehensive than ever before. Such progress enables understanding of multiscale aspects of high-risk natural phenomena and development of multi-platform and inter-disciplinary surveillance monitoring tools. The session welcomes contributions focusing on present and future perspectives in environmental remote sensing, from multispectral/hyperspectral optical and thermal sensors. Applications are encouraged to cover, but not limited to, the monitoring and characterization of environmental changes and natural hazards from volcanic and seismic processes, landslides, and soil science. Specifically, we are looking for novel solutions and approaches including the topics as follows: (i) state-of-the-art techniques focusing on novel quantitative methods; (ii) new applications for state-of-the-art sensors, including UAVs and other close-range systems; (iii) techniques for multiplatform data fusion.

Orals: Wed, 26 Apr | Room G2

Chairpersons: Annalisa Cappello, Gabor Kereszturi, Sabine Chabrillat
08:30–08:35
08:35–08:45
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EGU23-1083
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GI6.3
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ECS
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On-site presentation
Nafiseh Kakhani, Thomas Gläßle, Ruhollah Taghizadeh-Mehrjardi, Ndiye Michael Kebonye, and Thomas Scholten

Carbon is an essential element and contributor to healthy soil conditions as well as ecological soil function and productivity. Additionally, carbon is a component of all plants and animals on the planet and is a necessary component of life. Natural vegetation serves as a significant but highly dynamic carbon sink. When vegetation is removed quicker than it can regenerate, for example by harvesting crops or timber, soil carbon is depleted. Thus, understanding the environmental effects and dynamics of loss of vegetation is a crucial prerequisite to turning our natural resource management from a carbon emitter to a carbon sink to avoid that and achieve sustainability. At the same time, the spatial distribution of soil organic carbon is also highly heterogeneous, with variations in climate, other soil characteristics, and land use/land cover affecting how our ecosystem reacts to the loss of vegetation. Thus, to effectively improve green metrics and contribute to the creation of future policies, it is required to conduct research on the changes in vegetation and their effect on soil organic carbon and provide regionally appropriate management advice. Here, in this research, our goal is to examine the "individual treatment effects" (ITE), which are a personalized or individualized effect estimation of one variable on the output, and utilize causal inference to address them.  Using the LUCAS dataset, we explore the heterogeneous treatment effect of percent tree coverage (PTC), as a parameter of the density of trees on the ground, on the soil organic carbon content in Germany. We do this by leveraging some parameters, such as climate data, land use/land cover information, and other information from the soil. We thus offer a data-driven viewpoint for focusing on sustainable behaviors and effectively increasing soil organic carbon content levels.

How to cite: Kakhani, N., Gläßle, T., Taghizadeh-Mehrjardi, R., Kebonye, N. M., and Scholten, T.: Exploring the ‘Individual Treatment Effects’ (ITE) of Vegetation with Causal Inference on Soil Organic Carbon Prediction in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1083, https://doi.org/10.5194/egusphere-egu23-1083, 2023.

08:45–08:55
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EGU23-2114
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GI6.3
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Highlight
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On-site presentation
Nathan Torbick, Aoife Whelan, Nick Synes, Xiaodong Huang, and Vincent Cornwell

The adoption of regenerative agricultural practices is gaining traction as an approach to enhance soil health and sequester carbon to combat climate change. Several sustainability frameworks and programmes are now incentivizing producers to transition to regenerative farming. These evolving initiatives have created a need to build and operate Measurement, Reporting and Verification (MRV) platforms to track cropland practices and impacts. To help scale initiatives, we have developed an automated approach that leverages multi-source remote sensing, data science and machine learning for cost-effective, robust and transparent tracking of tillage practices. Our approach leverages time-series satellite observations from Sentinel-1 and Sentinel-2 constellations, along with ancillary data from SMAP, soils and weather. Within a hierarchical classification, these inputs are blended with dense, independent training data (i.e., “ground truth”) collected across Europe with tens of thousands of samples gathered across France, Belgium, Denmark and the UK. Training data includes observations of crop types and rotations, residue, soil disturbance and field conditions. Together, these multi-source data feed into gradient boosting and Convolutional Neural Networks to ultimately help seasonally classify tillage practices into conventional, reduced or no till at field scale for all major row crops. Withheld independent observations and data science best practices are used to tune model performance and class accuracy depending on regional schemes, residue categories and landscape practice variability. F1 score and Overall Accuracy achieve > 80% with some crop and tillage practice combinations (i.e. corn, soy, wheat conventional) > 0.9. In addition, we share lessons learnt and next challenges. With this approach, the Community of Practice can robustly track every field wall-to-wall over seasons and feed downstream applications, such as estimating Soil Organic Carbon and emissions process modelling. With these tools, and open operational data streams such as Copernucis, we can support scaling regenerative agriculture impacts and grow carbon farming initiatives and ecosystem service markets across Europe. 

How to cite: Torbick, N., Whelan, A., Synes, N., Huang, X., and Cornwell, V.: Tracking tillage practices across European croplands using multi-scale remote sensing and machine learning., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2114, https://doi.org/10.5194/egusphere-egu23-2114, 2023.

08:55–09:05
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EGU23-4049
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GI6.3
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ECS
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Virtual presentation
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Jacob Nieto, Nelly Lucero Ramírez Serrato, Mariana Patricia Jácome Paz, and Tania Ximena Ruiz Santos

Land use classification studies help to quantify the changes in forest cover that may occur at a given site over time. This quantification helps us understand the effect of the natural and anthropogenic processes over the study site. Activities such as agriculture, cattle ranching and illegal logging, which in turn are related to the evolution of the site's public policies, can be evaluated through classification studies. Tenosique area, in the southeast of Mexico, is a clear example of the consequences of these programs, being largely benefited by economic consent for agriculture and more for cattle ranching, and, suffering,  in 1974, a complete  turn in productivity activities because it was given full support in exploration and obtainment of hydrocarbons. This led to a crisis that left the area devastated and later became a protected area in 2008, which resulted in illegal logging, and land use for agriculture within the tropical forest, among others. With remote sensing, the task of quantifying the effect of public policies has become increasingly influential and many studies are being carried out to evaluate the current state of Tenosique. However, the results are known to depend directly on the images and methodologies used for this task.Because of this, this project, proposes, in a practical exercise, to determine how much these results may vary with respect to the images used as input for the supervised classification, and if this variation is significant enough to establish rules of operation on methodologies and determine ranges of the parameters of the images to perform a better land use classification. The aim of this project is to determine the margin of variability in the classification result over a given study area, using images from different satellite platforms, Landsat and RapidEye, together with the analysis of the properties of each image, when acquired by the satellite. In addition, the degree of affectation in the image by meteorological changes such as tropical haze in the source image and its respective corrected image was evaluated. The main results are:  individualization of complications and advantages derived from the resolution of the images, identification of the main steps for the possible corrections that can be needed for the images, advantages that are used for analyzing the metadata before doing some process to the images and finally, presenting a decision tree based on this information. It is important to emphasize that this study allows us to delimit the scope and limitations of the land use classifications made in the study area. Acknowledgments: Tania Ximena for the Planet images and Humberto Abaffy-Castillo, Ulises Gracía-Martínez and Mario Seinos-Jiménez for technical help in the project.

How to cite: Nieto, J., Ramírez Serrato, N. L., Jácome Paz, M. P., and Ruiz Santos, T. X.: Comparison of coverage obtained by land use classification using landsat and RapidEye. Case study: Tenosique, Tabasco, Mexico., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4049, https://doi.org/10.5194/egusphere-egu23-4049, 2023.

09:05–09:15
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EGU23-12111
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GI6.3
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On-site presentation
Zhigang Sun, Wanxue Zhu, Ehsan Eyshi Rezaei, Jinbang Peng, Danyang Yu, and Stefan Siebert

Accurate and in-time monitoring of cropping systems is critical to precision farming in order to facilitate decision-making for agronomic management and enhancing crop yield under changing climate. In this study, multi-source unmanned aerial vehicle (UAV) remote sensing observations were conducted at several key growing stages of crops at a standard wheat-maize cropping system field trials in the North China Plain from 2018 to 2020. Crop leaf area index, above-ground biomass, chlorophyll content, grain yield, and plant density were estimated using multi-source UAV remote sensing observations (including RGB, multi/hyperspectral, LiDAR, and thermal sensors) processed by machine/deep learning approaches.

In this study, we will give a comprehensive research introduction focusing on how to improve the estimation accuracy of the above crop growth variables via UAV remote sensing and machine/deep learning approaches, including three aspects:

(1) Data source and fusion, including the integration of multi-source UAV information for comprehensive maize growth monitoring, comparison of UAV-based point clouds with different densities for crop biomass estimation, and crop chlorophyll content estimation using multi-scale hyperspectral information.

(2) Optimization of UAV observation management: we will answer when is the most relevant phenological stage for maize yield estimation via high-frequent UAV observations; investigate extrapolation artefacts, validate the suitability and discuss the uncertainty of the UAV-based strategies for 'model calibration at a small site while applying these models at a large extent' for crop monitoring.

(3) Modeling improvement will give two cases to introduce improving crop biomass estimation accuracyand realize the plant density counting during the vigorous growing period employing deep learning.

How to cite: Sun, Z., Zhu, W., Eyshi Rezaei, E., Peng, J., Yu, D., and Siebert, S.: Multi-source UAV remote sensing and AI for crop growth monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12111, https://doi.org/10.5194/egusphere-egu23-12111, 2023.

09:15–09:25
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EGU23-12505
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GI6.3
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ECS
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On-site presentation
Moshe (Vladislav) Dubinin, Yagil Osem, Dan Yakir, and Tarin Paz-Kagan

Dryland forests are highly climate-sensitive are facing more frequent droughts and, consequently, increasing tree mortality, extreme wildfire events, and outbreaks of forest insects and pathogens. These changes, associated with climate change, are leading to biodiversity loss and the deterioration of related ecosystem services. Understanding the relationships between forest structure and function is essential for managing dryland forests to adapt to these changes. We studied the structure-function relationships in four dryland conifer forests distributed along a semiarid to sub-humid climatic aridity gradient. Forest structure was represented by leaf area index (LAI) and function by gross primary productivity (GPP), evapotranspiration (ET), and the derived efficiencies of water use (WUE= GPP/ET) and leaf area (LAE = GPP/LAI). The water and carbon fluxes at the ecosystem level were estimated by an empirical approach in which regression models were developed to relate multiple spectral data (VIs) derived from VENμS and Sentinel-2A satellites, combined with meteorological data, to local eddy covariance measurements from flux tower records available at three of the four study sites. The red-edge-based MERIS Terrestrial Chlorophyll Index (MTCI) from VENμS and Sentinel-2A showed strong correlations to flux tower GPP and ET measurements (R2cal >0.91, R2val >0.84). Using our approach, we showed that as LAI decreased with decreasing AI (dryer conditions), estimated GPP and ET decreased (R2>0.8 to LAI), while WUE (R2=0.68 to LAI) and LAE increased with decreasing AI. We propose that the higher WUE and LAE reflect an increased proportion of sun vs. shade leaves as LAI decreases. The results demonstrate the importance of high-resolution spectral and spatial data in low-density dry forests and the intricate structure-function interactions in the forests’ response to drying conditions.

How to cite: Dubinin, M. (., Osem, Y., Yakir, D., and Paz-Kagan, T.: Investigating the relationships between the leaf area index and forest functions of dryland conifer forests along an aridity gradient using VENµS and Sentinel-2 satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12505, https://doi.org/10.5194/egusphere-egu23-12505, 2023.

09:25–09:35
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EGU23-14746
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GI6.3
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ECS
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On-site presentation
Riccardo Scodellaro, Ilaria Cesana, Laura D'Alfonso, Margaux Bouzin, Maddalena Collini, Giuseppe Chirico, Roberto Colombo, Franco Miglietta, Marco Celesti, Dirk Schuettemeyer, Sergio Cogliati, and Laura Sironi

The accurate retrieval of Solar-Induced chlorophyll Fluorescence (SIF) is a pivotal target for Earth Observation since SIF can be easily monitored through optical remote sensing and provides unique information concerning the vegetation health status. Here, we propose i-φ-MaLe (metti il nome per esteso), a novel algorithm, which couples the Fourier analysis with a supervised machine learning-based procedure trained with the atmosphere-canopy radiative transfer (RT) SCOPE model.  i-φ-MaLe is the first method able to simultaneously retrieve, from the vegetation reflectance spectra, the Top Of Canopy SIF spectrum, the SIF spectrum corrected for leaf/canopy reabsorption (i.e. at photosystem level), the quantum efficiency (Fqe) and three canopy-related biophysical parameters (Leaf Area Index - LAI, Chlorophyll content - Cab and APAR) in few milliseconds. Validation procedures, based on the analysis of RT simulations, demonstrated that i-φ-MaLe, in experimental conditions (signal to noise ratio – SNR >= 500), estimates each biophysical parameter and SIF spectrum with a relative root mean square error (RRMSE) lower than 5%. In order to investigate the seasonal and daily dynamics of SIF, LAI, Cab, Fqe and APAR, the method has been also applied to field experimental data collected in the context of the AtmoFLEX and FLEXSense ESA campaigns, both at top-of-canopy (TOC) and tower (~100 meters) levels. Concerning the TOC scenario, the retrieved annual dynamic for SIF spectra has been compared with the results obtained by inversion-based methods, showing a good consistency amongthe different approaches (RRMSE ~ 10%). Moreover, SIF daily and annual dynamics, retrieved by excluding the oxygen spectral bands affected by the atmospheric reabsorption,  have been investigated for  high tower measurements. . In this context, i-φ-MaLe provided  promising results that can integrate and possibly overcome complex and computationally expensive atmospheric compensation techniques actually needed to retrieve fluorescence from oxygen absorptions bands. This study demonstrates a promising potential to exploit ground and tower spectral measurements with advanced processing algorithms, for improving our understanding on the link between canopy structure and physiological functioning of plants. Moreover, i-φ-MaLe can be straightforwardly employed to process reflectance spectra and open new perspectives in fluorescence retrieval at different scales.

How to cite: Scodellaro, R., Cesana, I., D'Alfonso, L., Bouzin, M., Collini, M., Chirico, G., Colombo, R., Miglietta, F., Celesti, M., Schuettemeyer, D., Cogliati, S., and Sironi, L.: i-φ-MaLe: a novel AI-phasor based method for a fast and accurate retrieval of multiple Solar-Induced Fluorescence metrics and biophysical parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14746, https://doi.org/10.5194/egusphere-egu23-14746, 2023.

09:35–09:45
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EGU23-6520
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GI6.3
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ECS
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On-site presentation
Vasudha Darbari, Hackney Christopher, Vasilopoulos Grigorios, Forsters Rodney, and Parsons Dan

The wetlands and lakes that make up more than 30% of Cambodia's terrain are home to a diverse range of resources and biodiversity. More than 46% of the population lives and works in these wetlands while 80% of the local population relies on their vital resources for sustenance such as fish, food, water and vegetables. This makes Cambodia one of the nations with the highest reliance on wetland and lake ecosystems in the world. On-going development in the region has boosted the rates of  urbanization. Urban expansion has deteriorated wetland ecosystems through land reclamation and infilling projects as well as hydrological and sediment cycle disruptions. It has also increased the demand for mined sand from the Mekong River. Mapping and monitoring the extent and distribution of wetland ecosystems in order to quantify the impact of human activities on these vital areas is critical for maintaining the ecological balance and promoting the sustainable development of an extensively eco-service dependent country such as Cambodia. In this study we combine spaceborne multispectral and radar remote sensing datasets with machine learning classification models and algorithms within the Google Earth Engine to monitor the changes observed in Cambodian wetlands through time. Our classifier is trained by comparing Sentinel 1 Synthetic Aperture Radar data to corresponding multispectral images captured from Landsat. We then use the classifier to monitor wetland extent through time from 1989 to present using merged Landsat 5 and 8 databases. With our maps and areal statistics, we identify the spatio-temporal trends and changes in wetland cover linked to climatic patterns and local anthropogenic influence connected to sand mining from the Mekong River and land infilling. In the last 15 years, about half the country’s wetlands have disappeared, with 15 out of 25 lakes near the capital completely infilled with sand that can be clearly observed with analysis of satellite data.

How to cite: Darbari, V., Christopher, H., Grigorios, V., Rodney, F., and Dan, P.: Mapping Cambodian Wetlands with Satellite Imagery and Google Earth Engine’s Machine Learning Algorithm., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6520, https://doi.org/10.5194/egusphere-egu23-6520, 2023.

09:45–09:55
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EGU23-6995
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GI6.3
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On-site presentation
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Felix Dacheneder

The detection of hydromorphological structures gained more attention during the last decades. Many approaches of different scopes, scales and purposes have been developed. They can either be classified as stand-alone methods, like the German River Habitat Survey, which evaluates the hydromorphological integrity on a catchment scale or as methods being part of an ecological assessment, which includes the estimation of hydromorphological characteristics on the scale of respective study sites. The main purposes of detecting hydromorphological structures are to investigate the spatial characteristics and temporal scale of change to collect reliable and comparable data in a sampling setup of an ecological multi habitat sampling. Especially river restoration projects often lack the detection of positive effects on aquatic biota induced by missing or wrong development of physical river habitat structures (PRHS).

Most methods available for determining PRHS are insufficient for this task as they lack sufficient temporal and spatial resolution. Examples thereof include overview methods based on topographic maps and remote sensing. On the other hand, visual assessment methods do not reach the required accuracy and objectiveness or are too general if too few hydromorphological structures are assessed. Therefore, this research proposes the combination of Unmanned Areal Vehicle (UAV) and high-resolution sensors. This combination creates high-resolution imagery or point clouds by using multispectral sensors or Lidar scanner.

In a case study of the river Lippe, the methods for detecting PRHS on Structure from Motion (SfM) high-resolution imagery with deep learning, based classification methods are applied. Results indicate the potential from different deep learning classification approaches to identify physical river habitat structures being able to assess the development over time.

How to cite: Dacheneder, F.: Detecting hydromorphological structures using an AI-based analysis of high-resolution drone imagery to access physical river habitat development, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6995, https://doi.org/10.5194/egusphere-egu23-6995, 2023.

09:55–10:05
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EGU23-11414
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GI6.3
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ECS
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On-site presentation
Justine Sarrau and Abdelgadir Abuelgasim

In the context of global sea level rising, coasts are directly impacted. The retreat to coastlines and submersion of anthropic installations are among the major impacts. It is thus imperative to continuously monitor the coastlines status and devise the means and techniques to effectively assess their status. The United Arab Emirates (UAE) for example is a country which has a long sandy coastline. In this research, an algorithm was developed that makes use of remote sensing temporal data to assess the variability of the coastline in the UAE. The algorithm is used to automatically extract the whole coastline between 1991 and 2021 from Landsat 5 and 8 satellite images. They were selected for 1991, 2001, 2013 and 2021 because of the availability of data, and the significant changes that have been done in coastal areas due to urban development during this period.

Only the Landsat spectral bands of green and near-infrared were utilized to calculate the spectral index of detection of the coast DDWI (Direct Difference Water Index). It is the first step of the algorithm developed. Then is used an automatic threshold Otsu to differentiate the land from water. The result is filled to remove the main artifacts and a canny edge detector is used to detect the coastline. At the end of the algorithm, the result is georeferenced because it lost it during the process. The georeferenced layer is polygonised so that the remaining artifacts are easier to remove. Then, a mask layer was created including boats, clouds, etc… and it is removed from the polygonised layer to get the final extracted coastline.

The preliminary findings of this study show that the sandbanks have increased during the period of the study along the Arabian Gulf waters, suggesting that the coastline is retreating. The results showed a development of the sandbanks towards the Arabian Gulf in several places along the northern coastline but also their general retreat on the north-western one. This can be explained by the sediment settlement or the backfills that have been done to create new islands especially around Abu Dhabi city and Dubai. The creation of mangroves plantations or port infrastructures in the same place has completely changed the coastline layout of the UAE.

On the other side of the UAE, along the sea of Oman, the sandbanks have retreated, suggesting either soil erosion by water currents or advancement of the coastline. The results show no significant change at all and no sandbanks. The only changes observed are linked to the anthropic modification of the coast. While the coastline did not change, the developed algorithm detected scattered sandbanks as the coastline. This confusion likely comes from the similar reflectance of sandbanks in shallow water with the sand of the coast. A further improvement for the developed algorithm will be pursued in the future to reduce such confusions.
This methodology is applicable worldwide, but it is necessary to monitor the results for sandy areas such as the Middle East.

How to cite: Sarrau, J. and Abuelgasim, A.: Temporal variations of United Arab Emirates coastline from 1991 to 2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11414, https://doi.org/10.5194/egusphere-egu23-11414, 2023.

10:05–10:15
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EGU23-14979
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GI6.3
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ECS
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On-site presentation
Juanjuan Yu, Xiufeng He, Mahdi Motagh, Peng Yang, and Jia Xu

Coastal aquaculture has become one of the main sources of animal protein and plays an important role in food and nutrition supplies and security around the world. Accurately mapping aquaculture areas is the basis for its sustainable management and use, and provides important support to policy development and implementation at regional, national, and global levels. Considering the concentrated and densely distributed characteristics of aquacultures, it is difficult to distinguish the dikes between aquacultures and identify small-scale aquacultures using medium-and low-resolution SAR images. GaoFen-3 (GF-3) is the first launched full-polarimetric C-band SAR satellite of China at metre-level resolution. This study aims to use a novel combination model to extract coastal aquacultures in the Yancheng coastal wetland, China on the basis of GF-3 Fully Polarimetric SAR Imagery. Polarimetric decomposition algorithms were applied to extract polarimetric scattering features and feature optimization was applied based on the separability index. To separate adjacent and even adhering ponds into individual aquaculture objects, we proposed a novel model that integrated two UNet++ subnetworks with the marker-controlled watershed (MCW) segmentation strategy to obtain more refined coastal aquaculture results. The accuracy assessment results demonstrated a considerable performance, with F1 greater than 95%, IoU greater than 90%, and insF1 higher than 85%. The experimental results indicate that the proposed algorithm achieved fairly high accuracy in aquaculture extraction and can effectively improve the boundary quality of individual aquacultures.

How to cite: Yu, J., He, X., Motagh, M., Yang, P., and Xu, J.: Coastal Aquaculture mapping using a novel combination model of GF-3 Fully Polarimetric SAR Imagery: A case study of Yancheng coastal wetland, China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14979, https://doi.org/10.5194/egusphere-egu23-14979, 2023.

Coffee break
Chairpersons: Annalisa Cappello, Gaetana Ganci, Gabor Kereszturi
10:45–10:55
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EGU23-13797
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GI6.3
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Virtual presentation
Lava flow delineation by using Sentinel2 and Landsat8 images: Kīlauea -Leilani 2018, Italy Etna 2021, La Palma Cumbre Vieja 2021 cases
(withdrawn)
massimo musacchio, Malvina Silvestri, Maria Fabrizia Buongiorno, Federico Rabuffi, and Sergio Falcone
10:55–11:05
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EGU23-15311
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GI6.3
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On-site presentation
Cristina Proietti, Emanuela De Beni, Massimo Cantarero, and Tullio Ricci

The 2021 eruptive activity at Mt Etna was characterized by 57 paroxysmal events at the South-East Crater, the most active among its four summit craters. These episodes of Strombolian activity and high lava fountains fed lava flows towards East, South, and South-West and caused ashfall in the surroundings of the volcano. In 2022 the SEC gave rise to only two paroxysms in February and effusive activity in May-June and since November (still ongoing). Although the impacted area does not include permanent infrastructures it is of high tourist attraction. Hence, timely mapping of each lava flow field was mandatory for hazard mitigation. The high frequency of the 2021 paroxysms, up to two events in 24 hours, forced us to implement a multidisciplinary approach based on various remote sensing techniques, with different spatial resolutions and revisiting time. In particular, several satellite images were processed, depending on data availability and weather conditions. Data acquired by Sentinel-2 MSI, Skysat, Landsat-8 OLI, and TIRS allowed us to map the lava flow fields at a spatial resolution ranging from 0.5 to 90 meters. High-spatial resolution (from 4.5 up to 55 cm) DEMs and orthomosaics were also realized elaborating the visible and thermal images acquired through Unmanned Aerial Systems (UASs) surveys. Moreover, data acquired by the thermal cameras of the Istituto Nazionale di Geofisica e Vulcanologia permanent network were re-projected into the topography for analyzing the lava flow field evolution at 5-meter spatial resolution. These multi-platform remote sensing data allowed for mapping the lava flows and compiling a geodatabase reporting the main geometrical parameters (e.g. length, area, average thickness, and volume). The resulting multi-sensor methodology enabled, for the first time on Etna, to timely and accurately characterize frequently occurring effusive events.

How to cite: Proietti, C., De Beni, E., Cantarero, M., and Ricci, T.: Timely mapping and quantification of volcanological parameters: the 2021-2022 Etna lava flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15311, https://doi.org/10.5194/egusphere-egu23-15311, 2023.

11:05–11:15
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EGU23-17477
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GI6.3
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On-site presentation
Carmine Gambardella, Roberto Moretti, Giuseppe Ciaburro, Dario Martimucci, Francesco Marconi, and Rosaria Parente

Campi Flegrei caldera (CFc; Southern Italy) is the archetype for volcanic risk occurring within a highly anthropized area. CFc was mostly shaped by the collapse following the Neapolitan Yellow Tuff eruption (NYT) ~15 ky BP, which generated about 50 km3 of volcanic products mainly deposited via huge pyroclastic flows. More than 50 eruptions from several volcanic centers were generated within the caldera after the NYT, with last eruption occurring in 1538 (Monte Nuovo eruption). Since the 1950s, CFc experiences a long-term unrest in the Pozzuoli area. After the 1969-72 and 1982-84 episode, uplift started again in 2004, raising serious concern to population and authorities also because of the recurrent seismic activity and the persistence of fumarolic emissions fed by the underlying hydrothermal system.

In this context, thermal monitoring of the caldera is a strategic issue for volcanic forecasting, considering that several areas are prone to the opening of volcanic vents. The large size of the onshore portion of the CFc (90 km2), the difficulties to access several anomalous sites and the huge degree of anthropization make the direct assessment of ground-thermal anomalies and the realization of periodic measurement campaigns very difficult, time-consuming and unsafe.

Here we report on a detailed airborne thermal mapping from a flight made on 15 April 2022 at ~6.30 am (local time). Thermal acquisition was performed with a wide-array broadband thermal sensor in conjunction with optical imagery in Red-Green-Blue bands via a 150 MP camera. The sensor platform and the aircraft ¾  including logistic facilities, agreements with military airports and authorizations to fly ¾  are a strategic asset of the BENECON. The high resolution of thermal mapping (instrumental accuracy: 0.05 °C on temperature; pixel size: 0.45m x 0.45m) in conjunction with real-time acquisition of optical images allows a straightforward discrimination of natural ground anomalies from thermal emissions and spots due to anthropic activities. Ground thermal anomalies related to volcanic-hydrothermal activity and associated with the caldera unrest are concentrated in the well-known Solfatara and Pisciarelli sites, whereas minor features are detected on the relief bordering the western side of Agnano plain, inside the Astroni crater and on the southern flank of Monte Nuovo, in line with results from existing ground surveys. At Solfatara and Pisciarelli, the shape of measured thermal anomalies matches that of CO2 fluxes interpolated from literature data. The consistency between heat fluxes computed from airborne-detected ground temperatures and soil CO2 fluxes (e.g., in the order of 100 MW for the Solfatara crater) confirms that steam condensation from hydrothermal activity is presently the dominant engine responsible for endogenous heat release at CFc.

The fast execution of the airborne survey, the rapid data processing and post-processing and the capability of detecting the most subtle anomaly prompt for periodic surveys of the CFc thermal flux aimed at 1) the tracking of existing anomalies 2) the rapid detection of new thermal features and 3) the building of time-series. Integration with optical and, in perspective, hyperspectral VNIR images foster an unprecedented capability to monitor the ongoing volcanic unrest.

How to cite: Gambardella, C., Moretti, R., Ciaburro, G., Martimucci, D., Marconi, F., and Parente, R.: Thermal features and heat budget of Campi Flegrei unrest caldera from fast, high-resolution airborne mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17477, https://doi.org/10.5194/egusphere-egu23-17477, 2023.

11:15–11:25
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EGU23-12720
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GI6.3
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ECS
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On-site presentation
Francesco Romeo, Luigi Mereu, Stefano Corradini, Luca Merucci, and Simona Scollo

The characterization of the eruption source parameters (EPS) of explosive eruptions is of vital importance to prevent damages, mitigate environmental impact and reduce aviation risks.  We consider highly explosive eruptions with a Volcanic Explosive Index (VEI) greater than 3. During these eruptions, a great number of volcanic particles are ejected into the atmosphere where they can remain suspended for several weeks. Satellite passive sensors can be adopted to monitor volcanoes due to their high spatial and temporal resolution. 

In this work we combine the Microwave (MW) and Millimetre-wave (MMW) observations with Thermal-InfraRed (TIR) radiometric data from Low Earth Orbit (LEO) satellites to have a complete characterization of the volcanic clouds. MW-MMW passive sensors are adopted to detect larger volcanic particles (i.e. size bigger than 20 µm) by working at lower frequencies. TIR observations are employed to study smaller particles due to the sensor settings which work at smaller wavelengths. We describe new physical-statistical methods together with machine learning techniques aiming at detecting and retrieving volcanic clouds masses of 2015 Calbuco, 2014 Kelud as well as other eruptions having high explosive activities worldwide. Concerning the detection, we compare the well-known split-window methods with a machine learning algorithm named Random Forest (RF). This work highlights how the machine learning model is suitable to automatically identify tephra contaminated pixels by combining different spectral information (i.e. MW-MMW and TIR) coming from different satellite platforms. Indeed, we used data coming from: Advanced Technology Microwave Sounder (ATMS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on board the Suomi-NPP LEO satellite; Microwave Humidity Sounder (MHS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on board the Metop series.  In terms of retrieval, the new developed Radiative Transfer Model Algorithm (RTMA) is designed to estimate the total columnar content (TCC) and in turn the mass, for both MW-MMW and TIR observations. The synthetic BTs (simulated by RTMA) are linked with the observed BTs to retrieve the volcanic clouds features. In this respect, two minimization techniques, the Maximum Likelihood Estimation (MLE) and the Neural Network (NN) architecture, are also compared and discussed. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands but also with previous studies on the deposit as well as other validated satellite retrieval methods. 

In conclusion, this work shows how the machine learning model can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near source volcanic cloud.

How to cite: Romeo, F., Mereu, L., Corradini, S., Merucci, L., and Scollo, S.: Volcanic cloud detection and retrieval by micro-millimetre-waves and thermal-infrared satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12720, https://doi.org/10.5194/egusphere-egu23-12720, 2023.

11:25–11:35
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EGU23-12310
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GI6.3
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Highlight
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On-site presentation
Giuseppe Puglisi, Alessandro Bonforte, Maria Fabriza Buongiorno, Lucia Cacciola, Francesco Guglielmino, Gaetana Ganci, Massimo Musacchio, Simona Scollo, Danilo Reitano, Malvina Silvestri, and Letizia Spampinato

The Geohazard Supersites and Natural Laboratories (GSNL) is an initiative of the Group of Earth Observation (GEO) that has started in 2007 with the Frascati declaration, in which the GeoHazards Community of Practice recommended to: “... stimulate international and intergovernmental effort to monitor and study selected reference (geologic hazards) sites, by establishing open access to relevant datasets according to GEO principles, to foster collaboration between various partners and end users”. Since the beginning the main idea has been the improvement of the hazard assessment by combining space- and ground-based datasets provided by the Space Agencies and the research institutions managing the in-situ observation systems, respectively.

According to the definition of Supersite, since the early stage of the GSLN initiative, Mt. Etna has been identified as one of the Supersites due to its almost continuous eruptive activity, the great amount of satellite and in-situ data available, and the advanced in-situ multi-parametric observing systems. Officially, Mt. Etna is a Permanent Supersite since 2014. The Space Agencies provide quotas of SAR and high-spatial resolution optical multispectral satellite data and INGV offers geophysical, geochemical, and volcanological data. The data are accessible via an open access platform implemented in the framework of the EC FP7 MED-SUV project, and is going to be integrated in the EPOS research infrastructure.

During the past few decades, Mt. Etna has erupted almost every year offering the optimal conditions to apply the Supersite concept; thus here we report some relevant examples of the integrated use of the space-and ground-based data applied to Mt. Etna’s eruptions, highlighting how such complementarity improved the monitoring of the eruptive events and the assessment of the associated hazards.

How to cite: Puglisi, G., Bonforte, A., Buongiorno, M. F., Cacciola, L., Guglielmino, F., Ganci, G., Musacchio, M., Scollo, S., Reitano, D., Silvestri, M., and Spampinato, L.: Integration between space- and ground-based observations in areas prone to volcanic hazard: the experience of Mt. Etna Supersite, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12310, https://doi.org/10.5194/egusphere-egu23-12310, 2023.

11:35–11:45
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EGU23-14315
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GI6.3
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ECS
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Highlight
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On-site presentation
Lorenzo Solari, Joanna Balasis-Levinsen, and Henrik Steen Andersen

The European Ground Motion Service (EGMS) allows a wide spectrum of users to access ground motion data over 30 European countries for free, under the Copernicus data policy. The EGMS aims to serve for various applications, of which geohazards are probably the primary target. Also, the Service establishes a baseline for studies dedicated to localised deformation affecting buildings and infrastructure in general.

The EGMS is the result of a massive computational effort to process thousands of Sentinel-1 images and derive three levels of products: (a) basic, i.e. line of sight (LOS) velocity maps in ascending and descending orbits referred to a local reference point; (b) calibrated, i.e. LOS velocity maps calibrated with a geodetic reference network and (c) ortho, i.e. components of motion (horizontal and vertical) anchored to the reference geodetic network. The EGMS is implemented under the responsibility of the European Environment Agency in the frame of the Copernicus Programme.

The EGMS baseline (2016-2020) and the first annual update (2016-2021) were made available to users in the summer of 2022 and in the first quarter of 2023, respectively. The EGMS products are displayed in the EGMS Explorer (https://egms.land.copernicus.eu/), where users can investigate the data in a 3D web interface, explore the temporal behaviour of ground motion through time series and download one or multiple data tiles. External web map services can be imported in the EGMS Explorer to ease the interpretation of the interferometric measurements.

The strategy to update satellite interferometric time series is a hot topic for wide area processing services. So that, one of the goals of this presentation is to show the EGMS update strategy, which should guarantee the best trade-off between the identification of new coherent targets and motion areas and the continuity of the Service in terms of technical implementation and noise level.

The expected wide usage of the EGMS products required to setup a validation system that has two goals: verify the usability of the data for the expected range of applications and assess the quality of the products with respect to service requirements. Validation is based on seven activities performed in sixteen different countries and in several validation sites, which are representative for thematic applications (e.g. mining-induced ground motion) in different environments of Europe. To guarantee reproducibility of results, the validation data (e.g. levelling or corner reflectors time series, landslide databases) will be made available to users according to licensing conditions. The results of the validation exercise will be available to users in Q3 2023.

How to cite: Solari, L., Balasis-Levinsen, J., and Andersen, H. S.: European Ground Motion Service: production status and validation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14315, https://doi.org/10.5194/egusphere-egu23-14315, 2023.

11:45–11:55
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EGU23-15413
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GI6.3
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On-site presentation
Mathieu Le Breton, Arthur Charléty, Nicolas Grunbaum, Éric Larose, and Laurent Baillet

Passive RFID tags are opening new capabilities of monitoring in geoscience [1], applied to landslide [2-3], snowpack [4] or riverine pebble monitoring. This study investigate the ability to localize passive RFID tags (working at 868 MHz) from the air in harsh conditions met in natural areas outdoors. The tags are localized with the synthetic aperture radar method (SAR)  with a mobile reader from above, installed on a rail and equiped with a differential GNSS. The tags are localized either directly on the ground, under a vegetal cover, or under a snow cover, and the localization accuracy is evaluated in each case. This technique opens the possibility to monitor ground displacement even under snow or vegetal coverage, that challenge most of existing displacement measurement techniques.

 

 

Related studies on the topic :

[1] Le Breton, M., Liébault, F., Baillet, L., Charléty, A., Larose, É., Tedjini, S., 2022. Dense and long-term monitoring of earth surface processes with passive RFID—a review. Earth-Science Reviews 234, 104 225. https://doi.org/10.1016/j.earscirev.2022.104225

[2] Charléty, A., Le Breton, M., Larose, E., Baillet, L., 2022. 2D Phase-Based RFID Localization for On-Site Landslide Monitoring. Remote Sensing 14, 3 577. https://doi.org/10.3390/rs14153577

[3] Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., Jaboyedoff, M., 2019. Passive radio-frequency identification ranging, a dense and weather-robust technique for landslide displacement monitoring. Engineering Geology 250, 1–10. https://doi.org/10.1016/j.enggeo.2018.12.027

[4] Le Breton, M., Larose, É., Baillet, L., Lejeune, Y., van Herwijnen, A., 2022. Monitoring snowpack SWE and temperature using RFID tags as wireless sensors. https://doi.org/10.5194/egusphere-2022-761

How to cite: Le Breton, M., Charléty, A., Grunbaum, N., Larose, É., and Baillet, L.: SAR localization of passive RFID tags under snow and vegetation using a mobile reader antenna, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15413, https://doi.org/10.5194/egusphere-egu23-15413, 2023.

Lunch break
Chairpersons: Annalisa Cappello, Gaetana Ganci, Gabor Kereszturi
14:00–14:10
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EGU23-17455
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GI6.3
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On-site presentation
Salvatore G.P. Virdis, Siwat Kongwarakom, Sangam Shrestha, Liew Ju Neng, Bachisio M. Padedda, Tatsaneewan Phoesri, and Aung Chit Moe

Southeast Asian lakes provide several ecosystem services and are an important natural resource for water supplies, industry, agriculture, shipping, fishing, and recreation. It has been demonstrated that they are highly vulnerable to anthropogenic and climate threats. Recent scientific findings clearly demonstrated that climate change has already significantly affected the SEA region and that these impacts will continue and expand as the pace of climate change accelerates. However, a deep understanding of "if" and "how" climate change as well as intensification of land uses may exacerbate those impacts on such vulnerable ecosystems across the whole region is lacking.

To contribute towards filling some of the existing knowledge gaps, in a renowned data scarce region, we present the results of a 3-year-long interdisciplinary research project entitled Climate Change Risk Assessment for Southeast Asian Lakes (CCRASEAL), led by the Asian Institute of Technology and funded by the Asia Pacific Network for Global Research (APN).

We present new insights on: i) historical, remote sensing derived, yearly land use changes from 1992 to 2021 estimated at basin scale across whole mainland SEA; ii) historical and future changes in climate respectively for the periods 1970-2006 and 2007-2100 using different downscaled CORDEX-SEA climate data at lake level; iii) detected and assessed climate and land use long-term trends and their coupled impacts on both monthly runoff at multi-basin scale level and lake surface areas of more than 700 water bodies. Finally, we detected and assessed the satellite-derived Lake Surface Water Temperature (LSWT) trends, an essential climate variable (ECV), within defined historical and future scenarios and across whole mainland SEA.

To achieve our results, we used and integrated multi-source and multi-resolution datasets made of satellite derived water and land products along with available climatic CORDEX-SEA climate datasets. Furthermore, we used a combination of conventional remote sensing, GIS, machine- and deep learning based processing approaches. In our studies we analysis possible spatial and temporal linkages between observed alterations to multiple-threats, to understand “if”, “when”, “how” and “where” climate and land use changes had affected and will affect SEA lakes.

Results have been validated using, when available, ground-based observation collected at national and regional scales.

How to cite: Virdis, S. G. P., Kongwarakom, S., Shrestha, S., Neng, L. J., Padedda, B. M., Phoesri, T., and Moe, A. C.: Are Southeast Asian lakes impacted by changes in climate and land-use? A historical and future scenario analysis., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17455, https://doi.org/10.5194/egusphere-egu23-17455, 2023.

14:10–14:20
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EGU23-1501
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GI6.3
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On-site presentation
Yi Yu, Luigi Renzullo, Siyuan Tian, and Brendan Malone

High spatial resolution land surface temperature (LST) (<= 100 m) has a considerable significance for small scale studies like agricultural applications and urban heat island studies. Originally developed for optical data, spatiotemporal fusion methods, such as the widely used Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), are gradually becoming promising approaches to generate high resolution thermal variables but still have shortcomings, such as an invalid assumption in thermal fields and the accumulation of systematic biases. Hence, we proposed a variant of the ESTARFM algorithm, referred as the unbiased ESTARFM (ubESTARFM), aiming to better accommodate the spatiotemporal approach to thermal studies. We evaluated the results derived from our method and the typical ESTARFM against both in-situ LST and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST over a continental scale of Australia. The results show that the ubESTARFM has a bias of 2.55 K, unbiased RMSE (ubRMSE) of 2.57 K, and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11290 samples at 12 sites, all of which are significantly better than that of the ESTARFM, with a bias of 4.73 K, ubRMSE of 3.80 K and R of 0.92. In the cross-satellite comparison, the ubESTARFM LST has a bias of -1.69 K, ubRMSE of 2.00 K, and R of 0.70 over 43 near clear-sky scenes, while the ESTARFM LST has a bias of 1.79 K, ubRMSE of 2.68 K, and R of 0.59. Overall, the ubESTARFM is able to avoid the accumulation of systematic bias, considerably reduce the deviation of uncertainty, and maintain a good level of correlation with validation datasets compared to the typical ESTARFM algorithm. It is a promising method to integrate reliable numeric values from coarse resolution LST and spatial heterogeneity from fine resolution LST, and may be further coupled with energy balance or radiative transfer models to better enable farm- or regional-scale water management strategy or decision making.

How to cite: Yu, Y., Renzullo, L., Tian, S., and Malone, B.: An unbiased spatiotemporal fusion approach to generate daily 100 m spatial resolution land surface temperature over a continental scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1501, https://doi.org/10.5194/egusphere-egu23-1501, 2023.

14:20–14:30
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EGU23-8857
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GI6.3
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ECS
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On-site presentation
Friederike Kästner, Theres Küster, Hannes Feilhauer, and Magdalena Sut-Lohmann

Across Europe there are 2.5 million potentially contaminated sites due to natural and anthropogenic activities. In this regard, phytoremediation approaches are need as a cost-effective and ecosystem-friendly technique to rehabilitate soil compared to conventional methods. Hyperspectral imaging provides an ideal method to improve and monitor existing bioremediation methods, using hyperaccumulator plants. In our study, the hyperaccumulator plant Brassica juncea showed a high tolerance to the accumulation of Cu, Zn and Ni. Hyperspectral measurements were conducted with a HySpex VNIR-SWIR hyperspectral sensor (408-2500 nm) in-situ and in the laboratory. To monitor and optimize the process of accumulation with hyperspectral imaging, we calculated different vegetation indices, related to metal-induced plant stress, such as TCARI/OSAVI, Chlorophyll Vegetation Index (CVI), Red-Edge Stress Vegetation Index (RSVI), Normalized Pigments Chlorophyll Index (NPCI), Red-Edge Inflection Point (REIP) and Disease Water Stress Index (DWSI), using various pre-processing steps (raw, smoothed and brightness corrected data). In addition, the relation between the different indices and the measured heavy metal content in the samples were tested with a multivariate technique using Partial Least Squares Regression (PLSR). Our results revealed, even with no pre-processed image data, changes in chlorophyll- and red-egde-related indices with increasing PTE concentration. With hyperspectral imaging we are already able to monitor differences of the PTE accumulation within the hyperaccumulator plant Brassica juncea.

How to cite: Kästner, F., Küster, T., Feilhauer, H., and Sut-Lohmann, M.: Identification and remediation-related monitoring of potential toxic elements (PTE) in the hyperaccumulator plant Brassica juncea with hyperspectral imaging., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8857, https://doi.org/10.5194/egusphere-egu23-8857, 2023.

14:30–14:40
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EGU23-14908
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GI6.3
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On-site presentation
Lev Labzovskii, Gerd-Jan van Zadelhoff, Gijsbert Tilstra, Jos De Kloe, and David Donovan

Here, we report results paving the way toward a new method for retrieving surface albedo at 355 nm from lidar surface returns (LSR) of Aeolus. We found that averaged monthly LSR estimates at 2.5 x 2.5 grid clearly varied depending on the land type with the signal strength descending as follows: snow, arid areas, vegetation, water surfaces. Most importantly, given the difference in the instrumental setup, Aeolus LSR exhibited unexpectedly high agreement with Lambertian Equivalent Reflectance from TROPOMI and GOME-2 with correlation coefficients (r) of ~0.87 at global scales for median estimates at the study period (r = 0.91 for TROPOMI-GOME-2) and regional estimates for 37 selected areas (r > 0.90) where the agreement is driven by land surfaces with lower agreement over water due to inherently different physics of Aeolus LSR. Aeolus LSR showed superior sensitivity to the change of land type from vegetation to arid, compared to GOME-2 or TROPOMI as indicated by the highest negative agreement between Aeolus LSR, compared to GOME-2 or TROPOMI. We anticipate that our results will lay the foundation for the multiyear surface UV albedo climatology during the entire Aeolus lifetime.

 

How to cite: Labzovskii, L., van Zadelhoff, G.-J., Tilstra, G., De Kloe, J., and Donovan, D.: New method for retrieval surface UV albedo from Lidar Surface Returns (LSR) of Aeolus, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14908, https://doi.org/10.5194/egusphere-egu23-14908, 2023.

14:40–14:50
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EGU23-16434
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GI6.3
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Highlight
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On-site presentation
Matthew Watson and Thomas Hunter and the PROVE Team

Volcanic ash presents a challenge for the aviation industry. 3D information is needed to be able to back-calculate dose – this is a key parameter in managing airspace. To recreate the ash cloud, multiangle observations are required – deal to perform visual and infrared observations. Other mission objectives using the can also be realised, for example, as volcanic ash clouds are the primary target, there is the possibility to map new magma extrusions, lava and pyroclastic flow movements. Thermal infrared data has also previously been used to observe volcanic cycles and better understand their behaviour. The visual images required for 3D construction of ash clouds can be used to create digital elevation models of terrain around volcanos which have application in disaster management and planning, and forest fires may also be included as targets of opportunity.

A CubeSat mission - Pointable Radiometer for Observing Volcanic Emissions (PROVE) Pathfinder - is proposed to monitor the ash cloud using both thermal infrared and visual cameras. The resulting 2U payload consists of a thermal infrared camera (FLIR Tau 2 with a 50mm lens) and a visual camera (a narrow field of view Basler ace ac5472-5gc with a Kowa LM75HC lens). Alongside this, a payload computer to communicate with the cameras and store data was selected (the BeagleBone Black Enhanced Industrial) with a custom PCB providing connections to the instruments and bus. The software to operate the payload takes the form of a custom scheduler for an imaging pass, sending commands to the camera systems (and to the bus) to take the required multiangle images for ash cloud reconstruction.

The engineering model of the payload is currently being tested at the European Space Agency’s CubeSat Support Facility with the support of the Education Office of the European Space Agency under the educational Fly Your Satellite! Test Opportunities programme. The team are undertaking a month of environmental testing including vibration and thermal vacuum tests. The aim of the testing campaign is to qualify the payload for launch.

How to cite: Watson, M. and Hunter, T. and the PROVE Team: The Student-Led Design and Testing of an Imaging CubeSat Payload, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16434, https://doi.org/10.5194/egusphere-egu23-16434, 2023.

Posters on site: Wed, 26 Apr, 16:15–18:00 | Hall X4

Chairpersons: Annalisa Cappello, Sabine Chabrillat, Gabor Kereszturi
X4.227
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EGU23-1444
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GI6.3
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ECS
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Zahra Dabiri, Daniel Hölbling, Sofia Margarita Delgado-Balaguera, Gro Birkefeldt Møller Pedersen, and Jan Brus

Lava flows can threaten populated areas, cause casualties and considerable economic damage. Therefore, understanding lava flows and their evolution is important because they can be linked to lava transport systems and eruption parameters. However, timely and accurate lava flow mapping in the field can be time-consuming and dangerous. Earth observation (EO) data plays an important role in improving lava flow mapping and monitoring. Synthetic Aperture Radar (SAR) data provide a unique opportunity to study lava flows, especially in areas with high cloud coverage during the year. Moreover, smoke and ash clouds can be partially penetrated by SAR. The freely available Sentinel-1 SAR data (C-band), with its high temporal and spatial resolution, opens new opportunities for studying lava flow evolution and lava morphology. However, Sentinel-1 data have mainly been used to study surface deformation using Differential Interferometric SAR (DInSAR) techniques, and the utilisation of SAR backscatter information for lava flow characterisation has not been thoroughly exploited.

The Fagradalsfjall volcanic system is located on the Reykjanes Peninsula in southwest Iceland. The eruption began on the 19th of March and lasted until the 18th of September 2021. The resulting lava flows cover an area of 4.8 km2 (Pedersen et al., 2022). Another eruption occurred in August 2022. We used time series of dual-polarisation, including VH (antenna sends vertical pulses and receives horizontal backscatter) and VV (antenna sends vertical pulses and receives horizontal backscatter), Sentinel-1 data to study the changes in lava flow extent and morphology during the 2021 and 2022 Fagradalsfjall eruption phases. The pre-processing of Sentinel-1 data included orbit state vector correction, radiometric calibration to reduce the radiometric biases caused by topographic variations, co-registration, and range doppler terrain correction. In addition to backscatter polarisations, we calculated the image texture using the grey-level co-occurrence matrix (GLCM) algorithm, including several measures such as contrast, homogeneity, and entropy. We used object-based segmentation and classification algorithms to delineate the lava extent and evaluated the applicability of different polarisations. To validate the mapping results, we used reference layers derived from high-resolution optical images available from Pedersen et al. (2022). The results showed that cross-polarisation was the most suitable for mapping the extent of lava. Additionally, the integration of texture information allowed us to distinguish lava types to some extent.

The results demonstrate the potential and challenges of utilising SAR backscatter information from Sentinel-1 data for studying the spatio-temporal lava flow evolution and mapping lava flow morphology, especially when the applicability of optical EO data is limited. 

Pedersen, G. B. M., Belart, J. M. C., Óskarsson, B. V., Gudmundsson, M. T., Gies, N., Högnadóttir, T., et al. (2022). Volume, Effusion Rate, and Lava Transport During the 2021 Fagradalsfjall Eruption: Results From Near Real-Time Photogrammetric Monitoring. Geophysical Research Letters, 49, 13, e2021GL097125. https://doi.org/10.1029/2021GL097125

How to cite: Dabiri, Z., Hölbling, D., Delgado-Balaguera, S. M., Pedersen, G. B. M., and Brus, J.: Lava flow mapping using Sentinel-1 SAR time series data: a case study of the Fagradalsfjall eruptions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1444, https://doi.org/10.5194/egusphere-egu23-1444, 2023.

X4.228
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EGU23-2199
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GI6.3
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Hyewon Yun, Yun-Soo Choi, and Sunghee Joo and the National Geographic Information Institute Korea Land Satellite Center

Korean National Land Satellite 1 has been launched with a mission to map national geospatial information and to monitor land resource and disasters on March 22, 2021. The satellite has a precise optical payload of 5 multi-spectral bands (Pan, R, G, B, and NIR). It observes the ground of 12 kilometers width at a 0.5m GSD (Ground Sample Distance) mainly over the Korean Peninsula and global areas of interest during at least four years.

The product of National Land Satellite is classified to 4 levels: Basic geometry image based on initial satellite position (Level 1); Precise Ortho-rectified image (Level 2);

Reproduced 2D/3D information only with Level 2 (Level 3); and Reproduced 2D/3D information with Precise image(Level 2/3) and other spatial information (Level 4). As the first 0.5m-scale satellite, Level 1 and Level 2 products are open and accessible to the Korean public. In case of Level 2 product, the average location accuracy shows about 1~4m in Korea, depending on the number of available Ground Control Points (GCP) and Level 2 product will produce North Korea Digital Map at 1:5,000 scale. The level 3 and level 4 will be serviced to the public in stage from 2023. The Korea national land satellite can be used to monitor disaster damage, especially for monitoring climate change caused by increasing greenhouse gas emissions through increasing plastic waste. In addition, it is expected that it can be used to generate high value-added spatial information such as 3D spatial information through convergence between various spatial information and land satellite information.

 

Acknowledgment: This work was supported by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government and Korea Environment Industry & Technology Institute (KEITI) through Plastic-Free Specialized Graduate School funded by Korea Ministry of Environment (MOE).

Keywords : #National Land Satellite, #CAS500, #High resolution, #0.5m, #Diaster #plastic waste #climate change

How to cite: Yun, H., Choi, Y.-S., and Joo, S. and the National Geographic Information Institute Korea Land Satellite Center: The Operation and Service of National Land Satellite 1, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2199, https://doi.org/10.5194/egusphere-egu23-2199, 2023.

X4.229
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EGU23-3070
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GI6.3
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ECS
Yuseon Lee, Jaewon Kim, and Youngmin Noh

As emissions from ships and marine sources account for a high proportion of fine particle emissions, interest in air pollutants generated in port areas and the need to prepare countermeasures are increasing. For port air pollutants, it is necessary to consider substances emitted from ships and various emission sources from the yard around ports. This study uses a scanning LiDAR system capable of observing PM10 and PM2.5 in a radius of up to 5 km at a high resolution of 30 m horizontally and left and right to check high-concentration pollutants generated around Dangjin Port(36.985476°N, 126.745613°E) in real time and corresponding substances tried to distinguish. The scanning LiDAR used in this study provides the Ångström exponent calculated from the extinction coefficient at both wavelengths of 1064 and 532 nm and the depolarization ratio at 532 nm. First, the Ångström exponent can confirm information about the particle size. In addition, the depolarization ratio is a parameter representing information on the asphericity of particles. It provides information on the classification of aerosol types depending on whether the particles are spherical or non-spherical. The concentration of fine particle generated was identified using the extinction coefficient, and the kind of particle was determined using the Ångström exponent and the depolarization ratio. The primary source of fine particle in the vicinity of Dangjin Port was an industrial complex, such as a steel mill located on the west side of Dangjin Port, and fine particle was also generated from the port's coal yard and moving ships. The diffusion direction of fine particle was closely related to the wind direction. The type of fine particle confirmed by a low Ångström exponent between 0 and 1 and a high depolarization ratio degree between 0.1 and 0.2 was confirmed as non-spherical scattering dust. Through this study, it was confirmed that it was possible to identify the generation and movement of fine particle in a wide area and to distinguish the types of particles using scanning lidar.

Acknowledgement

This work was supported by the “Graduate school of Particulate matter specialization.” of Korea Environment Industry & Technology Institute grant funded by the Ministry of Environment. Republic of Korea.

How to cite: Lee, Y., Kim, J., and Noh, Y.: A Study on Fine Particle Emission Characteristics in Dangjin Port Using High Resolution Scanning LiDAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3070, https://doi.org/10.5194/egusphere-egu23-3070, 2023.

X4.230
|
EGU23-3071
|
GI6.3
Jaewon Kim, Juseon Shin, Shohee Joo, and Youngmin Noh

Busan is Korea's largest port city. Considering the large size of the port, measurement through a single monitoring station has limitations in expressing the spatial distribution of fine particles. In this study, a Scanning LiDAR system was used to overcome the limitations of existing observations. Scanning LiDAR is a remote sensing device that uses a laser as a light source to calculate distance information. It can calculate fine particle mass concentration and distance information through signal analysis of collected light from laser light scattered backward by fine particles. It is possible to observe the fine particle concentration in real-time and continuously for 24 hours at a resolution of 30 m within a radius of 5 km and to check the spatial distribution of particulate matter using this. Scanning LiDAR is located on the rooftop of the 9th Engineering Building, Yongdang Campus, Pukyong National University, Korea (latitude: 35.11, longitude: 129.09, about 10m above ground), and was observed from March 2nd to April 28th, 2022. Residential areas, ports, industrial facilities, etc., are included in the observation range, and the average fine particle concentration by area was obtained by dividing it into six areas. ( (A) residential area, (B) steel mill, (C) Gamman Port, (D) redevelopment area, (E) shipyard, (F) berth ). Areas A, B, and C are located to the northeast of the port area, while Areas D, E, and F are located to the west and southwest. As a result of observation, the average concentration of PM2.5 and PM10 in the A, B, and C areas tended to be higher than those in D, E, and F. In the case of Area A, despite being residential, it has a high average concentration. This is because the fine particle is emitted from Area C, where ships and loading equipment are located, and Area B, where steel mills are located. This can be attributed to the diffusion and movement of fine particles discharged from the port area to the downwind side due to the influence of the south wind series, which is the main wind during the observation period.

Acknowledgement

This work was supported by the "Graduate school of Particulate matter specialization"of Korea Environment Industry & Technology Institute grant funded by the Ministry of Environment, Republic of Korea.

How to cite: Kim, J., Shin, J., Joo, S., and Noh, Y.: A Study on analysis of fine particle Distribution in Busan Port Area Using Scanning LiDAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3071, https://doi.org/10.5194/egusphere-egu23-3071, 2023.

X4.231
|
EGU23-3687
|
GI6.3
Christoph Raab and Viet Duc Nguyen

Crop type information derived from satellite remote sensing are of pivotal importance for quantifying crop growth and health status. However, such spatial information are not readily available for countries in Central and South Asia, where smallholder farmers play a dominant role in agricultural practice, and food security. In this study, we provide insights into crop type mapping for three study sites in the region: 1) Panfilov District in Kazakhstan, 2) Jaloliddin Balkhi District in Tajikistan, and 3) Multan District in Pakistan. A collection of Sentinel-2 and Sentinel-1 satellite data was used along with the random forest classification algorithm. To train and validate the classification model, field data were collected between May and October 2022 in each of the study areas. Our main objective was to evaluate the performance of a combined Sentinel-2 and Sentinel-1 mapping approach in comparison to a single source result. In addition, this contribution will provide insights into the performance with regard to crop type mapping accuracy of different temporal data aggregation intervals. Preliminary results indicate a small increase in overall accuracy for a combined Sentinel-2 and Sentinel-1 mapping approach. However, Sentinel-2 data might be sufficient for reliable crop type mapping, in case cloud coverage is not a constraint. Future studies might consider evaluating the potential benefit of using a full Sentinel-1 data set without temporal aggregation for mapping crop types.

How to cite: Raab, C. and Nguyen, V. D.: Crop type mapping in Central and South Asia using Sentinel-1 and Sentinel-2 remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3687, https://doi.org/10.5194/egusphere-egu23-3687, 2023.

X4.232
|
EGU23-3777
|
GI6.3
|
ECS
|
Bahruz Ahadov and Nilufar Karimli

To track global environmental change and evaluate the risk to sustainable development, analysts and decision-makers in government, civil society, finance, and industry need the fundamental geospatial data products known as Land Use and Land Cover Change (LULCC) maps. Our research studied LULCC variations in a timeframe of 5 years in the Gabala district. Sentinel 2 open-source products were used to compare and categorize the procedure over one-year time intervals. For this investigation, the discrete indexing method was developed and used. The approach we used was focused on obtaining multiple indices and using them to improve classification performance. The Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BSI), Normalized Difference Tillage Index (NDTI), and Salinity Index (SI) are the indices evaluated. The most crucial variables were determined and classified using the random forest classifier in LULCC. The Sentinel Application Platform of the European Space Agency (SNAP ESA) algorithm was used to analyze the process and performed over 90% accurate predictions when applied to the testing dataset. Results revealed that using the RS technique, time and cost-efficient analyses are possible and reliable for developing socioeconomic and ecological growth strategies.

How to cite: Ahadov, B. and Karimli, N.: Analyzing Land Use/Land Cover Changes and its Dynamics Using Remote Sensing Data: A case study of Gabala, Azerbaijan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3777, https://doi.org/10.5194/egusphere-egu23-3777, 2023.

X4.233
|
EGU23-7538
|
GI6.3
Kwonho Lee, Heeseob Kim, Seonghun Pyo, and Seunghan Park

Infrared remote sensing technique has been widely used for the characteristics of objects since it
has the advantage of higher atmospheric transmittance than visible wavelengths. However, the
Mid-Wave InfraRed (MWIR) region close to the visible band can be partially affected by solar
radiation, so the solar radiation and attenuation in the atmosphere cause errors in the target
detecting. In this study, an algorithm for retrieval of the mid-infrared surface temperature was
developed by using a combination of the GEO-KOMPSAT-2A (GK-2A) satellite and Landsat data.
Through the comparison with ground observations, it was found that the surface temperatures at
MWIR band retrieved are less than 3K, and a statistically significant level of mutual comparison
was obtained. Therefore, despite the limitations of the MWIR band, the new methodology can be
applied to determine the surface-level temperature through the coupling between the two
different orbit satellites.

Acknowledgement
This research was supported by the Korea Aerospace Research Institute (FR22H00W01) in
2022-2023.

How to cite: Lee, K., Kim, H., Pyo, S., and Park, S.: Surface temperature retrieval at mid-infrared band using a combination of low-orbit and geostationary orbit satellite imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7538, https://doi.org/10.5194/egusphere-egu23-7538, 2023.

X4.234
|
EGU23-5481
|
GI6.3
|
ECS
Suyoung Sim, Kyung-soo Han, Sungwon Choi, Noh-hun Seong, Daeseong Jung, Jongho Woo, and Nayeon Kim

 Surface reflectance is the product of removing atmospheric scattering and absorption effects from the Top-Of-Atmosphere (TOA) radiation using the Radiative Transfer Model (RTM), and it refers to the reflectance according to the solar and satellite zenith angles at the time of observation. Surface reflectance is an essential input data for other Level-2 calculation algorithms such as aerosol, cloud, ozone, gas tracers, etc. Therefore, if the surface reflectance data has missing value, it will lead to missing other products that use it. However, when there are clouds in the satellite image, there is a problem with that blank pixels are generated because the surface reflectance cannot be calculated. Therefore, in this study, we conducted an algorithm to calculate background surface reflectance (BSR) without missing values with high accuracy using GK-2B/Geostationary Environment Monitoring Spectrometer (GEMS) data. The BSR is an estimate of the surface reflectance under specific observation conditions (solar and satellite zenith angles) and is a product that avoids the calculation precedence dilemma between AOD and surface reflectance. In many studies, the BSR is mainly calculated using the minimum reflectance method, but it has limitations in not considering the angular conditions at the time of observation and the reflectance characteristics of the ground surface. To overcome these limitations, a realistic BSR calculation was performed considering the anisotropic reflectance characteristics of the surface according to the observation conditions through bi-directional reflectance distribution function (BRDF) modeling.

 Surface reflectance, which is an input variable for BRDF modeling, was calculated based on the Look-Up Table (LUT) generated using the Second Simulation of Satellite Signal in the Solar Spectrum (6SV) RTM. At this time, LUT interpolation was additionally performed through the 6d-interploation technique to resolve discontinuities that may occur in LUT-based atmospheric correction. For BRDF modeling, the kernel-based Roujean model was used, and the optimal synthesis period for BRDF modeling considering the characteristics of the GEMS satellite was selected. To evaluate the accuracy of BSR, the simulated BSR through the BRDF model and the observed surface reflectance were compared, and it was confirmed that the BSR showed higher accuracy than the minimum reflectance method. In the future, the BSR produced through this study is expected to have a great impact on improving the calculation accuracy of aerosol and atmospheric products of GEMS satellites.

How to cite: Sim, S., Han, K., Choi, S., Seong, N., Jung, D., Woo, J., and Kim, N.: A Study on the background surface reflectance retrieval of near-UV wavelength using GK-2B/GEMS data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5481, https://doi.org/10.5194/egusphere-egu23-5481, 2023.

X4.235
|
EGU23-5697
|
GI6.3
|
ECS
NaYeon Kim, Kyung-soo Han, Sungwon Choi, Noh-hun Seong, Daeseong Jung, Suyoung Sim, and Jongho Woo

Currently, research such as time-series vegetation index analysis, disaster monitoring, and aerosol monitoring are being conducted using high-resolution optical satellites. However, since each high spatial resolution satellite has differences in the spectral response of the two sensors, there is a limit of time-series monitoring when using satellite data fusion. In this study, the Spectral Band Adjustment Factor (SBAF) was calculated for Sentinel-2A and Landsat-8, which are high-resolution satellites, and cross-calibration was performed. When combining data from two satellites, it is necessary to overcome the difference in radiometric sensor characteristics of each satellite. The bias due to the difference in the spectral response of the two satellites was corrected through an adjustment factor derived from the EO-1 Hyperion data. As a result of applying SBAF, the difference in value was within 5%. In the future, based on the results derived from this study, it is expected to make a great contribution to continuous monitoring and time series analysis of aerosols including PM2.5.

※ This work was supported by the "Graduate school of Particulate matter specialization." of Korea Environmental Industry & Technology Institute grant funded by the Ministry of Environment, Republic of Korea.

How to cite: Kim, N., Han, K., Choi, S., Seong, N., Jung, D., Sim, S., and Woo, J.: Applicability evaluation of Spectral Band Adjustment Factor for Cross-Calibration using high resolution optical satellite, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5697, https://doi.org/10.5194/egusphere-egu23-5697, 2023.

X4.236
|
EGU23-6085
|
GI6.3
Young Baek Son and Jong-Kuk Choi

The Yellow Sea (YS) and East China Sea (ECS) have the world’s largest supply of floating algae. The golden tides (Sargassum horneri) appear mainly in the YS and ECS, but become entangled as they drift. The floating harmful macroalgae blooms (HMBs) obstructs navigation and is a huge socioeconomic problem in the vicinity of coastal areas. To determine the origin and movement trend of the golden tide in the YS and ECS, the multi-satellite sensor data (e.g. Sentinel-2 and GOCI) was used to detect the floating macroalgae which was determined by the Alternative Floating Algae Index (AFAI, Wang and Hu, 2016) and mapped over the study area using a 15-year data. 

The occurrence period of the golden tide from 2008 to 2019 determined that they were found between January and March in the China coast, and the patches of floating macroalgae in Jeju Island and the west coast of Korea were observed between March and May. The macroalgae was detached from the waters near the Yangtze River and Zhejiang Province, China and then floating into the east and north-east ward influenced by the Tsushima warm current or Kuroshio. The build-up of the gold tide was occurred in the middle of the ECS and pile-up of them was in the coast of Korea from March to May. Recently, changes have begun to appear in movement trend of the golden tide. During 2020 and 2021, the golden tide was found in the western coast of Korea on January and in the northern waters of Jeju Island, Korea on February, and at the same time, another large-scale patch was found in the waters near the mouth of the Yangtze River and Zhejiang Province, China. From the results, the golden tide outbreak occurred that first flowed in west coast of Korea and northern Jeju Island in the winter, and then another outbreak occurred in southern Jeju Island in spring. It was analyzed that the movement trend of the golden tide has changed in recent years that the golden tide presented in the YS and ECS have different origins such as Bohai Bay and near the Yangtze River and Zhejiang Province, China.

How to cite: Son, Y. B. and Choi, J.-K.: Tracing the golden tide outbreak in the Yellow Sea and East China Sea over a 15-year period using multi-satellite sensor data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6085, https://doi.org/10.5194/egusphere-egu23-6085, 2023.

X4.237
|
EGU23-6548
|
GI6.3
|
ECS
|
Highlight
Omar Andres Lopez Camargo, Kasper Johansen, Victor Angulo, Samer Almashharawi, and Matthew McCabe

The widespread use of diameter at breast height (DBH) and tree height attributes as a non-destructive indirect estimation of tree parameters (e.g., above-ground biomass, volume, age, and carbon stock) demands efficient and accurate surveying methods. However, traditional surveys, which are primarily manual, are often time-consuming, inaccurate, inconsistent, and might suffer from observer-bias. This study applies an agile quadruped robot, Spot from Boston Dynamics, and a mounted LiDAR system for mapping and measuring tree height, diameter at breast height (DBH), and tree volume. This project uses the Spot Enhanced Autonomy Payload (EAP) navigation module as the source of LiDAR data. The use of this module has two main advantages. First, Spot EAP's VLP-16 sensor is a low-beam LiDAR that, as demonstrated in previous research, is capable of estimating tree structural parameters while consuming less time and data than robust systems such as Terrestrial Laser Scanning (TLS). Second, using an existing payload as the primary source of data without disabling its default function results in more efficient payload capacity utilization and, as a result, lower energy consumption, in addition to making room for additional payloads. The experiment was conducted for 41 trees (23 Erythrina variegata and 18 Ficus altissima) in a park on the campus of King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. TLS data were used to compute the height and volume reference data, while manual measurements were used to obtain DBH reference data. The robot-derived point cloud generation methodology was based on a multiway registration approach in which a total of 76 scans were acquired from 4 different locations using multiple poses of the robot to overcome the short field of view of the LiDAR sensor. As a result of processing the scans, a point cloud for each of the trees was obtained. The height estimations, which consist of a difference within Z coordinates, obtained a mean absolute error (MAE) and a mean percentage error (MPE) of 6.71 cm and 1.31% respectively. The DBH estimation based on circle-fitting algorithms obtained an MAE and an MPE of 2.55 and 12.99% respectively. The volume estimation obtained a coefficient of determination of 0.93. When compared to the most recent approaches available in the literature, the results for height and volume were satisfactory, yielding higher accuracy than other studies in some cases. The results for DBH estimation were also comparable to those in the literature. The main sources of error were tree occlusion and inclined trees, both of which are solvable by including more scanning locations and increasing the robustness of software estimation. Consequently, the acquisition system is not a barrier to future improvements. This work successfully introduced one of the first methods for using agile robots in high throughput field phenotyping. The use of agile robots addresses some of the major challenges for deploying ground-based robotics in high throughput field phenotyping, allowing for a higher assessment frequency without causing soil compaction and damage, as well as bringing unprecedented adaptation to difficult terrains.

 

How to cite: Lopez Camargo, O. A., Johansen, K., Angulo, V., Almashharawi, S., and McCabe, M.: Using LiDAR on a Ground-based Agile Robot to Map Tree Structural Properties , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6548, https://doi.org/10.5194/egusphere-egu23-6548, 2023.

X4.238
|
EGU23-8888
|
GI6.3
|
ECS
|
Eirini Efstathiou and Vassilia Karathanassi

Landfills constitute a major environmental issue that needs to be handled, especially when they are located near large urban areas. In landfills, end up most of the city non-hazardous solid waste (mainly household waste), which are not appropriate for recovery/recycling and thus they are disposed in the ground for decomposition process. Monitoring of such sites is significantly important, due to the fact that the decomposition process - which includes the release of hot gases - is harmful to the environment and to the human health.  The increase of Land Surface Temperature (LST) in landfill sites and the methane gas emissions, which contribute to the greenhouse effect, can be monitored using remote sensing methods and techniques. This type of monitoring is very important for safeguarding the surrounding environment, especially in environmentally sensitive areas, as are those located close to densely populated areas, and therefore, many studies have been carried out focusing on the monitoring of the environmental impacts of landfills through remote sensing. In relevance with previous literature, the current study aims at monitoring the environmental impact of the active landfill site of Fyli – Ano Liosia, Attica, Greece. For the needs of the study, time series of Land Surface Temperature (LST) have been processed as extracted from Landsat 8-9 satellite imagery. The analyzed time period is from January 2021 to December 2022. LST data have been extracted from two areas within the landfill, one in the active landfill area and the second one in an area that has been rehabilitated and is no longer active. Furthermore, we selected to study LST data from a bare soil area which is located at a short distance from the landfill in order to find temperature deviation caused by the decomposition processes. The land surface temperatures inside the landfill have been compared with those of the bare soil as well as with the air temperature, which is provided by the weather station of Ano Liosia of METEO (infrastructure of National Observatory of Athens for weather forecasting). It has been observed that the LST in the active area of ​​the landfill is higher by 1°C-2°C compared to that in the inactive area of ​​the landfill, and by 2°C-3°C compared to the bare soil LST. A reversal of this phenomenon has been observed during the snowy winter months due to different snowmelt rates and possibly due to a slowdown of the decomposition process. The air temperature was found to be significantly lower than the LST, as expected.

How to cite: Efstathiou, E. and Karathanassi, V.: Monitoring the environmental conditions in landfill sites: a case study of Fyli - Ano Liosia, Attica Region, Greece, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8888, https://doi.org/10.5194/egusphere-egu23-8888, 2023.

X4.239
|
EGU23-9489
|
GI6.3
|
ECS
|
Highlight
Victor Angulo, Kasper Johansen, Jorge Rodriguez, Omar Lopez, Jamal Elfarkh, and Matthew McCabe

Hyperspectral (HS) images obtained from space are useful for monitoring different natural phenomena on regional to global scales. The Environmental Mapping and Analysis Program (EnMAP) is a satellite recently launched by Germany to monitor the environment and explore the capabilities of hyperspectral sensors in the 420 and 2450 nm range of the spectrum. However, the data captured by the EnMAP mission have a ground sampling distance (GSD) of 30 m. This limits the use of the data for some applications that require higher spatial resolution (<10 m). This study examines the potential for improving the resolution of hyperspectral data using high resolution multispectral (MS) data obtained by Cubesats. Specifically, this work uses the data captured by the PlanetScope constellation, which has more than 150 CubeSats in low Earth orbit, with a high spatial and temporal resolution. The approach adopted leverages (1) the spectral capability of the hyperspectral EnMAP sensor, with a bandwidth of 6.5 nm in the visible and near infrared (VNIR) range (420–1000 nm) and 10 nm in the SWIR range (900–2450 nm), and (2) the spatial capability of the multispectral PlanetScope data, with a GSD of 3 meters, to enable significant spatial improvements due to its high spatial resolution. The main components of this work include: (i) area of interest clipping (ii) data co-registration, (iii) HS-MS data fusion, and (iv) quality assessments using the Jointly Spectral and Spatial Quality Index (QNR). In this study, a 2 km x 2 km area of interest was selected in the Malaucene region of France, where six state-of-the-art HS-MS fusion methods were evaluated: (1) fast multi-band image fusion algorithm (FUSE), (2) coupled nonnegative matrix factorization (CNMF), (3) smoothing filtered-based intensity modulation (SFIMHS), (4) maximum a posteriori stochastic mixing model (MAPSMM), (5) Hyperspectral Superresolution (HySure), and (6) generalized laplacian pyramid hypersharpening (GLPHS). Quality assessments of the enhanced data showed that high spectral and spatial fidelity are maintained, with the best performing fusion method being FUSE with a QNR of 0.625 followed by the MAPSMM method with a QNR of 0.604. Overall, this study advocates the benefits associated with the fusion of hyperspectral and multispectral data to obtain enhanced EnMAP data at 3 m GSD. 

How to cite: Angulo, V., Johansen, K., Rodriguez, J., Lopez, O., Elfarkh, J., and McCabe, M.: Resolution-enhanced Hyperspectral EnMAP data: CubeSat-based high resolution data fusion approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9489, https://doi.org/10.5194/egusphere-egu23-9489, 2023.

X4.240
|
EGU23-10320
|
GI6.3
|
ECS
Daeseong Jung, Kyung-soo Han, Noh-hun Seong, Suyoung Sim, Jongho Woo, Nayeon Kim, and Sungwon Choi

To monitor the surface based on Earth observation optical satellites, accurate atmospheric correction of satellite images is required. Surface reflectance is calculated using a look-up table (LUT) based on a radiative transfer model. In addition, atmospheric gas components and geometric information of solar and satellite observations used in LUT construction are applied to each channel at equal intervals. However, the atmospheric gas components are sensitive to the atmospheric effect in a specific wavelength range of the satellite sensor. The higher the geometric information appears in the satellite observation area, the greater the variability of the atmospheric effect occurs because the moving distance of light increases. Because of this, LUT-based atmospheric correction at equal intervals generates discontinuities in surface reflectance in satellite images. In this study, to improve the quality of the surface reflectance applied with atmospheric correction, a Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) radiation transfer model was used to analyze the sensitivity of the surface reflectance for each channel according to the GEO-KOMPSAT-2A-based atmospheric gas component and the geometric information of the solar and satellite observations. After figuring out the variability of surface reflectance for each channel according to the intervals of variables used in LUT construction, an error analysis of surface reflectance was performed for the optimal LUT interval considering the interpolation technique. In the future, it is considered that the results of this study can be used to identify LUT-based surface reflectance characteristics for removing discontinuities in surface reflectance, including increasing the utilization of geostationary satellite images.

How to cite: Jung, D., Han, K., Seong, N., Sim, S., Woo, J., Kim, N., and Choi, S.: Sensitivity analysis and discontinuity removal of 6SV LUT-based surface reflectance for each channel: based on GEO-KOMPSAT-2A, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10320, https://doi.org/10.5194/egusphere-egu23-10320, 2023.

X4.241
|
EGU23-10586
|
GI6.3
Seungtaek Jeong, Jong-min Yeom, Jonghan Ko, Daewon Chung, and Sun-Gu Lee

The remote sensing-integrated crop model (RSCM) was designed to simulate crop growth processes and yield using remote sensing data. The RSCM is based on the radiation use efficiency (RUE) model and employs a within-season calibration procedure recalibrating the daily crop leaf area index (LAI) using satellite images. And it has functions to calculate daily biomass, evapotranspiration (ET), gross primary productivity (GPP), and net primary productivity from the LAI in addition to crop yield. In previous studies, the essential crop growth parameters required in the model, such as RUE, light extinction coefficient, specific leaf area, base temperature, etc., were determined through field experiments. And its performances were validated using various remote sensing data, including proximity sensing data, drone images, and satellite images. Among them, this study presented the application results with satellite images in the RSCM. The target crop is rice (Oryza Sativa), one of the world's major crops, and the study areas range from South Korea to Northeast Asia. Satellite images and meteorological data were used differently depending on the study sites. The types of satellite images used in this study are the RapidEye, the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra/Aqua satellite, and the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) of Communication, Ocean and the Meteorological (COMS) satellite. And gridded data for air temperature and solar radiation was acquired from the Korea Local Analysis and Prediction System (KLAPS) and the European Centre for Medium-Range Weather Forecasts (ECMFW). The primary application of the RSMC is to simulate rice yield, but some results showed crop growth factors such as biomass, LAI, GPP, and ET. In addition, the most recent study performed the early prediction of crop yield by combining deep learning with crop models. Through this study, it is possible to know the future utilization of the RSCM model in the agriculture and satellite application fields.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2022-00165154, "Development of Application Support System for Satellite Information Big Data").

How to cite: Jeong, S., Yeom, J., Ko, J., Chung, D., and Lee, S.-G.: Application cases of remote sensing-integrated crop model to simulate and predict crop yield with satellite images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10586, https://doi.org/10.5194/egusphere-egu23-10586, 2023.

X4.242
|
EGU23-12364
|
GI6.3
Veronika Kopackova-Strnadova

Acid mine drainage (AMD) is considered as one of the main factors causing water pollution in regions with historic or current mining activities. Its generation, release, mobility, and attenuation involves complex processes governed by a combination of physical, chemical, and biological factors. Clearly this phenomenon is highly dynamic depending on other external factors such as precipitations and ground water table fluctuations and conventional monitoring is time and resource-demanding. Recent research studies proved that imaging spectroscopy represents an alternative to conventional methods and an efficient way to characterize mines and assess the potential for AMD discharge while focusing on mapping those minerals serving as indicators of sub-aerial oxidation of pyrite (‘hot spots’) and the subsequent formation of AMD. In this study a potential of new PRISMA hyperspectral satellite sensor for multi-temporal AMD mappng was evaluated. The PRISMA AMD mineral mapping results were compared with existing ground truth data and other validated AMD maps derived using aerial high-resolution hyperspectral imaging data (e.g., CASI/SASI). To conclude, a spectral and spatial resolution of the PRISMA satellite data is sufficient to map this phenomenon at multi-temporal scale and PRISMA data has a potential to be operationally used in remediation projects and other environmental applications.

How to cite: Kopackova-Strnadova, V.: Mapping mining environmental impacts using PRISMA hyperspectral imagery: Acid mine drainage (AMD) example, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12364, https://doi.org/10.5194/egusphere-egu23-12364, 2023.

X4.243
|
EGU23-12461
|
GI6.3
Yeonsu Lee, Bokyung Son, and Jungho Im

Urban trees are important carbon sink in human settlements by absorbing carbon dioxide and storing them as biomass. As urban areas continue to expand, quantification of carbon storage (CS) in human settlements is becoming important. Usually, urban tree CS is extrapolated using total tree area statistics and carbon stocks per unit area. However, since urban trees show large variability due to diverse growing conditions, additional information such as vegetation vitality or three-dimensional structures should be considered in CS estimation. This study suggests a new two-step approach to estimate urban tree CS using forest tree carbon stocks and then correcting it to human settlements via machine learning (ML) regression models and remote sensing data. First, urban tree CS was estimated using a high-resolution urban tree canopy cover map which classified by deep-learning approach and forest tree carbon stocks which were calculated using merchantable growing stocks and biomass expansion factor (Step 1 CS). Second, urban tree CS was estimated via ML models using Step 1 CS, Sentinel-2 images, and airborne light detection and ranging (LiDAR) measurement as independent variables. As dependent variable, the field-measured CS values calculated using allometric equations and field-measured diameter at breast height using terrestrial LiDAR were utilized. Step 2 CS using random forest showed the best performance with a correlation coefficient of 0.90 and a root-mean-squared-error of 0.48. Tree height and normalized difference vegetation index appeared as important variables in estimating urban tree CS. Suggested model can estimate urban tree CS more sophisticatedly and spatially explicitly. The output, high-resolution urban tree CS map, can be used in urban planning to achieve carbon neutrality and pleasant urban environment.

 

How to cite: Lee, Y., Son, B., and Im, J.: Machine learning based two-step urban tree carbon storage estimation fusing airborne LiDAR, and Sentinel-2, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12461, https://doi.org/10.5194/egusphere-egu23-12461, 2023.

X4.244
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EGU23-13477
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GI6.3
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ECS
Mir Talas Mahammad Diganta, Md Galal Uddin, and Agnieszka I. Olbert

For the purposes of cost-effective and rapid surface water quality monitoring, the utilization of the cutting-edge satellite remote sensing (RS) technique has increased over the years. Recently, several studies have revealed that the RS technique severely suffers from particles present in the atmosphere, especially from aerosol particles. This interference significantly influences the quality of the information extracted from remote sensing measurements and produces much more uncertainty in retrieving optically active water quality indicators (e.g., chlorophyll-a, coloured dissolved organic matter, total suspended matter) from optically complex water bodies. Therefore, it is required to minimize the uncertainty within the remotely sensed data by reducing the impact of atmospheric interference through the atmospheric correction (AC) process. Currently, a series of algorithms have been utilized in the literature for treating the AC in the RS technique, among which ACIX-Aqua, ACOLITE, BAC, C2RCC, FLAASH, iCOR, l2gen, LaSRC, POLYMER, GRS, Sen2Cor, and 6SV are widely used. Since the development of the AC algorithms, its applications have increased in handling of big data, like as remote sensing data. Recently, several studies have revealed that the existing algorithms have produced a considerable uncertainty in the retrieval data due to the architectural complexity of algorithms. Although, the application of cutting-edge machine learning and artificial intelligence techniques is increasing for atmospheric correction process. Therefore, the aim of the research is to develop an efficient algorithm utilizing the publicly available AC algorithms and incorporating machine learning and artificial intelligence approaches in order to reduce atmospheric interference from the RS data. The results of the research could be helpful for retrieving various optically active water quality indicators most efficiently in terms of reducing the uncertainty in monitoring water quality.

Keywords: surface water quality; remote sensing; atmospheric correction, artificial intelligence; optically active water quality indicators.

How to cite: Diganta, M. T. M., Uddin, M. G., and Olbert, A. I.: Assessing the atmospheric correction algorithms for improving the retrieval data accuracy in the remote sensing technique, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13477, https://doi.org/10.5194/egusphere-egu23-13477, 2023.

X4.245
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EGU23-13492
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GI6.3
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ECS
Theodora Lendzioch, Jakub Langhammer, and Veethahavya Kootanoor Sheshadrivasan

The grain size distribution of gravel riverbed material is an essential parameter to estimate the sediment transportation, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Conventional and present methods of obtaining grain size distribution analysis of more extensive areas are time-consuming and remain challenging in effectively modeling sediment load. On this account, this paper appraised the role of employing the end-to-end data-driven GRAINet approach, a convolutional neural network (CNN) application, to predict and map the grain size distribution at particular locations over an entire gravel bar based on georeferenced drone-based orthoimagery. We conducted multiple drone surveys after post-flood events in the Javoří Brook Šumava National Park (Šumava NP) in Czechia over a small unregulated montane stream with an exposed gravel bar and frequently changed fluvial dynamics. The GRAINet model performances between the predicted mean diameter (dm) and ground truth diameter dm (human performance) produce the result of different loss functions, i.e., the mean absolute errors (MAEs), the mean squared errors (MSEs), and the root-mean-square errors (RMSEs). Corresponding averages of MAEs varied between 3 cm to  4.8 cm with standard deviations (STDs) of 1.7 cm and 3.8 cm, respectively. The averages of MSE ranged between 13 cm to 14.5 cm with  STDs of 12.7 cm and 12.8 cm, and RMSE of 3.2 cm to  5.6 cm with STDs of 1.6 cm and 4.6 cm, respectively. With high to moderate accuracies and lower computational costs than other deep learning approaches, the tested ensemble model shows that the integration of UAV remote sensing and machine learning (ML) provides a promising tool to help make decisions using timely mapped high-resolution grain size maps without access to direct object counts or locations.   

How to cite: Lendzioch, T., Langhammer, J., and Kootanoor Sheshadrivasan, V.: Automated mapping of grain size distributions from UAV imagery using the CNN-based GRAInet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13492, https://doi.org/10.5194/egusphere-egu23-13492, 2023.

X4.246
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EGU23-15349
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GI6.3
Luciano Galone, Emanuele Colica, Peter Iregbeyen, Luca Piroddi, Adam Gauci, Alan Deidun, Gianluca Valentino, and Sebastiano D'Amico

Pocket Beaches are small beaches limited by natural headlands, strongly jutting into the sea, free from direct sedimentary contributions that are not eroded from back-shore cliffs. Malta’s pocket beaches are one of the most significant geomorphologic features of the archipelago. They play an important role for a variety of ecological and economic reasons. In this sense, sediment (mostly sand) dynamics is the most relevant factor to consider in the beach system. Sediment movement can be driven by a variety of factors, including wave action, currents, wind and direct and indirect anthropic action, leading to extreme morphological modifications in some cases.

The SIPOBED (Satellite Investigation to study POcket BEach Dynamics) project seeks to develop a reliable and cost-effective tool capable of monitoring sediment dynamics using satellite and other remote sensing data in several selected Maltase Pocket Beach systems, by reconstructing the volume and distribution of sediment of the beaches system through time.

The monitoring of sandy coastal zones requires the analysis of sediment dynamics in the entire beach system, from the coastal dunes to the closure depth, where the influence of sea waves on the seabed is low. SIPOBED uses Interferometric SAR and Light Detection and Ranging (LIDAR) derived Digital Elevation Models (DEMs) to study the inland system dynamics. The DEMs are used to improve the co-registration of temporal SAR imagery and detect subtle changes between acquisitions. The underwater sediment dynamics monitoring is approached by tracking bathymetric changes using multispectral satellite and unmanned aerial vehicle (UAV) images. In situ bathymetric data is essential for calibrating and validating the model. This methodology allows for more frequent and cost-effective monitoring of changes in both the dune-beach system and the ocean floor compared to classical approaches, such as in situ topographic surveys and ship-based sonar surveys. The project also aims to determine the bedrock depth and geometry at the lower limit of the pocket beach system using near-surface geophysical techniques.

The monitoring of Maltese sandy coastal beaches can provide insights into the factors influencing sediment dynamics and improve our understanding of the processes that shape and reshape pocket beaches over time. The results of the SIPOBED project will contribute to developing a risk assessment and monitoring tool that combines the sediment dynamics process with their potential local impacts, resulting in a powerful instrument for decision-makers.

The SIPOBED project is financed by the Malta Council for Science and Technology (MCST, https://mcst.gov.mt/) through the Space Research Fund (Building capacity in the downstream Earth Observation Sector), a programme supported by the European Space Agency.

How to cite: Galone, L., Colica, E., Iregbeyen, P., Piroddi, L., Gauci, A., Deidun, A., Valentino, G., and D'Amico, S.: Satellite Investigation to study POcket BEach Dynamics in Malta. The SIPOBED project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15349, https://doi.org/10.5194/egusphere-egu23-15349, 2023.

Posters virtual: Wed, 26 Apr, 16:15–18:00 | vHall ESSI/GI/NP

Chairpersons: Annalisa Cappello, Sabine Chabrillat, Gabor Kereszturi
vEGN.17
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EGU23-12803
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GI6.3
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ECS
Monitoring Landcover changes of small-scale alluvial mining in Columbia Based on Multi-source Remote Sensing Data
(withdrawn)
Lifan Xiong and Jingyi Jiang