GI6.2 | The Remote Sensing and UASs approaches in Geoscience Research Platforms for the 21st century
The Remote Sensing and UASs approaches in Geoscience Research Platforms for the 21st century
Co-organized by EMRP2/ESSI4
Convener: Vincenzo De Novellis | Co-conveners: Misha KrassovskiECSECS, Antonello BonfanteECSECS, Carlo Alberto Brunori, Francesco Zucca, Riccardo Civico
Orals
| Mon, 24 Apr, 16:15–18:00 (CEST)
 
Room -2.31
Posters on site
| Attendance Mon, 24 Apr, 10:45–12:30 (CEST)
 
Hall X4
Posters virtual
| Attendance Mon, 24 Apr, 10:45–12:30 (CEST)
 
vHall ESSI/GI/NP
Orals |
Mon, 16:15
Mon, 10:45
Mon, 10:45
Remote sensing (RS) plays a fundamental role in the impact analysis and mitigation of the impacts of climate change and human activities, supporting the achievement of Sustainable Developments Goals of United Nations (e.g. SDG2, SDG11, SDG13, SDG15). For three decades, RS from satellite has been established as a powerful monitoring tool able to cover extended areas at low cost and with regular revisit capability. However, to face the current and expected future increase in the frequency of natural hazards, new technologies have been developed with the aims to improve the flexibility in data collections and resolution. A branch of these new developed technologies are the uncrewed aerial systems (UASs) equipped with different sensors (optical, microwave, near-infrared, thermal infrared sensors). They allow bridging the gap among spaceborne and ground-based RS data providing ultra-high resolution spatial data, with a significant advantage on the flexibility of flight scheduling and the environmental data collection. These multi-source UAS-sensing data drive new developments in the field of RS applications: the mapping of the modification induced by climate change, as by the erosion and landslides, by tectonic, volcanic or human processes, as well as the improvement of crop monitoring to support a sustainable precision agriculture. On those bases a number of synergy was observed between the “sensing technologies” in the Geosciences community. The session will really focus on the several aspects of this cooperation in terms of technology and team-work and how they answer to the needing of the SDG of United Nations. In this context, we encourage who is involved concurrence development and applications to show their most recent findings focused for example on: (i) reviewing the trends of satellite RS in terrain surveys before and after the geological phenomena in integration with UAS measurement systems; (ii) UAS configuration and specifications for precision agriculture, vegetation management; (iii) the changes with the use of UAS, for those doing remote measurement with the ability to control every segment of the RS chain.

Orals: Mon, 24 Apr | Room -2.31

Chairpersons: Vincenzo De Novellis, Francesco Zucca, Carlo Alberto Brunori
16:15–16:20
16:20–16:30
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EGU23-4057
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GI6.2
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ECS
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On-site presentation
Teng Zhang, Zhongjing Wang, Yingfu Tang, and Yujia Shi

Soil salinity mapping is essential for sustainable land development and water resources management. In situ sampling is time-consuming, laborious, and restricted by geographical conditions. Therefore, an efficient and accurate model is necessary to monitor and assess the spatio-temporal dynamic salinization at regional a scale. In this study, Shule River Basin (SLRB) is taken as an example to develop the soil salinity mapping model based on Landsat 8 OLI images using random forest (RF) algorithms. A series of extended soil salinity indexes (ESSIs) were generated by combining any two, three, or four spectral bands were combined in expressions that include one or more of the arithmetic operations: addition, subtraction, multiplication, division, square and rooting form. The features selected from ESSIs outperformed the features selected from soil salinity indexes (SSIs) used in references. The best selected indexes are (B7^2-B5^2)^0.5, (B4^2+B5^2-B6^2)^0.5, (B1*B5-B4*B6/(B1*B5+B4*B6))^0.5,(B2*B6-B3*B7/( B2*B6+B3*B7))^0.5. In addition, three partition sampling methods of the training set and validation set for long-tail distribution problems are compared. The results showed that the resampling method considering the long-tail distribution performs better than systematic resampling and random k-fold cross-validation. The regional soil salinity mapping results showed that most areas are seriously salt-affected in the whole basin, especially along the river and the southeast mountainous area, where the soil salinity classes are highly and even over-extremely saline. This study could have implications for agricultural schemes planning and salinization control.

How to cite: Zhang, T., Wang, Z., Tang, Y., and Shi, Y.: Mapping of Soils Salinity with Landsat 8 OLI Imagery and Random Forest Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4057, https://doi.org/10.5194/egusphere-egu23-4057, 2023.

16:30–16:40
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EGU23-4412
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GI6.2
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ECS
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Virtual presentation
Jiayi Wang, Kunfeng Qiu, and Jianan Fu

Unmanned Aerial Vehicle (UAV) can greatly improve the geological field mapping. However, applications of UAV in the investigations of the deposit still remain to be explored. The Liba gold deposit, located at the Li-Min gold belt to the western Qinling orogenic belt, is a typical open-pit gold deposit. The associated (local) landscape and geomorphology provide an excellent natural laboratory to explore the UAV in deposit field mapping. Here, UAV-based photogrammetry was performed to get the aerial photos across the mining area, as well asoutcrop information from the Liba gold mine. In the combination with a detailed field work, alteration zones with the regional faults can be efficiently interpreted and evaluated, both from the macro- to micro scale. According to the work, we established a general working flow of the usage of UAV deposit field exploration to improves the field work. By demonstrating the UAV-based technical applied in Liba, this work can strongly promote the understanding and interpretation of regional geology during the field work.

Key words: Open-pit Gold Deposit, Liba gold deposit, UAV-drone photogrammetry, Geological field mapping

How to cite: Wang, J., Qiu, K., and Fu, J.: An Application of UAV in Open-pit Gold Deposit Geological Field Mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4412, https://doi.org/10.5194/egusphere-egu23-4412, 2023.

16:40–16:50
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EGU23-7531
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GI6.2
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Virtual presentation
Serigne Mansour Diene, Romain Fernandez, Eric Goze, Ibrahima Diack, Marième Faye, Al Housseynou Dabo, Pape Oumar Ba Bousso, Alain Audebert, Olivier Roupsard, Louise Leroux, Modou Mbaye, Abdou Aziz Diouf, Moussa Diallo, and Idrissa Sarr

Agroforestry, the association between trees/shrubs and crops, a widespread practice in West Africa, is presented as a lever for ecological intensification to optimize cereal yields in the face of strong population growth and the fight against climate change. Within the framework of the EU-DESIRA SustainSAHEL project, we aim to develop techniques to spatially assess the effect of trees on millet yields on an intra-field scale using imagery from an UAV equipped with a multispectral camera combined with geostatistical approaches. Indeed, recent advances in earth observation technologies position the UAV as an effective tool for evaluating the agronomic performance of agroforestry systems and for taking into account the intra-field variability of yields caused by environmental conditions, agricultural practices or the presence of trees (Roupsard and al., 2020 ; Leroux and al., 2022). The objective of this study was to estimate millet yields intra-field variability using UAV and up-to-date geostatistical approaches.

The study was carried out over the 2018-2022 cropping seasons in one representative Faidherbia parkland of the groundnut basin of Senegal. To that end, a Random Forest (RF) algorithm was first calibrated to estimate millet yield at sub-plot scale using a thresholding classification to eliminate non-vegetation elements and also to integrate texture data, in order to take into account the spatial relationships between pairs of pixels. Millet yields data and vegetation and textural index from aerial images at a flight height of 25 meters acquired in farmers’ plots were used to calibrate the RF model. The RF model was used to upscale yield at the whole field scale thus allowing to obtain a map of millet yield. Then Voronoï diagram, with Faidherbia as a reference, was applied to each yield map, considering each Voronoï region as a zone of influence of its included Faidherbia. We then applied a transformation and rotation matrix to overlay all the zones of influence of a population of 50 Faidherbia by putting all the trees at the same geographical position. Finally, we build an atlas, which is an average structure representative of a population and which makes possible to detect the patterns and properties of the evolution of the population considered, to evaluate the distance and directional effect of Faidherbia on vegetation index of the population and then on millet yield.

The RF model is able to explain between 70 and 90 % of the millet yield variability. Then the analysis has shown that the tree has an influence on the millet stand density with a distance-decay effect from the tree. This stand density is about 60 % around the tree and 30 % at 15m from the tree.

Key words : Agroforestry, Uav, Machine learning, Image analysis, Geostatistics, Atlas

How to cite: Diene, S. M., Fernandez, R., Goze, E., Diack, I., Faye, M., Dabo, A. H., Bousso, P. O. B., Audebert, A., Roupsard, O., Leroux, L., Mbaye, M., Diouf, A. A., Diallo, M., and Sarr, I.: Assessment of the Faidherbia albida effect on millet yield using UAV images analysis and geostatistical techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7531, https://doi.org/10.5194/egusphere-egu23-7531, 2023.

16:50–17:00
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EGU23-8617
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GI6.2
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Highlight
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Virtual presentation
Marco Anzidei, Fawzi Doumaz, Alessandra Esposito, Daniele Trippanera, Antonio Vecchio, Massimo Fabris, Alessandro Bosman, and Tommaso Alberti

In the Aeolian Archipelago (southern Tyrrhenian Sea), Panarea Island and the islets of Bottaro, Lisca Bianca, Lisca Nera and Dattilo, is undergoing sea level rise, land subsidence, coastal erosion and beach retreat that are posing continuous threats to coastal stability and infrastructures built along the coastal zone. With the aim to assess the coastal changes by the end of 2100 according to the IPCC climatic scenarios, that predict a global sea level rise even more than 1 m, a detailed evaluation of the potential coastal flooding has been estimated in the frame of the PANDCOAST project, funded by the INGV.

This work focuses on the use of Unmanned Aerial Vehicles (UAVs) imagery combined with multibeam bathymetry data collected in different years for the generation of the very  high-resolution Digital Terrain and Marine Model (DTMM) of the Panarea Island and its archipelago. Scenarios are based on the determination of the current coastline position, high resolution Digital Terrain and Marine Models, vertical land movements and climatic projections.  The data fusion of detailed topographic data, up to 2 cm/pixel for the subaerial sector with sea level rise projections released by the Intergovernmental Panel on Climate Change (IPCC) for the SSP2.6 and SSP5 climatic scenarios for this area, are used to map the expected multi-temporal sea level rise scenarios for 2050 and 2100.

In the analysis have been incorporated the effects of the vertical land movements (VLM) as estimated by the Global Navigation Satellite System (GNSS) network located in the archipelago. Assuming constant rates of VLM for the next 80 years, relative sea level rise projections provide values between 31±11 cm by 2050 and 104±27 cm by 2100 for the IPCC AR6 SSP8.5 scenarios and at 27±10 cm by 2050 and 73±24 cm by 2100, for the IPCC AR6 SSP2.6 scenario, with small variations in the individual islets of the archipelago. With these scenarios, the lowest elevated coasts of the islands are exposed to increasing marine flooding, especially during storm surges and high water levels particularly heavy from the north-western sectors.

How to cite: Anzidei, M., Doumaz, F., Esposito, A., Trippanera, D., Vecchio, A., Fabris, M., Bosman, A., and Alberti, T.: Ultra High-Resolution terrestrial and marine DEMs drive Relative Sea Level Rise projections and flooding scenario for 2100 A.D. for the Island of Panarea (Southern Tyrrhenian Sea, Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8617, https://doi.org/10.5194/egusphere-egu23-8617, 2023.

17:00–17:10
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EGU23-10468
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GI6.2
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Highlight
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On-site presentation
Mel Rodgers, Rocco Malservisi, Robert Van Alphen, Taha Sadeghi Chorsi, Timothy Dixon, and Charles Connor

The use of unoccupied aerial systems (UAS) in geoscience has dramatically improved our ability to collect data at high resolution, minimal cost, and in rapid response to sudden events. The wide range of sensor and platform configurations gives scientists great flexibility in survey design and data collection. Satellite remote sensing data has exceptional spatial coverage and continues to increase its data acquisition to meter-level resolution. UAS data can image to the cm-level resolution but lacks the same spatial coverage as satellite. By combining and comparing UAS data with satellite and ground-based remote sensing data we can utilize the different strengths of these systems. Here we demonstrate various UAS applications in high-resolution topographic change, land use classification, and sub-surface geological mapping. We use UAS payloads such as RTK georeferenced RGB and multispectral images, lidar, and magnetic sensors to image surface changes and sub-surface structures. We demonstrate the need for post-processing (PPK) high precision GNSS rover locations over utilizing only RTK position information.

Florida, USA, is home to rapidly changing beaches and wetlands, which are highly susceptible to our changing climate and destructive storm events. We show examples from beaches and wetlands in Pinellas County, Florida, USA where we have a) imaged the emergence and development of a barrier island, b) developed automated land use classification using photogrammetry and multispectral data, c) evaluated the impacts of a major hurricane event on a recently renourished beach. Pacaya Volcano, Guatemala, is an active volcano with frequent lava flows and historical flank collapse events. Using a combination of satellite DEMs, ground-based Terrestrial Radar Interferometry data, and UAS RGB SfM-photogrammetry, we have imaged recent lava flows in high-resolution showing details of lava flow levees and other structures. By comparing our data to pre-eruption satellite DEMs we can evaluate the volume and morphology of recently emplaced lava flows. In addition, we have collected magnetic data over recent lava flows that allows us to image the sub-surface structure of the lava flows and model lava flow properties. UASs are a powerful tool for remote sensing, geodetic, and geophysical data collection. They augment satellite and ground-based methodologies and by combining multidisciplinary data from these platforms we can image the earth in greater spatial and temporal detail than ever before.  

How to cite: Rodgers, M., Malservisi, R., Van Alphen, R., Sadeghi Chorsi, T., Dixon, T., and Connor, C.: UAS applications in high-resolution topographic change, land use classification, and sub-surface geophysical mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10468, https://doi.org/10.5194/egusphere-egu23-10468, 2023.

17:10–17:20
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EGU23-12890
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GI6.2
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Highlight
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Virtual presentation
Claudia Spinetti, Marina Bisson, and Monica Palaseanu-Lovejoy

Stromboli is one of the most visited volcanoes in the world due to its persistent activity consisting in mild strombolian explosions with a frequency up to 25-30 events per hour. This activity is punctuated by more energetic explosions named major explosions, paroxysms and lava flow. These types of eruption can change drastically the morphology of the affected areas and cause volcanic phenomena highly impacting for the island, including heavy fallout of blocks and bombs on the flanks of the volcano, pyroclastic flows and tsunami waves. Paroxysms are highly dangerous phenomena for the tourists that climb the volcano and can cause serious problems also to the local people living on the two villages on the coast of the island. In order to map the areas affected by morphological changes, the thickness of deposits and the associate volume estimation of erupted products, we propose a study based on two techniques of remote sensing. First, we reconstruct the Stromboli topography, before and after an event, elaborating stereo pairs of Pleiades satellite and using as base an airborne LiDAR data at spatial resolution of 50 cm. Then we map the morphological changes giving an estimation of the relative areas and volumes. These results, discussed and compared with available field data, can help to better understand the impact of the event and provide indications useful in a territory planning aimed to mitigate the effects of such calamitous events.

How to cite: Spinetti, C., Bisson, M., and Palaseanu-Lovejoy, M.: Stromboli surface changes from Pleiades high-resolution space data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12890, https://doi.org/10.5194/egusphere-egu23-12890, 2023.

17:20–17:30
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EGU23-16686
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GI6.2
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Highlight
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Virtual presentation
Marina Bisson, Claudia Spinetti, Roberto Gianardi, Karen Strehlow, Emanuela De Beni, and Patrizia Landi

This study presents an application based on UAS optical data for mapping at very high spatial resolution the ballistic projectiles erupted during an explosive volcanic eruption. The novelty consists in the development of a GIS-based automate procedure that, elaborating high spatial resolution UAV optical imagery (RGB) acquired within few days from the explosive event, is able to reproduce the boundary of each ballistic projectile as georeferenced polygon feature. This procedure, applied for the first time at Stromboli volcano (Aeolian Archipelago, Italy), has reconstructed in 2D digital format the shape of the ballistic spatter clasts emplaced on the East flank of the volcano during the paroxysm of the 3rd July, 2019. The dimensions of the clasts, reproduced as polygon features stored in WGS 84 UTM 33 metric coordinates, range from 0.03 m2 (16 cm x 16 cm) to 4.23 m2 (~2 m x 2 m). Respect to the classic field survey, the application here presented is able to generate, in efficient and rapid way, a large amount of data and information on ballistic deposits, covering also the areas inaccessible and/or dangerous as particularly affected by ballistic fallout. Such application allowed  to better understand the dynamic of ballistics emplacement, providing a useful contribution to volcanic hazard mitigation.

How to cite: Bisson, M., Spinetti, C., Gianardi, R., Strehlow, K., De Beni, E., and Landi, P.: An automated GIS procedure for mapping ballistic projectiles by using UAVs imagery: the case of the 3rd July, 2019 paroxysm at Stromboli, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16686, https://doi.org/10.5194/egusphere-egu23-16686, 2023.

17:30–17:40
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EGU23-14035
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GI6.2
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On-site presentation
Abraham Mejia-Aguilar, Alexandros Theofanidis, Emilio Dorigatti, Ruth Sonnenschein, Ekaterina Chuprikova, and Liqiu Meng

Endemic pests are a fundamental part of forest ecosystems, they provide key ecosystem services such as nutrient cycling and support biodiversity. Still, massive outbreaks of these pests, triggered by events such as drought, windthrows, and snow breaks, can limit the provisioning of ecosystem services that are key for human populations such as water cycle regulation and, which can eventually trigger natural hazard events (e.g. landslides).

Unmanned Aerial Vehicles (UAVs) and miniaturized optical sensors can be used to support foresters in detecting, identifying, and quantifying pests and their diffusion by exploiting multispectral imagery at high resolution. Such platforms are especially suited for monitoring areas in mountain regions that are difficult to access.

This study focus on the pine processionary (Thaumetopoea pityocampa) and European bark beetle (Ips typographus) that affect many forests in the Province of South Tyrol, Italy. Here, we present an up-scale strategy that first identifies the presence of a pest at the centimeter level (ground and close-range scale) based on UAV-derived products on a plot level. We conducted three UAV-flight campaigns during the year corresponding to the insect-life cycle. Then, on the one hand, using simple RGB and NDVI mosaics the system delineates the trees, identifies nests (processionary) and quantifies their impact. On the other, using the NDVI time series collection the system classify healthy, infested or dead tree linked to the presence of bark beetle. The system classifies and quantifies its presence by presenting graduated symbol maps widely used by foresters. Then, we scale up to meter resolution (remote sensing scale) to detect changes due to certain conditions of stress that can link to the presence of the studied pests. The final aim is to create high-quality training datasets that will be exploited by remote sensing products (Sentinel) to study and cover wider areas.

How to cite: Mejia-Aguilar, A., Theofanidis, A., Dorigatti, E., Sonnenschein, R., Chuprikova, E., and Meng, L.: Up-scaling approach to monitor pests in Alpine forests: A case study in Vinschgau, South Tyrol, Italy., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14035, https://doi.org/10.5194/egusphere-egu23-14035, 2023.

17:40–17:50
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EGU23-15267
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GI6.2
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ECS
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On-site presentation
Robbie Ramsay, Alex Merrington, Jack Gillespie, and Steven Hancock

The Field Spectroscopy Facility (Edinburgh, UK) is a Natural Environment Research Council public funded body which maintains and provides cutting edge spectroscopy instrumentation and expertise to UK and international researchers. The facility primarily focuses on the provision of ground based spectroscopic instrumentation, often in support of airborne spectroscopic surveys, but was awarded a UKRI capital fund in 2019 for the development of a UAV spectroscopic sensor suite to fill the spatial resolution gap between airborne and ground measurements.

Developed as the “NERC Field Spectroscopy Facility UAV Suite”, the new instrument pool consists of various UAV platforms and spectroscopic sensors which can be loaned to UK and international researchers. Instruments include multispectral cameras with sensors matched to Sentinel-2 and WorldView-3 centre wavelengths; thermal cameras covering the SWIR to MIR region; a custom designed UV-VIS spectrometer for measurements of solar induced fluorescence; and the flagship sensor of the suite, a lightweight hyperspectral imager with LIDAR attachment covering the UV-VIS-SWIR region (350 to 2500 nm range).

In this presentation, we discuss the development of the FSF UAV suite, discussing our “chain” concept of development – calibration of sensors at our optical laboratory; integration of sensors onto UAVs; logistical planning of flights with associated ground-based data acquisition; and the development of custom processing chains of UAV acquired data. We will highlight select campaigns on which the UAV suite has been used, including macro plastic detection as part of ESA HyperDrone, ecological surveying of large peatlands in Northern Scotland, and support for the ESA-FLEX (solar induced fluorescence sensing) mission. We will also discuss the challenges involved in sensor integration, and provide insight into the novel solutions which we have employed during the development of the UAV suite.  

How to cite: Ramsay, R., Merrington, A., Gillespie, J., and Hancock, S.: The NERC Field Spectroscopy Facility UAV Suite, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15267, https://doi.org/10.5194/egusphere-egu23-15267, 2023.

17:50–18:00
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EGU23-16009
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GI6.2
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On-site presentation
Teodor Hanchevici, Piotr Zaborowski, Donald V. Sullivan, and Alex Robin

Multi-vendor operations of uncrewed vehicles as part of the observations, surveillance and surveying are already daily practice in many fields. The popularity of the integration platforms that manage multiple, sometimes simultaneously, systems is also already proven by the integration platforms' popularity. With new European regulations for the drone industry and the growing popularity of various (ground, water surface, underwater, aerial) systems exploitations, the need for situation awareness and planning that will be flexible and vendor lock-in free is leveraged. However, despite several recent efforts and some popular specifications that aim at becoming de-facto standards, civil operations' interoperability challenge is unsolved. To assess whether a shared data model is suitable for multi-domain, multi-heterogeneous vehicle use, and challenge it with real applications and demonstrate the exchange of command and control information, OGC members started an Interoperability Experiment in 2022. IE is based on a data model developed by Kongsberg Geospatial and partners under the Standards-based UxS Interoperability Test-bed (SUIT). The IE considers those other standards and specifications which were used in the SUIT work as well as other Command and Control practices from the aviation and marine communities. The presentation depicts selected use cases and scenarios and outlines the information model of the localized situation awareness and mission planning and operations. Being specific for autonomous vehicle operations, they extend the needs of generic geospatial representations. Authors will explain relations to other similar models like (LSTS, MavLink, UMAA, STANAG 4586, JAUS, C2INav) and modern geospatial data exchange standards like OGC SensorThings, Features, Moving Features, GeoPose.

How to cite: Hanchevici, T., Zaborowski, P., Sullivan, D. V., and Robin, A.: Common mission planning and situation awareness model for UxS Command and Control systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16009, https://doi.org/10.5194/egusphere-egu23-16009, 2023.

Posters on site: Mon, 24 Apr, 10:45–12:30 | Hall X4

Chairpersons: Carlo Alberto Brunori, Antonello Bonfante, Misha Krassovski
X4.187
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EGU23-14165
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GI6.2
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Highlight
Vincenzo De Novellis, Massimiliano Alvioli, Andrea Barone, Antonello Bonfante, Maurizio Buonanno, Raffaele Castaldo, Ada De Matteo, Federica Fiorucci, Susi Pepe, Paola Reichenbach, Michele Santangelo, Giuseppe Solaro, Pietro Tizzani, and Andrea Vitale

The following work focuses on the surveys that were carried out using optical sensors (photogrammetry) and LiDAR mounted on UAS platforms. The processing of the acquired images provided the necessary information for the development of high-precision digital terrain models that can be used as a basis for the subsequent modeling of the stability analysis of collapse phenomena with STONE, a three-dimensional rockfall simulation model. These surveys allowed us to localize the possible detachment sources and the inclusion of scenario-based seismic shaking as a trigger for rockfalls.

The areas filmed fall almost exclusively along the north-western slope of Mt. Epomeo and more precisely in the areas identified as locality Falanga (32 ha) and locality Frassitelli (123 ha) in the territory of the Municipality of Forio (Napoli) and only marginally in the Municipality of Serrara Fontana (Napoli). The slope surveyed has two distinct morphologies: 1) the north-west oriented sector (Falanga) delimited by extremely steep walls and by cliffs with variable vertical development, at the base of which there is a large sub-flat area delimited to the north by a new sudden jump in slope; 2) in the west sector (Frassitelli) the slope is instead more rounded, even if in various points there are areas with steep slopes and strongly fractured cliffs; this side is characterized by the presence of numerous tuff blocks, even of large dimensions, which have stopped at various altitudes after having detached themselves from the overlying sub-vertical walls.

We also used data from the Geoportale Nazionale Italiano managed by the Ministry of Environment and provided different kinds of spatial data. In particular, the archive contains an extensive LiDAR survey covering a substantial portion of Italy, with data stored at the intermediate processing level. For this research, we selected point clouds covering the Ischia island and we interpolated separately the two point clouds, using the module specifically designed to perform surface interpolation from vector points mapped by splines, within the GIS platform.

In conclusion, we interpreted the point-to-point difference between DSM and DTM as due to vegetation and exploited this information to infer modifications of ground parameters relevant to the simulations with Stone. We partially took into account disturbances due to the presence of anthropic structures and buildings using additional land cover data, which we correlated with point-to-point DSM – DTM differences.

How to cite: De Novellis, V., Alvioli, M., Barone, A., Bonfante, A., Buonanno, M., Castaldo, R., De Matteo, A., Fiorucci, F., Pepe, S., Reichenbach, P., Santangelo, M., Solaro, G., Tizzani, P., and Vitale, A.: The integrated use of LiDAR and photogrammetric techniques by the UAS platform for the mapping of rockfall processes in Ischia Island (Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14165, https://doi.org/10.5194/egusphere-egu23-14165, 2023.

X4.188
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EGU23-4516
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GI6.2
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Alexandra Restrepo, Aya Labnine, Rocco DiMatteo, Colin Edwards, Jamali Hamilton, Luis Quinto, Madison Tuohy, Alex Nikulin, and Timothy S. de Smet

Anti-personal/tank landmines, improvised explosive devices (IED), unexploded ordinances (UXO), and other abandoned explosive ordinances (EO) all pose long-lasting threats that are detrimental to areas of conflict. From 2015 to 2021, a total of 49,050 deaths/injuries were caused by EOs, and this number is only increasing. Current demining methods heavily rely on ground-based electromagnetic-induction (EMI); however, this method is costly, time consuming and puts personnel at risk. Recent advances in drone and remote sensing technology have allowed for the development of alternative remote methods to improve the efficiency in locating EOs. We used a Velodyne VLP-16 light detection and ranging (LiDAR) sensor attached to a DJI Matrice 600 drone platform to remotely identify EOs, specifically PFM-1 and VPMA-3 anti-personnel mines, TM-62M anti-vehicle mines, and 3 meter long 122 mm multibarrel rockets (MBRL). LiDAR data was acquired in dual return acquisition mode at 300 rpm and a flight speed of 1 m/s. Several of these EOs are being used in the current Russo-Ukrainian war, including: TM-62 anti-vehicle mines, PFM-1 landmines, and the MBRL rockets. Our LiDAR sensor was calibrated with a 18 m swath width to acquire 4630 points/m2  density and a 1.7 cm footprint resolution. The LiDAR data that was collected was post-processed to produce various derivative data such as: 3D point clouds, digital elevation models (DEM), digital surface models (DSMs), and derivative data products such as the total horizontal derivative (THD) filter. Processed data highlighted lateral spatial heterogeneity, which identified vertical and horizontal MBRLs, as well as surficial TM-62M anti-vehicle, TM62P anti-personnel mines and VPMA-3 landmines. PFM-1 landmines, the smallest of all EOs used, were not located, as the footprint resolution of the data collected was too small (1.7 cm) to clearly differentiate the ordinance from the environment. This pilot study allowed us to better understand the strengths and weaknesses of this method. We plan to further develop this technology by exploring the use of streamlined algorithms, applying alternative data processing workflows, and using sub-pixel techniques to improve the accuracy and efficiency of location. Refining data acquisition parameters, such as the speed and height of drone flight may also lead to further improvements in efficiency. In addition to location, a focus could also be placed on looking at intensity to identify material properties of EOs. 

How to cite: Restrepo, A., Labnine, A., DiMatteo, R., Edwards, C., Hamilton, J., Quinto, L., Tuohy, M., Nikulin, A., and de Smet, T. S.: Drones Paired with Hyperspectral Imaging Paired with LiDAR to Locate Explosive Ordnance, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4516, https://doi.org/10.5194/egusphere-egu23-4516, 2023.

X4.189
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EGU23-7272
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GI6.2
Junho Yeom, Aisha Javed, Taeheon Kim, and Youkyung Han

With the advent and development of UAV technologies, UAV images are widely used in various fields since UAV photogrammetry has many advantages in terms of cost and accessibility. In addition, UAV photogrammetry has the advantage of enabling precise 3D surveying because it acquires images of higher spatial resolution with higher overlap compared to traditional aerial photogrammetry. UAV photogrammetry requires ground control points (GCPs) that are dense and evenly distributed throughout the study area. GCP surveying is generally conducted on-site, unlike automated UAV flight and image acquisition, which is a primary factor hindering time and labor cost reduction. In addition, pre-processing, such as UAV orthophoto, point cloud data, and digital elevation model (DEM) production, is performed automatically according to designated parameters, whereas matching GCP survey information with the images involves the intervention of an analyst. Therefore, in this study, the automatic extraction of UAV GCP targets and their centroids was investigated to increase the utilization of UAV photogrammetry and reduce the cost. Sequential steps of image thresholding, boundary detection, and buffered labeling detected a candidate area where ground targets exist. Then, the Hough transform was applied to the target candidates to extract two dominant lines and their intersection point representing the target center. The proposed method extracts the GCP targets from the images with high accuracy, and it was confirmed that it could be applied to complex urban areas. In addition, the GCP targets and their centroid points were successfully extracted from various land covers.

How to cite: Yeom, J., Javed, A., Kim, T., and Han, Y.: Automatic Detection of UAV GCP Targets Using Line-Based Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7272, https://doi.org/10.5194/egusphere-egu23-7272, 2023.

X4.190
|
EGU23-7353
|
GI6.2
Accurate Interpretation of Urban Mangrove Stand Structure Based on Low-altitude Unmanned Aerial Vehicle System
(withdrawn)
Xiaoxue Shen, Chaoyang Zhai, Fang Yang, and Ruili Li
X4.191
|
EGU23-7771
|
GI6.2
|
ECS
Utilizing UAV-Based Hyperspectral Imaging to Detect Surficial Explosive Ordnance
(withdrawn)
Madison Tuohy, Jasper Baur, Gabriel Steinberg, Jalissa Pirro, Taylor Mitchell, Alex Nikulin, John Frucci, and Timothy De Smet
X4.192
|
EGU23-17308
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GI6.2
|
Salim Soltani, Hannes Feilhauer, Robbert Duker, and Teja Kattenborn

Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that CNNs accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied aerial vehicles (UAV). However, such tasks require ample training data to generate transferable CNN models. Reference data are commonly generated via geocoded in-situ observations or labeling of remote sensing data through visual interpretation. Both approaches are commonly laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photos are expected to be very heterogeneous, and often show a different perspective compared to the typical bird-perspective of remote sensing data. Still, crowd-sourced plant photos could be a valuable source to overcome the challenge of limited training data and reduce the efforts for field data collection and data labeling. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photos for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photos that share a more similar perspective to the remote sensing data. Therefore, we used three case studies to test our proposed approach using multiple RGB orthoimages acquired from UAV for the target plant species Fallopia japonica (F. japonica), Portulacaria Afra (P. afra), and 10 different tree species, respectively. For training the CNN models, we queried the iNaturalist database for photos of the target species and the surrounding species that are expected in the areas of each case study. We trained CNN models with an EfficientNet-B07 backbone. For applying these models based on the crowd-sourced data to the remote sensing imagery, we used a sliding window approach with a 10 percent overlap. The individual sliding-window-based predictions were spatially aggregated in order to create a high-resolution classification map. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photos can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photos used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications.

How to cite: Soltani, S., Feilhauer, H., Duker, R., and Kattenborn, T.: Transfer learning from citizen science photos enables plantspecies identification in UAV imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17308, https://doi.org/10.5194/egusphere-egu23-17308, 2023.

X4.193
|
EGU23-11014
|
GI6.2
|
ECS
|
You Chul Jeong, Jong-Seok Lee, Jisun Shin, and Young-Heon Jo

Marine environmental issues due to marine debris are worldwide phenomena. According to a press release from the Ministry of Oceans and Fisheries of Korea, marine waste collection from the coastal area increased yearly. In 2020, it collected 1.38 million tons, about 45% more than in 2018. To remove them, they were collected and monitored through field monitoring systems. However, it is very inefficient in terms of time and cost. Therefore, the remote sensing approach can be suited for classifying and investigating marine waste dumped in coastal areas. Previous studies have classified marine waste by combining remote sensing based on RGB images and artificial intelligence. However, actual marine waste is often damaged, or its shape is difficult to recognize through RGB images. This study was conducted to classify various wastes using multi-spectral camera and a convolution neural network (CNN) model. We first trained and tested CNN model using three wastes, such as a brown paper box, an orange-colored buoy, and a blue plastic basket with different spectral characteristics in the land environment. Then, we conducted the classification of marine waste using CNN model and multi-spectral images taken with Uncrewed Aerial Systems (UAS) in the marine environment around Socheongcho-Ocean Research Station (S-ORS). The CNN model were trained using 1,452 seawater and 1,319 clear plastic images around the S-ORS with 128 x 128-pixel size. We calculated precision, recall, f1-score, and accuracy, suggesting that the CNN model could be used to classify various marine wastes in the various ocean environment. Overall, these results can provide useful information for marine waste monitoring.

How to cite: Jeong, Y. C., Lee, J.-S., Shin, J., and Jo, Y.-H.: A study on classification and monitoring of marine debris using multi-spectral images and deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11014, https://doi.org/10.5194/egusphere-egu23-11014, 2023.

X4.194
|
EGU23-11869
|
GI6.2
|
ECS
Hadrien Michel, Marc Dumont, David Caterina, François Jonard, Itzel Isunza Manrique, Tom Debouny, and Frédéric Nguyen

Ancient metallurgical industries produced large amounts of residues, which were typically deposited in heaps or tailing ponds. The presence of such wastes could represent a potential source of pollution that may prevent the reuse of the sites. The NWE-REGENERATIS project aims to characterize different types of metallurgical deposits in order to improve their management and rehabilitation. The understanding of these sites is made difficult by their heterogeneous composition, complex morphology and dense vegetation.

Here, we explore the interest of integrating UAV surveys in geophysical characterization of NWE-REGENERATIS sites. First, our approach uses photogrammetry to build the digital surface model. Such models can be used to approach deposit volume and improve modelling of the sites. Those are crucial to carry accurate inversion of land-based geophysical data. Secondly, the multi-spectral measurements allow characterizing surface geochemical composition in order to define surface waste characteristics. These data could be used to explain surface electrical resistivity variation. Finally, areas with high metallurgical contents are highlighted with magnetic mapping. There, the ability of UAV to cover areas previously unattainable by land (dense vegetation and/or steep inclines) is key for a better understanding of the site.

This methodology is applied to multiple sites, including old iron and zinc factories or uncharacterized industrial landfill. We thus present strengths and weaknesses of each UAV mapping used to characterize metallurgical landfills.

How to cite: Michel, H., Dumont, M., Caterina, D., Jonard, F., Isunza Manrique, I., Debouny, T., and Nguyen, F.: How UAV improve past metallurgical deposits characterization for landfill regeneration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11869, https://doi.org/10.5194/egusphere-egu23-11869, 2023.

X4.195
|
EGU23-13115
|
GI6.2
|
ECS
Jamal Elfarkh, Kasper Johansen, Victor Angulo-Morales, Omar Lopez Camargo, and Matthew F. McCabe

Land surface temperature (LST) is crucial information that helps to understand and assess the interactions between the surface and the atmosphere. LST is a key parameter used in various applications including studies of irrigation, water use, vegetation health, urban heat island effects, and building insulation. In addition to several satellites that provide periodic images of surface temperature, unmanned aerial vehicle (UAV) platforms have been adapted to obtain higher spatio-temporal resolution thermal infrared (TIR) data. In fact, numerous research studies have investigated the accuracy and the processing method of UAV-based TIR images given its complexity and sensitivity to ambient conditions. However, the surface temperature is characterized by continuous and rapid variation over time, which is difficult to take into consideration in the processing of UAV-based orthomosaics. Here, we quantify this variation and discuss the environmental factors that lead to its amplification. Thermal images were collected over a fixed hovering position during periods of 15-20 min, representing the common duration of UAV flights. At different times of the day, we flew at different altitudes over sand, water, grass and olive trees. Before the quantification of the surface temperature variation, the thermal infrared data were evaluated against field-based measurements using calibrated Apogee sensors. The evaluation showed a significant error in the UAV-based thermal infrared data linked to wind speed, which increased the bias from -1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. The assessment of the LST values collected over the different surfaces showed a temperature variation while hovering ranging between 1.4 and 5 °C. In addition to wind effects, temperature variations while hovering were strongly linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. This research provides insights into the LST variation expected for standard UAV flights of 15-20 min under different environmental conditions, which should be taken into account during UAV-based thermal infrared data processing and may help interpret and quantify inconsistencies in UAV-based orthomosaics of LST.

How to cite: Elfarkh, J., Johansen, K., Angulo-Morales, V., Lopez Camargo, O., and F. McCabe, M.: Surface temperature variations observed from a thermal infrared camera mounted on a hovering UAV platform, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13115, https://doi.org/10.5194/egusphere-egu23-13115, 2023.

X4.196
|
EGU23-12229
|
GI6.2
|
ECS
Adrián Moncholi, Shari Van Wittenberghe, Maria Pilar Cendrero-Mateo, Luis Alonso, Marcos Jiménez, Katja Berger, Alasdair Mac Arthur, and José Moreno

Under the current climate change conditions, the early stress detection of crops and worldwide vegetation are crucial to promote sustainable agriculture and ecosystem management. With the upcoming European Space Agency’s Fluorescence Explorer-Sentinel 3 (FLEX-S3) tandem mission, vegetation fluorescence and the auxiliary parameters/traits needed to interpret solar-induced vegetation fluorescence (SIF) will become available at 300x300 m spatial resolution. Today, a variety of SIF-specialized UAS systems exist to retrieve the canopy-emitted SIF over larger areas, e.g., as a reference for airborne imaging SIF sensors. However, they lack the complementary sensors needed for a correct interpretation of the highly dynamic fluorescence emission.  In this study we present the FluoCat system, a unique UAS system which can be mounted either in a UAV or cable-suspended mobile platform. On board the FluoCat are mounted: a high-spectral resolution Piccolo Doppio dual spectrometer system, a MAIA-S2 multispectral camera and a TeAx Thermal Capture Fusion camera, which can be triggered simultaneously according to a pre-set protocol. The FluoCat system mimics the FLEX-S3 sensor configuration, by using a multi-sensor system integrating the visible, NIR and thermal spectral regions providing complete datasets to assess the actual vegetation stress. In this context a field campaign was conducted in the experimental site ‘Las Tiesas’ in Barrax, Spain, with the aim to (1) apply sampling protocols to obtain spatially representative canopy reflectance and SIF measurements, and (2) provide accurate ground truth measurements for real (i.e., leaf) surface reflectance and effective surface fluorescence measurements, linkable to the real photosynthetic performance. Further we demonstrate the development of a sensor synergy product, combining canopy physiological and structural information to reveal real surface physiological stress-related energy emission. The ‘sunlit green fluorescence’ is a synergy product combining the top-of-canopy fluorescence and the fractional vegetation cover of the sunlit vegetation. This synergy product improved the estimation of the effective surface fluorescence flux, using the leaf fluorescence emission as reference, by reducing the errors from 36 % to 18 % (band 687 nm); and from 24 % to 6 % (band 760 nm). Real surface properties and products referring to the actual photosynthetic surface behavior are promising quantitative proxies to assess the impact of climate change and/or management practices on crop lands or even whole ecosystems. With this study we show how innovative proximal sensing platforms can help to develop new data processing schemes combining all required information for the quantitative assessment of vegetation health, even before visible damage occurs. The further processing and normalization of first-derived stress proxies such as SIF can generate further in-depth early stress detection, directly related to the photosynthetic light reactions, and further global carbon assessment. These developments are in direct support for the global monitoring of early vegetation stress under a changing global climate.

How to cite: Moncholi, A., Van Wittenberghe, S., Cendrero-Mateo, M. P., Alonso, L., Jiménez, M., Berger, K., Mac Arthur, A., and Moreno, J.: Real surface vegetation functioning and early stress detection using visible-NIR-thermal sensor synergies: from UAS to future satellite applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12229, https://doi.org/10.5194/egusphere-egu23-12229, 2023.

X4.197
|
EGU23-16685
|
GI6.2
|
Josef Wagner, Inbal Becker-Reshef, Shabarinath Nair, Sergii Skakun, Yuval Sadeh, Sheila Baber, Blake Munshel, Andrew Zolli, and Françoise Nerry

The invasion of Ukraine by Russian forces was expected to have global impact on food trade and security, since Ukraine is a breadbasket cereals and oil seeds producer. The NASA Harvest « Rapid Agricultural Assessment for Policy Support » (RAAPS) team was triggered early in the conflict to provide answers to the following questions : 

(i) How much winter cereals, winter oil seeds and summer crops were planted in Ukraine during the 2021-2022 cropping season?

(ii) What proportion of those crops fell under the Russian occupied area? 

(iii) How much cropland was left unplanted in 2022 due to the war?

As insights had to be produced within season, the NASA Harvest RAAPS team produced the first ever, Ukraine scale in-season crop type map based on Planet Labs 3 meter spatial -, 4 bands spectral -, and daily  temporal – resolution data.  Since   no   labeled   datasets   were   available   early   enough   in-season  for applying supervised machine learning techniques, cropland was progressively mapped   into   four   classes   (winter   cereals,   rapeseed,   summer   crops   and barren/non cultivated plots), using semi-supervised clustering techniques and heuristical thresholdings. Expert domain  knowledge  allowed to cope  with missing ground truth training data. First, active cropland was separated into winter crops and potential summer crops. K-means clustering of April and May Planet images, followed by visual cluster assignment, allowed to efficiently separate green crops (winter crops) from barren soils (potential summer crops). Then, another K-means clustering allowed to split winter crops into winter cereals and rapeseed as of end of May, based  on the intense yellow flowering signal of the latter. Finally a set of NDVI based heuristics was applied on potential summer crops in order to assess if green-up happened or not. Crops which   did   not   green   up   as   of   the   11th   of   July   2022   were   considered barren/non-planted. 

Road side ground surveyed crop type information collected in free Ukraine has been provided by Kussul & al. (2022) in August 2022. Validation against this data provided an overall accuracy of 94 % and a mean F1-score of 91 % for winter cereals, rapeseed and summer crops. No unplanted fields  were collected as part of the ground campaign. Several assessments of proportional area per crop type and occupation status were performed throughout the growing season, as occupation boundaries kept moving. As of the 11th of July 2022, 23.03 % of Ukraines cropland was occupied. 55.29 % of all detected barren fields were located within occupied territories, mainly scattered around the front line. 33.9 % of all winter crops were under occupied territory when harvest ready (mid July). 

This crop type map was used for computing harvested area, estimating yield and   for   production computation. Following NASA EarthObservatory articles were published,   providing   information   to   the   public   and   private   sector :   (i) https://earthobservatory.nasa.gov/images/150025/measuring-wars-effect-on-a-global-breadbasket    (ii) https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-ukraine-than-expected 

How to cite: Wagner, J., Becker-Reshef, I., Nair, S., Skakun, S., Sadeh, Y., Baber, S., Munshel, B., Zolli, A., and Nerry, F.: In - season progressive crop type mapping in war affected Ukraine, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16685, https://doi.org/10.5194/egusphere-egu23-16685, 2023.

Posters virtual: Mon, 24 Apr, 10:45–12:30 | vHall ESSI/GI/NP

Chairpersons: Antonello Bonfante, Vincenzo De Novellis
vEGN.23
|
EGU23-15546
|
GI6.2
|
ECS
Manuel Quintanilla-Albornoz, Joaquim Bellvert, Ana Pelechá, Jaume Casadesus, Omar García-Tejera, and Xavier Miarnau

The almond production has increased by doubling their hectares under irrigation treatments in Spain. In a context of water scarcity, the estimation of Evapotranspiration (ET) and its components, Transpiration (T) and Evaporation (E), are key variables to monitor and manage the water resources. High-resolution ET can be retrieved from surface energy flux modeling, such as a Two-Source Energy Balance (TSEB) model, using an Unmanned Aerial System (sUAS). sUAS equipped with Thermal and Multispectral cameras allows us to obtain the main parameters required in TSEB. Currently, there are no studies that evaluate the T obtained with TSEB Priestley Taylor (TSEB-PT) and TSEB-2T models in tree-scale almonds under different irrigation treatments (IR) and production systems (PS). In this context, we evaluated the T retrieved with TSEB-PT and TSEB-2T models using Sap Flows sensor in trees with three PS, Open Vase with Minimal Pruning (OVMP), Central Axis (CA) and Hedgerow (HGR), and three levels IR, Full Irrigation (FI), Mild Stressed (MS) and Stressed (SS). Five flights were conducted from March 2021 to July 2021 to analyze the almond growing season with an aircraft equipped with a thermal and multispectral camera. Leaf area index (LAI), stem water potential (Ψstem) and Fractional Intercepted Photosynthetically Active Radiation (fIPAR) was also measured concomitant to image acquisition. PS presents significant differences in fractional canopy cover (F_C), tree height (H_C), LAI and Sap Flow transpiration (Tsf). The two TSEB models show a generalized overestimation with a BIAS of 0.99 and 1.22 for TSEB-2T and TSEB-PT respectively. TSEB-PT presented worse statistics and R2 decreases in the more intensive production system. HGR has equal or greater LAI but lower F_C, which would imply an overestimation of canopy temperature (T_C) by the PT method. This is in addition to the difficulty of setting the PT coefficient according to the context of the crop. The overestimation in both models could be associated with an error in Campbell (1998) Radiative Transfer Model used to estimate transmittance, which has an error of 0.14 RMSE and 0.12 BIAS compared with fIPAR. Our results suggest the use of TSEB-2T with high resolution images considering the current available technology that allows us to estimate T_C and T_S separately, especially in intensive or super-intensive almond crops. To improve the T estimation, it is recommended to use in situ PAR measurement to decrease the influence of LAI measurements on the models.

How to cite: Quintanilla-Albornoz, M., Bellvert, J., Pelechá, A., Casadesus, J., García-Tejera, O., and Miarnau, X.: Assessment of transpiration in different almond production systems with two-source energy balance models using high-resolution aerial imagery., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15546, https://doi.org/10.5194/egusphere-egu23-15546, 2023.