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  • Artikel  (125)
  • machine learning
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  • 1
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    Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence (2023)
    MDPI AG
    In:  Remote Sensing vol. 15 no. 3334
    Details
    Publikationsdatum: 2024-06-01
    Beschreibung: Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.
    Schlagwort(e): mangrove forests ; forest inventory ; monitoring ; habitat mapping ; UAV ; UAS ; artificial ; intelligence ; machine learning ; instance segmentation ; semantic segmentation ; above ground biomass ; carbon stock
    Repository-Name: National Museum of Natural History, Netherlands
    Materialart: info:eu-repo/semantics/article
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
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    Digitizing the coral reef: Machine learning of underwater spectral images enables dense taxonomic mapping of benthic habitats (2022)
    In:  Methods in Ecology and Evolution vol. 14 no. 2, pp. 596-613
    Details
    Publikationsdatum: 2024-06-01
    Beschreibung: Coral reefs are the most biodiverse marine ecosystems, and host a wide range of taxonomic diversity in a complex spatial community structure. Existing coral reef survey methods struggle to accurately capture the taxonomic detail within the complex spatial structure of benthic communities. We propose a workflow to leverage underwater hyperspectral image transects and two machine learning algorithms to produce dense habitat maps of 1150 m2 of reefs across the Curaçao coastline. Our multi-method workflow labelled all 500+ million pixels with one of 43 classes at taxonomic family, genus or species level for corals, algae, sponges, or to substrate labels such as sediment, turf algae and cyanobacterial mats. With low annotation effort (only 2% of pixels) and no external data, our workflow enables accurate (Fbeta of 87%) survey-scale mapping, with unprecedented thematic detail and with fine spatial resolution (2.5 cm/pixel). Our assessments of the composition and configuration of the benthic communities of 23 image transects showed high consistency. Digitizing the reef habitat and community structure enables validation and novel analysis of pattern and scale in coral reef ecology. Our dense habitat maps reveal the inadequacies of point sampling methods to accurately describe reef benthic communities.
    Schlagwort(e): coral reefs ; habitat mapping ; hyperspectral imaging ; machine learning ; survey scale mapping ; thematic detail
    Repository-Name: National Museum of Natural History, Netherlands
    Materialart: info:eu-repo/semantics/article
    Format: application/pdf
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  • 3
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    Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects (2024)
    The Royal Society
    In:  Philosophical Transactions of the Royal Society B: Biological Sciences vol. 379 no. 1904
    Details
    Publikationsdatum: 2024-05-17
    Beschreibung: Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects—from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.
    Schlagwort(e): biodiversity monitoring ; machine learning ; moths ; camera trap
    Repository-Name: National Museum of Natural History, Netherlands
    Materialart: info:eu-repo/semantics/article
    Format: application/pdf
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  • 4
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    Monitoring sources of volcanic activity at Mount Etna using pattern recognition techniques on infrasound signals (2024)
    Oxford University Press on behalf of the Royal Astronomical Society and the German Geophysical Society
    Details
    Publikationsdatum: 2024-05-10
    Beschreibung: Infrasound monitoring plays an important role in the framework of the surveillance of Mt. Etna, Europe’s largest active volcano. Compared to seismic monitoring, which is particularly effective for buried sources, infrasound signals mirror the activity of shallow sources like Strombolian explosions or degassing. The interpretation of infrasound signals is difficult to the untrained eye, as we have to account for volcanic and non-volcanic sources. The problem of handling large and complex data sets can be tackled with machine learning, namely pattern recognition techniques. Here, we focus on so-called ‘Unsupervised Learning’, where we identify groups of patterns being similar to each other. The degree of similarity is based on a metric measuring the distance among the features of the patterns. This work aims at the identification of typical regimes of infrasound radiation and their relation to the state of volcanic activity at Mt. Etna. For this goal, we defined features describing any infrasound pattern. These features were obtained using wavelet transform. We applied ‘Self-Organizing Maps’ (SOM) to the features projecting them to a 2-D representation space—the ‘map’. An intriguing aspect of SOM resides in the fact that the position of the patterns on the map can be expressed by a colour code, in a manner that similar patterns are assigned a similar colour code. This simplified representation of multivariate patterns allows to follow the development of their characteristics with time efficiently. During a training phase we considered a reference data set, which encompassed a large variety of scenarios. We identified typical groups of patterns which correspond to a specific regime of activity, being representative of the state of the volcano or noise conditions. These groups form areas on the 2-D maps. In a second step, we considered a test data set, which was not used during the training phase. Applying the same pre-processing as for the training data, we blindly assigned the test patterns to the regimes found before, identifying the one whose colour code is most similar to the one calculated to the test pattern. We are thus able to assess the validity of the prediction. The classification scheme presented provides a reliable assessment of the state of activity and adds useful and supplementary details to the results of the real-time automatic system in operation at Istituto Nazionale di Geofisica e Vulcanologia—Osservarorio Etneo. This is of particular importance when no visible information of the volcanic activity is available either for unfavourable meteorological conditions or during night time.
    Beschreibung: IMPACT PROJECT (INGV Department strategic Projects - 2019)
    Beschreibung: Published
    Beschreibung: 1-16
    Beschreibung: JCR Journal
    Schlagwort(e): infrasound ; volcano monitoring ; self-organization ; time-series analysis ; machine learning ; volcanic hazards and risks ; 04.06. Seismology ; 04.08. Volcanology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 5
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    Enhancing detection of volcanic ash clouds from space with convolutional neural networks (2024)
    Elsevier
    Details
    Publikationsdatum: 2024-03-15
    Beschreibung: Volcanic ash cloud detection is a crucial component of volcano monitoring and a valuable tool for investigating ash cloud dispersion, which is paramount for enhancing the safety of human settlements and air traffic. The latest generation of high-resolution satellite sensors (e.g., EUMETSAT MSG Spinning Enhanced Visible and InfraRed Imager, SEVIRI) provides radiometric estimates for monitoring volcanic clouds on a global scale efficiently and timely. However, these radiometric intensities are not always discriminative enough to detect volcanic ash clouds due to the spectral limitations of these instruments and the complex nature of some volcanic clouds, such as low concentration resulting in an averaged detected radiometric estimate comparable to the background. Here, we evaluate the ability of a Convolutional Neural Network (CNN) to detect and track the dispersion of volcanic ash clouds into the atmosphere, exploiting a variety of spatial and spectral intensity information mainly coming from SEVIRI Ash RGB images. We train a deep CNN model through transfer learning, and demonstrate that the trained models overcome the limitations of algorithms based solely on pixel intensity, whether traditional or machine learning, resulting in increased performance compared to other methods. We illustrate the operation of this model using the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022.
    Beschreibung: Published
    Beschreibung: 108046
    Beschreibung: OSV3: Sviluppo di nuovi sistemi osservazionali e di analisi ad alta sensibilità
    Beschreibung: JCR Journal
    Schlagwort(e): Volcano explosive eruptions ; satellite remote sensing ; volcanic ash clouds ; machine learning ; deep learning ; Etna volcano ; 04.08. Volcanology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 6
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    Classification of Seismic Signals at Vulcano, Italy, using Unsupervised Learning Techniques (2024)
    Details
    Publikationsdatum: 2024-03-12
    Beschreibung: We analyze the seismic signals recorded on the island of Vulcano (Italy) during a volcano unrest that started in 2021. From mid-September 2021 onward, a high number of very long-period and long-period events occurred, accompanied by large emissions of CO2 and the increased temperature of fumaroles at various sites of the island. The complexity of the seismic signals recorded during the unrest made standard amplitude-based monitoring techniques, such as RSAM, questionable, as part of the signals are not volcanogenic, such as the frequent close-by passage of ships. We therefore study the inventory of the recorded signals by exploiting machine learning procedures, in particular unsupervised classification techniques. Our studies aim at identifying varying classes of seismic events possibly related to volcanic dynamic as well as irrelevant signals, such as man-made noise. Self-Organizing Maps and Cluster Analysis were applied. As a result, we are able to visualize the development of signal characteristics efficiently. This can provide a useful contribution to volcanic surveillance purposes, which aim to identify changes heralding a “Vulcanian” eruption, an eruptive style with strong explosive characteristics.
    Beschreibung: Published
    Beschreibung: Lacco Ameno, Ischia Island (Italy)
    Beschreibung: OST5 Verso un nuovo Monitoraggio
    Schlagwort(e): seismic signals ; machine learning ; Vulcano ; classification ; 04.06. Seismology ; 05.06. Methods ; 04.08. Volcanology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: Oral presentation
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  • 7
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    Using machine learning for the classification of Seismic Signals at Vulcano, Italy (2024)
    GFZ German Research Centre for Geosciences
    Details
    Publikationsdatum: 2024-01-29
    Beschreibung: A Vulcanian eruption is described as an eruptive style with strong explosive characteristics. The name derives from the island of Vulcano in Italy, the first place in which it was observed during the last eruptive activity between 1888 and 1890. In this paper we analyze the seismicity recorded at Vulcano during a seismic unrest starting in September 2021 and still present as of November 2022. The distinctive feature of this seismicity is the presence of a variety of signals, most of which have a very long period (\textasciitilde0.5 s) signature. Low frequency content is interpreted as due to fluid involvement. Therefore, the high occurrence rate of VLP seismicity is a potential indication of pressure buildup within the volcanic system, and may herald phreatomagmatic activity (usually the first stage of a Vulcanian eruption), with serious consequences for inhabitants and tourists.Our analyses exploit machine learning procedures, with particular reference to pattern classification, at the aim of identifying varying classes of seismic events and trace their evolution over time. This classification can be useful for surveillance purposes contributing, along with other early warning methods, to reduce the devastating consequences of eruptions for people and property.
    Beschreibung: Published
    Beschreibung: Berlino (Germania)
    Beschreibung: OST5 Verso un nuovo Monitoraggio
    Schlagwort(e): seismic activity ; machine learning ; events classification ; Vulcano ; Aeolian Islands ; VLP seismicity ; 04.06. Seismology ; 04.08. Volcanology ; 05.06. Methods
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: Conference paper
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  • 8
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    A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning (2023)
    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Details
    Publikationsdatum: 2023-02-22
    Beschreibung: Dataset: Distribution of dissolved barium in seawater determined using machine learning
    Beschreibung: We present a spatially and vertically resolved global grid of dissolved barium concentrations ([Ba]) in seawater determined using Gaussian Process Regression machine learning. This model was trained using 4,345 quality-controlled GEOTRACES data from the Arctic, Atlantic, Pacific, and Southern Oceans. Model output was validated by assessing the accuracy of [Ba] simulations in the Indian Ocean, noting that none of the 1,157 Indian Ocean data were seen by the model during training. We identify a model that can accurate predict [Ba] in the Indian Ocean using six features: depth, temperature, salinity, dissolved oxygen, dissolved phosphate, and dissolved nitrate. This model achieves a mean absolute percentage deviation of 6.3 %. This model was used to simulate [Ba] on a global basis using predictor data from the World Ocean Atlas 2018. The global model of [Ba] is on a 1°x 1° grid with 102 depth levels from 0 to 5,500 m. The dissolved [Ba] output was then used to simulate dissolved Ba* (barium-star), which is the difference between 'observed' and [Ba] predicted from co-located [Si]. Lastly, [Ba] data were combined with temperature, salinity, and pressure data from the World Ocean Atlas to calculate the saturation state of seawater with respect to barite. The model reveals that the volume-weighted mean oceanic [Ba] and and saturation state are 89 nmol kg–1 and 0.82, respectively. These results imply that the total marine Ba inventory is 122±8 ×10¹² mol. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/885506
    Beschreibung: NSF Division of Ocean Sciences (NSF OCE) OCE-2023456, NSF Division of Ocean Sciences (NSF OCE) OCE-2048604
    Schlagwort(e): barium ; barite ; machine learning
    Repository-Name: Woods Hole Open Access Server
    Materialart: Dataset
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  • 9
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    A dataset from a multi-station analysis of volcanic tremor at Mt. Etna, Italy, in 2021 (2022)
    Details
    Publikationsdatum: 2022-10-28
    Beschreibung: This work was designed within the project IMPACT (A multidisciplinary Insight on the kinematics and dynamics of Magmatic Processes at Mt. Etna Aimed at identifying preCursor phenomena and developing early warning sysTems). IMPACT belongs to the Progetti Dipartimentali INGV [DIP7], https://progetti.ingv.it/index.php/it/progetti-dipartimentali/vulcani/impact#informazioni-sul-progetto.
    Beschreibung: Published
    Beschreibung: 6V. Pericolosità vulcanica e contributi alla stima del rischio
    Schlagwort(e): Etna ; volcano unrest ; volcanic tremor ; machine learning ; pattern classification ; identification of thresholds ; 04.06. Seismology ; 04.08. Volcanology ; 05.06. Methods ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: web product
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  • 10
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    Sea Wave Data Reconstruction Using Micro-Seismic Measurements and Machine Learning Methods (2022)
    Frontiers Media S.A.
    Details
    Publikationsdatum: 2022-06-17
    Beschreibung: Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However, being exposed to a harsh environment, they are not reliable, need frequent maintenance, and the datasets feature many gaps. To overcome the previous limitations, we propose a system including a buoy, a micro-seismic measuring station, and a machine learning algorithm. The working principle is based on measuring the micro-seismic signals generated by the sea waves. Thus, the machine learning algorithm will be trained to reconstruct the missing buoy data from the micro-seismic data. As the micro-seismic station can be installed indoor, it assures high reliability while the machine learning algorithm provides accurate reconstruction of the missing buoy data. In this work, we present the methods to process the data, develop and train the machine learning algorithm, and assess the reconstruction accuracy. As a case of study, we used experimental data collected in 2014 from the Northern Tyrrhenian Sea demonstrating that the data reconstruction can be done both for significant wave height and wave period. The proposed approach was inspired from Data Science, whose methods were the foundation for the new solutions presented in this work. For example, estimating the period of the sea waves, often not discussed in previous works, was relatively simple with machine learning. In conclusion, the experimental results demonstrated that the new system can overcome the reliability issues of the buoy keeping the same accuracy.
    Beschreibung: Assist in Gravitation and Instrumentation srl Istituto Nazionale di Geofisica e Vulcanologia
    Beschreibung: Published
    Beschreibung: 798167
    Beschreibung: 4A. Oceanografia e clima
    Beschreibung: JCR Journal
    Schlagwort(e): sea swell ; machine learning ; ocean waves ; micro-seismic data ; sea state ; sea wave period ; buoy ; Marine Science ; Oceanography
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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