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  • 1
    In: Remote Sensing, MDPI AG, Vol. 15, No. 12 ( 2023-06-13), p. 3095-
    Abstract: Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 2
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 16, No. 6 ( 2015-12-01), p. 2559-2576
    Abstract: A single-model 16-member ensemble is used to investigate how external model factors can affect model performance. Ensemble members are constructed with the land surface model (LSM) Joint UK Land Environment Simulator (JULES), with different choices of meteorological forcing [in situ, NCEP Climate Forecast System Reanalysis (CFSR)/CFSv2, or Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] and ancillary datasets (in situ or remotely sensed), and with four time step modes. Effects of temporal averaging are investigated by comparing the hourly, daily, monthly, and seasonal ensemble performance against snow depth and water equivalent, soil temperature and moisture, and latent and sensible heat fluxes from one forest site and one clearing in the boreal ecozone of Finnish Lapland. Results show that meteorological data are the largest source of uncertainty; differences in ancillary data have little effect on model results. Although generally informative and representative, aggregated performance metrics fail to identify “right results for the wrong reasons”; to do so, scrutinizing of time series and of interactions between variables is necessary. Temporal averaging over longer intervals improves metrics—with the notable exception of bias, which increases—by reducing the effects of internal data and model variability on model response. Model evaluation during shoulder seasons (fall minus spring) identifies weaknesses in the reanalyses datasets that conventional seasonal performance (winter minus summer) neglects. In view of the importance of snow on the range of results obtained with the same model, let alone identical simulations using different temporal averaging, it is recommended that systematic evaluation, quantification of errors, and uncertainties in snow-covered regions be incorporated in future efforts to standardize evaluation methods of LSMs.
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2015
    detail.hit.zdb_id: 2042176-X
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  • 3
    In: Global Change Biology, Wiley, Vol. 26, No. 2 ( 2020-02), p. 876-887
    Abstract: The role of plant phenology as a regulator for gross ecosystem productivity (GEP) in peatlands is empirically not well constrained. This is because proxies to track vegetation development with daily coverage at the ecosystem scale have only recently become available and the lack of such data has hampered the disentangling of biotic and abiotic effects. This study aimed at unraveling the mechanisms that regulate the seasonal variation in GEP across a network of eight European peatlands. Therefore, we described phenology with canopy greenness derived from digital repeat photography and disentangled the effects of radiation, temperature and phenology on GEP with commonality analysis and structural equation modeling. The resulting relational network could not only delineate direct effects but also accounted for possible effect combinations such as interdependencies (mediation) and interactions (moderation). We found that peatland GEP was controlled by the same mechanisms across all sites: phenology constituted a key predictor for the seasonal variation in GEP and further acted as a distinct mediator for temperature and radiation effects on GEP. In particular, the effect of air temperature on GEP was fully mediated through phenology, implying that direct temperature effects representing the thermoregulation of photosynthesis were negligible. The tight coupling between temperature, phenology and GEP applied especially to high latitude and high altitude peatlands and during phenological transition phases. Our study highlights the importance of phenological effects when evaluating the future response of peatland GEP to climate change. Climate change will affect peatland GEP especially through changing temperature patterns during plant phenologically sensitive phases in high latitude and high altitude regions.
    Type of Medium: Online Resource
    ISSN: 1354-1013 , 1365-2486
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2020313-5
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Meteorology and Atmospheric Physics Vol. 133, No. 2 ( 2021-04), p. 281-294
    In: Meteorology and Atmospheric Physics, Springer Science and Business Media LLC, Vol. 133, No. 2 ( 2021-04), p. 281-294
    Type of Medium: Online Resource
    ISSN: 0177-7971 , 1436-5065
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 232907-4
    detail.hit.zdb_id: 863-1
    detail.hit.zdb_id: 1462145-9
    SSG: 16,13
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  • 5
    In: Remote Sensing, MDPI AG, Vol. 14, No. 14 ( 2022-07-12), p. 3357-
    Abstract: Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 6
    In: Earth System Science Data, Copernicus GmbH, Vol. 10, No. 1 ( 2018-01-25), p. 173-184
    Abstract: Abstract. In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. With broad spatial coverage and high temporal resolution, networked cameras can provide information about snow cover and vegetation status, serve as ground truths to Earth observations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (https://doi.org/10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.
    Type of Medium: Online Resource
    ISSN: 1866-3516
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2475469-9
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  • 7
    Online Resource
    Online Resource
    Copernicus GmbH ; 2021
    In:  The Cryosphere Vol. 15, No. 1 ( 2021-01-27), p. 369-387
    In: The Cryosphere, Copernicus GmbH, Vol. 15, No. 1 ( 2021-01-27), p. 369-387
    Abstract: Abstract. The capability of time-lapse photography to retrieve snow depth time series was tested. Historically, snow depth has been measured manually by rulers, with a temporal resolution of once per day, and it is a time-consuming activity. In the last few decades, ultrasonic and/or optical sensors have been developed to obtain automatic and regular measurements with higher temporal resolution and accuracy. The Finnish Meteorological Institute Image Processing Toolbox (FMIPROT) has been used to retrieve the snow depth time series from camera images of a snow stake on the ground by implementing an algorithm based on the brightness difference and contour detection. Three case studies have been illustrated to highlight potentialities and pitfalls of time-lapse photography in retrieving the snow depth time series: Sodankylä peatland, a boreal forested site in Finland, and Gressoney-La-Trinité Dejola and Careser Dam, two alpine sites in Italy. This study presents new possibilities and advantages in the retrieval of snow depth in general and snow depth time series specifically, which can be summarized as follows: (1) high temporal resolution – hourly or sub-hourly time series, depending on the camera's scan rate; (2) high accuracy levels – comparable to the most common method (manual measurements); (3) reliability and visual identification of errors or misclassifications; (4) low-cost solution; and (5) remote sensing technique – can be easily extended in remote and dangerous areas. The proper geometrical configuration between camera and stake, highlighting the main characteristics which each single component must have, has been proposed. Root mean square errors (RMSEs) and Nash–Sutcliffe efficiencies (NSEs) were calculated for all three case studies comparing with estimates from both the FMIPROT and visual inspection of images directly. The NSE values were 0.917, 0.963 and 0.916, while RMSEs were 0.039, 0.052 and 0.108 m for Sodankylä, Gressoney and Careser, respectively. In terms of accuracy, the Sodankylä case study gave better results. The worst performances occurred at Careser Dam located at 2600 m a.s.l., where extreme weather conditions and a low temporal resolution of the camera occur, strongly affecting the clarity of the images.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2393169-3
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  • 8
    In: Biogeosciences, Copernicus GmbH, Vol. 16, No. 2 ( 2019-01-21), p. 223-240
    Abstract: Abstract. The surface albedo time series, CLARA-A2 SAL, was used to study trends in the snowmelt start and end dates, the melting season length and the albedo value preceding the melt onset in Finland during 1982–2016. In addition, the melt onset from the JSBACH land surface model was compared with the timing of green-up estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Moreover, the melt onset was compared with the timing of the greening up based on MODIS data. Similarly, the end of snowmelt timing predicted by JSBACH was compared with the melt-off dates based on the Finnish Meteorological Institute (FMI) operational in situ measurements and the Fractional Snow Cover (FSC) time-series product provided by the EU FP7 CryoLand project. It was found that the snowmelt date estimated using the 20 % threshold of the albedo range during the melting period corresponded well to the melt estimate of the permanent snow layer. The longest period, during which the ground is continuously half or more covered by snow, defines the permanent snow layer (Solantie et al., 1996). The greening up followed within 5–13 days the date when the albedo reached the 1 % threshold of the albedo dynamic range during the melting period. The time difference between greening up and complete snowmelt was smaller in mountainous areas than in coastal areas. In two northern vegetation map areas (Northern Karelia–Kainuu and Southwestern Lapland), a clear trend towards earlier snowmelt onset (5–6 days per decade) and increasing melting season length (6–7 days per decade) was observed. In the forested part of northern Finland, a clear decreasing trend in albedo (2 %–3 % per decade in absolute albedo percentage) before the start of the melt onset was observed. The decreasing albedo trend was found to be due to the increased stem volume.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2158181-2
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  • 9
    Online Resource
    Online Resource
    The Electromagnetics Academy ; 2006
    In:  Progress In Electromagnetics Research Vol. 56 ( 2006), p. 263-281
    In: Progress In Electromagnetics Research, The Electromagnetics Academy, Vol. 56 ( 2006), p. 263-281
    Type of Medium: Online Resource
    ISSN: 1559-8985
    Language: English
    Publisher: The Electromagnetics Academy
    Publication Date: 2006
    detail.hit.zdb_id: 2241936-6
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  • 10
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Geosciences Vol. 13, No. 3 ( 2023-03-22), p. 92-
    In: Geosciences, MDPI AG, Vol. 13, No. 3 ( 2023-03-22), p. 92-
    Abstract: Ultrasonic sensors are one of the most common automatic monitoring methods in operational snow depth monitoring with reliable results. On the other hand, there is significant uncertainty when measuring small snow depths ( 〈 2 cm), thus it cannot provide binary snow presence (on/off) information. The use of webcams in monitoring snow cover has proven to be successful in recent studies and applications. In this study, we applied an adaptive thresholding technique on images from webcams to obtain reliable snow on/off information to complement the ultrasonic snow depth measurements. Camera and ultrasonic sensor data from two weather stations in Finland were studied. The webcam data was processed using FMIPROT (Finnish Meteorological Institute Image Processing Tool) software, operating in a cloud computing environment, which can generate near real-time data. Our results indicate that webcam-derived data can be successfully used for quality control or as auxiliary data to support operational ultrasonic sensor measurements and provide a cost-effective improvement to operational monitoring capabilities. Webcam monitoring is especially useful during the melting season when the snow depth is below 15 mm, with accuracy values between 72% and 94%.
    Type of Medium: Online Resource
    ISSN: 2076-3263
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655946-8
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