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Article ; Online: Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images.

Entezami, Alireza / De Michele, Carlo / Arslan, Ali Nadir / Behkamal, Bahareh

2022 Volume 22, Issue 13

Abstract: The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide ... ...

Abstract	The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher-student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.
MeSH term(s)	Algorithms ; Environmental Monitoring/methods ; Humans ; Interferometry/methods ; Neural Networks, Computer ; Radar
Language	English
Publishing date	2022-06-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22134964
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data

Entezami, Alireza / Arslan, Ali Nadir / De Michele, Carlo / Behkamal, Bahareh

Remote Sensing. 2022 July 12, v. 14, no. 14

2022

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 ... ...

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.
Keywords	algorithms ; artificial intelligence ; automation ; bioinformatics ; synthetic aperture radar ; United Kingdom
Language	English
Dates of publication	2022-0712
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14143357
Database	NAL-Catalogue (AGRICOLA)

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Article: Accuracy of Copernicus Altimeter Water Level Data in Italian Rivers Accounting for Narrow River Sections

Deidda, Cristina / De Michele, Carlo / Arslan, Ali Nadir / Pecora, Silvano / Taburet, Nicolas

Remote Sensing. 2021 Nov. 05, v. 13, no. 21

2021

Abstract: Information about water level is essential for hydrological monitoring and flood/drought risk assessment. In a large part of Italian river network, in situ instruments for measuring water level are rare or lacking. Here we consider the satellite ... ...

Abstract	Information about water level is essential for hydrological monitoring and flood/drought risk assessment. In a large part of Italian river network, in situ instruments for measuring water level are rare or lacking. Here we consider the satellite measurements of water level retrieved by Copernicus altimetric missions (Sentinel 3A, Sentinel 3B, Jason 2/3), and compare these with in situ data, from 19 gauging stations in Italy with a river section in the range of [50, 555] m. The results highlight the potentiality of altimetric satellite measurements for water level retrieval in a case study of Italian rivers. By comparing synchronous satellite and in situ water level difference (i.e., difference between two successive measurements in time of satellite data compared to the difference of two successive measurements in time of in situ data), we found a median value of Pearson correlation of 0.79 and 0.37 m of RMSE. Then, from water level differences, we extracted the satellite water level values with two different procedures: (1) assuming as the initial water level of the satellite measurements the first joint measurement (satellite–in situ data) and (2) calibrating the initial water level, minimizing the mean absolute error metric. The results show the feasibility of using satellite data for water level retrieval in an operative and automatic perspective.
Keywords	altimeters ; case studies ; drought ; hydrology ; remote sensing ; risk assessment ; rivers ; satellites ; Italy
Language	English
Dates of publication	2021-1105
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13214456
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Snow depth estimation by time-lapse photography

Bongio, Marco / Arslan, Ali Nadir / Tanis, Cemal Melih / Michele, Carlo

eISSN: 1994-0424

Finnish and Italian case studies

2019

Abstract: We explored the potentiality of time-lapse photography method to estimate the snow depth in boreal forested and alpine regions. Historically, the snow depth has been measured manually by rulers or snowboards, with a temporal resolution of once per day, ... ...

Abstract	We explored the potentiality of time-lapse photography method to estimate the snow depth in boreal forested and alpine regions. Historically, the snow depth has been measured manually by rulers or snowboards, with a temporal resolution of once per day, and a time-consuming activity. In the last decades, ultrasonic and/or optical sensors have been developed to obtain automatic measurements with higher temporal resolution and accuracy, defining a network of sensors within each country. The Finnish Meteorological Institute Image processing tool (FMIPROT) is used to retrieve the snow depth from images of a snow stake on the ground collected by cameras. An “ad-hoc” algorithm based on the brightness difference between snowpack and stake’s markers has been developed. We illustrated three case studies (case study 1-Sodankylä Peatland, case study 2-Gressoney la Trinitè Dejola, and case study 3-Careser dam) to highlight potentialities and pitfalls of the method. The proposed method provides, respect to the existing methods, new possibilities and advantages in the estimation of snow depth, which can be summarized as follows: 1) retrieving the snow depth at high temporal resolution, and an accuracy comparable to the most common method (manual measurements); 2) errors or misclassifications can be identified simply with a visual observation of the images; 3) estimating the spatial variability of snow depth by placing more than one snow stake on the camera’s view; 4) concerning the well-known under catch problem of instrumental pluviometer, occurring especially in mountain regions, the snow water equivalent can be corrected using high-temporal digital images; 5) the method enables retrieval of snow depth in avalanche, dangerous and inaccessible sites, where there is in general a lack of data; 6) the method is cheap, reliable, flexible and easily extendible in different environments and applications. We analyzed cases in which this method can fail due to poor visibility conditions or obstruction on the camera’s view. Defining a simple procedure based on ensemble of simulations and a post processing correction we can reproduce a snow depth time series without biases. Root Mean Square Errors (RMSE) and Nash Sutcliffe Efficiency (NSE) are calculated for all three case studies comparing with both estimates from the FMIPROT and visual observations of images. For the case studies, we found NSE = 0.917 , 0.963, 0.916 respectively for Sodankylä, Gressoney and Careser. In terms of accuracy, the first case study gave better results (RMSE equal to 3.951 · 10 −2 m, 5.242 · 10 −2 m, 10.78 · 10 −2 m, respectively). The worst performances occurred at Careser dam located at 2600 m a.s.l. where extreme weather conditions occur, strongly affecting the clarity of the images. For Sodankylä case study, we showed that the proposed method can improve the measurements obtained by a Campbell snow depth ultrasonic sensor. According to results, we provided also useful information about the proper geometrical configuration stake-camera and the related parameters, which allow to retrieve reliable snow depth time series.
Subject code	551
Language	English
Publishing date	2019-10-07
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Ecosystem Services Related to Carbon Cycling - Modeling Present and Future Impacts in Boreal Forests.

Holmberg, Maria / Aalto, Tuula / Akujärvi, Anu / Arslan, Ali Nadir / Bergström, Irina / Böttcher, Kristin / Lahtinen, Ismo / Mäkelä, Annikki / Markkanen, Tiina / Minunno, Francesco / Peltoniemi, Mikko / Rankinen, Katri / Vihervaara, Petteri / Forsius, Martin

Frontiers in plant science

2019 Volume 10, Page(s) 343

Abstract: Forests regulate climate, as carbon, water and nutrient fluxes are modified by physiological processes of vegetation and soil. Forests also provide renewable raw material, food, and recreational possibilities. Rapid climate warming projected for the ... ...

Abstract	Forests regulate climate, as carbon, water and nutrient fluxes are modified by physiological processes of vegetation and soil. Forests also provide renewable raw material, food, and recreational possibilities. Rapid climate warming projected for the boreal zone may change the provision of these ecosystem services. We demonstrate model based estimates of present and future ecosystem services related to carbon cycling of boreal forests. The services were derived from biophysical variables calculated by two dynamic models. Future changes in the biophysical variables were driven by climate change scenarios obtained as results of a sample of global climate models downscaled for Finland, assuming three future pathways of radiative forcing. We introduce continuous monitoring on phenology to be used in model parametrization through a webcam network with automated image processing features. In our analysis, climate change impacts on key boreal forest ecosystem services are both beneficial and detrimental. Our results indicate an increase in annual forest growth of about 60% and an increase in annual carbon sink of roughly 40% from the reference period (1981-2010) to the end of the century. The vegetation active period was projected to start about 3 weeks earlier and end ten days later by the end of the century compared to currently. We found a risk for increasing drought, and a decrease in the number of soil frost days. Our results show a considerable uncertainty in future provision of boreal forest ecosystem services.
Language	English
Publishing date	2019-03-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2019.00343
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Book ; Online: Monitoring changes in forestry and seasonal snow using surface albedo during 1982–2016 as an indicator

Manninen, Terhikki / Aalto, Tuula / Markkanen, Tiina / Peltoniemi, Mikko / Böttcher, Kristin / Metsämäki, Sari / Anttila, Kati / Pirinen, Pentti / Arslan, Ali Nadir

eISSN: 1726-4189

2018

Abstract: The surface albedo time series CLARA-A2 SAL was used to study trends in the snow melt start and end dates, the melting season length and the albedo value preceding the melt onset in Finland during 1982–2016. The results were compared with corresponding ... ...

Abstract	The surface albedo time series CLARA-A2 SAL was used to study trends in the snow melt start and end dates, the melting season length and the albedo value preceding the melt onset in Finland during 1982–2016. The results were compared with corresponding snow melt timing calculated using the land ecosystem model JSBACH. In addition, the melt onset was compared with the greening-up timing based on MODIS data. Likewise the end of snow melt was compared with the melt-off day product by SYKE based on Fractional Snow Cover time-series provided by Copernicus CryoLand service and the FMI operational end of snow melt dates based on in situ measurements. It turned out that the albedo threshold 20 % of the melting season dynamic variation corresponded well to the melt estimate of the permanent snow layer. The greening-up followed the albedo threshold 1 % within 5–13 days, more rapidly in mountainous areas and more slowly on coastal areas. In two northern vegetation map areas a clear trend to earlier snow melt onset (0.5–0.6 days per year) and increasing melting season length (0.6–0.7 days per year) was observed. In the forested part of northern Finland a clear decreasing trend in albedo (0.2 %–0.3 % per year in absolute albedo percentage) before the start of the melt onset was observed. The increased stem volume explained the trend.
Subject code	550 ; 551
Language	English
Publishing date	2018-08-28
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Refining the role of phenology in regulating gross ecosystem productivity across European peatlands

Koebsch, Franziska / Sonnentag, Oliver / Järveoja, Järvi / Peltoniemi, Mikko / Alekseychik, Pavel / Aurela, Mika / Arslan, Ali Nadir / Dinsmore, Kerry / Gianelle, Damiano / Helfter, Carole / Jackowicz‐Korczynski, Marcin / Korrensalo, Aino / Leith, Fraser / Linkosalmi, Maiju / Lohila, Annalea / Lund, Magnus / Maddison, Martin / Mammarella, Ivan / Mander, Ülo /

Minkkinen, Kari / Pickard, Amy / Pullens, Johannes W. M / Tuittila, Eeva‐Stiina / Nilsson, Mats B / Peichl, Matthias

Global change biology. 2020 Feb., v. 26, no. 2

2020

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 ... ...

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.
Keywords	air temperature ; altitude ; canopy ; climate change ; ecosystems ; gross primary productivity ; latitude ; peatlands ; phenology ; photography ; photosynthesis ; seasonal variation ; structural equation modeling ; thermoregulation ; vegetation
Language	English
Dates of publication	2020-02
Size	p. 876-887.
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	1281439-8
ISSN	1365-2486 ; 1354-1013
ISSN (online)	1365-2486
ISSN	1354-1013
DOI	10.1111/gcb.14905
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: European In-Situ Snow Measurements: Practices and Purposes.

Pirazzini, Roberta / Leppänen, Leena / Picard, Ghislain / Lopez-Moreno, Juan Ignacio / Marty, Christoph / Macelloni, Giovanni / Kontu, Anna / von Lerber, Annakaisa / Tanis, Cemal Melih / Schneebeli, Martin / de Rosnay, Patricia / Arslan, Ali Nadir

Sensors (Basel, Switzerland)

2018 Volume 18, Issue 7

Abstract: In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called "A European network ...

Abstract	In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called "A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction". Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.
Language	English
Publishing date	2018-06-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s18072016
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Refining the role of phenology in regulating gross ecosystem productivity across European peatlands.

Koebsch, Franziska / Sonnentag, Oliver / Järveoja, Järvi / Peltoniemi, Mikko / Alekseychik, Pavel / Aurela, Mika / Arslan, Ali Nadir / Dinsmore, Kerry / Gianelle, Damiano / Helfter, Carole / Jackowicz-Korczynski, Marcin / Korrensalo, Aino / Leith, Fraser / Linkosalmi, Maiju / Lohila, Annalea / Lund, Magnus / Maddison, Martin / Mammarella, Ivan / Mander, Ülo /

Minkkinen, Kari / Pickard, Amy / Pullens, Johannes W M / Tuittila, Eeva-Stiina / Nilsson, Mats B / Peichl, Matthias

Global change biology

2019 Volume 26, Issue 2, Page(s) 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.
MeSH term(s)	Climate Change ; Ecosystem ; Photosynthesis ; Seasons ; Temperature
Language	English
Publishing date	2019-12-03
Publishing country	England
Document type	Journal Article
ZDB-ID	1281439-8
ISSN	1365-2486 ; 1354-1013
ISSN (online)	1365-2486
ISSN	1354-1013
DOI	10.1111/gcb.14905
Database	MEDical Literature Analysis and Retrieval System OnLINE

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