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Special Issue "Earth Observation and In-Situ Sensing for Risk Assessment from Natural Threats"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 September 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Fabio Dell'Acqua

University of Pavia, Italy - Ticinum Aerospace s.r.l., Pavia, Italy
Website 1 | Website 2 | E-Mail
Interests: remote sensing; Earth observation; risk management

Special Issue Information

Dear Colleagues,

An increasing awareness is emerging that, although extremely useful in a large pool of applications, space-based sensing, alone, is not sufficient to reach the desired accuracy, reliability, precision, and especially completeness of data requested in some cases. In the context of risk assessment, where missing details can have a great impact on the accuracy of projected disaster scenarios, it is important to guarantee a reasonable completeness of the data kit; to meet such an ambitious goal, consistent and operational monitoring systems are needed to integrate spaceborne acquisitions at a variety of spatial and temporal resolutions. On the other hand, the development of sensor networks and mobile-based crowdsourcing appear to be offering the required technical means to generate the complementary data and support the integration of space-based and in-situ sensing.

From the above context, this Special Issue was conceived, open to the submission of both review and original research articles, related to the exploitation of spaceborne Earth observation (EO) and in-situ sensors for risk assessment from natural threats. Special attention will be devoted to the emerging paradigms in both sensing contexts, like “open data”, “big data”, “machine learning”, “crowdsourcing” and “participative sensing”. The Special Issue welcomes contributions ranging from exposure and vulnerability assessment, to geospatial methods for risk scenario analysis, to sensor networks, as well as innovative approaches using sensor fusion and deep learning. Original contributions on hazard and damage assessment are also encouraged.

Prof. Dr. Fabio Dell'Acqua
Guest Editor

Keywords

  • Spaceborne earth observation
  • In-situ sensing
  • Participative sensing
  • Crowdsourcing
  • Sensor networks
  • Risk assessment
  • Natural hazards

Published Papers (8 papers)

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Research

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Open AccessArticle European In-Situ Snow Measurements: Practices and Purposes
Sensors 2018, 18(7), 2016; https://doi.org/10.3390/s18072016
Received: 8 May 2018 / Revised: 13 June 2018 / Accepted: 18 June 2018 / Published: 22 June 2018
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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
[...] Read more.
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. Full article
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Open AccessArticle Wind Gust Measurement Techniques—From Traditional Anemometry to New Possibilities
Sensors 2018, 18(4), 1300; https://doi.org/10.3390/s18041300
Received: 21 March 2018 / Revised: 12 April 2018 / Accepted: 13 April 2018 / Published: 23 April 2018
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Abstract
Information on wind gusts is needed for assessment of wind-induced damage and risks to safety. The measurement of wind gust speed requires a high temporal resolution of the anemometer system, because the gust is defined as a short-duration (seconds) maximum of the fluctuating
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Information on wind gusts is needed for assessment of wind-induced damage and risks to safety. The measurement of wind gust speed requires a high temporal resolution of the anemometer system, because the gust is defined as a short-duration (seconds) maximum of the fluctuating wind speed. Until the digitalization of wind measurements in the 1990s, the wind gust measurements suffered from limited recording and data processing resources. Therefore, the majority of continuous wind gust records date back at most only by 30 years. Although the response characteristics of anemometer systems are good enough today, the traditional measurement techniques at weather stations based on cup and sonic anemometers are limited to heights and regions where the supporting structures can reach. Therefore, existing measurements are mainly concentrated over densely-populated land areas, whereas from remote locations, such as the marine Arctic, wind gust information is available only from sparse coastal locations. Recent developments of wind gust measurement techniques based on turbulence measurements from research aircraft and from Doppler lidar can potentially provide new information from heights and locations unreachable by traditional measurement techniques. Moreover, fast-developing measurement methods based on Unmanned Aircraft Systems (UASs) may add to better coverage of wind gust measurements in the future. In this paper, we provide an overview of the history and the current status of anemometry from the perspective of wind gusts. Furthermore, a discussion on the potential future directions of wind gust measurement techniques is provided. Full article
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Open AccessFeature PaperArticle A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data
Sensors 2018, 18(2), 591; https://doi.org/10.3390/s18020591
Received: 27 October 2017 / Revised: 13 December 2017 / Accepted: 12 January 2018 / Published: 14 February 2018
Cited by 1 | PDF Full-text (26879 KB) | HTML Full-text | XML Full-text
Abstract
Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a
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Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data—such as municipality-level records of crop seeding—for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using “good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task. Full article
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Open AccessArticle Assessing Crop Coefficients for Natural Vegetated Areas Using Satellite Data and Eddy Covariance Stations
Sensors 2017, 17(11), 2664; https://doi.org/10.3390/s17112664
Received: 30 September 2017 / Revised: 7 November 2017 / Accepted: 9 November 2017 / Published: 18 November 2017
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Abstract
The Food and Agricultural Organization (FAO) method for potential evapotranspiration assessment is based on the crop coefficient, which allows one to relate the reference evapotranspiration of well irrigated grass to the potential evapotranspiration of specific crops. The method was originally developed for cultivated
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The Food and Agricultural Organization (FAO) method for potential evapotranspiration assessment is based on the crop coefficient, which allows one to relate the reference evapotranspiration of well irrigated grass to the potential evapotranspiration of specific crops. The method was originally developed for cultivated species based on lysimeter measurements of potential evapotranspiration. Not many applications to natural vegetated areas exist due to the lack of available data for these species. In this paper we investigate the potential of using evapotranspiration measurements acquired by micrometeorological stations for the definition of crop coefficient functions of natural vegetated areas and extrapolation to ungauged sites through remotely sensed data. Pastures, deciduous and evergreen forests have been considered and lower crop coefficient values are found with respect to FAO data. Full article
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Open AccessArticle How Well Can We Extract the Permanent Displacement from Low-Cost MEMS Accelerometers?
Sensors 2017, 17(11), 2643; https://doi.org/10.3390/s17112643
Received: 5 October 2017 / Revised: 14 November 2017 / Accepted: 15 November 2017 / Published: 16 November 2017
Cited by 4 | PDF Full-text (2776 KB) | HTML Full-text | XML Full-text
Abstract
Following the recent establishment of a high-density seismic network equipped with low-cost micro-electro-mechanical system (MEMS) P-wave-alert-device (P-Alert) by the earthquake early warning (EEW) research group at the National Taiwan University, a large quantity of strong-motion records from moderate-magnitude earthquakes (M
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Following the recent establishment of a high-density seismic network equipped with low-cost micro-electro-mechanical system (MEMS) P-wave-alert-device (P-Alert) by the earthquake early warning (EEW) research group at the National Taiwan University, a large quantity of strong-motion records from moderate-magnitude earthquakes (ML > 6) around Taiwan has been accumulated. Using a data preprocessing scheme to recover the dynamic average embedded within the P-Alert data, we adopted an automatic baseline correction approach for the P-Alert accelerograms to determine the coseismic deformation (Cd). Comparisons between the Cd values determined using global positioning system (GPS) data, strong-motion records from the P-Alert network, and data from the Taiwan Strong Motion Instrumentation Program (TSMIP) demonstrates that the near-real-time determination of Cd values (>2 cm), which provide crucial information for seismic hazard mitigation, is possible using records from low-cost MEMS accelerometers. Full article
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Open AccessArticle Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
Sensors 2017, 17(8), 1864; https://doi.org/10.3390/s17081864
Received: 30 June 2017 / Revised: 8 August 2017 / Accepted: 10 August 2017 / Published: 12 August 2017
Cited by 1 | PDF Full-text (6624 KB) | HTML Full-text | XML Full-text
Abstract
We present the NEFOCAST project (named by the contraction of “Nefele”, which is the Italian spelling for the mythological cloud nymph Nephele, and “forecast”), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields
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We present the NEFOCAST project (named by the contraction of “Nefele”, which is the Italian spelling for the mythological cloud nymph Nephele, and “forecast”), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields over the regional territory based on the use of a widespread network of new-generation Eutelsat “SmartLNB” (smart low-noise block converter) domestic terminals. Though primarily intended for interactive satellite services, these devices can also be used as weather sensors, as they have the capability of measuring the rain-induced attenuation incurred by the downlink signal and relaying it on an auxiliary return channel. We illustrate the NEFOCAST system architecture, consisting of the network of ground sensor terminals, the space segment, and the service center, which has the task of processing the information relayed by the terminals for generating rain field maps. We discuss a few methods that allow the conversion of a rain attenuation measurement into an instantaneous rainfall rate. Specifically, we discuss an exponential model relating the specific rain attenuation to the rainfall rate, whose coefficients were obtained from extensive experimental data. The above model permits the inferring of the rainfall rate from the total signal attenuation provided by the SmartLNB and from the link geometry knowledge. Some preliminary results obtained from a SmartLNB installed in Pisa are presented and compared with the output of a conventional tipping bucket rain gauge. It is shown that the NEFOCAST sensor is able to track the fast-varying rainfall rate accurately with no delay, as opposed to a conventional gauge. Full article
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Open AccessArticle An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network
Sensors 2017, 17(7), 1481; https://doi.org/10.3390/s17071481
Received: 20 February 2017 / Revised: 9 June 2017 / Accepted: 13 June 2017 / Published: 23 June 2017
Cited by 3 | PDF Full-text (2184 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since
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This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since its foundation in July 2011. The sites from which measurements are being collected represent a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and a range of the most typical land use/cover types found in Wales. At present, WSMN consists of a total of nine monitoring sites across the area with a concentration of sites in three sub-areas around the region of Aberystwyth located in Mid-Wales. The dataset of composed of 0–5 (or 0–10) cm soil moisture, soil temperature, precipitation, and other ancillary data. WSMN data are provided openly to the public via the International Soil Moisture Network (ISMN) platform. At present, WSMN is also rapidly expanding thanks to funding obtained recently which allows more monitoring sites to be added to the network to the wider community interested in using its data. Full article
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Open AccessConcept Paper E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time
Sensors 2017, 17(12), 2766; https://doi.org/10.3390/s17122766
Received: 30 September 2017 / Revised: 16 November 2017 / Accepted: 19 November 2017 / Published: 29 November 2017
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Abstract
In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are
[...] Read more.
In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48–72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved. Full article
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