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Special Issue "Workshop Sensing A Changing World 2012"

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 October 2012)

Special Issue Editor

Guest Editor
Dr. Arend Ligtenberg

Wageningen University and Research Centre, GAIA building 101 at the campus WUR, Wageningen, The Netherlands
Website | E-Mail

Special Issue Information

Dear Colleagues,

Three years ago the first workshop on Sensing a Changing World was held in Wageningen bringing together 80 researchers from 12 countries (http://www.mdpi.com/journal/sensors/special_issues/sensing-a-changing-world/).
Now, three  years later, developments in sensor technology have advanced rapidly. Technological platforms matured, standards are accepted, and various (mobile) sensors became widely available. As a result the number of applications which implement sensors (including human-sensors) and sensor web concepts are increasing, leading to new research challenges. Current developments in sensor technology provide increasing opportunities to analyse human behaviour and monitor environmental processes of a changing world. Access to vast amounts of data from mobile (e.g., GPS, mobile phones), in situ (e.g., meteorological, groundwater, seismic) and remote sensing sensors provides scientific researchers with interesting spatial-temporal data sets.
The main goal of this second edition, which is organized with support of the MODAP project (http://www.modap.org), is to present and explore the current state-of-the-art developments, impacts and research challenges for sensor web technology in the context of environmental monitoring and analyses.
The workshop results in an overview of the current state-of-the art developments and identification of future research challenges to improve the application of various sensor concepts and techniques in the environmental sciences domains. We encourage submission of both conceptual and application oriented contributions for the following topics (but are not limited to):

  • Knowledge discovery from sensory data sets
  • Scale issues in processing and application of spatial temporal sensory data
  • Real-time and on-demand representation and visualization of sensor data
  • Information extraction from (informal) sensor network data
  • Integration of sensor webs and dynamical modelling
  • Development of location and movement based services
  • Standardized access to sensor data and the linkage to spatial data infrastructures.
  • Application of mobile sensor based applications (transportation, urban, tourism, cellular census, location based services etc.)
  • In situ and earth observation sensor applications (groundwater, air-quality, river management, agriculture, extreme events etc.)

Dr. Arend Ligtenberg
Guest Editor

Related Special Issue

Published Papers (4 papers)

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Research

Open AccessArticle The Aeroflex: A Bicycle for Mobile Air Quality Measurements
Sensors 2013, 13(1), 221-240; doi:10.3390/s130100221
Received: 27 September 2012 / Revised: 6 December 2012 / Accepted: 17 December 2012 / Published: 24 December 2012
Cited by 20 | PDF Full-text (1388 KB) | HTML Full-text | XML Full-text
Abstract
Fixed air quality stations have limitations when used to assess people's real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used
[...] Read more.
Fixed air quality stations have limitations when used to assess people's real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used as an additional tool to fill this void. In this publication we present the Aeroflex, a bicycle for mobile air quality monitoring. The Aeroflex is equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass and black carbon concentrations at a high resolution (up to 1 second). Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data pre-processing and data visualization. The Aeroflex is designed with adaptability, reliability and user friendliness in mind. Over the past years, the Aeroflex has been successfully used for high resolution air quality mapping, exposure assessment and hot spot identification. Full article
(This article belongs to the Special Issue Workshop Sensing A Changing World 2012)
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Open AccessArticle Mobile Devices for Community-Based REDD+ Monitoring: A Case Study for Central Vietnam
Sensors 2013, 13(1), 21-38; doi:10.3390/s130100021
Received: 16 November 2012 / Revised: 6 December 2012 / Accepted: 13 December 2012 / Published: 20 December 2012
Cited by 17 | PDF Full-text (1224 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic
[...] Read more.
Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic process of assessing forest stands properties with respect to reference data, adopting the current REDD+ requirements for implementing monitoring at national levels is a challenging task. Recently, the advancement in Information and Communications Technologies (ICT) and mobile devices has enabled local communities to monitor their forest in a basic resource setting such as no or slow internet connection link, limited power supply, etc. Despite the potential, the use of mobile device system for community based monitoring (CBM) is still exceptional and faces implementation challenges. This paper presents an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. This paper also assesses the accuracy and reliability of CBM data and proposes a way to fit them into national REDD+ Monitoring, Reporting and Verification (MRV) scheme. The system performance is evaluated at Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were tracked. The results show that the local community is able to provide data with accuracy comparable to expert measurements (index of agreement greater than 0.88), but against lower costs. Furthermore, the results confirm that communities are more effective to monitor small scale forest degradation due to subsistence fuel wood collection and selective logging, than high resolution remote sensing SPOT imagery. Full article
(This article belongs to the Special Issue Workshop Sensing A Changing World 2012)
Open AccessArticle A Laboratory Goniometer System for Measuring Reflectance and Emittance Anisotropy
Sensors 2012, 12(12), 17358-17371; doi:10.3390/s121217358
Received: 19 September 2012 / Revised: 7 December 2012 / Accepted: 11 December 2012 / Published: 13 December 2012
Cited by 10 | PDF Full-text (659 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a laboratory goniometer system for performing multi-angular measurements under controlled illumination conditions is described. A commercially available robotic arm enables the acquisition of a large number of measurements over the full hemisphere within a short time span making it much
[...] Read more.
In this paper, a laboratory goniometer system for performing multi-angular measurements under controlled illumination conditions is described. A commercially available robotic arm enables the acquisition of a large number of measurements over the full hemisphere within a short time span making it much faster than other goniometers. In addition, the presented set-up enables assessment of anisotropic reflectance and emittance behaviour of soils, leaves and small canopies. Mounting a spectrometer enables acquisition of either hemispherical measurements or measurements in the horizontal plane. Mounting a thermal camera allows directional observations of the thermal emittance. This paper also presents three showcases of these different measurement set-ups in order to illustrate its possibilities. Finally, suggestions for applying this instrument and for future research directions are given, including linking the measured reflectance anisotropy with physically-based anisotropy models on the one hand and combining them with field goniometry measurements for joint analysis with remote sensing data on the other hand. The speed and flexibility of the system offer a large added value to the existing pool of laboratory goniometers. Full article
(This article belongs to the Special Issue Workshop Sensing A Changing World 2012)
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Open AccessArticle Where and When Should Sensors Move? Sampling Using the Expected Value of Information
Sensors 2012, 12(12), 16274-16290; doi:10.3390/s121216274
Received: 27 September 2012 / Revised: 14 November 2012 / Accepted: 20 November 2012 / Published: 26 November 2012
Cited by 2 | PDF Full-text (504 KB) | HTML Full-text | XML Full-text
Abstract
In case of an environmental accident, initially available data are often insufficient for properly managing the situation. In this paper, new sensor observations are iteratively added to an initial sample by maximising the global expected value of information of the points for decision
[...] Read more.
In case of an environmental accident, initially available data are often insufficient for properly managing the situation. In this paper, new sensor observations are iteratively added to an initial sample by maximising the global expected value of information of the points for decision making. This is equivalent to minimizing the aggregated expected misclassification costs over the study area. The method considers measurement error and different costs for class omissions and false class commissions. Constraints imposed by a mobile sensor web are accounted for using cost distances to decide which sensor should move to the next sample location. The method is demonstrated using synthetic examples of static and dynamic phenomena. This allowed computation of the true misclassification costs and comparison with other sampling approaches. The probability of local contamination levels being above a given critical threshold were computed by indicator kriging. In the case of multiple sensors being relocated simultaneously, a genetic algorithm was used to find sets of suitable new measurement locations. Otherwise, all grid nodes were searched exhaustively, which is computationally demanding. In terms of true misclassification costs, the method outperformed random sampling and sampling based on minimisation of the kriging variance. Full article
(This article belongs to the Special Issue Workshop Sensing A Changing World 2012)
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