Monitoring landslide displacements with the Geocube wireless network of low-cost GPS

Benoît, Lionel; Briole, Pierre; Martin, Olivier; Thom, Christian; Malet, Jean‐Philippe; Ulrich, Patrice

doi:10.1016/j.enggeo.2015.05.020

Cited by 145 publications

(60 citation statements)

References 22 publications

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“…Not many papers revealed information whether the IoT devices were placed for long-term or only temporarily in order to get some measurements or perform sampling. Long-term sensor deployment was observed in water monitoring systems [14], in air quality monitoring [16], in monitoring landslide displacements [17] (using low-power 10-Watt solar panels), in deep-ocean tsunami measuring [18] (battery-powered with four-year lifetime), in weather monitoring stations [29] and in wildlife monitoring [20]. Long-term deployments were provisioned where sensor replacement was expensive or difficult.…”

Section: Disposable Vs Long-term Iot Sensorsmentioning

confidence: 99%

“…Collar tags placed on animals can be considered mobile devices [19], [20]. On the other hand, sensors were placed statically in cases of monitoring some phenomenon or potential disaster, such as landslides [17], flooding [14], [30], tsunami [18], [28], forest fire [25] and earthquakes [26]. Static sensors were also used to measure air quality [16].…”

Section: Mobile Vs Static Iot Devicesmentioning

confidence: 99%

“…Some reliability problems occurred during installation of sensors at the field. The geo-cubes used to monitor landslide displacements in [17], experienced loss of observations due to a lack of sealing and not proper solar panels used as energy sources. The sensors used to assess irrigation water well suitability were forced to become sealed after sensing [31], because exposure to air affected the measurements.…”

Section: Reliabilitymentioning

confidence: 99%

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Geospatial Analysis and the Internet of Things

Kamilaris

Ostermann

2018

IJGI

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Geospatial analysis offers large potential for better understanding, modelling and visualizing our natural and artificial ecosystems, using Internet of Things as a pervasive sensing infrastructure. This paper performs a review of research work based on the IoT, in which geospatial analysis has been employed in environmental informatics. Six different geospatial analysis methods have been identified, presented together with 26 relevant IoT initiatives adopting some of these techniques. Analysis is performed in relation to the type of IoT devices used, their deployment status and data transmission standards, data types employed, and reliability of measurements. This paper scratches the surface of this combination of technologies and techniques, providing indications of how IoT, together with geospatial analysis, are currently being used in the domain of environmental research. IntroductionThe Internet of Things (IoT) includes technologies and research disciplines that enable the Internet to reach out into the real world of physical objects [1]. The vision of IoT involves seamless integration of physical devices to the internet/web, by means of well-understood, accepted and used protocols, technologies and programming/description languages, for efficient human-to-machine or machine-to-machine communication [2]. Internet-enabled things are equipped with sensors, measuring the physical world and its phenomena such as temperature, humidity, radiation, electromagnetism, noise, chemicals etc. Moreover, the rise of multi-sensory mobile phones offers advanced sensing capabilities, such as measuring proximity, acceleration and location, recording audio/noise, sensing electromagnetism or capturing images and videos [3]. An important characteristic of the measurements performed by Internet-enabled things (i.e. sensors, mobile phones, cameras etc.) is the geo-location where each measurement was done. As the physical or artificial environments are generally quite complex, being characterized by a wide variety of parameters, a rich collection of measurements in space and time is necessary in order to model and understand these ecosystems [4]. Hence, sensory-based IoT information needs to be aggregated, stored and analyzed for more elaborate and holistic reasoning and inference. To exploit this variety of IoT measurements in type, space and time, geospatial analysis is used in order to provide high-quality analytics and insights [5].The contribution of this paper is to survey IoT applications that employ analytic operations that focus on location, examining the various geospatial analysis techniques employed. It is noted that we refer to the IoT as used in environmental informatics, i.e. sensing equipment and measurement systems recording

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Section: Disposable Vs Long-term Iot Sensorsmentioning

confidence: 99%

Section: Mobile Vs Static Iot Devicesmentioning

confidence: 99%

Section: Reliabilitymentioning

confidence: 99%

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Geospatial Analysis and the Internet of Things

Kamilaris

Ostermann

2018

IJGI

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“…The GPS receiver is the GeoCube devel-oped at IGN. The GeoCube (Benoit et al, 2015) is a multi-sensor instrument that embeds a LEA-6T-001 u-blox (u-blox, 2014) GPS module chip. It records carrier-phase raw measurements on L1 4 band of GPS constellation.…”

Section: Hardwarementioning

confidence: 99%

Study of Lever-Arm Effect Using Embedded Photogrammetry and on-Board GPS Receiver on Uav for Metrological Mapping Purpose and Proposal of a Free Ground Measurements Calibration Procedure

Daakir¹,

Pierrot-Deseilligny²,

Bosser³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Nowadays, Unmanned Aerial Vehicle (UAV) on-board photogrammetry knows a significant growth due to the democratization of using drones in the civilian sector. Also, due to changes in regulations laws governing the rules of inclusion of a UAV in the airspace which become suitable for the development of professional activities. Fields of application of photogrammetry are diverse, for instance: architecture, geology, archaeology, mapping, industrial metrology, etc. Our research concerns the latter area. Vinci-ConstructionTerrassement is a private company specialized in public earthworks that uses UAVs for metrology applications. This article deals with maximum accuracy one can achieve with a coupled camera and GPS receiver system for direct-georeferencing of Digital Surface Models (DSMs) without relying on Ground Control Points (GCPs) measurements. This article focuses specially on the lever-arm calibration part. This proposed calibration method is based on two steps: a first step involves the proper calibration for each sensor, i.e. to determine the position of the optical center of the camera and the GPS antenna phase center in a local coordinate system relative to the sensor. A second step concerns a 3d modeling of the UAV with embedded sensors through a photogrammetric acquisition. Processing this acquisition allows to determine the value of the lever-arm offset without using GCPs.

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“…GPS allows the coverage of a large area and has acceptable accuracy; the observations are independent of the weather conditions and can be divided between stations (Mora et al 2003). Because of the wide use of GPS, some studies were conducted to improve the use of this technique in landslide displacement monitoring, such as by optimizing the monitoring equipment and system in order to reduce the monitoring cost (Chen et al 2000;Benoit et al 2015) and by analyzing the methodology and observations to increase the accuracy of the GPS monitoring system (Ifadis 2000;Malet et al 2002;Hastaoglu & Sanli 2011). The reliable observations of the GPS monitoring system make it possible to analyze the deformation and mechanism of landslides (Cencetti et al 2000;Rizzo 2002;Coe et al 2003;Squarzoni et al 2005;Br€ uckl et al 2006;Peyret et al 2008;Nordvik & Nyrnes 2009;Zabuski et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Landslide displacement analysis based on fractal theory, in Wanzhou District, Three Gorges Reservoir, China

Gui

Yin

Glade

2016

Geomatics, Natural Hazards and Risk

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Slow moving landslide is a major disaster in the Three Gorges Reservoir area. It is difficult to compare the deformation among different parts of this kind of landslide through GPS measurements when the displacement of different monitoring points is similar in values. So far, studies have been seldom carried out to find out the information hidden behind those GPS monitoring data to solve this problem. Therefore, in this study, three landslides were chosen to perform landslide displacement analysis based on fractal theory. The major advantage of this study is that it has not only considered the values of the displacement of those GPS monitoring points, but also considered the moving traces of them. This allows to reveal more information from GPS measurements and to obtain a broader understanding of the deformation history on different parts of a unique landslide, especially for slow moving landslides. The results proved that using the fractal dimension as an indicator is reliable to estimate the deformation of each landslide and to represent landslide deformation on both spatial and temporal scales. The results of this study could make sense to those working on landslide hazard and risk assessment and land use planning.

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Monitoring landslide displacements with the Geocube wireless network of low-cost GPS

Cited by 145 publications

References 22 publications

Geospatial Analysis and the Internet of Things

Geospatial Analysis and the Internet of Things

Study of Lever-Arm Effect Using Embedded Photogrammetry and on-Board GPS Receiver on Uav for Metrological Mapping Purpose and Proposal of a Free Ground Measurements Calibration Procedure

Landslide displacement analysis based on fractal theory, in Wanzhou District, Three Gorges Reservoir, China

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