Abstract
Risk prevention is recognized as one of the most critical aspects of the policies of environmental monitoring. Because of the limited resources and the large amount of structures used for erosion control and slope protection, the Civil Protection and the Italian Forestry Carabinieri are not able to supervise them directly, with enough frequency. The present work is aimed to develop an innovative technique for periodically monitoring those structures, combining Mobile Crowd-Sensing (MCS) technology with photogrammetry and GIS. The experiments were performed in the Nature Reserve of Tirone (a protected natural area located inside the Vesuvius National Park in Naples) by analysing the metric reconstruction of two structures (a small weir and a log crib wall), before and after an accident, artificially generated for simulating a hydrogeological event or an act of vandalism, in order to evaluate GCPs influence. The procedure was split into four main phases: periodic acquisition of sets of photos with common smartphones and their transmission via the Internet; elaboration of the threedimensional model starting from a subset of selected pictures; comparison between the generated and the previous model; database update and programming of the subsequent monitoring. The accuracy of photogrammetric reconstructions was evaluated comparing the reconstruction with and without Ground Control Points (GCPs). The results show the models extracted without GCPs are satisfactory, since they allow to retrieve dimensional information of the examined constructions and to detect any instability. Models, generated using GCPs, are more detailed, but the processing and operational time is strongly higher.
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References
ISTAT annuario statistico italiano 2017, pp. 5–55. ISBN 978-88-458-1933-9. https://www.istat.it/it/files/2017/12/Asi-2017.pdf. Accessed 20 Mar 2019
Varnes, D.J.: Landslide hazard zonation: a review of principles and practice (1984). ISBN 92-3-101895-7. https://unesdoc.unesco.org/ark:/48223/pf0000063038
Angelats, E., Parés, M.E., Kumar, P.: Feasibility of smartphone based photogrammetric point clouds for the generation of accessibility maps. In: 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2, Riva del Garda, Italy, 4–7 June 2018
Caradonna, G., Tarantino, E., Scaioni, M., Figorito, B.: Multi-image 3D reconstruction: a photogrammetric and structure from motion comparative analysis. In: Gervasi, O., et al. (eds.) ICCSA 2018, Part V. LNCS, vol. 10964, pp. 305–316. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95174-4_25
Nocerino, E., Poiesi, F., Remondino, F., Van Gool, L.: Point clouds from smartphones. GIM Int. 32(3), 18–21 (2018)
Masiero, A., Fissore, F., Pirotti, F., Guarnieri, A., Vettore, A.: Toward the use of smartphones for mobile mapping. Geo-Spatial Inf. Sci. 19(3), 210–221 (2016). https://doi.org/10.1080/10095020.2016.1234684
Di grazia, F., Cantelli, L., Fabbri, S., Grumiero, B.: Citizen – photographers help environmental monitoring thanks to a photogrammetric approach, Geophysical Research Abstracts, vol. 20, EGU2018-18099, 2018 EGU General Assembly 2018 (2018)
Triglia, A., Iadanza, C., Bussettini, M., Lastoria, B.: Dissesto idrogeologico in Italia: pericolosità ed indicatori di rischio – Edizione 2018. Ispra, Rapporti 287/2018 (2018)
Micheletti, N., Chandler, J.H., Lane, S.N.: Investigating the geomorphological potential of freely available and accessible structure-from-motion photogrammetry using a smartphone. Earth Surf. Process. Landf. 40, 473–486 (2015)
Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104–4113 (2016)
Nocerino, E., et al.: 3D reconstruction with a collaborative approach based on smartphones and a cloud-based server. In: 2017 5th International Workshop LowCost 3D – Sensors, Algorithms, Applications. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W8, Hamburg, Germany, 28–29 November 2017
Capolupo, A., Pindozzi, S., Okello, C., Fiorentino, N., Boccia, L.: Photogrammetry for environmental monitoring: the use of drones and hydrological models for detection of soil contaminated by copper. Sci. Total Environ. 514, 298–306 (2015). https://doi.org/10.1016/j.scitotenv.2015.01.109. ISSN: 0048-9697
Capolupo, A., Pindozzi, S., Okello, C., Boccia, L.: Indirect field technology for detecting areas object of illegal spills harmful to human health: application of drones, photogrammetry and hydrological models. Geospatial Health 8, 699–707 (2014). https://doi.org/10.4081/gh.2014.298. ISSN: 1970-7096
Capolupo, A., Cervelli, E., Pindozzi, S., Boccia, L.: Assessing volumetric and geomorphologic changes of terraces in Amalfi Coast using photogrammetric technique. In: International Conference AIIA, Bari, Italy, 5–8 July 2017
Capolupo, A., Kooistra, L., Boccia, L.: A novel approach for detecting agricultural terraced landscapes from historical and contemporaneous photogrammetric aerial photos. Int. J. Appl. Earth Obs. Geoinf. 73, 800–810 (2018)
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This study was financed by the I.Z.S.Me/C.I.R.AM “Campania trasparente”.
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Boccia, L., Capolupo, A., Esposito, G., Mansueto, G., Tarantino, E. (2019). A Crowd-Sensing System for Geomatics Applications. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_23
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DOI: https://doi.org/10.1007/978-3-030-24305-0_23
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