Potential of remote sensors in the detection of mass graves
DOI:
https://doi.org/10.22335/rlct.v16i3.1973Keywords:
Remote sensing, Mass graves, Spectral index, Machine learning, Multispectral imaging, OrthophotoAbstract
The identification of mass graves in Colombia has become an essential part of uncovering the truth of the violent conflict. This activity is mostly done manually, and in order to identify burial sites the killer’s or the family’s input is necessary. However, these methods, aside from being unreliable, are extremely complicated and expensive.
Research has been carried out, in other countries, to increase the success of this work, based on geophysical resistivity, magnetometry or ground penetration radar. These methods can only be used in a restricted range, aside from the added difficulties when they are utilised in hard-to-reach places such as swamps, marshes, or places where active armed conflict exists, which could mean the presence of landmines. Hence the need to use long range methods that avoid direct contact, expediting searches and reducing the cost in time and money. This article explores the use of data obtained from remote sensing to identify how the health of vegetation is affected by the content of organic matter present in burial sites, correlating these with the spectral indices NDVI, GNDVI, and GCI.
To obtain images of the experimental area where the burial simulacrum was carried out, with animals and human bones as organic matter, a Sequoia Parrot multispectral sensor, carried by a UAV DJI Phantom 3 Advanced, was used to gather the images in the Red, Green and nIR wave frequencies in order to measure the vegetation indices and gauge the plants’ health over and around the burial sites. The results obtained allowed to educe the places where the experimental graves were located after over a decade of being buried.
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References
Auravant. (2023). Índices de vegetación y su interpretación: NDVI, GNDVI, MSAVI2, NDRE y NDWI. Auravant. Agricultura de Precisión. https://www.auravant.com/blog/agricultura-de-precision/indices-devegetacion- y-como-interpretarlos/#queesgndvi
Ballarina, M., Ballettia, C., & Guerra, F. (2015). Action cameras and low-cost aerial vehicles in archaeology. Proc. SPIE 9528, Videometrics, Range Imaging, and Applications XIII, 952813. https://doi.org/10.1117/12.2184692
Blau, S., Sterenberg, J., Weeden, P., Urzedo, F., Wright, R., & Watson, C. (2018). Exploring non-invasive approaches to assist in the detection of clandestine human burials: Developing a way forward. Forensic Sciences Research, 3(4), 320-342. https://doi.org/10.1080/20961790.2018.1493809
Brabazon, H., DeBruyb, J. M., Lenaghab, S. C., Li, F., Mundorff, A. Z., Steadman, D. W., & Stewart, C. N. (2020). Plants to remotely detect human decomposition? Trends in Plant Science, 25(10), 947-949.
Davenport, G. (2018). Remote sensing technology in forensic investigations: Geophysical techniques to locate clandestine graves and hidden evidence. In T. Y. Group (Ed.), CRC Press.
Davenport, G. C. (2001). Remote sensing applications in forensic investigations. Historical Archaeology, 35(1), 87-100. https://doi.org/10.1007/bf03374530
Doro, K. O., Kolapkar, A. M., Bank, C.-G., Wescott, D. J., & Mickleburgh, H. L. (2022). Geophysical imaging of buried human remains in simulated mass and single graves: Experiment design and results from pre-burial to six months after burial. Forensic Science International, (335), 111289. https://doi.org/10.1016/j.forsciint.2022.111289
Equitas. (2015).Un radar para encontrarlos. Cartilla sobre el Modelamiento Espacial y Estadístico Predictivo (MESP). https://es.scribd.com/doc/289476071/Un-radarpara- encontrarlos
Evers, R., & Masters, P. (2018). The application of low-altitude near-infrared aerial photography for detecting clandestine burials using a UAV and low-cost unmodified digital camera. Forensic Science International, (289), 408-418. https://doi.org/10.1016/j.forsciint.2018.06.020
FAFG (Fundación de Antropología Forense de Guatemala). (2013). Asistiendo desde 1997 a las familias en la búsqueda e identificación de sus seres queridos desaparecidos. FAFG. https://www.fafg.org/
Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271-282. https://doi.org/10.1078/0176-1617-00887
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOSMODIS. Remote Sensing of Environment, 58(3), 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7
González Gutiérrez, L. G. (2023). Identificación de fosas comunes mediante teledetección, aeronaves no tripuladas (UAV) e índices de vegetación [Proyecto de Grado, Universidad de Cundinamarca]. Repositorio Digital Universidad de Cundinamarca.
IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales). (2017). Atlas climatológico de Colombia. Imprenta Nacional de Colombia.
Kalacska, M., & Bell, L. S. (2006). Remote sensing as tool for the deteccion of clandestine mass graves. Canadian Society of Forensic Science Journal, 39(1), 1-13. https://doi.org/10.1080/00085030.2006.10757132
Kalacska, M., Bell, L., Sánchez-Azofeifa, A., & Celli, T. (2009). The application of remote sensing for detecting mass graves: An experimental case study from Costa Rica. Journal of Forensic Sciences, 54(1), 159- 166. https://doi.org/10.1111/j.1556-4029.2008.00938.x
Mejía, A. A. (2024). Metodología para la identificación de fosas comunes a partir de imágenes multiespectrales [Tesis de maestría, Universidad Nacional de Colombia]. Repositorio Institucional Universidad Nacional de Colombia.
Molina, C. M. (2016). Metodología para la búsqueda de fosas a partir de la interpretación de anomalías en los datos obtenidos mediante la aplicación geofísica de alta resolución [Tesis de doctorado, Universidad Nacional de Colombia]. Repositorio Institucional Universidad Nacional de Colombia.
Molina, C. M., Pringle, J. K., & Hernández, O. (2013). Experiments to detect clandestine graves from interpreted high resolution geophysical anomalies. https://ui.adsabs. harvard.edu/abs/2013AGUSMNS21C..03M/ abstract
Molina, C. M., Pringle, J. K., Saumett, M., & Evans, G. T. (2016). Geophysical monitoring of simulated graves with resistivity, magnetic susceptibility, conductivity and GPR in Colombia, South America. Forensic Science International, 106-115. https://doi.org/10.1016/j.forsciint.2016.02.009
Molina, C. M., Pringle, J. K., Saumett, M., & Hernández, O. (2015). Preliminary results of sequential monitoring of simulated clandestine graves in Colombia, South America, using ground penetrating radar and botany. Forensic Science International, 61-70. https://doi.org/doi=10.1016/j.forsciint.2014.12.011
Molina, C. M., Wisniewski, K., Heaton, V., Pringle, J. K., Ávila, E. F., Herrera, L. A., …, & Baena, A. (2022). Monitoring of simulated clandestine graves of dismembered victims using UAVs, electrical tomography, and GPR over one year to aid investigations of human rights violations in Colombia, South America. Journal of Forensic Sciences, 67(3), 1060-1071. https://doi.org/10.1111/1556-4029.14962
Norton, E. A. (2019). A multi-temporal approach to using multispectral remote sensing for the prospection of clandestine mass graves in temperate environments. [Tesis de doctorado, Bournemouth University]. Insititutional Repository Bournemouth University
Rouse, J., Haas, R., Schell, J., & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. (N. SP-351, Ed.) Third Earth Resources Technology Satellite–1 Symposium, 1 Sect. A (pp. 309-317). https://ntrs.nasa.gov/api/citations/19740022614/downloads/19740022614.pdf Ruffell, A., & McKinley, J. (2014). Forensic geomorphology. Geomorphology, 206, 14-22. https://doi.org/10.1016/j.geomorph.2013.12.020
RUV (Registro Único de Víctimas). (2017). Registro Único de Victimas (RUV). Unidad para las Víctimas. https://www.unidadvictimas. gov.co/registro-unico-de-victimas-ruv/
Silván-Cárdenas, J., Caccavari-Garza, A., Quinto-Sánchez, M., Madrigal-Gómez, J., Coronado-Juárez, E., & Quiroz-Suárez, D. (2021). Assessing optical remote sensing for grave detection. Forensic Science International, 329, 111064. https://doi.org/10.1016/j.forsciint.2021.111064
SIRDEC (Sistema de Información Red de Desaparecidos y Cadáveres). (2017). Registro Nacional de Desaparecidos. Sistema de Información Red de Desaparecidos y Cadáveres. https://siclico.medicinalegal.gov.co/consultasPublicas/
Urbanová, P., Jurda, M., Vojtíšek, T., & Krajsa, J. (2017). Using drone-mounted cameras for on-site body documentation: 3D mapping and active survey. Forensic Science International, 281, 52-62. https://doi.org/10.1016/j. forsciint.2017.10.027
Witmer, F. D. (2015). Remote sensing of violent conflict: Eyes from above. International Journal of Remote Sensing, 36(9), 2326- 2352. https://doi.org/10.1080/01431161.2015.1035412
Wu, C., Niu, Z., & Gao, S. (2010). Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize. Journal of Geophysical Research, 115(D12). https://doi.org/10.1029/2009JD013023
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