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Vol. 6 No. 1 (2022): APRIL

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Avoiding Mistakes in Drone Usage in Participatory Mapping: Methodological Considerations during the Pandemic

https://doi.org/10.24259/fs.v6i1.14117
Naufal Naufal
Department of Forestry, Muhammadiyah Makassar University, Makassar, Indonesia
https://orcid.org/0000-0003-1629-1108
Andi Asriadi
Department of Natural Resource Management, Sulawesi Community Foundation, Makassar, Indonesia
https://orcid.org/0000-0003-0375-5356
Sutrisno Absar
Department of Cooperation and Knowledge Management, Sulawesi Community Foundation, Makassar, Indonesia
https://orcid.org/0000-0002-9354-0756

Corresponding Author(s) : Naufal Naufal

[email protected]

Forest and Society, Vol. 6 No. 1 (2022): APRIL

Abstract

Participatory mapping has continued to evolve with the onset of new methodologies and technology. Conventional methods for sketching have now expanded to incorporate the use of drone imagery and other sophisticated mapping approaches as a base map. However, the use of ultra-high resolution drone imagery does not mean that it will facilitate more participatory processes nor improve the quality of data and uses of information. Indeed, it has long been known that ultra-high spatial resolution can cause misinterpretation.   During COVID-19, innovations are emerging to apply more remote technologies in participatory mapping. Mobility concerns, requirements, and preferences for physical distancing discourages active participation of local communities and are especially complex in contexts involving Indigenous People. This paper specifically explores the mistakes that can arise from over-reliance on employing drones as a tool in participatory mapping methods.  This paper is based on a case study of participatory mapping conducted at 43 villages (around forest area) of Central Sulawesi Province and West Sulawesi Province. The participatory mapping was carried out by the Sulawesi Community Foundation (SCF) from 2019-2021. The result of the study found at least six signs of potentially negative outcomes from the use of ultra-high resolution drone imagery, starting from disorientation, misperception over the periods of drone acquisition, homogeneous land cover conditions, similar types of plants, numerous signs of nature, and labeling affixed on map. We also encourage the development of ultra-high-resolution drone imagery to take place under certain conditions and see its role as an interpretation dictionary or as a targeted tool in local contexts. In addition, we found that the level of active participation in participatory mapping during the Pandemic was higher than before the pandemic but requires some improvisations in meeting design

Keywords

Participatory mapping Drones Pandemic Orthomosaic Interpretation
Naufal, N., Asriadi, A., & Absar , S. (2022). Avoiding Mistakes in Drone Usage in Participatory Mapping: Methodological Considerations during the Pandemic. Forest and Society, 6(1), 226-242. https://doi.org/10.24259/fs.v6i1.14117
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References
  1. Abdel-Basset, M., Chang, V., & Nabeeh, N. A. (2021). An intelligent framework using disruptive technologies for COVID-19 analysis. Technological Forecasting and Social Change, 163, 120431. https://doi.org/10.1016/J.TECHFORE.2020.120431
  2. Álvarez Larrain, A., Greco, C., & Tarragó, M. (2021). Participatory mapping and UAV photogrammetry as complementary techniques for landscape archaeology studies: an example from north-western Argentina. Archaeological Prospection, 28(1), 47–61. https://doi.org/10.1002/arp.1794
  3. Brambach, F., Leuschner, C., Tjoa, A., & Culmsee, H. (2017). Diversity, endemism, and composition of tropical mountain forest communities in Sulawesi, Indonesia, in relation to elevation and soil properties. Perspectives in Plant Ecology, Evolution and Systematics, 27, 68–79. https://doi.org/10.1016/J.PPEES.2017.06.003
  4. Brem, A., Viardot, E., & Nylund, P. A. (2021). Implications of the coronavirus (COVID-19) outbreak for innovation: Which technologies will improve our lives? Technological Forecasting and Social Change, 163, 120451. https://doi.org/10.1016/J.TECHFORE.2020.120451
  5. Chambers, R. (2006). Participatory Mapping and Geographic Information Systems: Whose Map? Who Is Empowered and Who Disempowered? Who Gains and Who Loses?. The Electronic Journal on Information Systems in Developing Countries, 25(1), 1-11. https://doi.org/10.1002/j.1681-4835.2006.tb00163.x
  6. Churiyah, M., Sholikhan, S., Filianti, F., & Sakdiyyah, D. A. (2020). Indonesia Education Readiness Conducting Distance Learning in Covid-19 Pandemic Situation. International Journal of Multicultural and Multireligious Understanding, 7(6), 491. https://doi.org/10.18415/ijmmu.v7i6.1833
  7. Citterio, A., & Piégay, H. (2009). Overbank sedimentation rates in former channel lakes: characterization and control factors. Sedimentology, Vol. 56(N 2 (February 2009)), 461–482. https://doi.org/10.1111/j.1365-3091.2008.00979.x
  8. Colloredo-Mansfeld, M., Laso, F. J., & Arce-Nazario, J. (2020). Uav-based participatory mapping: Examining local agricultural knowledge in the Galapagos. In Drones (Vol. 4, Issue 4, pp. 1–13). MDPI AG. https://doi.org/10.3390/drones4040062
  9. Dandois, J. P., Olano, M., Ellis, E. C., Baghdadi, N., Kerle, N., & Thenkabail, P. S. (2015). Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure. 7, 13895–13920. https://doi.org/10.3390/rs71013895
  10. Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of The Total Environment, 739, 140033. https://doi.org/10.1016/J.SCITOTENV.2020.140033
  11. González-García, J., Swenson, R. L., & Gómez-Espinosa, A. (2020). Real-time kinematics applied at unmanned aerial vehicles positioning for orthophotography in precision agriculture. Computers and Electronics in Agriculture, 177, 105695. https://doi.org/10.1016/J.COMPAG.2020.105695
  12. Goswami, R., Roy, K., Dutta, S., Ray, K., Sarkar, S., Brahmachari, K., Nanda, M. K., Mainuddin, M., Banerjee, H., Timsina, J., & Majumdar, K. (2021). Multi-faceted impact and outcome of COVID-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies. Agricultural Systems, 189, 103051. https://doi.org/10.1016/J.AGSY.2021.103051
  13. Haqiqi, I., & Horeh, M. B. (2021). Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP). Agricultural Systems, 190, 103132. https://doi.org/10.1016/J.AGSY.2021.103132
  14. Iese, V., Wairiu, M., Hickey, G. M., Ugalde, D., Salili, D. H., Walenenea Jr, J., ... & Ward, A. C. (2021). Impacts of COVID-19 on agriculture and food systems in Pacific Island countries (PICs): Evidence from communities in Fiji and Solomon Islands. Agricultural Systems, 190, 103099. https://doi.org/10.1016/J.AGSY.2021.103099
  15. Kotivuori, E., Kukkonen, M., Mehtätalo, L., Maltamo, M., Korhonen, L., & Packalen, P. (2020). Forest inventories for small areas using drone imagery without in-situ field measurements. Remote Sensing of Environment, 237, 111404. https://doi.org/10.1016/J.RSE.2019.111404
  16. Middendorf, B. J., Faye, A., Middendorf, G., Stewart, Z. P., Jha, P. K., & Prasad, P. V. V. (2021). Smallholder farmer perceptions about the impact of COVID-19 on agriculture and livelihoods in Senegal. Agricultural Systems, 190, 103108. https://doi.org/10.1016/J.AGSY.2021.103108
  17. Orengo, H. A., & Garcia-Molsosa, A. (2019). A brave new world for archaeological survey: Automated machine learning-based potsherd detection using high-resolution drone imagery. Journal of Archaeological Science, 112, 105013. https://doi.org/10.1016/J.JAS.2019.105013
  18. Paneque-Gálvez, J., Mccall, M. K., Napoletano, B. M., Wich, S. A., & Koh, L. P. (2014). Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas. 5, 1481–1507. https://doi.org/10.3390/f5061481
  19. Radjawali, I., & Pye, O. (2017). Drones for justice: inclusive technology and river-related action research along the Kapuas. Geographica Helvetica, 72(1), 17-27. https://doi.org/10.5194/gh-72-17-2017
  20. Rostan, J. C., Juget, J., & Brun, A. M. (1997). Sedimentation rates measurements in former channels of the upper Rhône river using Chernobyl 137Cs and 134Cs as tracers. Science of The Total Environment, 193(3), 251–262. https://doi.org/10.1016/S0048-9697(96)05348-X
  21. Rowan, N. J., & Galanakis, C. M. (2020). Unlocking challenges and opportunities presented by COVID-19 pandemic for cross-cutting disruption in agri-food and green deal innovations: Quo Vadis? Science of The Total Environment, 748, 141362. https://doi.org/10.1016/J.SCITOTENV.2020.141362
  22. Schiefer, F., Kattenborn, T., Frick, A., Frey, J., Schall, P., Koch, B., & Schmidtlein, S. (2020). Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 170, 205–215. https://doi.org/10.1016/J.ISPRSJPRS.2020.10.015
  23. Sidiq, A. (2021). Critical Approaches to GIS and Spatial Mapping in Indonesia Forest Management and Conservation. Forest and Society, 5(2), 190–195. https://doi.org/10.24259/fs.v5i2.10921
  24. Singh, K. K., & Frazier, A. E. (2018). A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications. International Journal of Remote Sensing, 39(15–16), 5078–5098. https://doi.org/10.1080/01431161.2017.1420941
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