Technologies for environmental monitoring of the city

O. Bahatska, N. Pasichnyk, О. Opryshko
Abstract

IoT technologies in the Big Data concept can radically change approaches in agricultural practices, but it is necessary to work out methods of processing and interpreting information that can be effective in crop practice. Since the dimensions of plants are too small for satellite imagery, the development of technologies can be done on trees whose dimensions are sufficient for their identification in satellite imagery. The purpose of the work is to identify and assess the condition of plantations, in particular trees, with the determination of their positioning on satellite images of megacities. Digital photographs created by optical and infrared lenses of the Obolonskyi district of Kyiv were used for the research. It was found that in the optical range for objects under direct sunlight, plant identification is possible, while shaded areas are identified with significant errors. When using the index for IR shooting IRtree = C1 - C2 + 100 it was possible to identify individual ranges that belong to the crown of trees and grass in direct sunlight and to some extent in the shade, which could not be achieved with the index for optical range GBtree = G - B + 100. Monochrome infrared and optical images were not suitable for plant identification, because when objects were in the shadow of buildings, the ranges of intensity of the color components of plants were superimposed on the ranges of foreign objects. For infrared and optical satellite images, spectral indices have been proposed that take into account several color components to assess the condition of plantations. For tree crowns under direct sunlight, approximately the same results were obtained for the proposed indices. However, the indices proposed for infrared photography are more selective, as they were able to identify separately the crowns of trees and plants on lawns, both in direct sunlight and in the shade of buildings

Keywords

indices, satellite monitoring, IoT, biogas

Suggested citation
Bahatska, O., Pasichnyk, N., & Opryshko, О. (2021). Technologies for environmental monitoring of the city. Scientific Reports of the National University of Life and Environmental Sciences of Ukraine, 17(5),156-166. https://doi.org/10.31548/dopovidi2021.05.014
References
  1. Raciti, S.M., Hutyra, L.R., & Newell, J.D. (2014). Mapping carbon storage in urban trees with multi-source remote sensing data: Relationships between biomass, land use, and demographics in Boston neighborhoods. Science of The Total Environment, 500-501, 72-83. https://doi.org/10.1016/j.scitotenv.2014.08.070.
  2. Talavera, J.M., Tobón, L.E., Gómez, J.A., Culman, M.A., Aranda, J.M., Parra, D.T., Quiroz, L.A., Hoyos, A., & Garreta, L.E. (2017). Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture, 142(A), 283-297. https://doi.org/10.1016/j.compag.2017.09.015.
  3. Lee, J.T.E., Ee, A.W.L., & Tong, Y.W. (2018). Environmental impact comparison of four options to treat the cellulosic fraction of municipal solid waste (CF-MSW) in green megacities. Waste Management, 78, 677-685. https://doi.org/10.1016/j.wasman.2018.06.043.
  4. Tong, H., Yao, Z., Lim, J.W., Mao, L., Zhang, J., Ge, T.S., Peng, Y.H., Wang, C.H., & Tong, Y.W. (2018). Harvest green energy through energy recovery from waste: A technology review and an assessment of Singapore. Renewable and Sustainable Energy Reviews, 98, 163-178. https://doi.org/10.1016/j.rser.2018.09.009.
  5. Golub, B., Hudz, A., Dudnyk, A., & Bushma, A. (2019). Production of biotechnological objects using business intelligence. In 2019 9th International Conference on Advanced Computer Information Technologies, ACIT 2019 - Proceedings (pp. 200-204). https://doi.org/10.1109/ACITT.2019.8780061.
  6. Jeppesen, J.H., Ebeid, E., Jacobsen, R.H., & Toftegaard, T.S. (2018). Open geospatial infrastructure for data management and analytics in interdisciplinary research. Computers and Electronics in Agriculture, 145, 130-141. https://doi.org/10.1016/j.compag.2017.12.026.
  7. Lysenko, V., & Dudnyk, A. (2016). Automation of biotechnological objects. In Modern Problems of Radio Engineering, Telecommunications and Computer Science, Proceedings of the 13th International Conference on TCSET 2016 (pp. 44-46). https://doi.org/10.1109/TCSET.2016.7451963.
  8. Kiktev, N., Chichikalo, N., Rozorinov, H., Filippov, R., & Khort, D. (2018). Infocomunication technology for determination of coal ash-content on the conveyor line. In 2018 International Scientific-Practical Conference on Problems of Infocommunications Science and Technology, PIC S and T 2018 - Proceedings (pp. 535-538). https://doi.org/10.1109/INFOCOMMST.2018.8632108.
  9. dos Santos, U.J.L., Pessin, G., da Costa, C.A., & da Rosa Righi, R. (2019). AgriPrediction: A proactive internet of things model to anticipate problems and improve production in agricultural crops. Computers and Electronics in Agriculture, 161, 202-213. https://doi.org/10.1016/j.compag.2018.10.010.
  10. Al-Turjman, F. (2019). The road towards plant phenotyping via WSNs: An overview. Computers and Electronics in Agriculture, 161, 4-13. https://doi.org/10.1016/j.compag.2018.09.018.
  11. Kiktev, N., Chichikalo, N., Rozorinov, H., Filippov, R., & Khor, D. (2018). Infocomunication technology for determination of coal ash-content on the conveyor line. In 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T) (pp. 535-538). https://doi.org/10.1109/INFOCOMMST.2018.8632108.
  12. Ouyang, F., Cheng, H., Lan, Y., Zhang, Y., Yin, X., Hu, J., Peng, X., Wang, G., & Chen, S. (2019). Automatic delivery and recovery system of Wireless Sensor Networks (WSN) nodes based on UAV for agricultural applications. Computers and Electronics in Agriculture, 162, 31-43. https://doi.org/10.1016/j.compag.2019.03.025.
  13. Banđur, Đ., Jakšić, B., Banđur, M., & Jović, S. (2019). An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture. Computers and Electronics in Agriculture, 156, 500-507. https://doi.org/10.1016/j.compag.2018.12.016.
  14. Liao, M.S., Chen, S.F., Chou, C.Y., Chen, H.Y., Yeh, S.H., Chang, Y.C., & Jiang, J.A. (2017). On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system. Computers and Electronics in Agriculture, 136, 125-139. https://doi.org/10.1016/j.compag.2017.03.003.
  15. Lysenko, V., Komarchuk, D., Opryshko, O., Pasichnyk, N., & Zaets, N. (2017). Determination of the not uniformity of illumination in process monitoring of wheat crops by UAVs. In 2017 4th International Scientific-Practical Conference Problems of Infocommunications Science and Technology, PIC S and T 2017 - Proceedings (pp. 265-267). https://doi.org/10.1109/INFOCOMMST.2017.8246394.
  16. Shvorov, S., Komarchuk, D., Pasichnyk, N., Opryshko, O., Gunchenko, Y., & Kuznichenko, S. (2018). UAV navigation and management system based on the spectral portrait of terrain. In 2018 IEEE 5th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC) - Proceedings (pp. 68-71). https://doi.org/10.1109/MSNMC.2018.8576304.
  17. Svensgaard, J., Jensen, S.M., Westergaard, J.C., Nielsen, J., Christensen, S., & Rasmussen, J. (2019). Can reproducible comparisons of cereal genotypes be generated in field experiments based on UAV imagery using RGB cameras? European Journal of Agronomy, 106, 49-57. https://doi.org/10.1016/j.eja.2019.03.006.
  18. Zheng, H., Zhou, X., He, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2020). Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV). Computers and Electronics in Agriculture, 169, 105223. https://doi.org/10.1016/j.compag.2020.105223.
  19. Dolia, M., Lysenko, V., Pasichnyk, N., Opryshko, O., Komarchuk, D., Miroshnyk, V., Lendiel, T., & Martsyfei, A. (2019). Information technology for remote evaluation of after effects of residues of herbicides on winter crop rape. In 2019 3rd International Conference on Advanced Information and Communications Technologies, AICT 2019 - Proceedings (pp. 469-473). https://doi.org/10.1109/AIACT.2019.8847850.