Portable Multispectral Camera for Environmental Monitoring
https://doi.org/10.32603/1993-8985-2025-28-5-66-82
Abstract
Introduction. Passive spectral remote sensing techniques have come a long way to become the main source of information on the Earth’s surface and atmosphere. Multispectral and hyperspectral cameras are now produced on a mass scale; however, their wider use is still impeded by high cost. Commercially available affordable complementary metal–oxide semiconductor (CMOS) image sensors provide a suitable basis for the development of low-cost multispectral cameras.
Aim. To design and test a portable multispectral camera intended for environmental monitoring in the field.
Materials and methods. The camera design is based on a single CMOS image sensor. Spectral bands are selected by interchangeable interference filters installed in two gear wheels. The optical system of the camera forms a parallel beam of light before its passing through a filter followed by focusing in the sensor plane. Filter switching is performed by a stepping motor. Its rotation, as well as image acquisition and storage, is controlled by a Raspberry Pi single-board computer. Multispectral images were processed using scripts in the Python language.
Results. The optical design of the newly created camera was tested to assess the size and spectral uniformity of its field of view. In addition, the camera was used to obtain several images of vegetation cover. Further, spatial distributions of two vegetation indices – the normalized difference vegetation index (NDVI) and the green–red vegetation index (GRVI) – were calculated. These distributions allowed areas occupied by vegetation to be successfully detected and coniferous trees to be separated from deciduous ones.
Conclusion. The results obtained have confirmed the feasibility of the proposed optical and mechanical design for remote assessment of the ecological status of vegetation cover.
About the Authors
Viktor S. GoryainovRussian Federation
Viktor S. Goryainov, Cand. Sci. (Eng.) (2019), Associate Professor of the Department of Photonics,
5F, Professor Popov St., St Petersburg 197022.
Jacques B. Ngoua Ndong Avele
Russian Federation
Jacques B. Ngoua Ndong Avele, Master in quantum and optical electronics (2019, Saint Petersburg Electrotechnical University), Postgraduate student of the Department of Radiotechnical Systems
5F, Professor Popov St., St Petersburg 197022.
Adam B. Mazoya
United Republic of Tanzania
Adam B. Mazoya, Postgraduate studies in optical and optoelectronic devices and complexes in Saint Petersburg Electrotechnical University (2023); Associate Professor of the Department of Electronics and Telecommunications of University of Dodoma,
Benjamin Mkapa rd., 1, Iyumbu, Dodoma 41218, Tanzania.
Sergey A. Tarasov
Russian Federation
Sergey A. Tarasov, Dr Sci. (Eng.) (2016), Head of the Department of Photonics,
5F, Professor Popov St., St Petersburg 197022.
References
1. Krinov E. L. Spectral Reflectance Properties of Natural Formations. Ottawa, National research council of Canada, 1953, 268 p.
2. Buznikov A. A. Space Spectrophotometry of The Natural Environment from Manned Orbital Stations. J. of Optical Technology. 2015, vol. 82, no. 7, pp. 487–493. doi: 10.1364/JOT.82.000487
3. Goryainov V. S., Buznikov A. A., Kostikov E. V. Redesigning the Portable RSS Spectrometer. Proc. of Saint Petersburg Electrotechnical University. 2020, no. 2, pp. 5–16. (In Russ.)
4. Raizer V. Optical Remote Sensing of Ocean Hydrodynamics. Boca Raton, CRC Press, 2019, 296 p. doi: 10.1201/9781351119184
5. Barnett T. L., Juday R. D. Skylab S191 VisibleInfrared Spectrometer. Applied Optics. 1977, vol. 16, no. 4, pp. 967–972. doi: 10.1364/AO.16.000967
6. Goetz A. F. H., Srivastava V. Mineralogical Mapping in the Cuprite Mining District, Nevada. Proc. of the Airborne Imaging Spectrometer Data Anal. Workshop. Pasadena. Jet Propulsion Laboratory, 1985, pp. 22−31.
7. Goetz A. F. H. Three Decades of Hyperspectral Remote Sensing of the Earth: a Personal View. Remote Sensing of Environment. 2009, vol. 113, pp. S.5–S.16. doi: 10.1016/j.rse.2007.12.014
8. Vane G., Goetz A. F. H., Wellman J. B. Airborne Imaging Spectrometer: a New Tool for Remote Sensing. IEEE Trans. Geosci. Remote Sens. 1984, vol. GE22, no. 6, pp. 546–549. doi: 10.1109/TGRS.1984.6499168
9. Middleton E. M., Campbell P. K. E., Ong L., Landis D. R., Zhang Q., Neigh C. S., Huemmrich K. F., Ungar S. G., Mandl D. J., Frye S. W., Ly V. T., Cappelaere P. G., Chien S. A., Franks S., Pollack N. H. Hyperion: the First Global Orbital Spectrometer, Earth Observing-1 (EO-1) Satellite (2000–2017). IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), Fort Worth, 23–28 July 2017. IEEE, 2017, pp. 3039–3042. doi: 10.1109/IGARSS.2017.8127639
10. Qian S.-E. Hyperspectral Satellites, Evolution, and Development History. IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, vol. 14, pp. 7032–7056. doi: 10.1109/JSTARS.2021.3090256
11. Benhadj I., Livens S., Esposito M., Vercruyssen N., Van Dijk C., Soukup M., Marchi A. Z., Maresi L. HyperScout-1 Inflight Calibration and Product Validation. Intern. J. of Remote Sensing. 2024, vol. 45, no. 7, pp. 2486–2517. doi: 10.1080/01431161.2024.2331979
12. Wang J., He Z., Shu R., Xu R., Chen K., Li C. Visible and Near‐Infrared Imaging Spectrometer Aboard Chinese Chang'E 3 Spacecraft. Optical Payloads for Space Missions. Chichester, Wiley, 2016, pp. 121–139. doi: 10.1002/9781118945179.ch5
13. Praks J., Niemelä P., Näsilä A., Kestilä A., Jovanovic N., Riwanto B., Tikka T., Leppinen H., Vainio R., Janhunen P. Miniature Spectral Imager in-Orbit Demonstration Results from Aalto-1 Nanosatellite Mission. IEEE Intern. Geoscience and Remote Sensing Symp., Valencia, 22–27 July 2018. IEEE, 2018, pp. 1986–1989. doi: 10.1109/IGARSS.2018.8517658
14. Zhao X., Xiao Z., Kang Q., Li Q., Fang L. Overview of the Fourier Transform Hyperspectral Imager (HSI) Boarded on HJ-1A Satellite. IEEE Intern. Geoscience and Remote Sensing Symp., Honolulu, 25– 30 July 2010. IEEE, 2010, pp. 4272–4274. doi: 10.1109/IGARSS.2010.5649250
15. Ngom N. M., Baratoux D., Bolay M., Dessertine A., Saley A. A., Baratoux L., Mbaye M., Faye G., Yao A. K., Kouamé K. J. Artisanal Exploitation of Mineral Resources: Remote Sensing Observations of Environmental Consequences, Social and Ethical Aspects. Surveys in Geophysics. 2023, vol. 44, pp. 225–247. doi: 10.1007/s10712-022-09740-1
16. Lammoglia S.-K., Akpa Y. L., Danumah J. H., Brou Y. L. A., Kassi J. N. High-Resolution Multispectral and RGB Dataset from UAV Surveys of Ten Cocoa Agroforestry Typologies in Côte d'Ivoire. Data in Brief. 2024, vol. 55, art. no. 110664. doi: 10.1016/j.dib.2024.110664
17. Belcore E., Piras M., Pezzoli A., Massazza G., Rosso M. Raspberry Pi 3 Multispectral Low-Cost Sensor for UAV Based Remote Sensing. Case Study in South-West Niger. ISPRS, 2019, vol. XLII-2/W13, pp. 207–214. doi: 10.5194/isprs-archives-XLII-2-W13-207-2019
18. Adão T., Hruška J., Pádua L., Bessa J., Peres E., Morais R., Sousa J. J. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens. 2017, vol. 9, no. 11, art. no. 1110. doi: 10.3390/rs9111110
19. Jang G., Kim J., Yu J.-K., Kim H.-J., Kim Y.-H., Kim D.-W., Kim K.-H., Lee C., Chung Y. S. Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application. Remote Sensing. 2020, vol. 12, no. 6, art. no. 998. doi: 10.3390/rs12060998
20. Baranov P. S., Chirkunova A. A. Television Camera of the Visible and Near-Infrared Ranges. J. of the Russin Universities. Radioelectronics. 2015, vol. 4, pp. 47–56. (In Russ.)
21. Ovchinnikov A. V. Multispectral Camera with Sequential Formation of Images. J. of Radioelectronics. 2021, no. 5, pp. 1–11. (In Russ.) doi: 10.30898/1684-1719.2021.5.6
22. Mazeh F., El Sahili J., Zaraket H. Low-Cost NDVI Platform for Land Operation: Passive and Active. IEEE Sensors Let. 2021, vol. 5, no. 10, art. no. 5500804. doi: 10.1109/LSENS.2021.3112822
23. Morales A., Guerra R., Horstrand P., Diaz M., Jimenez A., Melian J., Lopez S., Lopez J. F. A Multispectral Camera Development: From the Prototype Assembly until Its Use in a UAV System. Sensors. 020, vol. 20, art. no. 6129. doi: 10.3390/s20216129
24. Monteiro F., Bexiga V., Chaves P., Godinho J., Henriques D., Melo-Pinto P., Nunes T., Piedade F., Pimenta N., Sustelo L., Fernandes A. M. Classification of Fish Species Using Multispectral Data from a LowCost Camera and Machine Learning. Remote Sensing. 2023, vol. 15, art. no. 3952. doi: 10.3390/rs15163952
25. Lopez-Ruiz N., Granados-Ortega F., Carvajal M. A., Martinez-Olmos A. Portable Multispectral Imaging System Based on Raspberry Pi. Sensor Review. 2017, vol. 37, no. 3, pp. 322–329. doi: 10.1108/SR-12-2016-0276
26. Noguera M., Millan B., Andújar J. M. New, LowCost, Hand-Held Multispectral Device for In-Field FruitRipening Assessment. Agriculture. 2023, vol. 13, no. 1, p. 4. doi: 10.3390/agriculture13010004
27. Shiddiq M., Herman H., Arief D. S., Fitra E., Husein I. R., Ningsih S. A. Wavelength Selection of Multispectral Imaging for Oil Palm Fresh Fruit Ripeness Classification. Applied Optics. 2022, vol. 61, no. 17, pp. 5289–5298. doi: 10.1364/AO.450384
28. OmniVision. OV5647 datasheet. Preliminary specification. Available at: https://cdn.sparkfun.com/ datasheets/Dev/RaspberryPi/ov5647_full.pdf (accessed: 09.06.2025).
29. Goryainov V. S., Buznikov A. A. A Study of the Influence of Copper Sulfate on the Spectral Properties of Common Buckwheat (Fagopyrum Esculentum). J. of Physics: Conf. Ser. 2021, vol. 2103, no. 1, art. no. 012155. doi: 10.1088/1742-6596/2103/1/012155
30. Sonobe R., Wang Q. Assessing the Xanthophyll Cycle in Natural Beech Leaves with Hyperspectral Reflectance. Functional Plant Biology. 2016, vol. 43, no. 5, pp. 438–447. doi: 10.1071/FP15325
31. Gitelson A. A., Zur Y., Chivkunova O. B., Merzlyak M. N. Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochem. Photobiol. 2002, vol. 75, no. 3, pp. 272–281. doi: 10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2
32. Sims D. A., Gamon J. A. Relationships Between Leaf Pigment Content and Spectral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sensing of Environment. 2002, vol. 81, no. 2–3, pp. 337–354. doi: 10.1016/S0034-4257(02)00010-X
33. Strong L. L., Gilmer D. S., Brass J. A. Inventory of Wintering Geese with a Multispectral Scanner. J. of Wildlife Management. 1991, vol. 55, no. 2, pp. 250–259.
34. Liu C. C., Chen Y. H., Wen H. L. Supporting the Annual International Black-Faced Spoonbill Census with a Low-Cost Unmanned Aerial Vehicle. Ecological Informatics. 2015, vol. 30, pp. 170–178. doi: 10.1016/j.ecoinf.2015.10.008
35. O'Neill C. J., Roberts J. J., Cozzolino D. Identification of Beef Cattle Categories (Cows and Calves) and Sex Based on the Near Infrared Reflectance Spectroscopy of Their Tail Hair. Biosystems Engineering. 2017, vol. 162, pp. 140–146. doi: 10.1016/j.biosystemseng.2017.07.007
36. Terletzky P., Ramsey R. D., Neale C. M. U. Spectral Characteristics of Domestic and Wild Mammals. GIScience & Remote Sensing. 2012, vol. 49, no. 4, pp. 597–608. doi: 10.2747/1548-1603.49.4.597
37. Trivedi M. M., Wyatt C. L., Anderson D. R. A Multispectral Approach to Remote Detection of Deer. Photogramm. Eng. Remote Sens. 1982, vol. 48, no. 12, pp. 1879–1889.
38. Yang Z., Wang T., Skidmore A. K., De Leeuw J., Said M. Y., Freer J. Spotting East African Mammals in Open Savannah from Space. PloS One. 2014, vol. 9, no. 12, p. e115989. doi: 10.1371/journal.pone.0115989
39. Platonov N. G., Mordvintsev I. N., Rozhnov V. V. The Possibility of Using High Resolution Satellite Images for Detection of Marine Mammals. Biology Bulletin. 2013, vol. 40, pp. 197–205. doi: 10.1134/S1062359013020106
40. Cubaynes H. C., Fretwell P. T., Bamford C., Gerrish L., Jackson J. A. Whales from Space: Four Mysticete Species Described Using New VHR Satellite Imagery. Marine Mammal Science. 2019, vol. 35, no. 2, pp. 466–491. doi: 10.1111/mms.12544
41. LaRue M. A., Rotella J. J., Garrott R. A., Siniff D. B., Ainley D. G., Stauffer G. E., Porter C. C., Morin P. J. Satellite Imagery Can Be Used to Detect Variation in Abundance of Weddell Seals (Leptonychotes Weddellii) in Erebus Bay, Antarctica. Polar Biology. 2011, vol. 34, pp. 1727–1737. doi: 10.1007/s00300-011-1023-0
42. Motohka T., Nasahara K. N., Oguma H., Tsuchida S. Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology. Remote Sens. 2010, vol. 2, pp. 2369–2387. doi: 10.3390/rs2102369
Review
For citations:
Goryainov V.S., Ngoua Ndong Avele J.B., Mazoya A.B., Tarasov S.A. Portable Multispectral Camera for Environmental Monitoring. Journal of the Russian Universities. Radioelectronics. 2025;28(5):66-82. https://doi.org/10.32603/1993-8985-2025-28-5-66-82




























