Journal of Korean Society of Rural Planning (농촌계획)

Korean Society of Rural Planning (한국농촌계획학회)
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Development of Stream Cover Classification Model Using SVM Algorithm based on Drone Remote Sensing

드론원격탐사 기반 SVM 알고리즘을 활용한 하천 피복 분류 모델 개발

  • Jeong, Kyeong-So (Dept. of Agricultural and Rural Engineering, Chungbuk National University) ;
  • Go, Seong-Hwan (Dept. of Agricultural and Rural Engineering, Chungbuk National University) ;
  • Lee, Kyeong-Kyu (Construction Management Division, Chungcheongbuk-do Provincial Government) ;
  • Park, Jong-Hwa (Dept. of Agricultural and Rural Engineering, Chungbuk National University)
  • 정경수 (충북대학교 농업생명환경대학 지역건설공학과) ;
  • 고승환 (충북대학교 농업생명환경대학 지역건설공학과) ;
  • 이경규 (충청북도 도청 건설관리과) ;
  • 박종화 (충북대학교 농업생명환경대학 지역건설공학과)
  • Received : 2024.01.23
  • Accepted : 2024.02.07
  • Published : 2024.02.28
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Abstract

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

Keywords

Acknowledgement

이 논문은 충북대학교 국립대학육성사업(2023) 지원을 받아 작성되었음.

References

  1. Belsky, A. J., Matzke, A., and Uselman, S., 1999, Survey of livestock influences on stream and riparian ecosystems in the western United States, Journal of Soil and water Conservation, 54(1), 419-431. 
  2. Dang, N. H. and Maurer, O., 2021, Place-Related Concepts and Pro-Environmental behavior in tourism research: A Conceptual Framework, Sustainability, 13(21), 11861. 
  3. Del Tanago, M. G., Martinez-Fernandez, V., Aguiar, F. C., Bertoldi, W., Dufour, S., de Jalon, D. G., Garofano-Gomez, V., Mandzukovski, D., and Rodriguez-Gonzalez, P. M., 2021, Improving river hydromorphological assessment through better integration of riparian vegetation: Scientific evidence and guidelines, Journal of Environmental Management, 292, 112730. 
  4. Ferreira, V., Albarino, R., Larranaga, A., LeRoy, C. J., Masese, F. O., and Moretti, M. S., 2023, Ecosystem services provided by small streams: an overview, Hydrobiologia, 850(12), 2501-2535.  https://doi.org/10.1007/s10750-022-05095-1
  5. Ge, J., Meng, B., Liang, T., Feng, Q., Gao, J., Yang, S., Huang, X. and Xie, H., 2018, Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China, Remote Sensing of Environment, 218, 162-173.  https://doi.org/10.1016/j.rse.2018.09.019
  6. Hashemi-Beni, L. and Gebrehiwot, A. A., 2021, Flood extent mapping: an integrated method using deep learning and region growing using UAV optical data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2127-2135.  https://doi.org/10.1109/JSTARS.2021.3051873
  7. Huete, A. R., 1988, A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25(3), 295-309.  https://doi.org/10.1016/0034-4257(88)90106-X
  8. James, K. and Bradshaw, K., 2020, Detecting plant species in the field with deep learning and drone technology, Methods in Ecology and Evolution, 11(11), 1509-1519.  https://doi.org/10.1111/2041-210X.13473
  9. Kavzoglu, T. and Colkensen, I., 2009, A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoinformation, 11, 352-359.  https://doi.org/10.1016/j.jag.2009.06.002
  10. Kazemi Garajeh, M., Weng, Q., Hossein Haghi, V., Li, Z., Kazemi Garajeh, A., and Salmani, B., 2022, Learning-based methods for detection and monitoring of shallow flood-affected areas: impact of shallow-flood spreading on vegetation density, Canadian Journal of Remote Sensing, 48(4), 481-503.  https://doi.org/10.1080/07038992.2022.2072277
  11. Kuo, B. C., Ho, H. H., Li, C. H., Hung, C. C., and Taur, J. S., 2013, A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 317-326.  https://doi.org/10.1109/JSTARS.2013.2262926
  12. Laliberte, A. S. and Rango, A., 2009, Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery, IEEE Transactions on Geoscience and Remote Sensing, 47(3), 761-770.  https://doi.org/10.1109/TGRS.2008.2009355
  13. Lee, D. H., Kim, H. J., and Park, J. H., 2021, UAV, a farm map, and machine learning technology convergence classification method of a corn cultivation area, Agronomy, 11(8), 1554. 
  14. Li, D., Wang, G., Qin, C., and Wu, B., 2021, River extraction under bankfull discharge conditions based on sentinel-2 imagery and DEM data, Remote Sensing, 13(14), 2650. 
  15. Li, M., Wu, P., Wang, B., Park, H., Yang, H., and Wu, Y., 2021, A deep learning method of water body extraction from high resolution remote sensing images with multisensors, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3120-3132.  https://doi.org/10.1109/JSTARS.2021.3060769
  16. Lin, J., Huang, J., Prell, C., and Bryan, B. A., 2021, Changes in supply and demand mediate the effects of land-use change on freshwater ecosystem services flows, Science of the Total Environment, 763, 143012. 
  17. Melgani, F., and Bruzzone, L., 2004, Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on geoscience and remote sensing, 42(8), 1778-1790.  https://doi.org/10.1109/TGRS.2004.831865
  18. Mountrakis, G., Im, J., and Ogole, C., 2011, Support vector machines in remote sensing: A review, ISPRS journal of photogrammetry and remote sensing, 66(3), 247-259.  https://doi.org/10.1016/j.isprsjprs.2010.11.001
  19. McFeeters, S. K., 1996, The use of the Nnormalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17(7), 1425-1432.  https://doi.org/10.1080/01431169608948714
  20. Nandi, I., Srivastava, P. K., and Shah, K., 2017, Floodplain mapping through support vector machine and optical/infrared images from Landsat 8 OLI/TIRS sensors: Case study from Varanasi, Water Resources Management, 31, 1157-1171.  https://doi.org/10.1007/s11269-017-1568-y
  21. Naiman, R. J., Decamps, H., and Pollock, M., 1993, The role of riparian corridors in maintaining regional biodiversity, Ecological Applications, 3(2), 209-212.  https://doi.org/10.2307/1941822
  22. Peters, D. P., Rivers, A., Hatfield, J. L., Lemay, D. G., Liu, S., and Basso, B., 2020, Harnessing AI to transform agriculture and inform agricultural research, IT Professional, 22(3), 16-21.  https://doi.org/10.1109/MITP.2020.2986124
  23. Ren, L., Liu, Y., Zhang, S., Cheng, L., Guo, Y., and Ding, A., 2020, Vegetation properties in human-impacted riparian zones based on unmanned aerial vehicle (UAV) imagery: An analysis of river reaches in the Yongding River Basin, Forests, 12(1), 22. 
  24. Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W., 1974, Monitoring vegetation systems in the Great Plains with ERTS, NASA Spec. Publ, 351(1), 309. 
  25. Xu, X., Chen, Y., Zhang, J., Chen, Y., Anandhan, P., and Manickam, A., 2021, A novel approach for scene classification from remote sensing images using deep learning methods, European Journal of Remote Sensing, 54(sup2), 383-395.  https://doi.org/10.1080/22797254.2020.1790995
  26. Yang, Q., Shi, L., Han, J., Yu, J., and Huang, K., 2020, A near real-time deep learning approach for detecting rice phenology based on UAV images, Agricultural and Forest Meteorology, 287, 107938.