드론원격탐사 기반 SVM 알고리즘을 활용한 하천 피복 분류 모델 개발
Development of Stream Cover Classification Model Using SVM Algorithm based on Drone Remote Sensing
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (26)
- Belsky, A. J., "Survey of livestock influences on stream and riparian ecosystems in the western United States", Journal of Soil and water Conservation, 54(1), pp.419~431 (1999)
- Dang, N. H., "Place-Related Concepts and Pro-Environmental behavior in tourism research : A Conceptual Framework", Sustainability, 13(21), pp.11861~ (2021)
- Del Tánago, M. G., "Improving river hydromorphological assessment through better integration of riparian vegetation : Scientific evidence and guidelines", Journal of Environmental Management, 292, pp.112730~ (2021)
- Ferreira, V., "Ecosystem services provided by small streams : an overview", Hydrobiologia, 850(12), pp.2501~2535 (2023)
- Ge, J., "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, pp.162~173 (2018)
- Hashemi-Beni, L., "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, pp.2127~2135 (2021)
- Huete, A. R., "A soil-adjusted vegetation index(SAVI)", Remote Sensing of Environment, 25(3), pp.295~309 (1988)
- James, K., "Detecting plant species in the field with deep learning and drone technology", Methods in Ecology and Evolution, 11(11), pp.1509~1519 (2020)
- Kavzoglu, T., "A kernel functions analysis for support vector machines for land cover classification", International Journal of Applied Earth Observation and Geoinformation, 11, pp.352~359 (2009)
- Kazemi Garajeh, M., "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), pp.481~503 (2022)
- Kuo, B. C., "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), pp.317~326 (2013)
- Laliberte, A. S., "Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle(UAV)imagery", IEEE Transactions on Geoscience and Remote Sensing, 47(3), pp.761~770 (2009)
- Lee, D. H., "UAV, a farm map, and machine learning technology convergence classification method of a corn cultivation area", Agronomy, 11(8), pp.1554~ (2021)
- Li, D., "River extraction under bankfull discharge conditions based on sentinel-2 imagery and DEM data", Remote Sensing, 13(14), pp.2650~ (2021)
- L i, M., "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, pp.3120~3132 (2021)
- Lin, J., "Changes in supply and demand mediate the effects of land-use change on freshwater ecosystem services flows", Science of the Total Environment, 763, pp.143012~ (2021)
- Melgani, F., "Classification of hyperspectral remote sensing images with support vector machines", IEEE Transactions on geoscience and remote sensing, 42(8), pp.1778~1790 (2004)
- Mountrakis, G., "Support vector machines in remote sensing : A review", ISPRS journal of photogrammetry and remote sensing, 66(3), pp.247~259 (2011)
- McFeeters, S. K., "The use of the Nnormalized Difference Water Index(NDWI)in the delineation of open water features", International Journal of Remote Sensing, 17(7), pp.1425~1432 (1996)
- Nandi, I., "Floodplain mapping through support vector machine and optical", infrared images from Landsat 8 OLI, Water Resources Management, pp.2017, 31[DOI]
- Naiman, R. J., "The role of riparian corridors in maintaining regional biodiversity", Ecological Applications, 3(2), pp.209~212 (1993)
- Peters, D. P., "Harnessing AI to transform agriculture and inform agricultural research", IT Professional, 22(3), pp.16~21 (2020)
- Ren, L., "Vegetation properties in humanimpacted riparian zones based on unmanned aerial vehicle(UAV)imagery : An analysis of river reaches in the Yongding River Basin", Forests, 12(1), pp.22~ (2020)
- Rouse, J. W., "Monitoring vegetation systems in the Great Plains with ERTS", NASA Spec. Publ, 351(1), pp.309~ (1974)
- Xu, X., "A novel approach for scene classification from remote sensing images using deep learning methods", European Journal of Remote Sensing, 54(sup2), pp.383~395 (2021)
- Yang, Q., "A near real-time deep learning approach for detecting rice phenology based on UAV images", Agricultural and Forest Meteorology, 287, pp.107938~ (2020)