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http://dx.doi.org/10.11108/kagis.2019.22.4.086

Analysis of Cropland Spectral Properties and Vegetation Index Using UAV  

LEE, Geun-Sang (Dept. of Cadastre & Civil Engineering, Vision College of Jeonju)
CHOI, Yun-Woong (Dept. of Civil Construction Engineering, Chosun College of Science & Technology)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.22, no.4, 2019 , pp. 86-101 More about this Journal
Abstract
Remote sensing technology has been continuously developed both quantitatively and qualitatively, including platform development, exploration area, and exploration functions. Recently, the use cases and related researches in the agricultural field are increasing. Also, since it is possible to detect and quantify the condition of cropland and establish management plans and policy support for cropland and agricultural environment, it is being studied in various fields such as crop growth abnormality determination and crop estimation based on time series information. The purpose of this study was to analyze the vegetation index for agricultural land reclamation area using a UAV equipped with a multi-spectral sensor. In addition, field surveys were conducted to evaluate the accuracy of vegetation indices calculated from multispectral image data obtained using UAV. The most appropriate vegetation index was derived by evaluating the correlation between vegetation index calculated by field survey and vegetation index calculated from UAV multispectral image, and was used to analyze vegetation index of the entire area.
Keywords
Remote sensing; Spectral property; Vegetation index; UAV;
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1 Laliberte, A.S., A. Rango, J.E. Herrick, E.L. Fredrickson and L. Burkett. 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments 69(1):1-14.   DOI
2 Lee, K.D., S.I. Na, S.C. Baek, K.D. Park, J.S. Choi, S.J. Kim, H.J. Kim, H.S. Yun, and S.Y. Hong, 2015, Estimating the amount of nitrogen in hairy vetch on paddy fields using unmaned aerial vehicle imagery, Korean Journal of Soil Science and Fertilizer 48(5):384-390 (in Korean with English abstract).   DOI
3 Lee, K.D., Y.E. Lee, C.W. Park, S.Y. Hong and S.I. Na. 2016. Study on Reflectance and NDVI of Aerial Images using a Fixed-Wing UAV"Ebee". Korean Journal of Soil Science and Fertilizer 49(6):731-742 (in Korean with English abstract).   DOI
4 Lee, G.S., Y.W. Choi, K. Jung and G.S. Cho. 2015. Analysis of the Spatial Information Accuracy According to Photographing Direction of Fixed Wing UAV. Journal of the Korean Cadastre Information Associstion 17(3):141-149
5 Lee, G.S., Y.W. Choi, S.B. Lee and S.G. Kim. 2016. Estimation of reservoir area and capacity curve equation using UAV photogrammetry. Journal of the Korean Society for Geo-spatial Information Science 24(3):93-101   DOI
6 Lee, J.W., G.A. Park, H.K. Joh, K.H. Lee, S.I. Na, J.H. Park, and S.J. Kim. 2011. Analysis of relationship between vegetation indices and crop yield using KOMPSAT (KoreaMulti-Purpose SATellite)-2 imagery and field investigation data. Journal of The Korean Society of Agricultural Engineers 53(3):75-82   DOI
7 Morel, A. and L. Prieur. 1977. Analysis of variation in ocean. Limnology and Oceanography 22:709-722.   DOI
8 Kim, K.Y. and S.I. Na. 2019. Vegetation Map Service System Using UAV Imagery and Sample Field Data on Major Cultivation Regions. Journal of the Korean Society for Geospatial Information Science 27(1):33-41   DOI
9 Booth, D.T., S.E. Cox, T. Meikle and H.R. Zuuring. 2008. Ground-cover measurements: assessing correlation among aerial and ground-based methods. Environmental Management 42(6):1091-1100.   DOI
10 Birth, G.S. and G.R. McVey. 1968. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer. American Society of Agronomy, 60:640-643.   DOI
11 Broge, N.H. and J.V. Mortensen. 2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment 81(1):45-57.   DOI
12 Toevs, G.R., J.W. Karl, J.J. Taylor, G.S. Spurrier, M. Karl, M.R. Bobo and J.E. Herrick. 2011. Consistent indicators and methods and a scalable sample design to meet assessment, inventory, and monitoring information needs across scales. Rangelands 33:14-20.
13 Rouse, J,W., R.H. Haas, J.A. Schell and D.W. Deering. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings, Third Earth Resources Technology Satellite-1 Symposium, Volume 1: Technical Presentations, section A. pp.309-317.
14 Shin, Y.H., J.H. Park and M.S. Park. 2003. Spectral reflectance characteristics and vegetation indices of field crops. KCID journal 10(2):43-54
15 Su, T.C. and H.T. Chou. 2015. Application of multispectral sensors carried on unmanned aerial vehicle(UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu reservoir in Kinmen, Taiwan. Remote Sensing 7(8):10078-10097.   DOI
16 Zaman, B., A. Jensen, S.R. Clemens and M. McKee. 2014. Retrieval of spectral reflectance of high resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle. Photogrammetric Engineering & Remote Sensing 80(12):1139-1150.   DOI
17 Tomas, J.R. and H.W. Gausman. 1977. Leat reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agronomy Journal 69:799-802.   DOI
18 Thompson, L.J., Y. Shi, R.B. Ferguson. 2017. Getting Started with Drones in Agriculture. NebGuide, Nebraska Extension Publications. 12pp.
19 Watanabea, Y. and Y. Kawaharab. 2016. UAV photogrammetry for monitoring changes in river topography and vegetation. Procedia Engineering 154:317-325.   DOI
20 Candiago, S., F. Remondino, M.D. Giglio, M. Dubbini, and M. Gattelli. 2015. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sensing 7:4026-4047.   DOI
21 Flynn, K.F. and S.C. Chapra. 2014. Remote Sensing of submerged aquatic vegetation in a shallow Non-turbid river using an unmanned aerial vehicle. Remote Sensing 6:12815-12836.   DOI
22 Choi, Y.W., J.H. You and G.S. Cho. 2015. Accuracy analysis of UAV data processing using DPW. Journal of the Korean Society for Geospatial Information Science 23(4):3-10   DOI
23 Dieter H., Z. Werner, S. Gunter, and S. Peter. 2005. Monitoring of gas pipelines - a civil UAV application. Aircraft Engineering and Aerospace Technology 77:352-360.   DOI
24 Feng, Q., J. Liu, and J. Gong. 2015. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sensing 7:1074-1094.   DOI
25 Gitelson, A., Y.J. Kaufman and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3):289-298.   DOI
26 Hardisky, M. A., V. Klemas and R.M. Smart. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering & Remote Sensing 49:77-83.
27 Hassan, F.M., H.S. Lim and M.Z. Mat Jafri. 2011. CropCam UAV for Land Use/Land Cover mapping over Penang Island, Malaysia, Pertanika Journal of Science & Technology, 19:69-76.
28 Herwitz, S.R., L.F. John, S.E. Dunagan, R.G. Higgins, D.V. Sullivan, J. Zheng, B.M. Lobitz, J.G. Leung, B.A. Gallmeyer, M. Aoyagi, R.E. Slye and J.A. Brass. 2004. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support, Computers and Electronics in Agriculture 44:49-61.   DOI
29 Na, S.I., S.Y. Hong, Y.H. Kim, K.D. Lee, and S.Y. Jang. 2013. Prediction of rice yield in Korea using paddy rice NPP index: Application of MODIS data and CASA model. Korean Journal of Remote Sensing 29(5):461-476   DOI
30 Na, S.I., S.Y. Hong, C.W. Park, K.D. Kim, K.D. Lee. 2016. Estimation of highland Kimchi cabbage growth using UAV NDVI and agro-meteorological factors. Korean Journal of Soil Science and Fertilizer 49(5):420-428 (in Korean with English abstract).   DOI
31 Na, S.I., C.W. Park, Y.K. Cheong, C.S. Kang, I.B. Choi and K.D. Lee. 2016. Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing: An Application of Unmanned Aerial Vehicle and Field Investigation Data. Korean Journal of Remote Sensing 32(5):483-497   DOI
32 Nam W.H., T. Tadesse, B.D. Wardlow, M.W. Jang and S.Y. Hong. 2015. Satellite-based hybrid drought assessment using vegetation drought response index in South Korea (VegDRISKorea). Journal of the Korean Society of Agricultural Engineers 57(4):1-9   DOI
33 Pajares, G. 2015. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering & Remote Sensing 81(4):281-329.   DOI
34 Huete, A.R. and H.Q. Liu. 1994. An error and sensitivity analysis of the atmosphericand soil-correcting variants of the NDVI for the MODIS-EOS. IEEE Transactions on Geoscience and Remote Sensing 32(4):897-905.   DOI
35 Park, J.K. and J.H. Park. 2015. Crops classification using imagery of unmanned aerial vehicle(UAV). Journal of the Korean Society of Agricultural Engineers 57(6):91-97   DOI
36 Pearson, R.L. and L.D. Miller. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proceeding of the Eighth International Symposium on Remote Sensing of Environment, II, Michigan, Ann Arbor, pp. 1357-1381.
37 Rock, B.N., J.E. Vogelmann, D.L. Williams, A.F. Vogehnann and T. Hoshizaki. 1986. Remote detection of forest damage. Bioscience 36:439-445.   DOI
38 Hong, S.Y., J.N. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y.H. Kim, K.D. Lee, S.H. Kim, G.Y. Kim, and K.M. Shim. 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea. Korean Journal of Remote Sensing 28(5):509-520   DOI
39 Huete, A.R. 1988. A soil-adjusted vegetation index(SAVI). Remote Sensing of Environment 25(3):259-309.
40 Hunt, E.R., J.H. Everitt, J.C. Ritchie, M.S. Moran, D.T. Booth, G.L. Anderson, P.E. Clark and M.S. Seyfried. 2003. Applications and research using remote sensing for rangeland management. Photogrammetric Engineering & Remote Sensing 69(6):675-693.   DOI
41 Im, S.H., S.I. Hassan, L.M. Dang, K.B. Min and H. Moon. 2018. Analysis of Fusarium Wilt Based on Normalized Difference Vegetation Index for Radish Field Images from Unmanned Aerial Vehicle. The Transactions of the Korean Institute of Electrical Engineers 67(10):1353-1357   DOI
42 Jeong, S.H., Y.W. Choi and G.S. Cho. 2018. Basic Data Investigation Method of Crop Insurance using Spatial Information Based on UAV. Journal of the Korean Society for Geospatial Information Science 26(3):61-68   DOI
43 Jung, K.S., Y.S. Kim, and S.R. Oh. 2015. Technical Development of Flood Damage Estimation using UAV. Water for future, 48(1):51-59
44 Kim, D.I., Y.S. Song, G. Kim, and C.W. Kim. 2014. A study on the application of UAV for korean land monitoring. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 32(1):29-38   DOI
45 Hornbuckle, J., J. Brinkhoff, C. Ballester and S. North. 2016, Using Satellite And Drones For Water And Nitrogen Management Decision Making, CRDC.