References
- Brezonik, P., Menken, K.D., Bauer, M., (2005). Landsat-based remote sensing of lakewater quality characteristics, including chlorophyll and colored dissolvedorganic matter (CDOM). Lake Reserv. Manage. 21 (4), 373-382. https://doi.org/10.1080/07438140509354442
- Chen, Z., Hu, C. and Muller-Karger, F. (2007) Monitoring turbidity in tampa bay usingMODIS/Aqua 250-m imagery. Remote Sens. Environ. 109(2), 207-220. https://doi.org/10.1016/j.rse.2006.12.019
- Chen, L., Tan, C. H., Kao, S. J. and Wang, T. S. (2008) Improvement of remote monitoringon water quality in a subtropical reservoir by incorporating grammaticalevolution with parallel genetic algorithms into satellite imagery. Water Res. 42(1-2), 296-306. https://doi.org/10.1016/j.watres.2007.07.014
- Choe, E. Y., Lee, J. W and Lee, J. K. (2011) Estimation of Chlorophyll-a Concentrations in the Nakdong River Using High-Resolution Satellite Image, Korean Journal of Remote Sensing, 27, 5, 613-623. https://doi.org/10.7780/kjrs.2011.27.5.613
- De Figueiredo, D. R., Azeiteiro, U. M., Esteves, S .M., Gon Alves, F. J. M., Pereira, M. J. (2004) Microcystin-producing blooms-a serious global public health issue 1, Ecotoxicology and Environmental Safety, 59, 151-163. https://doi.org/10.1016/j.ecoenv.2004.04.006
- Flynn, K. F. and Chapra, S. C. (2014) Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle, Remote Sens., 6, 12815-12836. https://doi.org/10.3390/rs61212815
- Gitelson, A. A., Dall'Olmo, G., Moses, W., Rundquist, D. C., Barrow, T., Fisher, T. R., Gurlin, D. and Holz, J. (2008) A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation, Remote Sensing of Environment, 112, 3582-3593. https://doi.org/10.1016/j.rse.2008.04.015
- Govender, M., Chetty, K. and Bulcock, H. (2007) A review of hyperspectral remote sensing and its application in vegetation and water resource studies, Water SA, 33(2), 145-152.
- Han, L. and Rundquist, D. (1997) Comparison of NIR/RED Ratio and First Derivative of Reflectance in Estimating Algal-Chlorophyll Concentration: A Case Study in a Turbid Reservoir, Remote Sensing of Environment, 62, 253-261. https://doi.org/10.1016/S0034-4257(97)00106-5
- Kutser, T., Metsamaa, L., Strombeck, N. and Vahtmae. E. (2006) Monitoring cyanobacterial blooms by satellite remote sensing, Estuarine, Coastal and Shelf Science, 67, 303-312. https://doi.org/10.1016/j.ecss.2005.11.024
- Lee, H. Kang, T. G., Nam, G. B., Ha, R. and Cho, K. H. (2015) Remote Estimation Models for Deriving Chlorophyll-a Concentration using Optical Properties in Turbid Inland Waters : Application and Valuation, Journal of Korean Society on Water Environment, 31, 3, 272-285. https://doi.org/10.15681/KSWE.2015.31.3.272
- Lowe, G. D. (2004) Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 20, 91-110.
- Mishra, S. and Mishra, D. R. (2012) Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sensing of Environment, 117, 394-406. https://doi.org/10.1016/j.rse.2011.10.016
- Moses, W., Gitelson, A., Berdnikov, S. and Povazhnyy, V. (2009) Satellite estimation of chlorophyll-a concentration using the red and NIR bands of MERIS-The Azov Sea case study, IEEE Geoscience and Remote Sensing Letters, 6, 845-849. https://doi.org/10.1109/LGRS.2009.2026657
- Moses, W. J., Gitelson, A. A., Berdnikov, S., Bowles, J. H., Povazhnyi, V., Saprygin, V., Wagner, J.E. and Patterson, K. W. (2014) HICO-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters. IEEE Geosci. RemoteSens. Lett. 11, 1111-1115. https://doi.org/10.1109/LGRS.2013.2287458
- 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. https://doi.org/10.14358/PERS.81.4.281
- Su, T. C. and Chou, H. T. (2015) Application of multispectral sensors carried onunmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: a case study of Tain-Pu reservoir in Kinmen, Taiwan. Remote Sens. 7(8), 10078-10097.
- Tebbs, E. J., Remedios, J. J. and Harper, D. M. (2013) Remote sensing of chlorophyll-a as ameasure of cyanobacterial biomass in Lake Bogoria, a hypertrophic,saline-alkaline, flamingo lake, using Landsat ETM+. Remote Sens. Environ. 135, 92-106. https://doi.org/10.1016/j.rse.2013.03.024
- Teixeira, M. R. and Rosa, M. J. (2006) Comparing dissolved air flotation and conventional sedimentation to remove cyanobacterial cells of Microcystis aeruginosa: part I: the key operating conditions, Separation and Purification Technology, 52, 84-94. https://doi.org/10.1016/j.seppur.2006.03.017
- Tong, A. and He, Y. (2017) Estimating and mapping chlorophyll content for a heterogeneous grassland: Comparing prediction power of a suite of vegetation indices across scales between years, ISPRS Journal of Photogrammetry and Remote Sensing, 126, 146-167. https://doi.org/10.1016/j.isprsjprs.2017.02.010
- Vega, F. A., Ramirez, F. G., Saiz, M. P. and Rosua, F. O. (2015) Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop, biosystems engineering, 132, 19-27. https://doi.org/10.1016/j.biosystemseng.2015.01.008
- Watanabea, Y. and Kawaharab, Y. (2016) UAV photogrammetry for monitoring changes in river topography and vegetation, Procedia Engineering, 154, 317-325. https://doi.org/10.1016/j.proeng.2016.07.482
- Xie, X., Xu, Y., Liu, Q., Hu, F., Cai, T., Jiang, N. and Xiong, H. (2015) A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform, J. Ambient Intell Human Comput., 6, 835-843. https://doi.org/10.1007/s12652-015-0319-2
- Zhang, Y., Pulliainen, J. T., Koponen, S. S. and Hallikainen, M. T. (2003) Water qualityretrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf ofFinland. IEEE Trans. Geosci. Remote Sens., 41(3), 622-629. https://doi.org/10.1109/TGRS.2003.808906
- Zaman, B., Jensen, A., Clemens, S. R. and McKee, M. (2014) Retrieval of spectralreflectance of high resolution multispectral imagery acquired with anautonomous unmanned aerial vehicle: AggieAirTM. Photogramm. Eng. RemoteSens., 80(12), 1139-1150. https://doi.org/10.14358/PERS.80.12.1139
Cited by
- Monitoring algal bloom in river using unmanned aerial vehicle(UAV) imagery technique vol.32, pp.6, 2018, https://doi.org/10.11001/jksww.2018.32.6.573
- 인 제거 입상소재를 적용한 여과수로 설계인자의 실험적 결정 vol.33, pp.1, 2017, https://doi.org/10.11001/jksww.2019.33.1.009