предыдущий|следующий

Remote sensing of total suspended matter concentration and turbidity in a macrophytic lake

  • role： First author第一作者
  • Affiliation：

    College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China

    Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

  • Email：yinc0416@163.com
  • Introduction：曹引，1991年生，男，工程师，研究方向为流域水资源遥感及水循环模型数据同化。E-mail：yinc0416@163.com
CAO Yin12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

  • Email：yeyuntao@iwhr.com
  • Introduction：冶运涛，1983年生，男，高级工程师，研究方向为水信息与智慧流域、流域水循环模拟及水资源遥感。E-mail：yeyuntao@iwhr.com
YE Yuntao2*,  
  • Affiliation：

    Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

ZHAO Hongli2,  
  • Affiliation：

    Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

JIANG Yunzhong2,  
  • Affiliation：

    College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China

    Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

WANG Hao12

реферат

Remote sensing has been recognized as an effective tool for monitoring water quality in inland waters, especially in algal lakes. However, water quality in macrophytic lakes where aquatic vegetation grows because of mixed pixels is difficult to retrieve. In this study, a classification retrieval method and image data acquired between July 2014 and June 2015 using GF-1 WFV and HJ-1A/1B CCD sensors were proposed to monitor Total Suspended Matter (TSM) and turbidity in a macrophytic lake, namely, the Weishan Lake, considering aquatic vegetation phenology. In the classification retrieval method, the Weishan Lake was divided into water overlying aquatic vegetation and water area using normalized difference water index. First, a qualitative method was proposed to retrieve TSM concentration and turbidity in water overlying aquatic vegetation considering aquatic vegetation phenology. In the qualitative method, time series MODIS NDVI data were used to obtain the time–spectrum curves of aquatic vegetation in the Weishan Lake for identifying different aquatic vegetation phenological periods. The characteristics of aquatic vegetation in different phenological periods were used to estimate the TSM concentration and turbidity. Second, single band, band ratio, and partial least squares models were applied to retrieve the TSM concentration and turbidity in the water area. Finally, the temporal and spatial variations in the TSM concentration and turbidity along with aquatic vegetation in the entire Weishan Lake were analyzed. Results showed that the three main aquatic vegetation areas in the Weishan Lake, namely,Potamogeton lucens, Myriophyllum spicatum, and Potamogeton crispus, have different phenological periods. P. lucens and M. spicatum started to grow in spring, reached their peak at the end of summer, and gradually died in autumn. P. crispus started to grow in April, reached its peak at the end of spring, and quickly died in the early summer. Different aquatic vegetation areas had various indicators of TSM concentration and turbidity in different phenological periods. The TSM concentration was less than 15 mg/L in the water overlying three aquatic vegetation areas in the growth stage. The turbidity in the water overlyingP. lucens, M. spicatum, and P. crispus in the growth stage were less than 30 and 15 NTU. The death of P. lucens and M. spicatum did not result in the deterioration of water quality because of their extensive death time. However, the death of P. crispus within a short time resulted in serious deteriorations of water quality. The TSM concentration and turbidity were 15–145 mg/L and 30–140 NTU, correspondingly, in the water overlying P. crispus in the death stage. The TSM concentration and turbidity in the Weishan Lake had a significant temporal–spatial variability. The TSM concentration and turbidity in the Southwestern Weishan Lake where P. lucens and M. spicatum grew had low levels in the seasons. By contrast, the TSM concentration and turbidity in the Northeastern Weishan Lake with P. crispus growth had low levels in spring. However, they became large in summer because of the quick death of P. crispus and gradually decreased in autumn and winter. A classification retrieval method coupled with quantitative monitoring in the water overlying aquatic vegetation and qualitative monitoring in the water area was proposed to monitor the TSM concentration and turbidity in the Weishan Lake considering aquatic vegetation phenology. This method effectively monitored the temporal and spatial variations in water quality in the entire Weishan Lake. The findings indicated that the proposed method can be used to monitor the water quality in other macrophytic lakes.

ключеви́че слова́

remote sensing;macrophytic lake;aquatic vegetation;time-spectrum curve;phenology;remote sensing monitoring;GF-1;HT-1

References

  1. 1.
    Abo El-lil A H. 2006. Evaluation of the efficiency of some hydrophytes for trapping suspended matters from different aquatic ecosystems. Biotechnology, 5(1): 90–97
  2. 2.
    Ali K A and Ortiz J D. 2016. Multivariate approach for chlorophyll-a and suspended matter retrievals in Case II type waters using hyperspectral data. Hydrological Sciences Journal, 61(1): 200–213
  3. 3.
    Birk S and Ecke F. 2014. The potential of remote sensing in ecological status assessment of coloured lakes using aquatic plants. Ecological Indicators, 46: 398–406
  4. 4.
    Bolpagni R, Bresciani M, Laini A, Pinardi M, Matta E, Ampe E M, Giardino C, Viaroli P and Bartoli M. 2014. Remote sensing of phytoplankton-macrophyte coexistence in shallow hypereutrophic fluvial lakes. Hydrobiologia, 737(1): 67–76
  5. 5.
    Bonansea M, Rodriguez M C, Pinotti L and Ferrero S. 2015. Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina). Remote Sensing of Environment, 158: 28–41
  6. 6.
    Brooks C, Grimm A, Shuchman R, Sayers M and Jessee N. 2015. A satellite-based multi-temporal assessment of the extent of nuisanceCladophora and related submerged aquatic vegetation for the Laurentian Great Lakes. Remote Sensing of Environment, 157: 58–71
  7. 7.
    Cao Y, Ye Y T, Zhao H L, Jiang Y Z, Wang H, Shang Y Z and Wang J F. 2018. Remote sensing of water quality based on HJ-1A HSI imagery with modified discrete binary particle swarm optimization-partial least squares (MDBPSO-PLS) in inland waters: A case in Weishan Lake. Ecological Informatics, 44: 21–32
  8. 8.
    Chang S Y, Wu T and Zhao J J. 2016. Effect of planting different submerged macrophytes groups on water purification of Beidagang Reservoir. Chinese Journal of Environmental Engineering, 10(1): 439–444
  9. 9.
    Dekker A G, Phinn S R, Anstee J, Bissett P, Brando V E, Casey B, Fearns P, Hedley J, Klonowski W, Lee Z P, Lynch M, Lyons M, Mobley C and Roelfsema C. 2011. Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments. Limnology and Oceanography: Methods, 9(9): 396–425
  10. 10.
    Dörnhöfer K and Oppelt N. 2016. Remote sensing for lake research and monitoring - Recent advances. Ecological Indicators, 64: 105–122
  11. 11.
    Duan H T, Ma R H, Xu X F, Kong F X, Zhang S H, Kong W J, Hao J Y and Shang L L. 2009. Two-decade reconstruction of algal blooms in China's Lake Taihu. Environmental Science and Technology, 43(10): 3522–3528
  12. 12.
    Giardino C, Bresciani M, Valentini E, Gasperini L, Bolpagni R and Brando V E. 2015. Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake. Remote Sensing of Environment, 157: 48–57
  13. 13.
    Giardino C, Bresciani M, Villa P and Martinelli A. 2010. Application of remote sensing in water resource management: the case study of Lake Trasimeno, Italy. Water Resources Management, 24(14): 3885–3899
  14. 14.
    Guo C C, Yu G H and Wang G X. 2007. Removal of Potamogeton crispus to suspended sediment and N,P of water. 2007. Journal of Soil and Water Conservation, 21(3): 108–111, 117
  15. 15.
    Güttler F N, Niculescua S and Gohinb F. 2013. Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western–northwestern Black Sea coastal zone. Remote Sensing of Environment, 132: 86–101
  16. 16.
    Hestir E L, Brando V E, Bresciani M, Giardino C, Matta E, Villa P and Dekker A G. 2015. Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission. Remote Sensing of Environment, 167: 181–195
  17. 17.
    Hicks B J, Stichbury G A, Brabyn L K, Allan M G and Ashraf S. 2013. Hindcasting water clarity from Landsat satellite images of unmonitored shallow lakes in the Waikato region, New Zealand. Environmental Monitoring and Assessment, 185(9): 7245–7261
  18. 18.
    Hou X J, Feng L, Duan H T, Chen X L, Sun D Y and Shi K. 2017. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China. Remote Sensing of Environment, 190: 107–121
  19. 19.
    Huang C C, Li Y M, Yang H, Li J S, Chen X, Sun D Y, Le C F, Zou J and Xu L J. 2014. Assessment of water constituents in highly turbid productive water by optimization bio-optical retrieval model after optical classification. Journal of Hydrology, 519: 1572–1583
  20. 20.
    Huang L, Wu N C, Tang T, Li D F and Cai Q H. 2010. Enrichment and removal of nutrients in eutrophic water by aquatic macrophytes. China Environmental Science, 30(S1): 1–6
  21. 21.
    Hunter P D, Gilvear D J, Tyler A N, Willby N J and Kelly A. 2010. Mapping macrophytic vegetation in shallow lakes using the Compact Airborne Spectrographic Imager (CASI). Aquatic Conservation: Marine and Freshwater Ecosystems, 20(7): 717–727
  22. 22.
    Jiang Y Z, Ye Y T and Wang H. 2010. Discussion on intelligent regulation technology architecture for river basin based on internet of things. Water Resources Informatization(4): 1–5, 10
  23. 23.
    Jiang Y Z, Ye Y T and Wang H. 2011. Smart basin and its prospects for application. Systems Engineering—Theory and Practice, 31(6): 1174–1181
  24. 24.
    Jiang Y Z, Ye Y T and Wang H. 2014. Intelligent control and emergency treatment system of water quality and quantity for the interconnected river system network based on the internet of things. Systems Engineering—Theory and Practice, 34(7): 1895–1903
  25. 25.
    Kim W, Moon J E, Park Y and Ishizaka J. 2016. Evaluation of chlorophyll retrievals from Geostationary Ocean Color Imager (GOCI) for the North-East Asian region. Remote Sensing of Environment, 184: 482–495
  26. 26.
    Li J S, Wu D, Wu Y F, Liu H X, Shen Q and Zhang H. 2009. Identification of algae-bloom and aquatic macrophytes in Lake Taihu from in-situ measured spectra data. Journal of Lake Sciences, 21(2): 215–222
  27. 27.
    Li Y, Zhang L F, Huang C P, Wang J N and Cen Y. 2016. Monitor of cyanobacteria bloom in Lake Taihu from 2001 to 2013 based on MODIS temporal spectral data. Spectroscopy and Spectral Analysis, 36(4): 1406–1411
  28. 28.
    Liu Z H, Li Y M, Lü H, Xu Y F, Xu X, Huang J Z, Tan J and Guo Y L. 2011. Inversion of suspended matter concentration in Lake Chaohu based on partial least-squares regression. Journal of Lake Sciences, 23(3): 357–365
  29. 29.
    Lobo F L, Costa M P F and Novo E M L M. 2015. Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities. Remote Sensing of Environment, 157: 170–184
  30. 30.
    Matthews M W, Bernard S and Winter K. 2010. Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS. Remote Sensing of Environment, 114(9): 2070–2087
  31. 31.
    Oppelt N M, Schulze F, Doernhoefer K, Eisenhardt I and Bartsch I. 2012. Hyperspectral classification approaches for intertidal macroalgae habitat mapping: a case study in Heligoland. Optical Engineering, 51(11): 111703
  32. 32.
    Panigrahi S, Wikner J, Panigrahy R C, Satapathy K K and Acharya B C. 2009. Variability of nutrients and phytoplankton biomass in a shallow brackish water ecosystem (Chilika Lagoon, India). Limnology, 10(2): 73–85
  33. 33.
    Qiao N, Huang C P, Zhang L F, Zhao H L, Ye Y T, Cen Y and Li Y. 2016. Remote sensing inversion of total suspended matter concentration in typical shallow macrophytic lake. Journal of Hubei University (Natural Science), 38(6): 510–516
  34. 34.
    Qin B Q. 2002. Approaches to mechanisms and control of eutrophication of shallow Lakes in the middle and lower reaches of the Yangze River. Journal of Lake Sciences, 14(3): 193–202
  35. 35.
    Scheffer M, Szabó S, Gragnani A, Van Nes E H, Rinaldi S, Kautsky N, Norberg J, Roijackers R M M and Franken R J M. 2003. Floating plant dominance as a stable state. Proceedings of the National Academy of Sciences of the United States of America, 100(7): 4040–4045
  36. 36.
    Shen Q S, Zhou Q L, Shao S G, Liu C, Zhang L and Fan C X. 2014. Estimation of in-situ sediment nutrients release at the submerged plant induced black bloom area in Lake Taihu. Journal of Lake Sciences, 26(2): 177–184
  37. 37.
    Shi K, Zhang Y L, Zhu G W, Liu X H, Zhou Y Q, Xu X, Qin B Q, Liu G and Li Y M. 2015. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250 m MODIS-Aqua data. Remote Sensing of Environment, 164: 43–56
  38. 38.
    Shields E C and Moore K A. 2016. Effects of sediment and salinity on the growth and competitive abilities of three submersed macrophytes. Aquatic Botany, 132: 24–29
  39. 39.
    Song K S, Li L, Tedesco L P, Li S, Duan H T, Liu D W, Hall B E, Du J, Li Z C, Shi K and Zhao Y. 2013. Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model. Remote Sensing of Environment, 136: 342–357
  40. 40.
    Srivastava J K, Chandra H, Kalra S J S, Mishra P, Khan H and Yadav P. 2017. Plant–microbe interaction in aquatic system and their role in the management of water quality: a review. Applied Water Science, 7(3): 1079–1090
  41. 41.
    Villa P, Laini A, Bresciani M and Bolpagni R. 2013. A remote sensing approach to monitor the conservation status of lacustrine Phragmites australis beds. Wetlands Ecology and Management, 21(6): 399–416
  42. 42.
    Wang J D, Sheng Y W and Tong T S D. 2014. Monitoring decadal lake dynamics across the Yangtze Basin downstream of Three Gorges Dam. Remote Sensing of Environment, 152: 251–269
  43. 43.
    Wang J Q, Zheng Y F and Wang G X. 2011. Influences of Potamogeton crispus population on the lake water quality distribution. Environmental Science, 32(2): 416–422
  44. 44.
    Wu Z B, Wang Y N, Liu B Y, Zhou Q H and Zhang Y Y. 2009. Allelopathic effects of phenolic compounds present in submerged macrophytes on Microcystis aeruginosa. Allelopathy Journal, 23(2): 403–410
  45. 45.
    Zhang B, Shen Q, Li J S, Zhang H and Wu D. 2009. Retrieval of three kinds of representative water quality parameters of Lake Taihu from hyperspectral remote sensing data. Journal of Lake Sciences, 21(2): 182–192
  46. 46.
    Zhang M, Yin C B, Zhang C Y, Wang L and Liu Z Q. 2015. Ecological function of submerged plant potamogeton crispus and its application status. Soil and Water Conservation in China(3): 50–53
  47. 47.
    Zhang Y, Wang S R, Duan C Q, Jiao L X, Wang Y R and Gao Q S. 2018. Spatial-temporal variations of nitrogen and phosphorus forms in sediment porewater as affected by submerged plant in Lake Dianchi. Journal of Lake Sciences, 30(2): 314–325
  48. 48.
    Zhang Y L, Shi K, Liu X H, Zhou Y Q and Qin B Q. 2014. Lake topography and wind waves determining seasonal-spatial dynamics of total suspended matter in turbid Lake Taihu, China: Assessment using long-term high-resolution MERIS data. PLoS One, 9(5): e98055
  49. 49.
    Zhu Q, Li J S, Zhang F F, Shen Q, Lin H, Wang L J and Zhu L. 2016. Distinguishing cyanobacteria bloom and aquatic plants in Lake Taihu based on Hyperspectral Imager for the Coastal Ocean images. Remote Sensing Technology and Application, 31(5): 879–885
  50. 50.
    Zhu Y F, Zhu L, Li J G, Chen Y J, Zhang Y H, Hou H Q, Ju X and Zhang Y Z. 2017. The study of inversion of chlorophyll a in Taihu based on GF-1 WFV image and BP neural network. Acta Scientiae Circumstantiae, 37(1): 130–137

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