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Research progress of aquatic vegetation remote sensing in shallow lakes

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

  • Email：jhluo@niglas.ac.cn
  • Introduction：罗菊花，1981年生，女，副研究员，研究方向为水生/湿生植被遥感。E-mail： jhluo@niglas.ac.cn
LUO Juhua,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

YANG Jingzhicheng,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

DUAN Hongtao,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

LU Lirong,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

SUN Zhe,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

XIN Yihao

résumé

In shallow lakes or reservior, aquatic vegetation plays an important role in purifying water, maintaining the balance of lake ecosystems, supporting socioeconomic functions and protecting lake ecological environment. However, an excessive amount of macrophytes, especially floating-leaved vegetation, can have some negative effects on lake ecology. For example, the addition of large amounts of plant material to the lake bottom can cause lake silting and accelerate lake swamping; the release of pollutants into the lake water when the plants die and decay can result in water pollution. Therefore, it is very important to map spatiotemporal distribution and their changes of aquatic vegetation and then to retrieve biochemical parameters such as coverage and biomass for ecological restoration and management of lakes. Remote sensing techniques have become powerful and effective tools for mapping aquatic vegetation types and their changes over a large area and a long period. In this paper, with the theme of aquatic vegetation remote sensing, we reviewed and summarized the major progresses and methods of remote sensing application in aquatic vegetation in shallow lakes by literature review. We found the research topics in aquatic vegetation remote sensing mainly included hyperspectral analyses, classification and mapping, parameter inversion, change detection, and so on. We also offered a literature statistical diagram of classification methods for mapping aquatic vegetation, and found decision tree was the most popular and machine learning was becoming more and more popular in all mapping methods. Finally, we discussed existing major challenges, potential solutions and future prospects in aquatic vegetation remote sensing, including developing a multi-parameter method for mapping different species of submerged vegetation, expanding the spatial-temporal scale of inversion models in parameters in application and making full use of the advantages of UAV (unmanned aerial vehicle) coupled with hyperspectral and multispectral sensors for mapping and parameter inversion in aquatic vegetation.

mots-clés

Aquatic vegetation;submerged aquatic vegetation;Biomass;Remote sensing;Classification;Change detection

References

  1. 1.
    Byrd K B, O’Connell J L, Di Tommaso S and Kelly M. 2014. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sensing of Environment, 149: 166-180
  2. 2.
    Chen S Q, Xu Q J, Li F S and Liu J. 2008. Influence of environmental factors on the distribution and growth of aquatic macrophytes. Journal of Biology, 25(2): 11-15
  3. 3.
    Chen Z Y, Lei Z X, Zhou J, Wen F C and Chen J K. 2000. Monthly quantitative and biomass dynamics of six submerged macrophytes populations in Liangzi Lake. Acta Hydrobiologica Sinica, 24(6): 582-588
  4. 4.
    Cui X H, Chen J K and Li W. 1999. Survey methods on aquatic macrophyte vegetation in lakes in the middle and lower reaches of Changjiang River. Journal of Wuhan Botanical Research, 17(4): 357-361
  5. 5.
    Corti Meneses N, Baier S, Geist J and Schneider T. 2017. Evaluation of green-lidar data for mapping extent, density and height of aquatic reed beds at lake chiemsee, bavaria—germany. Remote Sensing, 9(12): 1308.
  6. 6.
    Costa M. 2005. Estimate of net primary productivity of aquatic vegetation of the Amazon floodplain using Radarsat and JERS‐1. International Journal of Remote Sensing, 26(20): 4527-4536
  7. 7.
    Costa M P F, Niemann O, Novo E and Ahern F. 2002. Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat. International Journal of Remote Sensing, 23(7): 1401-1426
  8. 8.
    Dogan O K, Akyurek Z and Beklioglu M. 2009. Identification and mapping of submerged plants in a shallow lake using quickbird satellite data. Journal of Environmental Management, 90(7): 2138-2143
  9. 9.
    Gao Y N, Gao J F, Wang J, Wang S S, Li Q, Zhai S H and Zhou Y. 2017. Estimating the biomass of unevenly distributed aquatic vegetation in a lake using the normalized water-adjusted vegetation index and scale transformation method. Science of the Total Environment, 601-602: 998-1007
  10. 10.
    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
  11. 11.
    Han X X, Chen X L and Feng L. 2015. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sensing of Environment, 156: 426-437
  12. 12.
    Han X X, Feng L, Hu C M and Chen X L. 2018. Wetland changes of China’s largest freshwater lake and their linkage with the Three Gorges Dam. Remote Sensing of Environment, 204: 799-811
  13. 13.
    Held P and von Deimling J S. 2019. New feature classes for acoustic habitat mapping—A multibeam echosounder point cloud analysis for mapping submerged aquatic vegetation (SAV). Geosciences, 9(5): 235
  14. 14.
    Hestir E L, Khanna S, Andrew M E, Santos M J, Viers J H, Greenberg J A, Rajapakse S S and Ustin S L. 2008. Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sensing of Environment, 112(11): 4034-4047
  15. 15.
    Hou X J, Feng L, Chen X L and Zhang Y L. 2018. Dynamics of the wetland vegetation in large lakes of the Yangtze Plain in response to both fertilizer consumption and climatic changes. ISPRS Journal of Photogrammetry and Remote Sensing, 141: 148-160
  16. 16.
    Husson E, Ecke F and Reese H. 2016. Comparison of manual mapping and automated object-based image analysis of non-submerged aquatic vegetation from very-high-resolution UAS images. Remote Sensing, 8(9): 724
  17. 17.
    Janssen A B G, Teurlincx S, An S Q, Janse J H, Paerl H W and Mooij W M . 2014. Alternative stable states in large shallow lakes? Journal of Great Lakes Research, 40(4): 813-826
  18. 18.
    Jiang H, Zhao D H, Cai Y and An A Q. 2012. A method for application of classification tree models to map aquatic vegetation using remotely sensed images from different sensors and dates. Sensors, 12(9): 12437-12454
  19. 19.
    Jing R, Deng L, Zhao W J and Gong Z N. 2016. Object-oriented aquatic vegetation extraction approach based on visible vegetation indices. Chinese Journal of Applied Ecology, 27(5): 1427-1436
  20. 20.
    Jin B F and Guo Y H. 2001. Primary studies on the reproductive characteristics of potanogeton maackianus. Acta Hydrobiologica Sinica, 25(5): 439-448
  21. 21.
    Kellndorfer J, Walker W, Pierce L, Dobson C, Fites J A, Hunsaker C, Vona J and Clutter M. 2004. Vegetation height estimation from shuttle radar topography mission and national elevation datasets. Remote Sensing of Environment, 93(3): 339-358
  22. 22.
    Liang Q C, Zhang Y C, Ma R H, Loiselle S, Li J and Hu M Q. 2017. A MODIS-based novel method to distinguish surface cyanobacterial scums and aquatic macrophytes in Lake Taihu. Remote Sensing, 9(2): 133
  23. 23.
    Liu X H, Zhang Y L, Shi K, Zhou Y Q, Tang X M, Zhu G W and Qin B Q. 2015. Mapping aquatic vegetation in a large, shallow eutrophic lake: a frequency-based approach using multiple years of MODIS data. Remote Sensing, 7(8): 10295-10320
  24. 24.
    Luo J H, Duan H T, Ma R H, Jin X L, Li F, Hu W P, Shi K and Huang W J. 2017. Mapping species of submerged aquatic vegetation with multi-seasonal satellite images and considering life history information. International Journal of Applied Earth Observation and Geoinformation, 57: 154-165
  25. 25.
    Luo J H, Li X C, Ma R H, Li F, Duan H T, Hu W P, Qin B Q and Huang W J. 2016a. Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake, China. Ecological Indicators, 60: 503-513
  26. 26.
    Luo J H, Ma R H, Duan H T, Hu W P, Zhu J G, Huang W J and Lin C. 2014. A new method for modifying thresholds in the classification of tree models for mapping aquatic vegetation in Taihu Lake with satellite images. Remote Sensing, 6(8): 7442-7462
  27. 27.
    Luo J H, Ma R H, Feng H H and Li X C. 2016b. Estimating the total nitrogen concentration of reed canopy with hyperspectral measurements considering a non-uniform vertical nitrogen distribution. Remote Sensing, 8(10): 789
  28. 28.
    Luo J H, Pu R L, Duan H T, Ma R H, Mao Z G, Zeng Y, Huang L S and Xiao Q T. 2020. Evaluating the influences of harvesting activity and eutrophication on loss of aquatic vegetations in Taihu Lake, China. International Journal of Applied Earth Observation and Geoinformation, 87: 102038
  29. 29.
    Luo S Z, Wang C, Xi X H, Pan F F, Qian M J, Peng D L, Nie S, Qin H M and Lin Y. 2017. Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 58: 107-117
  30. 30.
    Ma R H, Duan H T, Gu X H and Zhang S X. 2008. Detecting aquatic vegetation changes in Taihu Lake, China using multi-temporal satellite imagery. Sensors, 8(6): 3988-4005
  31. 31.
    Massicotte P, Bertolo A, Brodeur P, Hudon C, Mingelbier M and Magnan P. 2015. Influence of the aquatic vegetation landscape on larval fish abundance. Journal of Great Lakes Research, 41(3): 873-880
  32. 32.
    Mobley C D. 2001. Radiative transfer in the ocean//Steele J H, ed. Encyclopedia of Ocean Sciences. San Diego: Academic Press, 2321-2330
  33. 33.
    Murphy F, Schmieder K, Baastrup-Spohr L, Pedersen O and Sand‐Jensen K. 2018. Five decades of dramatic changes in submerged vegetation in Lake constance. Aquatic Botany, 144: 31-37
  34. 34.
    Mutanga O, Adam E and Cho M A. 2012. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18: 399-406
  35. 35.
    Oyama Y, Matsushita B and Fukushima T. 2015. Distinguishing surface cyanobacterial blooms and aquatic macrophytes using Landsat/TM and ETM+ shortwave infrared bands. Remote Sensing of Environment, 157: 35-47
  36. 36.
    Pande-Chhetri R, Abd-Elrahman A and Jacoby C. 2014. Classification of submerged aquatic vegetation in black river using hyperspectral image analysis. Geomatica, 68(3): 169-182
  37. 37.
    Pang C C, Wu S Q, Lai X J and Wu D. 2014. Water flow with submerged vegetation and its effect on water turbidity. Research of Environmental Sciences, 27(5): 498-504
  38. 38.
    Poerschmann J, Weiner B, Wedwitschka H, Zehnsdorf A, Koehler R and Kopinke F D. 2015. Characterization of biochars and dissolved organic matter phases obtained upon hydrothermal carbonization of Elodea nuttallii. Bioresource Technology, 189: 145-153
  39. 39.
    Pu R L, Bell S, Meyer C, Baggett L and Zhao Y C. 2012. Mapping and assessing seagrass along the western coast of Florida using Landsat TM and EO-1 ALI/Hyperion imagery. Estuarine, Coastal and Shelf Science, 115: 234-245
  40. 40.
    Qing S, A R N, Shun B, Zhao W J, Bao Y H and Hao Y L. 2020. Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986-2018. Ecological Indicators, 112: 106073
  41. 41.
    Rogers K H and Breen C M. 1983. An investigation of macrophyte, epiphyte and grazer interactions//Wetzel P G, ed. Periphyton of Freshwater Ecosystems. Dordrecht: Springer
  42. 42.
    Sawaya K E, Olmanson L G, Heinert N J, Brezonik P L and Bauer W E. 2003. Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88(1/2): 144-156
  43. 43.
    Shi H, Li X W, Niu Z C, Li J Y, Li Y and Li N. 2016. Remote sensing information extraction of aquatic vegetation in Lake Taihu based on random forest model. Journal of Lake Sciences, 28(3): 635-644
  44. 44.
    Shi L L, Jia Y F, Zuo A J, Ma T H, Lei J L, Lei G C and Wen L. 2018. Dynamic change of vegetation cover and productivity of Poyang Lake wetland based on MODIS EVI time series. Biodiversity Science, 26(08): 828-837
  45. 45.
    Singh G, Reynolds C, Byrne M and Rosman B. 2020. A remote sensing method to monitor water, aquatic vegetation, and invasive water hyacinth at national extents. Remote Sensing, 12(24): 4021
  46. 46.
    Soana E, Naldi M and Bartoli M. 2012. Effects of increasing organic matter loads on pore water features of vegetated (Vallisneria spiralis L.) and plant-free sediments. Ecological Engineering, 47: 141-145
  47. 47.
    Spears B M, Mackay E B, Yasseri S, Gunn I D M, Waters K E, Andrews C, Cole S, De Ville M, Kelly A, Meis S, Moore A L, Nürnberg G K, van Oosterhout F, Pitt J A, Madgwick G, Woods H J and Lürling M. 2016. A meta-analysis of water quality and aquatic macrophyte responses in 18 lakes treated with lanthanum modified bentonite (Phoslock®). Water Research, 97: 111-121
  48. 48.
    Tian Y Q, Yu Q, Zimmerman M J, Flint S and Waldron M C. 2010. Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing. Freshwater Biology, 55(8): 1658-1673
  49. 49.
    Underwood E C, Mulitsch M J, Greenberg J A, Whiting M L, Ustin S L and Kefauver S C. 2006. Mapping invasive aquatic vegetation in the Sacramento-San Joaquin Delta using hyperspectral imagery. Environmental Monitoring and Assessment, 121(1/3): 47-64
  50. 50.
    Villa P, Bresciani M, Bolpagni R, Pinardi M and Giardino C. 2015. A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sensing of Environment, 171: 218-233
  51. 51.
    Villa P, Pinardi M, Bolpagni R, Gillier J M, Zinke P, Nedelcuţ F and Bresciani M. 2018. Assessing macrophyte seasonal dynamics using dense time series of medium resolution satellite data. Remote Sensing of Environment, 216: 230-244
  52. 52.
    Visser F, Buis K, Verschoren V and Schoelynck J. 2018. Mapping of submerged aquatic vegetation in rivers from very high-resolution image data, using object-based image analysis combined with expert knowledge. Hydrobiologia, 812(1): 157-175
  53. 53.
    Visser F, Wallis C and Sinnott A M. 2013. Optical remote sensing of submerged aquatic vegetation: opportunities for shallow clearwater streams. Limnologica, 43(5): 388-398
  54. 54.
    Wang Q, Zhou X D, Luo J H and Chen C. 2015. The remote sensing monitoring of dominant species of submerged vegetation of Lake Taihu with the consideration of their living histories. Journal of Lake Sciences, 27(5): 953-961
  55. 55.
    Wang Z H, Xin C L, Sun Z, Luo J H and Ma R H. 2019. Automatic extraction method of aquatic vegetation types in small shallow lakes based on sentinel-2 data: a case study of cuiping lake. Remote Sensing Information, 34(5): 132-141
  56. 56.
    Whiteside T G and Bartolo R E. 2015. Mapping aquatic vegetation in a tropical wetland using high spatial resolution multispectral satellite imagery. Remote Sensing, 7(9): 11664-11694
  57. 57.
    Wiegleb G and Brux H. 1991. Comparison of life history characters of broad-leaved species of the genus Potamogeton L. I. General characterization of morphology and reproductive strategies. Aquatic Botany, 39(1/2): 131-146
  58. 58.
    Wolter Peter T, Carol A Johnston, and Gerald J Niemi. 2007. Mapping submergent aquatic vegetation in the US Great Lakes using Quickbird satellite data. International Journal of Remote Sensing, 26(23): 5255-5274
  59. 59.
    Xiao C, Wang X F, Xia J and Liu G H. 2010. The effect of temperature, water level and burial depth on seed germination of Myriophyllum spicatum and Potamogeton malaianus. Aquatic Botany, 92(1): 28-32
  60. 60.
    Yadav S, Yoneda M, Susaki J, Tamura M, Ishikawa K and Yamashiki Y. 2017. A satellite-based assessment of the distribution and biomass of submerged aquatic vegetation in the optically shallow Basin of Lake Biwa. Remote Sensing, 9(9): 966
  61. 61.
    Yang J Z C, Luo J H, Lu L R, Sun Z, Cao Z G, Zeng Q F and Mao Z G. 2021. Changes in aquatic vegetation communities based on satellite images before and after pen aquaculture removal in East Lake Taihu. Journal of Lake Sciences, 33(2): 507-517
  62. 62.
    Yang Q X. 1998. Ecological functions of aquatic vegetation in East Taihu Lake and its reasonable regulation. Journal of Lake Sciences, 10(1): 67-72
  63. 63.
    Ye C, Li C H, Yu H C, Song X F, Zou G Y and Liu J. 2011. Study on ecological restoration in near-shore zone of a eutrophic lake, Wuli Bay, Taihu Lake. Ecological Engineering, 37(9): 1434-1437
  64. 64.
    Yuan L and Zhang L. 2006. Identification of the spectral characteristics of submerged plant vallisneria spiralis. Acta Ecologica Sinica, 26(4): 1005-1010
  65. 65.
    Zhang S X, Duan H T and Gu X H. 2008, Remote sensing information extraction of hydrophytes based on the retrieval of water transparency in Lake Taihu, China. Journal of Lake Sciences, 20(2): 184-190
  66. 66.
    Zhang Y L, Jeppesen E, Liu X H, Qin B Q, Shi K, Zhou Y Q, Thomaz S M and Deng J M. 2017. Global loss of aquatic vegetation in lakes. Earth-Science Reviews, 173: 259-265
  67. 67.
    Zhang Y L, Liu X H, Qin B Q, Shi K, Deng J M and Zhou Y Q. 2016. Aquatic vegetation in response to increased eutrophication and degraded light climate in Eastern Lake Taihu: implications for lake ecological restoration. Scientific Reports, 6: 23867
  68. 68.
    Zhao D H, Jiang H, Yang T W, Cai Y, Xu D L and An S Q. 2012. Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds. Journal of Environmental Management, 95(1): 98-107
  69. 69.
    Zhao D H, Lv M T, Jiang H, Cai Y, Xu D L and An S Q. 2013. Spatio-temporal variability of aquatic vegetation in taihu lake over the past 30 years. PLoS One, 8(6): e66365
  70. 70.
    Zhou G H, Ma Z Q, Sathyendranath S, Platt T, Jiang C and Sun K. 2018. Canopy reflectance modeling of aquatic vegetation for algorithm development: global sensitivity analysis. Remote Sensing, 10(6): 837
  71. 71.
    Zhou G H, Niu C Y, Xu W J, Yang W N, Wang J W and Zhao H J. 2015. Canopy modeling of aquatic vegetation: a radiative transfer approach. Remote Sensing of Environment, 163: 186-205
  72. 72.
    Zhou G H, Yang S, Sathyendranath S and Platt T. 2020. Canopy modeling of aquatic vegetation: a geometric optical approach (AVGO). Remote Sensing of Environment, 245: 111829

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