Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T10:15:30.391Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatiotemporal monitoring of hydrilla [Hydrilla verticillata (L. f.) Royle] to aid management actions

Published online by Cambridge University Press:  16 May 2019

Abhishek Kumar Open the ORCID record for Abhishek Kumar [Opens in a new window] ,
Christopher Cooper ,
Caren M. Remillard ,
Shuvankar Ghosh ,
Austin Haney ,
Frank Braun ,
Zachary Conner ,
Benjamin Page ,
Kenneth Boyd  and
Susan Wilde
...Show all authors
Abhishek Kumar*
Affiliation:
Graduate Student, Department of Geography, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Christopher Cooper
Affiliation:
Graduate Student, Department of Geography, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Caren M. Remillard
Affiliation:
Graduate Student, Department of Geography, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Shuvankar Ghosh
Affiliation:
Graduate Student, Department of Geography, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Austin Haney
Affiliation:
Undergraduate Student, Warnell School of Forestry and Natural Resources, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Frank Braun
Affiliation:
Undergraduate Student, Warnell School of Forestry and Natural Resources, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Zachary Conner
Affiliation:
Undergraduate Student, Department of Environmental Studies and Sciences, Ithaca College, Ithaca, NY, USA, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Benjamin Page
Affiliation:
Graduate Student, Department of Geography, and NASA DEVELOP National Program, University of Georgia, Athens, GA, USA
Kenneth Boyd
Affiliation:
Natural Resources Specialist, US Army Corps of Engineers, Savannah District, Savannah, GA, USA
Susan Wilde
Affiliation:
Associate Professor, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA
Deepak R. Mishra
Affiliation:
Professor, Center for Geospatial Research, Department of Geography, University of Georgia, Athens, GA, USA.
*
*Author for Correspondence: Abhishek Kumar, Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Hydrilla is an invasive aquatic plant that has rapidly spread through many inland water bodies across the globe by outcompeting native aquatic plants. The negative impacts of hydrilla invasion have become a concern for water resource management authorities, power companies, and environmental scientists. The early detection of hydrilla infestation is very important to reduce the costs associated with control and removal efforts of this invasive species. Therefore, in this study, we aimed to develop a tool for rapid, frequent, and large-scale monitoring and predicting spatial extent of hydrilla habitat. This was achieved by integrating in situ and Landsat 8 Operational Land Imager satellite data for Lake J. Strom Thurmond, the largest US Army Corps of Engineers lake east of the Mississippi River, located on the border of Georgia and South Carolina border. The predictive model for presence of hydrilla incorporated radiometric and physical measurements, including remote-sensing reflectance, Secchi disk depth (SDD), light-attenuation coefficient (Kd), maximum depth of colonization (Zc), and percentage of light available through the water column (PLW). The model-predicted ideal habitat for hydrilla featured high SDD, Zc, and PLW values, low values of Kd. Monthly analyses based on satellite images showed that hydrilla starts growing in April, reaches peak coverage around October, begins retreating in the following months, and disappears in February. Analysis of physical and meteorological factors (i.e., water temperature, surface runoff, net inflow, precipitation) revealed that these parameters are closely associated with hydrilla extent. Management agencies can use these results not only to plan removal efforts but also to evaluate and adapt their current mitigation efforts.

Keywords

Prashant Jha, Montana State UniversityHydrillaHydrilla verticillata (L. f.) RoyleLake J. Strom ThurmondLandsat 8 OLIpercentage of light available through the water column (PLW)prediction toolremote sensingSecchi disk depth (SDD)
Type
Research Article
Information
Weed Technology , Volume 33 , Issue 3 , June 2019 , pp. 518 - 529
Copyright
© Weed Science Society of America, 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ackleson, SG, Klemas, V (1987) Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay: a comparison of Landsat MSS to TM imagery. Remote Sens Environ 22:235–248CrossRefGoogle Scholar
Batiuk, R, Orth, R, Moore, K, Stevenson, JC, Dennison, W, Staver, L, Carter, V, Rybicki, N, Hickman, R, Kollar, S, Bieber, S (1992) Chesapeake Bay Submerged Aquatic Vegetation Habitat Requirements and Restoration Targets: A Technical Synthesis. Annapolis, Maryland: Chesapeake Bay ProgramGoogle Scholar
Betsill, RK, Van Den Avyle, MJ (1994) Spatial heterogeneity of reservoir zooplankton: a matter of timing? Hydrobiologia 277:63–70CrossRefGoogle Scholar
Blanco, A, Qu, JJ, Roper, WE (2012) Spectral signatures of hydrilla from a tank and field setting. Front Earth Sci 6:453–460CrossRefGoogle Scholar
Bowes, G, Van, TK, Garrard, LA, Haller, WT (1977) Adaptation to low light levels by hydrilla. J Aquat Plant Manag 15:32–35Google Scholar
Brooks, C, Grimm, A, Shuchman, R, Sayers, M, Jessee, N (2015) A satellite-based multi-temporal assessment of the extent of nuisance Cladophora and related submerged aquatic vegetation for the Laurentian Great Lakes. Remote Sens Environ 157:58–71CrossRefGoogle Scholar
Canfield, ED, Langeland, KA Jr, Linda, SB, Haller, WT (1985) Relations between water transparency and maximum depth of macrophyte colonization in lakes. J Aquat Plant Manage 23:25–28Google Scholar
Chambers, PA, Kalff, J (1985) Depth distribution and biomass of submersed aquatic macrophyte communities in relation to Secchi depth. Can J Fish Aquat Sci 42:701–709CrossRefGoogle Scholar
Cho, H, Mishra, DR, Kirui, P. Wood, J (2012) Remote sensing of submerged aquatic vegetation. in Escalante-Ramirez, B, ed. Remote Sensing– Applications. Rijeka, Croatia: InTech Open. https://www.intechopen.com/books/remote-sensing-applications/remote-sensing-of-submerged-aquatic-vegetationGoogle Scholar
Cho, H, Ogashawara, I, Mishra, DR, White, J, Kamerosky, A, Morris, L, Clarke, C, Simpson, A, Banisakher, D (2014) Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon, FL. GISci Remote Sens 51:120–138CrossRefGoogle Scholar
Cook, CDK, Lüönd, R (1982) A revision of the genus Hydrilla (Hydrocharitaceae). Aquat Bot 13:485–504CrossRefGoogle Scholar
Dash, P, Walker, N, Mishra, DR, D’Sa, E, Ladner, S (2012) Atmospheric correction and vicarious calibration of Oceansat-1 Ocean Color Monitor (OCM) Data in Coastal Case 2 Waters. Remote Sens 4:1716–1740CrossRefGoogle Scholar
Dennison, WC (1987) Effects of light on seagrass photosynthesis, growth and depth distribution. Aquat Bot 27:15–26CrossRefGoogle Scholar
Dennison, WC, Orth, RJ, Moore, KA, Stevenson, JC, Carter, V, Kollar, S, Bergstrom, PW, Batiuk, RA (1993) Assessing water quality with submersed aquatic vegetation. Habitat requirements as barometers of Chesapeake Bay health. Bioscience 43:86–94CrossRefGoogle Scholar
Duarte, CM (1991) Seagrass depth limits. Aquat Bot 40:363–377CrossRefGoogle Scholar
Duarte, CM, Kalff, J (1987) Latitudinal influences on the depths of maximum colonization and maximum biomass of submerged angiosperms in lakes. Can J FishAquat Sci 44:1759–1764CrossRefGoogle Scholar
Fuller, LM, Aichele, SS, Minnerick, RJ (2004) Predicting Water Quality by Relating Secchi-Disk Transparency and Chlorophyll A Measurement to Satellite Imagery for Michigan Inland Lakes, August 2002. Lansing, MI: US Geological Survey Fact Sheet 2007–3022Google Scholar
Green, LT, Herron, EM, Gold, AJ (2010) URI Watershed Watch Revised Lake and Pond Monitoring Manual. Kingston, RI: College of the Environmental and Life Sciences, University of Rhode Island. Contribution #5047Google Scholar
Haller, WT, Sutton, DL, Barlowe, WC (1974) Effects of salinity on growth of several aquatic macrophytes. Ecology 55:891–894CrossRefGoogle Scholar
Hamabata, E, Kobayashi, Y (2002) Present status of submerged macrophyte growth in Lake Biwa: recent recovery following a summer decline in the water level. Lakes Reserv Res Manag 7:331–338CrossRefGoogle Scholar
Hestir, EL, Khanna, S, Andrew, ME, Santos, MJ, Viers, JH, Greenberg, JA, Rajapakse, SS, Ustin, SL (2008) Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sens Environ 112:4034–4047CrossRefGoogle Scholar
Hu, C (2009) A novel ocean color index to detect floating algae in the global oceans. Remote Sens Environ 113:2118–2129CrossRefGoogle Scholar
Jain, MS, Kalamdhad, AS (2018) A review on management of Hydrilla verticillata and its utilization as a potential nitrogen-rich biomass for compost or biogas production. Bioresour Technol Reports 1:69–78CrossRefGoogle Scholar
Jakubauskas, ME, Peterson, DL, Campbell, SW, Noyelles, F Jr Campbell, SD Penny, D (2002) Mapping and monitoring invasive aquatic plant obstructions in navigable waterways using satellite multispectral imagery. in Morain, S, Budge, A, eds. Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings. Denver, CO. Vol. XXXIV, part 1. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 9 p.Google Scholar
Kemp, WM, Batleson, R, Bergstrom, P, Carter, V, Gallegos, CL, Hunley, W, et al. (2004) Habitat requirements for submerged aquatic vegetation in Chesapeake Bay: Water quality, light regime, and physical-chemical factors. Estuaries 27:363–377CrossRefGoogle Scholar
Langeland, KA (1996) Hydrilla verticillata (LF) Royle (Hydrocharitaceae), the perfect aquatic weed. Castanea 61:293–304Google Scholar
Luo, J, Li, X, Ma, R, Li, F, Duan, H, Hu, W, Qin, B, Huang, W (2016) Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake, China. Ecol Indic 60:503–513CrossRefGoogle Scholar
Ma, R, Duan, H, Gu, X, Zhang, S (2008) Detecting aquatic vegetation changes in Taihu Lake, China using multi-temporal satellite imagery. Sensors (Basel) 8:3988–4005CrossRefGoogle ScholarPubMed
Madsen, JD, Wersal, RM (2017) A review of aquatic plant monitoring and assessment methods. Aquat Plant Manag 55:1–12Google Scholar
Malthus, TJ (2017) Bio-optical Modeling and Remote Sensing of Aquatic Macrophytes. Pages 263–308 in Mishra, DR, Ogashawara, I, Gitelson, AA, eds. Bio-optical Modeling and Remote Sensing of Inland Waters. Amsterdam, the Netherlands: ElsevierCrossRefGoogle Scholar
McCann, AJ, Arkin, LN, Williams, JD (1996) Nonindigenous Aquatic and Selected Terrestrial Species of Florida. Status, Pathway and Time of Introduction, Present Distribution, and Significant Ecological and Economic Effects. Gainesville, FL: National Biological Service, Southeastern Biological Science Center. 256 pGoogle Scholar
Middleboe, AL, Markager, S (1997) Depth limits and minimum light requirements of freshwater macrophytes. Freshwater Biol 37:553–568CrossRefGoogle Scholar
Mishra, DR, Narumalani, S, Rundquist, D, Lawson, M (2005) High resolution ocean color remote sensing of benthic habitats: a case study at the Roatan Island, Honduras. IEEE Trans Geosci Remote Sens 43:1592–1604CrossRefGoogle Scholar
NASA Giovanni (2017) The Bridge between Data and Science. https://giovanni.gsfc.nasa.gov/giovanni/. Accessed: July 13, 2017CrossRefGoogle Scholar
Nelson, SAC, Cheruvelil, KS, Soranno, PA (2006) Satellite remote sensing of freshwater macrophytes and the influence of water clarity. Aquat Bot 85:289–298CrossRefGoogle Scholar
Page, BP, Kumar, A, Mishra, DR (2018) A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom. Int J App Earth Observ Geoinf 66:69–81CrossRefGoogle Scholar
Pimentel, D, Zuniga, R, Morrison, D (2005) Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol Econ 52:273–288CrossRefGoogle Scholar
Rotta, LH, Mishra, DR, Alcântara, EH, Imai, NN (2016) Analyzing the status of submerged aquatic vegetation using novel optical parameters. Int J Remote Sens 37:3786–3810CrossRefGoogle Scholar
Rotta, LH, Mishra, DR, Watanabe, F, Rodrigues, T, Alcântara, E, Imai, NN (2018) Analyzing the feasibility of a space-borne sensor (SPOT-6) to estimate the height of submerged aquatic vegetation (SAV) in inland waters. ISPRS J Photogramm Remote Sens 144:341–356CrossRefGoogle Scholar
Sawaya, KE, Olmanson, LG, Heinert, NJ, Brezonik, PL, Bauer, ME (2003) Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sens Environ 88:144–156CrossRefGoogle ScholarPubMed
Shuchman, RA, Sayers, MJ, Brooks, CN (2013) Mapping and monitoring the extent of submerged aquatic vegetation in the Laurentian great lakes with multi-scale satellite remote sensing. J Great Lakes Res 39:78–89CrossRefGoogle Scholar
Silva, TSF, Costa, MPF, Melack, JM, Novo, EMLM (2008) Remote sensing of aquatic vegetation: theory and applications. Environ Monit Assess 140:131–145Google Scholar
Sousa, WTZ, Thomaz, SM, Murphy, KJ, Silveira, MJ, Mormul, RP (2009) Environmental predictors of the occurrence of exotic Hydrilla verticillata (L.f.) Royle and native Egeria najas Planch. in a sub-tropical river floodplain: the Upper River Paraná, Brazil. Hydrobiologia 632:65–78CrossRefGoogle Scholar
Steward, KK (1991) Light requirements for growth of monoecious hydrilla from the Potomac River. Fla Sci 54:204–214Google Scholar
Steward, KK, Van, TK (1987) Comparative studies of monoecious and dioecious hydrilla (Hydrilla) biotypes. Weed Sci 35:204–210Google Scholar
[USACE] US Army Corps of Engineers (1990) Water Quality Studies: Richard B. Russell and J. Strom Thurmond Lakes. Third Annual Interim Report. Vicksburg, MI: Waterways Experiment Station, Vicksburg, Mississippi. Misc. Paper EL-90–1Google Scholar
[USACE] US Army Corps of Engineers (2014) Aquatic Plant Management Plan for US Army Corps of Engineers, Savannah District Water Resources Projects South Carolina and Georgia. Appendix A CY 2014 Update Annual Aquatic Plant Treatment Plan and Summary of Previous Year’s Management Program. Savannah, GA: US Army Corps of EngineersGoogle Scholar
[USACE] US Army Corps of Engineers (2016) Aquatic Plant Management Plan for US Army Corps of Engineers, Savannah District Water Resources Projects South Carolina and Georgia. Appendix A CY 2016 Update Annual Aquatic Plant Treatment Plan and Summary of Previous Year’s Management Program. Savannah, GA: US Army Corps of EngineersGoogle Scholar
[USACE] US Army Corps of Engineers (2018) Savannah District Water Management, Historic Project Data. http://water.sas.usace.army.mil/gmap/historicData.cfm. Accessed: March 21, 2018CrossRefGoogle Scholar
US Geological Survey (2018a) Specimen observation data for Hydrilla verticillata (L.f.) Royle, nonindigenous aquatic species database. https://nas.er.usgs.gov/viewer/omap.aspx?SpeciesID=6. Accessed: March 15, 2018CrossRefGoogle Scholar
US Geological Survey (2018b) National Water Information System. USGS 02193900 Thurmond Lake near Plum Branch, SC. https://waterdata.usgs.gov/usa/nwis/uv?02193900. Accessed: March 3, 2019.CrossRefGoogle Scholar
Vant, WN, Davies-Colley, RJ, Clayton, JS, Coffey, BT (1986) Macrophyte depth limits in North Island (New Zealand) lakes of differing clarity. Hydrobiologia 137:55–60CrossRefGoogle Scholar
Wilde, S, Murphy, T, Hope, CP, Habrun, SK, Kempton, J, Birrenkott, A, Wiley, F, Bowerman, WW, Lewitus, AJ (2005) Avian vacuolar myelinopathy linked to exotic aquatic plants and a novel cyanobacterial species. Environ Toxicol 20:348–353CrossRefGoogle Scholar
Wilde, S, Johansen, J, Wilde, H, Jiang, P, Bartelme, B, Haynie, R (2014) Aetokthonos hydrillicola gen. et sp. nov.: epiphytic cyanobacteria on invasive aquatic plants implicated in avian vacuolar myelinopathy. Phytotaxa 181:243–260Google Scholar
Wolter, P, Johnston, C, Niemi, GJ (2007) Mapping submergent aquatic vegetation in the US Great Lakes using Quickbird satellite data. Int J Remote Sens 26:5255–5274CrossRefGoogle Scholar
Yadav, S, Yoneda, M, Susaki, J, Tamura, M, Ishikawa, K, 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 Sens 9:966Google Scholar