Skip to main content
Log in

Identification of most spectrally distinguishable phenological stage of invasive Phramites australis in Lake Erie wetlands (Canada) for accurate mapping using multispectral satellite imagery

  • Original Paper
  • Published:
Wetlands Ecology and Management Aims and scope Submit manuscript

Abstract

Phragmites australis (Cav.) Trin. ex Steudel subspecies australis is one of the worst plant invaders in wetlands of North America. Remote sensing is the most cost-effective method to track its spread given its widespread distribution and rapid colonization rate. We hypothesize that the morphological and/or physiological features associated with different phenological states of Phragmites can influence their reflectance signal and thus affect mapping accuracies. We tested this hypothesis by comparing classification accuracies of cloud-free images acquired by Landsat 7, Landsat 8, and Sentinel 2 at roughly monthly intervals over a calendar year for two wetlands in southern Ontario. We used the Support Vector Machines classification and employed field observations and image acquired from unmanned aerial vehicle (8 cm) to perform accuracy assessments. The highest Phragmites producer’s, user’s, and overall accuracy (96.00, 91.11, and 88.56% respectively) were provided by images acquired in late summer and fall period. During this period, green, Near Infrared, and Short-Wave Infrared bands generated more unique reflectance signals for Phragmites. Both Normalized Difference Vegetation Index and Normalized Difference Water Index showed significant difference between Phragmites and the most confused classes (cattail; Typha latifolia L., and meadow marsh) during the late summer and fall period. Since meadow marsh separated out best from Phragmites and cattail in the February image, we used it to mask the meadow marsh in the July image to reduce confusion. The unique reflectance signal of Phragmites in late summer and fall is likely due to prolonged greenness of Phragmites when compared to other wetland vegetation, large, distinct inflorescence, and the water content of Phragmites during this period.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Adam E, Mutanga O, Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetl Ecol Manag 18:281–296

    Article  Google Scholar 

  • Ashley EP, Robinson JT (1996) Road mortality of amphibians, reptiles and other wildlife on the Long Point Causeway, Lake Erie, Ontario. Can Field Nat 110:403–412

    Google Scholar 

  • Bellavance M-E, Brisson J (2010) Spatial dynamics and morphological plasticity of common reed (Phragmites australis) and cattails (Typha sp.) in freshwater marshes and roadside ditches. Aquat Bot 93:129–134

    Article  Google Scholar 

  • Bostater CR, Ghir T, Bassetti L, Hall C, Reyeier E, Lowers R, Holloway-Adkins K, Virnstein R (2004) Hyperspectral remote sensing protocol development for submerged aquatic vegetation in shallow waters. Proceedings of SPIE 5233, Remote Sensing of the Ocean and Sea Ice 2003; Event: Remote Sensing, 2003, Barcelona, Spain 5233:199–215. https://doi.org/10.1117/12.541191

  • Bourgeau-Chavez L, Endres S, Battaglia M et al (2015) Development of a bi-national Great Lakes coastal wetland and land use map using three-season PALSAR and Landsat imagery. Remote Sens 7:8655–8682

    Article  Google Scholar 

  • Chambers RM, Meyerson LA, Saltonstall K (1999) Expansion of Phragmites australis into tidal wetlands of North America. Aquat Bot 64:261–273

    Article  Google Scholar 

  • Dalponte M, Bruzzone L, Gianelle D (2008) Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Trans Geosci Remote Sens 46:1416–1427

    Article  Google Scholar 

  • Delegido J, Verrelst J, Alonso L, Moreno J (2011) Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 11:7063–7081

    Article  PubMed  Google Scholar 

  • Dewey SA, Price KP, Ramsey D (1991) Satellite remote sensing to predict potential distribution of dyers woad (Isatis tinctoria). Weed Technol 5:479–484

    Article  Google Scholar 

  • Environment and Climate Change Canada (2011) Big Creek National Wildlife Area. In: aem. https://www.canada.ca/en/environment-climate-change/services/national-wildlife-areas/locations/big-creek.html. Accessed 26 Mar 2018

  • Everitt JH, Anderson GL, Escobar DE et al (1995) Use of remote sensing for detecting and mapping leafy spurge (Euphorbia esula). Weed Technol 9:599–609

    Article  Google Scholar 

  • Everitt JH, Escobar DE, Alaniz MA et al (1996) Using spatial information technologies to map Chinese tamarisk (Tamarix chinensis) Infestations. Weed Sci 44:194–201

    Article  CAS  Google Scholar 

  • Everitt JH, Escobar DE, Davis MR (2001) Reflectance and image characteristics of selected noxious rangeland species. J Range Manag 54:208–208

    Article  Google Scholar 

  • Fabre S, Lesaignoux A, Olioso A, Briottet X (2011) Influence of water content on spectral reflectance of leaves in the 3–15 μm domain. IEEE Geosci Remote Sens Lett 8:143–147

    Article  Google Scholar 

  • Sanchez-Flores E, Rodriguez-Gallegos H, Yool SR (2008) Plant invasions in dynamic desert landscapes. A field and remote sensing assessment of predictive and change modeling. J Arid Environ 72(3):189–206

    Article  Google Scholar 

  • Frampton WJ, Dash J, Watmough G, Milton EJ (2013) Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J Photogramm Remote Sens 82:83–92

    Article  Google Scholar 

  • Fuller DO (2005) Remote detection of invasive Melaleuca trees (Melaleuca quinquenervia) in South Florida with multispectral IKONOS imagery. Int J Remote Sens 26:1057–1063

    Article  Google Scholar 

  • Gao J, Liu Y (2008) Mapping of land degradation from space: a comparative study of Landsat ETM+ and ASTER data. Int J Remote Sens 29(14):4029–4043

    Article  Google Scholar 

  • Gibert JM (2015) Rondeau Provincial Park Invasive Phragmites Management Program 2008–2014 Summary Report and Recommended Next Steps (Unpublished manuscript)

  • Glass WR, Corkum LD, Mandrak NE (2012) Spring and summer distribution and habitat use by adult threatened spotted gar in Rondeau Bay, Ontario, using radiotelemetry. Trans Am Fish Soc 141:1026–1035

    Article  Google Scholar 

  • Gualtieri JA, Cromp RF (1999) Support vector machines for hyperspectral remote sensing classification. In: 27th AIPR Workshop: Advances in Computer-Assisted Recognition. International Society for Optics and Photonics, pp 221–233

  • Guerschman JP, Paruelo JM, Bella CD et al (2003) Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data. Int J Remote Sens 24:3381–3402

    Article  Google Scholar 

  • Harris Geospatial (2018) Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) https://www.harrisgeospatial.com/docs/FLAASH.html Accessed 20 June 2018

  • Hestir EL, Khanna S, Andrew ME et al (2008) Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sens Environ 112:4034–4047. https://doi.org/10.1016/j.rse.2008.01.022

    Article  Google Scholar 

  • Hill MJ (2013) Vegetation index suites as indicators of vegetation state in grassland and savanna: an analysis with simulated SENTINEL 2 data for a North American transect. Remote Sens Environ 137:94–111

    Article  Google Scholar 

  • Holbrook JE (1838) North American herpetology, vol 4. J. Dobson, Philadelphia

    Google Scholar 

  • Huang C, Asner GP (2009) Applications of remote sensing to alien invasive plant studies. Sensors 9:4869–4889. https://doi.org/10.3390/s90604869

    Article  PubMed  Google Scholar 

  • Huang H, Gong P, Clinton N, Hui F (2008) Reduction of atmospheric and topographic effect on Landsat TM data for forest classification. Int J Remote Sens 29:5623–5642

    Article  Google Scholar 

  • Immitzer M, Vuolo F, Atzberger C (2016) First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens 8:166

    Article  Google Scholar 

  • Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159

    Article  Google Scholar 

  • Laba M, Downs R, Smith S et al (2008) Mapping invasive wetland plants in the hudson river national estuarine research reserve using quickbird satellite imagery. Remote Sens Environ 112:286–300

    Article  Google Scholar 

  • Lelong B, Lavoie C, Jodoin Y, Belzile F (2007) Expansion pathways of the exotic common reed (Phragmites australis): a historical and genetic analysis. Divers Distrib 13:430–437

    Article  Google Scholar 

  • Lopez RD, Edmonds CM, Neale AC, Slonecker T, Jones KB, Heggem DT, Lyon JG, Jaworski E, Garofalo D, Williams D (2004) Accuracy assessments of airborne hyper-spectral data for mapping opportunistic plant species in fresh-water coastal wetlands. In: Lunetta RS, Lyon JG (eds) Remote sensing and GIS accuracy assessment. CRC Press, New York, pp 253–267

    Chapter  Google Scholar 

  • Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28:823–870

    Article  Google Scholar 

  • Lyon JG, McCarthy J (1995) Wetland and environmental applications of GIS. CRC Press, Boca Raton

    Google Scholar 

  • Mann DL, Nelson JG (1980) Ideology and wildlands management: the case of Rondeau Provincial Park, Ontario. Environ Manag 4:111–124

    Article  Google Scholar 

  • Marcaccio JV, Chow-Fraser P (2016) Mapping options to track invasive Phragmites australis in the Great Lakes basin in Canada. Proceedings of 3rd International Conference “Water resources and wetlands”. Tulcea

  • Marcaccio, Chow-Fraser P (2018) Mapping invasive Phragmites australis in highway corridors using provincial orthophoto databases in Ontario

  • Marcaccio JV, Markle CE, Chow-Fraser P (2016) Use of fixed-wing and multi-rotor unmanned aerial vehicles to map dynamic changes in a freshwater marsh. J Unmanned Veh Syst 4:193–202

    Article  Google Scholar 

  • Markle CE, Chow-Fraser P (2018) Effects of European common reed on Blanding’s turtle spatial ecology. J Wildl Manag 25:15. https://doi.org/10.1002/jwmg.21435

    Article  Google Scholar 

  • Marks M, Lapin B, Randall J (1994) Phragmites australis (P. communis): threats, management and monitoring. Nat Areas J 14:285–294

    Google Scholar 

  • McLaughlin C (1993) Ecosystem management in Rondeau Provincial Park. Altern J 19:6

    Google Scholar 

  • McNabb CD, Batterson TR (1991) Occurrence of the common reed, Phragmites australis, along roadsides in Lower Michigan. Alma, Mich Acad

    Google Scholar 

  • Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790. https://doi.org/10.1109/TGRS.2004.831865

    Article  Google Scholar 

  • Meloche C, Murphy SD (2006) Managing tree-of-heaven (Ailanthus altissima) in parks and protected areas: a case study of Rondeau Provincial Park (Ontario, Canada). Environ Manag 37:764–772. https://doi.org/10.1007/s00267-003-0151-x

    Article  Google Scholar 

  • Mestre H (1935) The absorption of radiation by leaves and algae. Cold Spring Harb Symp Quant Biol 3:191–209. https://doi.org/10.1101/SQB.1935.003.01.023

    Article  CAS  Google Scholar 

  • Meyerson LA, Saltonstall K, Windham L et al (2000) A comparison of Phragmites australis in freshwater and brackish marsh environments in North America. Wetl Ecol Manag 8:89–103

    Article  CAS  Google Scholar 

  • Morel A, Bélanger S (2006) Improved detection of turbid waters from ocean color sensors information. Remote Sens Environ 102:237–249

    Article  Google Scholar 

  • Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

    Article  Google Scholar 

  • Oetter DR, Cohen WB, Berterretche M et al (2001) Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data. Remote Sens Environ 76:139–155

    Article  Google Scholar 

  • Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetl Ecol Manag 10:381–402

    Article  Google Scholar 

  • Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J Remote Sens 26(5):1007–1011

    Article  Google Scholar 

  • Peterson EB (2005) Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data. Int J Remote Sens 26:2491–2507. https://doi.org/10.1080/01431160500127815

    Article  Google Scholar 

  • Rasolofoharinoro M, Blasco F, Bellan MF, Aizpuru M, Gauquelin T, Denis J (1998) A remote sensing based methodology for mangrove studies in Madagascar. Int J Remote Sens 19(10):1873–1886

    Article  Google Scholar 

  • Resasco J, Hale AN, Henry MC, Gorchov DL (2007) Detecting an invasive shrub in a deciduous forest understory using late-fall Landsat sensor imagery. Int J Remote Sens 28:3739–3745

    Article  Google Scholar 

  • Rickey MA, Anderson RC (2004) Effects of nitrogen addition on the invasive grass Phragmites australis and a native competitor Spartina pectinata. J Appl Ecol 41:888–896

    Article  Google Scholar 

  • Rupasinghe PA, Simic Milas A, Arend K, Simonson MA, Mayer C, Mackey S (2018) Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data. Int J Remote Sens 40:3008

    Article  Google Scholar 

  • Saltonstall K (2002) Cryptic invasion by a non-native genotype of the common reed, Phragmites australis, into North America. Proc Natl Acad Sci 99:2445–2449

    Article  CAS  PubMed  Google Scholar 

  • Schmidt KS, Skidmore AK (2001) Exploring spectral discrimination of grass species in African rangelands. Int J Remote Sens 22:3421–3434

    Article  Google Scholar 

  • Sinclair TR, Schreiber MM, Hoffer RM (1968) Pathway of solar radiation through leaves 1. Agron J 65(2):276–283

    Article  Google Scholar 

  • Sohn Y, McCoy RM (1997) Mapping desert shrub rangeland using spectral unmixing and modeling spectral mixtures with TM data. Photogramm Eng Remote Sens 63:707–716

    Google Scholar 

  • Stratoulias D, Balzter H, Sykioti O et al (2015) Evaluating sentinel-2 for lakeshore habitat mapping based on airborne hyperspectral data. Sensors 15:22956–22969

    Article  PubMed  Google Scholar 

  • Tottrup C (2004) Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing. Int J Remote Sens 25:717–730

    Article  Google Scholar 

  • Tuanmu M-N, Viña A, Bearer S et al (2010) Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sens Environ 114:1833–1844

    Article  Google Scholar 

  • Tulbure MG, Johnston CA, Auger DL (2007) Rapid invasion of a Great Lakes coastal wetland by non-native Phragmites australis and Typha. J Gt Lakes Res 33:269–279

    Article  Google Scholar 

  • Uddin M, Robinson RW, Caridi D et al (2014) Suppression of native Melaleuca ericifolia by the invasive Phragmites australis through allelopathic root exudates. Am J Bot 101:479–487

    Article  Google Scholar 

  • Vapnik VN, Kotz S (1982) Estimation of dependences based on empirical data. Springer, New York

    Google Scholar 

  • Wilcox DA (2012) Response of wetland vegetation to the post-1986 decrease in Lake St. Clair water levels: seed-bank emergence and beginnings of the Phragmites australis invasion. J Gt Lakes Res 38:270–277

    Article  Google Scholar 

  • Wilcox I (n.d.) Wetland Meadow Marsh Community - surface area, supply-based (Lake Ontario & Thousand Islands). http://www.losl.org/twg/pi/pi_meadowmarsh-e.html. Accessed 17 May 2018

  • Wilcox KL, Petrie SA, Maynard LA, Meyer SW (2003) Historical distribution and abundance of Phragmites australis at long point, Lake Erie, Ontario. J Gt Lakes Res 29:664–680

    Article  Google Scholar 

  • Williams AP, Hunt ER Jr (2002) Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens Environ 82:446–456

    Article  Google Scholar 

  • Wilstxtter R, Stoll A (1918) Untersuchungen fiber die Assimilation derKohlensiiure. Julius Springer Berl 344

  • Young BE, Young G, Hogg AR (2011) Using Landsat TM NDVI change detection to identify Phragmites infestation in southern Ontario coastal wetlands. Ont. Min. Nat. Resour., Inventory Monitoring and Assessment, Peterborough: 32

  • Zhang X, Friedl MA, Schaaf CB et al (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–475

    Article  Google Scholar 

  • Zhang C, Xie Z (2013) Object-based vegetation mapping in the Kissimmee River watershed using HyMap data and machine learning techniques. Wetlands 33(2):233–244. https://doi.org/10.1007/s13157-012-0373-x

    Article  Google Scholar 

  • Zheng B, Myint SW, Thenkabail PS, Aggarwal RM (2015) A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. Int J Appl Earth Obs Geoinform 34:103–112. https://doi.org/10.1016/j.jag.2014.07.002

    Article  Google Scholar 

Download references

Acknowledgements

Partial funding for this study came from a grant to PC-F from the Highway Infrastructure Innovation Funding Program from the Ministry of Transportation of Ontario. We thank C. Markle and J. Marcaccio for their assistance in assembling relevant data for this study. We are grateful to the helpful comments provided by anonymous reviewers on an earlier draft of this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prabha Amali Rupasinghe.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rupasinghe, P.A., Chow-Fraser, P. Identification of most spectrally distinguishable phenological stage of invasive Phramites australis in Lake Erie wetlands (Canada) for accurate mapping using multispectral satellite imagery. Wetlands Ecol Manage 27, 513–538 (2019). https://doi.org/10.1007/s11273-019-09675-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11273-019-09675-2

Keywords

Navigation