Wetlands Ecology and Management

, Volume 26, Issue 1, pp 63–86 | Cite as

The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware

  • Melanie K. Vanderhoof
  • Hayley E. Distler
  • Megan W. Lang
  • Laurie C. Alexander
Original Paper


The dependence of downstream waters on upstream ecosystems necessitates an improved understanding of watershed-scale hydrological interactions including connections between wetlands and streams. An evaluation of such connections is challenging when, (1) accurate and complete datasets of wetland and stream locations are often not available and (2) natural variability in surface-water extent influences the frequency and duration of wetland/stream connectivity. The Upper Choptank River watershed on the Delmarva Peninsula in eastern Maryland and Delaware is dominated by a high density of small, forested wetlands. In this analysis, wetland/stream surface water connections were quantified using multiple wetland and stream datasets, including headwater streams and depressions mapped from a lidar-derived digital elevation model. Surface-water extent was mapped across the watershed for spring 2015 using Landsat-8, Radarsat-2 and Worldview-3 imagery. The frequency of wetland/stream connections increased as a more complete and accurate stream dataset was used and surface-water extent was included, in particular when the spatial resolution of the imagery was finer (i.e., <10 m). Depending on the datasets used, 12–60% of wetlands by count (21–93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50–94% of the watershed contributing direct surface water runoff to streamflow. This finding suggests that our interpretation of the frequency and duration of wetland/stream connections will be influenced not only by the spatial and temporal characteristics of wetlands, streams and potential flowpaths, but also by the completeness, accuracy and resolution of input datasets.


Connectivity Depressions Forested wetlands Headwater streams Inundation Lidar 



This work was funded by the U.S. EPA Office of Research and Development, National Center for Environmental Assessment (EPA-USGS IA- 92410201-1, Multi-scale analyses and hydrologic simulation models of wetland/stream hydrologic connectivity in the Prairie Pothole Region). We would like to thank everyone who assisted in collecting field data for validation purposes. This includes Greg McCarty, Vincent Kim, Jason Todd, Sergio Torres, and Derek Raisanen. Thank you to Di Ana Mendiola for her help with processing the Radarsat-2 imagery. Thank you also to Jay Christensen, Charles Lane and anonymous reviewers for their valuable comments. Findings and conclusions in this presentation are those of the authors and the U.S. Geological Survey. They do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency or the U.S. Fish and Wildlife Service. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.


This work was funded by the U.S. EPA Office of Research and Development, National Center for Environmental Assessment (EPA-USGS IA- 92410201-1, Multi-scale analyses and hydrologic simulation models of wetland/stream hydrologic connectivity in the Prairie Pothole Region).

Supplementary material

11273_2017_9554_MOESM1_ESM.docx (40 kb)
Supplementary material 1 (DOCX 40 kb)


  1. Adler-Golden SM, Berk A, Bernstein LS, Richtsmeierl S, Acharyal PK, Matthew MW, Anderson GP, Allred CL, Jeong LS, Chetwynd JH (1998) FLAASH, A MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations. In: AVIRIS 1998 proceedings. JPL Publication 97–21:1–6Google Scholar
  2. Adler-Golden SM, Matthew MW, Bernstein LS, Levine RY, Berk A, Richtsmeier SC, Acharya PK, Anderson GP, Felde G, Gardner J, Hike M, Jeong LS, Pukall B, Mello J, Ratkowski A, Burke HH (1999) Atmospheric correction for shortwave spectral imagery based on MODTRAN4. SPIE Proc Imaging Spectrom V 3753:61–69CrossRefGoogle Scholar
  3. Alsdorf DE, Rodríguez E, Lettenmaier DP (2007) Measuring surface water from space. Rev Geophys 45(RG2002):1–24Google Scholar
  4. Baker ME, Weller DE, Jordan TE (2007) Effects of stream map resolution on measures of riparian buffer distribution and nutrient retention potential. Landsc Ecol 22(7):973–992CrossRefGoogle Scholar
  5. Blann KL, Anderson JL, Sands GR, Vondracek B (2009) Effects of agri-cultural drainage on aquatic ecosystems: a review. Crit Rev Environ Sci Technol 39:909–1001. doi: 10.1080/10643380801977966 CrossRefGoogle Scholar
  6. Chabot D, Bird DM (2013) Small unmanned aircraft: precise and convenient new tools for surveying wetlands. J Unmanned Veh Syst 1(1):15–24CrossRefGoogle Scholar
  7. Chu X (2015) Delineation of pothole-dominated wetlands and modeling of their threshold behaviors. J Hydrol Eng D5015003:1–11. doi: 10.1061/(ASCE)HE.1943-5584.0001224 Google Scholar
  8. Clewley D, Whitcomb J, Moghaddam M, McDonald K, Chapman B, Bunting P (2015) Evaluation of ALOS PALSAR data for high-resolution mapping of vegetated wetlands in Alaska. Remote Sens 7(6):7272–7297CrossRefGoogle Scholar
  9. Cloude S, Pottier E (1997) An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans Geosci Remote Sens 35(1):68–78CrossRefGoogle Scholar
  10. Cohen MJ, Creed IF, Alexander L, Basu NB, Calhoun AJK, Craft C, D’Amico E, DeKeyser E, Fowler L, Golden HE, Jawitz JW, Kalla P, Kirkman LK, Lane CR, Lang M, Leibowitz SG, Lewis DB, Marton J, McLaughlin DL, Mushet DM, Raanan-Kiperwas H, Rains MC, Smith L, Walls SC (2016) Do geographically isolated wetlands influence landscape functions? Proc Natl Acad Sci 113:1978–1986. doi: 10.1073/pnas.1512650113 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Creed IF, Sanford SE, Beall FD, Molot LA, Dillon PJ (2003) Cryptic wetlands: integrating hidden wetlands in regression models of the export of dissolved organic carbon from forested landscapes. Hydrol Processes 17:3629–3648CrossRefGoogle Scholar
  12. Dahl TE (1990) Wetlands losses in the United States, 1780’s to 1980’s. Report to the Congress (No. PB-91-169284/XAB). National Wetlands Inventory, St. Petersburg, FL (USA)Google Scholar
  13. De Laney TA (1995) Benefits to downstream flood attenuation and water quality as a result of constructed wetlands in agricultural landscapes. J Soil Water Conserv 50:620–626Google Scholar
  14. Díaz-Uriarte R, Alvarez de Andrés S (2006) Gene selection and classification of microarray data using random forest. BMC Bioinf 7(3):1–13Google Scholar
  15. Downing D, Nadeau TL, Kwok R (2007) Technical and scientific challenges in implementing Rapanos “Water of the United States”. Nat Resour Environ 22(1):45–63Google Scholar
  16. Epting SM (2017) Using landscape metrics to predict hydrologic connectivity patterns between forested wetlands and streams in a coastal plain watershed. Thesis, University of Maryland. doi: 10.13016/M2SB88
  17. Evenson GR, Golden HE, Lane CR, D’Amico E (2015) Geographically isolated wetlands and watershed hydrology: a modified model analysis. J Hydrol 529:240–256CrossRefGoogle Scholar
  18. Evenson GR, Golden HE, Lane CR, D’Amico E (2016) An improved representation of geographically isolated wetlands in a watershed-scale hydrologic model. Hydrol Processes. doi: 10.1002/hyp.10930 Google Scholar
  19. Fenstermacher DE, Rabenhorst MC, Lang MW, McCarty GW, Needelman BA (2014) Distribution, morphometry, and land use of Delmarva Bays. Wetlands 34(6):1219–1228CrossRefGoogle Scholar
  20. Fleiss JL (1981) Statistical methods for rates and proportions, 2nd edn. Wiley, New YorkGoogle Scholar
  21. Forbes AD (1995) Classification-algorithm evaluation: five performance measures based on confusion matrices. J Clin Monitor Comput 11(3):189–206CrossRefGoogle Scholar
  22. Freeman TA (1991) Calculating catchment-area with divergent flow based on a regular grid. Comput Geosci 17:413–422CrossRefGoogle Scholar
  23. Freeman A, Durden S (1998) A three-component scattering model for polarimetric SAR data. IEEE Trans Geosci Remote Sens 36(3):963–973CrossRefGoogle Scholar
  24. Freeman EA, Frescino TS, Moisen GG (2016) ModelMap: an R package for model creation and map production, R package version 4.6-12. R Foundation for Statistical Computing, ViennaGoogle Scholar
  25. Frohn RC, D’Amico E, Lane C, Autry B, Rhodus J, Liu H (2012) Multi-temporal sub-pixel Landsat ETM + classification of isolated wetlands in Cuyahoga County, Ohio, USA. Wetlands 32:289–299CrossRefGoogle Scholar
  26. Gleason RA, Tangen BA, Laubhan MK, Kermes KE, Euliss NH Jr (2007) Estimating water storage capacity of existing and potentially restorable wetland depressions in a subbasin of the Red River of the North. U.S. Geological Survey Open-File Report 2007–1159, 36pGoogle Scholar
  27. Golden HE, Sander HA, Lane CR, Zhao C, Price K, D’Amico E, Christensen JR (2016) Relative effects of geographically isolated wetlands on streamflow: a watershed-scale analysis. Ecohydrology 9(1):21–38CrossRefGoogle Scholar
  28. Green AA, Berman M, Switzer P, Craig MD (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26(1):65–74CrossRefGoogle Scholar
  29. Habtezion N, Nasab MT, Chu X (2016) How does DEM resolution affect microtopographic charteristics, hydrologic connectivity, and modelling of hydrologic processes? Hydrol Processes. doi: 10.1002/hyp.10967 Google Scholar
  30. Halabisky M, Moskal LM, Gillespie A, Hannam M (2016) Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984–2011). Remote Sens Environ 177:171–183CrossRefGoogle Scholar
  31. Hansen WF (2001) Identifying stream types and management implications. For Ecol Manag 143(1–3):39–46CrossRefGoogle Scholar
  32. Heine RA, Lant CL, Sengupta RR (2004) Development and comparison of approaches for automated mapping of stream channel networks. Ann Assoc Am Geogr 94(3):477–490CrossRefGoogle Scholar
  33. Hess LL, Melack JM, Affonso AG, Barbosa C, Gastil-Buhl M, Novo EMLM (2015) Wetlands of the lowland Amazon basin: extent, vegetative cover, and dual-season inundated area as mapped with JERS-1 synthetic aperture radar. Wetlands 35(4):745–756CrossRefGoogle Scholar
  34. Homer C, Dewitx J, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold N, Wickham J, Megown K (2015) Completion of the 2011 National Land Cover Database for the conterminous United States—representing a decade of land cover change information. Photogramm Eng Remote Sens 81:345–354Google Scholar
  35. Huang S, Young C, Feng M, Heidemann K, Cushing M, Mushet DM, Liu S (2011) Demonstration of a conceptual model for using LiDAR to improve the estimation of floodwater mitigation potential of Prairie Pothole Region wetlands. J Hydrol 405(3–4):417–426CrossRefGoogle Scholar
  36. Huang C, Peng Y, Lang M, Yeo IY, McCarty G (2014) Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data. Remote Sens Environ 141:231–242CrossRefGoogle Scholar
  37. James LA, Watson DG, Hansen WF (2007) Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA. Catena 71(1):132–144CrossRefGoogle Scholar
  38. Jensen AM, Hardy T, McKee M, Chen YQ (2011) Using a multispectral autonomous unmanned aerial remote sensing platform (AggieAir) for riparian and wetlands applications. In: Geoscience and remote sensing symposium (IGARSS), 2011 IEEE International 3413–3416Google Scholar
  39. Jin H, Huang C, Lang MW, Yeo IY, Stehman SV (2017) Monitoring of wetland inundation dynamics in the Delmarva Peninsula using Landsat time-series imagery from 1985 to 2011. Remote Sens Environ 190:26–41CrossRefGoogle Scholar
  40. Kahara SN, Mockler RM, Higgins KF, Chipps SR, Johnson RR (2009) Spatiotemporal patterns of wetland occurrence in the prairie pothole region of eastern South Dakota. Wetlands 29(2):678–689CrossRefGoogle Scholar
  41. Kandus P, Karszenbaum H, Pultz T, Parmuchi G, Bava J (2001) Influence of flood conditions and vegetation status on the radar backscatter of wetland ecosystems. Can J Remote Sens 6:651–662CrossRefGoogle Scholar
  42. Lane CR, D’Amico E (2010) Calculating the ecosystem service of water storage in isolated wetlands using LiDAR in North Central Florida, USA. Wetlands 30(5):967–977CrossRefGoogle Scholar
  43. Lang MW, Kasischke ES (2008) Using C-band synthetic aperture radar data to monitor forested wetland hydrology in Maryland’s Coastal Plain, USA. IEEE Trans Geosci Remote Sens 4:535–546CrossRefGoogle Scholar
  44. Lang MW, McCarty GW (2009) Lidar intensity for improved detection of inundation below the forest canopy. Wetlands 29(4):1166–1178CrossRefGoogle Scholar
  45. Lang M, McDonough O, McCarty G, Oesterling R, Wilen B (2012) Enhanced detection of wetland-stream connectivity using LiDAR. Wetlands 32:461–473CrossRefGoogle Scholar
  46. Lang M, McCarty G, Oesterling R (2013) Topographic metrics for improved mapping of forested wetlands. Wetlands 33(1):141–155CrossRefGoogle Scholar
  47. Lee JS, Pottier E (2009) Polarimetric radar imaging: from basics to applications. CRC Press, Boca RatonCrossRefGoogle Scholar
  48. Leibowitz SG, Mushet DM, Newton WE (2016) Intermittent surface water connectivity: Fill and spill versus fill and merge dynamics. Wetlands. doi: 10.1007/s13157-016-0830-z
  49. Liaw A, Wiener M (2015) Breiman and Cutler’s random forests for classification and regression, R package version 4.6-12. R Foundation for Statistical Computing, ViennaGoogle Scholar
  50. Lide RF, Meentemeyer VG, Pinder JE III, Beatty LM (1995) Hydrology of a Carolina bay located on the upper coastal plain of western South Carolina. Wetlands 15(1):47–57CrossRefGoogle Scholar
  51. Lindsay JB (2014) The Whitebox geospatial analysis tools project and open-access GIS. In: Proceedings of the GIS research UK 22nd annual conference, GlasgowGoogle Scholar
  52. Lowe WH, Likens GE (2005) Moving headwater streams to the head of the class. Bioscience 55(3):196–197CrossRefGoogle Scholar
  53. Lowrance R, Altier LS, Newbold D, Schnabel RR, Groffman PM, Denver JM, Correll DL, Gilliam JW, Robinson JL, Brinsfield RB, Staver KW, Lucas W, Todd AH (1997) Water quality functions of riparian forest buffers in Chesapeake Bay watersheds. Environ Manag 21(5):687–712CrossRefGoogle Scholar
  54. Marton JM, Creed I, Lewis D, Lane CR, Basu N, Cohen MJ, Craft C (2015) Geographically isolated wetlands are important biogeochemical reactors on the landscape. Bioscience 65(4):408–418CrossRefGoogle Scholar
  55. Masek JG, Vermote EF, Saleous N, Wolfe R, Hall EF, Huemmrich F, Gao F, Kutler J, Teng-Kui L (2006) A Landsat surface reflectance data set for North America, 1990–2000. IEEE Geosci Remote Sens Lett 3:68–72CrossRefGoogle Scholar
  56. McCauley LA, Anteau MJ (2014) Generating nested wetland catchments with readily-available digital elevation data may improve evaluations of land-use change on wetlands. Wetlands 34(6):1123–1132CrossRefGoogle Scholar
  57. McDonough OT, Lang MW, Hosen JD, Palmer MA (2015) Surface hydrologic connectivity between Delmarva Bay wetlands and nearby streams along a gradient of agricultural alteration. Wetlands 35(1):41–53CrossRefGoogle Scholar
  58. McLaughlin DL, Kaplan DA, Cohen MJ (2014) A significant nexus: geographically isolated wetlands influence landscape hydrology. Water Resour Res 50(9):7153–7166CrossRefGoogle Scholar
  59. Murphy PNC, Oglivie J, Meng FR, Arp P (2008) Stream network modelling using lidar and photogrammetric digital elevation models: a comparison and field verification. Hydrol Processes 22(12):1747–1754CrossRefGoogle Scholar
  60. Mushet DM, Calhoun AJK, Alexander LC, Cohen MJ, DeKeyser ES, Fowler L, Lane CR, Lang MW, Rains MC, Walls SC (2015) Geographically Isolated Wetlands: rethinking a Misnomer. Wetlands 35:423–431CrossRefGoogle Scholar
  61. Nachshon U, Ireson A, van der Kamp G, Davies SR, Wheater HS (2014) Impacts of climate variability on wetland salinization in the North American prairies. Hydrol Earth Syst Sci 18:1251–1263. doi: 10.5194/hess-18-1251-2014 CrossRefGoogle Scholar
  62. Nadeau TL, Rains MC (2007) Hydrological connectivity between headwater streams and downstream waters: how science can inform policy. J Am Water Resour Assoc 43(1):118–133CrossRefGoogle Scholar
  63. Niemuth ND, Wangler B, Reynolds RE (2010) Spatial and temporal variation in wet area of wetlands in the prairie pothole region of North Dakota and South Dakota. Wetlands 30:1053–1064CrossRefGoogle Scholar
  64. NOAA National Climatic Data Center (2014) Data tools: 1981–2010 normals. Accessed 28 Oct 2014
  65. Padilla M, Stehman SV, Chuvieco E (2014) Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sens Environ 144:187–196CrossRefGoogle Scholar
  66. Parmuchi M, Karszenbaum H, Kandus P (2002) Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based classifier. Can J Remote Sens 28(2):175–186CrossRefGoogle Scholar
  67. Pyzoha JE, Callahan TJ, Sun G, Trettin CC, Miwa M (2008) A conceptual hydrologic model for a forested Carolina bay depressional wetland on the Coastal Plain of South Carolina, USA. Hydrol Processes 22:2689–2698CrossRefGoogle Scholar
  68. Rains MC, Dahlgren RA, Fogg GE, Harter T, Williamson RJ (2008) Geological control of physical and chemical hydrology in California vernal pools. Wetlands 28:347–362CrossRefGoogle Scholar
  69. Rover J, Wright CK, Euliss NH Jr, Mushet DM, Wylie BK (2011) Classifying the hydrologic function of Prairie Potholes with remote sensing and GIS. Wetlands 31:319–327CrossRefGoogle Scholar
  70. Sass GZ, Creed IF (2008) Characterizing hydrodynamics on boreal landscapes using archived synthetic aperture radar imagery. Hydrol Processes 22:1687–1699CrossRefGoogle Scholar
  71. Schalles JF, Shure DJ (1989) Hydrology, community structure and productivity patterns of a dystrophic Carolina bay wetland. Ecol Monogr 59(4):365–385CrossRefGoogle Scholar
  72. Schlaffer S, Chini M, Dettmering D, Wagner W (2016) Mapping wetlands in Zambia using seasonal backscatter signatures derived from ENVISaT ASaR time series. Remote Sens 8(5):1–24Google Scholar
  73. Schmitt A, Brisco B (2013) Wetland monitoring using the curvelet-based change detection method on polarimetric SAR imagery. Water 5:1036–1051CrossRefGoogle Scholar
  74. Schmitt A, Wendleder A, Hinz S (2015) The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation. ISPRS J Photogramm Remote Sens 102:122–139CrossRefGoogle Scholar
  75. Sethre PR, Rundquist BC, Todhunter PE (2005) Remote detection of Prairie Pothole ponds in the Devils Lake basin, North Dakota. GISci Remote Sens 42:277–296CrossRefGoogle Scholar
  76. Sharit RR, Gibbons JW (1982) The ecology of evergreen shrub bogs, pocosins and Carolina bays of the Southeast: a community profile. FWS/OBS-82/04. U.S. Fish and Wildlife Service, Office of Biological Services, Washington DC, 93pGoogle Scholar
  77. Shaw DA, Vanderkamp G, Conly FM, Pietroniro A, Martz L (2012) The fill-spill hydrology of prairie wetland complexes during drought and deluge. Hydrol Processes 26:3147–3156CrossRefGoogle Scholar
  78. Shaw DA, Pietroniro A, Martz LW (2013) Topographic analysis for the prairie pothole region of Western Canada. Hydrol Processes 27:3105–3114Google Scholar
  79. Shi Z, Fung KB (1994) A comparison of digital speckle filters. In: Proceedings of IGARSS 94, August 8–12, 1994, 2129–2133Google Scholar
  80. Simon RN, Tormos T, Danis PA (2015) Very high spatial resolution optical and radar imagery in tracking water level fluctuations of a small inland reservoir. Int J Appl Earth Obs Geoinf 38:36–39CrossRefGoogle Scholar
  81. Snodgrass JW, Bryan AL Jr, Lide RF, Smith GM (1996) Factors affecting the occurrence and structure of fish assemblages in isolated wetlands of the upper coastal plain, U.S.A. Can J Fish Aquat Sci 53:443–454CrossRefGoogle Scholar
  82. Spence C (2007) On the relation between dynamic storage and runoff: a discussion on thresholds, efficiency, and function. Water Resour Res 43(W12416):1–11. doi: 10.1029/2006WR005645 Google Scholar
  83. Spence C, Phillps RW (2015) Refining understanding of hydrological connectivity in a boreal catchment. Hydrolo Processes 29(16):3491–3503CrossRefGoogle Scholar
  84. Stolt MH, Baker JC (1995) Evaluation of National Wetland Inventory maps to inventory wetlands in the southern blue ridge of Virginia. Wetlands 15:346–353CrossRefGoogle Scholar
  85. Sun G, Callahan TJ, Pyzoha JE, Trettin CC (2006) Modeling the climatic and subsurface stratigraphy controls on the hydrology of a Carolina bay wetland in South Carolina, USA. Wetlands 26(2):567–580CrossRefGoogle Scholar
  86. Tiner RW (1999) Wetland indicators: a guide to wetland identification, delineation, classification, and mapping. CRC Press, Boca RatonCrossRefGoogle Scholar
  87. Tiner RW (2003) Geographically isolated wetlands of the United States. Wetlands 23(3):494–516CrossRefGoogle Scholar
  88. Tiner RW (ed) (2009) Status report for the National Wetlands Inventory Program: 2009. U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation, Branch of Resource and Mapping Support, Arlington, VAGoogle Scholar
  89. Touzi R, Deschamps A, Rother G (2007) Wetland characterization using polarimetric RADARSAT-2 capability. Can J Remote Sens 33(1):S56–S67CrossRefGoogle Scholar
  90. Tromp-van Meerveld HJ, McDonnell JJ (2006) Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis. Water Resour Res 42(W02411):1–11Google Scholar
  91. Turin G (1960) An introduction to matched filters. IRE Trans Inf Theory 6:311–329CrossRefGoogle Scholar
  92. U.S. EPA (2015) Connectivity of streams and wetlands to downstream waters: A review and synthesis of the scientific evidence (Final Report). EPA/600/R-14/475F. U.S. Environmental Protection Agency, Washington DCGoogle Scholar
  93. U.S. Geological Survey (2000) The national hydrography dataset concepts and content.
  94. U.S. Geological Survey (2013). The National Hydrography Dataset (NHD). U.S. Geological Survey, Reston, Virginia.
  95. U.S. Army Corps of Engineers (1987) Corps of Engineers wetlands delineation manual. Wetlands Research Program Technical Report Y-87-1, Environmental Laboratory, Waterways Experiment StationGoogle Scholar
  96. U.S. Fish and Wildlife Service (2010) National Wetlands Inventory website. U.S. Department of the Interior, Fish and Wildlife Service, Washington DC.
  97. Van der Kamp G, Hayashi M (2009) Groundwater-wetland ecosystem interaction in the semiarid glaciated plains of North America. Hydrogeol J 17(1):203–214CrossRefGoogle Scholar
  98. Vanderhoof MK, Alexander LC (2015) The role of lake expansion in altering the wetland landscape of the Prairie Pothole Region. Wetlands. doi: 10.1007/s13157-015-0728-1 Google Scholar
  99. Vanderhoof MK, Alexander LC, Todd MJ (2016a) Temporal and spatial patterns of wetland extent influence variability of surface water connectivity in the Prairie Pothole Region, United States. Landsc Ecol 31(4):805–824CrossRefGoogle Scholar
  100. Vanderhoof MK, Christensen JR, Alexander LC (2016b) Patterns and drivers for wetland connections in the Prairie Pothole Region, United States. Wetl Ecol Manag. doi: 10.1007/s11273-016-9516-9 Google Scholar
  101. Vanderhoof MK, Distler HE, Mendiola DA, Lang M (2017) Integrating Radarsat-2, lidar and Worldview-3 imagery to maximize detection of forested inundation extent in the Delmarva Peninsula, USA. Remote Sens. doi: 10.3390/rs9020105 Google Scholar
  102. Wang L, Liu H (2006) An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int J Geogr Inf Sci 20(2):193–213CrossRefGoogle Scholar
  103. White DC, Lewis MM (2011) A new approach to monitoring spatial distribution and dynamics of wetlands and associated flows of Australian Great Artesian Basin springs using QuickBird satellite imagery. J Hydrol 408(1–2):140–152CrossRefGoogle Scholar
  104. White B, Ogilvie J, Campbell DMH, Hiltz D, Gauthier B, Chisholm KH, Wen HK, Murphy PNC, Arp PA (2012) Using the cartographic depth-to-water index to locate small streams and associated wet areas across landscapes. Can Water Resour J 37(4):333–347CrossRefGoogle Scholar
  105. Whiteside TG, Bartolo RE (2015) Use of WorldView-2 time series to establish a wetland monitoring program for potential offsite impacts of mine site rehabilitation. Int J Appl Earth Obs Geoinf 42:24–37CrossRefGoogle Scholar
  106. Wilcox BP, Dean DD, Jacob JS, Sipocz A (2011) Evidence of surface connectivity for Texas Gulf Coast depressional wetlands. Wetlands 31:451–458CrossRefGoogle Scholar
  107. Wu Q, Lane C, Liu H (2014) An effective method for detecting potential woodland vernal pools using high-resolution LiDAR data and aerial imagery. Remote Sens 6:11444–11467CrossRefGoogle Scholar
  108. Wu Q, Deng C, Chen Z (2016) Automated delineation of karst sinkholes from LiDAR-derived digital elevation models. Geomorphology 266:1–10CrossRefGoogle Scholar
  109. Yuan T, Lee H, Jung HC (2015) Toward estimating wetland water level changes based on hydrological sensitivity analysis of PALSAR backscattering coefficients over different vegetation fields. Remote Sens 7:3153–3183CrossRefGoogle Scholar

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© Springer Science+Business Media Dordrecht (outside the USA) 2017

Authors and Affiliations

  1. 1.Geosciences and Environmental Change Science CenterU.S. Geological SurveyDenverUSA
  2. 2.Department of Geographical SciencesUniversity of MarylandCollege ParkUSA
  3. 3.US Fish and Wildlife Service National Wetland InventoryFalls ChurchUSA
  4. 4.Office of Research and Development, National Center for Environmental AssessmentU.S. Environmental Protection AgencyWashingtonUSA

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