Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling

Estuarine, Coastal and Shelf Science

Volumn 202, Issue , 2018, Pages 193-211

  Rogers, Jeffrey N ^a,b   Parrish, Christopher E ^c,d   Ward, Larry G ^a,d   Burdick, David M ^d  

^a Univ of New Hampshire   (United States)

^b CENTER FOR COASTAL STUDIES   (United States)

^c Oregon State University   (United States)

^d UNIVERSITY OF NEW HAMPSHIRE   (United States)

Author keywords

CART; DEM correction; Random Forests; Regression trees; Spartina alterniflora; TreeNet Stochastic Gradient Boosting

Indexed keywords

ACCURACY ASSESSMENT; DIGITAL ELEVATION MODEL; ECOLOGICAL ZONATION; GRASS; LIDAR; PREDICTION; REGRESSION ANALYSIS; SALTMARSH; STOCHASTICITY; TOPOGRAPHY; UNCERTAINTY ANALYSIS; VEGETATION DYNAMICS; WAVEFORM ANALYSIS;

CAPE COD; MASSACHUSETTS; UNITED STATES;

SPARTINA ALTERNIFLORA;

EID: 85044365631     PISSN: 02727714     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecss.2017.11.034     Document Type: Article

References (88)

1. Abdullah, Q.A., A star is born: the state of new lidar technologies. Photogramm. Eng. Remote Sens. 82 (2016), 307–312.
2. Adams, T., Beets, P., Parrish, C., Extracting more data from LiDAR in forested areas by analyzing waveform shape. Remote Sens. 4 (2012), 682–702.
3. Anderson, C.M., Treshow, M., A review of environmental and genetic factors that affect height in Spartina alterniflora Loisel (Salt-Marsh Cord Grass). Estuaries 3 (1980), 168–176.
4. Anderson, J.E., Plourde, L.C., Martin, M.E., Braswell, B.H., Smith, M.L., Dubayah, R.O., Hofton, M.A., Blair, J.B., Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest. Remote Sens. Environ. 112 (2008), 1856–1870.
5. Andrew, M.E., Ustin, S.L., Habitat suitability modelling of an invasive plant with advanced remote sensing data. Divers. Distrib. 15 (2009), 627–640.
6. Artigas, F.J., Yang, J.S., Hyperspectral remote sensing of marsh species and plant vigour gradient in the New Jersey Meadowlands. Int. J. Remote Sens. 26 (2005), 5209–5220.
7. Bertness, M.D., Ellison, A.M., Determinants of pattern in a New England salt marsh plant community. Ecol. Monogr. 57 (1987), 129–147.
8. Bourennane, H., Couturier, A., Pasquier, C., Chartin, C., Hinschberger, F., Macaire, J.-J., Salvador-Blanes, S., Comparative performance of classification algorithms for the development of models of spatial distribution of landscape structures. Geoderma 219–220 (2014), 136–144.
9. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
10. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., Classification and Regression Trees. 1984, Wadsworth, Belmont, CA.
11. Bretar, F., Chauve, A., Bailly, J.S., Mallet, C., Jacome, A., Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands. Hydrol. Earth Syst. Sci. 13 (2009), 1531–1544.
12. Brock, J., Sallenger, A., Airborne Topographic LIDAR Mapping for Coastal and Resource Management. U.S. Geological Survey Open File Report, 01-46, 2001, 4.
13. Buffington, K.J., Dugger, B.D., Thorne, K.M., Takekawa, J.Y., Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes. Remote Sens. Environ. 186 (2016), 616–625.
14. Burrough, P.A., McDonell, R.A., Principles of Geographical Information Systems. 1998, Oxford University Press, New York.
15. Byrd, K.B., Kelly, M., Salt marsh vegetation response to edaphic and topographic changes from upland sedimentation in a pacific estuary. Wetlands 26 (2006), 813–829.
16. Chaplot, V., Darboux, F., Bourennane, H., Leguedois, S., Silvera, N., Phachomphon, K., Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology 77 (2006), 126–141.
17. Chu-Agor, M.L., Munoz-Carpena, R., Kiker, G., Emanuelsson, A., Linkov, I., Exploring vulnerability of coastal habitats to sea level rise through global sensitivity and uncertainty analyses. Environ. Model. Softw. 26 (2011), 593–604.
18. Cooper, H.M., Fletcher, C.H., Chen, Q., Barbee, M.M., Sea-level rise vulnerability mapping for adaptation decisions using LiDAR DEMs. Prog. Phys. Geogr. 37 (2013), 745–766.
19. Costanza, R., D'Arge, R., DeGroot, R., Farber, S., Grasso, M., The value of the world's ecosystem services and natural capital. Nature 387 (1997), 253–280.
20. Crooks, S., Schutten, J., Sheern, G.D., Pye, K., Davy, A.J., Drainage and elevation as factors in the restoration of salt marsh in Britain. Restor. Ecol. 10 (2002), 591–602.
21. Derrig, R., Francis, L., Distinguishing the forest from the TREES: a comparison of tree-based data mining methods. Variance 2 (2008), 184–208.
22. Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A., Chazdon, R.L., Weishampel, J.F., Prince, S.D., Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sens. Environ. 79 (2002), 305–319.
23. Fagherazzi, S., Carniello, L., D'Alpaos, L., Defina, A., Critical bifurcation of shallow microtidal landforms in tidal flats and salt marshes. Proc. Natl. Acad. Sci. U. S. A. 103 (2006), 8337–8341.
24. Fernandez-Nunez, M., Burningham, H., Ojeda Zujar, J., Improving accuracy of LiDAR-derived digital terrain models for saltmarsh management. J. Coast. Conserv. (Springer Sci. Bus. Media B.V.) 21 (2017), 209–222.
25. Friedman, J.H., Multivariate adaptive regression splines. Ann. Stat. 19 (1991), 1–67.
26. Friedman, J.H., Stochastic gradient boosting. Comput. Stat. Data Anal. 38 (2002), 367–378.
27. Friedman, J.H., Fast sparse regression and classification. Int. J. Forecast. 28 (2012), 722–738.
28. Gopfert, J., Heipke, C., Assessment of Lidar DTM in coastal vegetated areas. Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci. 36 (2006), 79–85.
29. Graham, D., Sault, M., Bailey, C.J., National Ocean Service shoreline - past, present, and future. J. Coast. Res., 2003, 14–32.
30. Griffin, P.J., Theodose, T., Dionne, M., Landscape patterns of forb pannes across a northern New England salt marsh. Wetlands 31 (2011), 25–33.
31. Gutierrez, A.G., Schnabel, S., Contador, J.F.L., Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecol. Model. 220 (2009), 3630–3637.
32. Hladik, C., Alber, M., Accuracy assessment and correction of a LIDAR-derived salt marsh digital elevation model. Remote Sens. Environ. 121 (2012), 224–235.
33. Hladik, C., Alber, M., Classification of salt marsh vegetation using edaphic and remote sensing-derived variables. Estuar. Coast. Shelf Sci. 141 (2014), 47–57.
34. Hladik, C., Schalles, J., Alber, M., Salt marsh elevation and habitat mapping using hyperspectral and LIDAR data. Remote Sens. Environ. 139 (2013), 318–330.
35. Jutzi, B., Stilla, U., Range determination with waveform recording laser systems using a Wiener Filter. ISPRS J. Photogram. Remote Sens. 61 (2006), 95–107.
36. Kirwan, M.L., Murray, A.B., Donnelly, J.P., Corbett, D.R., Rapid wetland expansion during European settlement and its implication for marsh survival under modern sediment delivery rates. Geology 39 (2011), 507–510.
37. Konisky, R., Role of simulation models in understanding the salt marsh restoration process. Roman, C.T., Burdick, D., (eds.) Tidal Marsh Restoration: a Synthesis of Science and Practice, 2012, Island Press, Washington, 253–276.
38. Lefor, M.W., Kennard, W.C., Civco, D.L., Relationship of salt marsh plant distributions to tidal levels in Connecticut, USA. Environ. Manag. 11 (1987), 61–68.
39. Lefsky, M.A., Cohen, W.B., Parker, G.G., Harding, D.J., Lidar remote sensing for ecosystem studies. BioScience 52 (2002), 19–30.
40. Mallet, C., Bretar, F., Full-waveform topographic lidar: state-of-the-art. ISPRS J. Photogram. Remote Sens. 64 (2009), 1–16.
41. McClure, A., Liu, X., Hines, E., Ferner, M.C., Evaluation of error reduction techniques on a LIDAR-derived salt marsh digital elevation model. J. Coast. Res. 32 (2016), 424–433.
42. Mckee, K., Patrick, J.W.H., The relationship of smooth cordgrass (Spartina alterniflora) to tidal datums: a review. Estuaries 11 (1988), 143–151.
43. Medeiros, S., Hagen, S., Weishampel, J., Angelo, J., Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density. Remote Sens. 7 (2015), 3507–3525.
44. Meissner, K., Fiorentino, D., Schnurr, S., Arbizu, P.M., Huettmann, F., Holst, S., Brix, S., Svavarsson, J., Distribution of benthic marine invertebrates at northern latitudes - an evaluation applying multi-algorithm species distribution models. J. Sea Res. 85 (2014), 241–254.
45. Mendelssohn, I.A., McKee, K.L., Patrick, W.H., Oxygen deficiency in Spartina alterniflora roots - metabolic adaptation to anoxia. Science 214 (1981), 439–441.
46. Mitsch, W.J., Gosselink, J.G., Wetlands. third ed., 2000, John Wiley & Sons, New Jersey.
47. Montane, J.M., Torres, R., Accuracy assessment of lidar saltmarsh topographic data using RTK GPS. Photogramm. Eng. Remote Sens. 72 (2006), 961–967.
48. Morris, J.T., Porter, D., Neet, M., Noble, P., Schmidt, L., Lapine, L.A., Jensen, J.R., Integrating Lidar elevation data, multispectral imagery and neural network modeling for marsh characterization. Int. J. Remote Sens. 26 (2005), 5221–5234.
49. Morris, J.T., Sundareshwar, P.V., Nietch, C.T., Kjerfve, B., Cahoon, D.R., Responses of coastal wetlands to rising sea level. Ecology 83 (2002), 2869–2877.
50. Morsy, S., Shaker, A., El-Rabbany, A., LaRocque, P.E., AIRBORNE multispectral lidar data for land-cover classification and land/water mapping using different spectral indexes. ISPRS Ann. Photogram. Remote Sens. Spatial Inf. Sci., 3, 2016.
51. Muss, J.D., Aguilar-Amuchastegui, N., Mladenoff, D.J., Henebry, G.M., Analysis of waveform lidar data using shape-based metrics. IEEE Geosci. Remote Sens. Lett. 10 (2013), 106–110.
52. NOAA, Tides and Currents. 2013, NOAA Center for Operational Oceanographic Products and Services http://tidesandcurrents.noaa.gov/station_info.shtml?stn=8446121.
53. NOAA, NOAA Continually Updated Shoreline Product (CUSP). 2016, National Oceanic and Atmospheric Administration https://shoreline.noaa.gov/data/datasheets/cusp.html.
54. NOAA, Benthic Terrain Modeler. 2017, NOAA Office for Coastal Management https://coast.noaa.gov/digitalcoast/tools/btm.
55. NOAA, Estimation of Vertical Uncertainties in VDatum. 2017, NOAA National Ocean Service https://vdatum.noaa.gov/docs/est_uncertainties.html.
56. Ornes, W.H., Kaplan, D.I., Macronutrient status of tall and short forms of Spartina alterniflora in a South Carolina salt marsh. Mar. Ecol. Prog. Ser. 55 (1989), 63–72.
57. Parrish, C.E., Jeong, I., Nowak, R.D., Smith, R.B., Empirical comparison of full-waveform lidar algorithms: range extraction and discrimination performance. Photogramm. Eng. Remote Sens. 77 (2011), 825–838.
58. Parrish, C.E., Rogers, J.N., Calder, B.R., Assessment of waveform features for lidar uncertainty modeling in a coastal salt marsh environment. IEEE Geosci. Remote Sens. Lett. 11 (2014), 569–573.
59. Pennings, S.C., Bertness, M.D., Salt marsh communities. Bertness, M.D., Gaines, S.D., Hay, M.E., (eds.) Marine Community Ecology, 2001, Sinauer Associates Inc., Sunderland, MA, 289–316.
60. Populus, J., Barreau, G., Faxilleau, J., Kerdreux, M., L'Yavanc, J., Assessment of the Lidar topographic technique over a coastal area. Proceedings of CoastGIS’01: 4th International Symposium on GIS and Computer Mapping for Coastal Zone Management, Halifax, Nova Scotia, 2001, 11.
61. Portnoy, J.W., Roman, C.T., Smith, S.M., Gwilliam, E., Estuarine habitat restoration at Cape Cod National Seashore: the Hatches Harbor prototype. Park Sci., 22, 2003, 8.
62. Reimold, R.J., Gallagher, J.l., Thompson, D.E., Remote sensing of tidal marsh. Photogramm. Eng. Remote Sens. 39 (1973), 477–488.
63. Ries, L., Areas of influence for IDW-interpolation with isotropic environmental data. Catena 20 (1993), 199–205.
64. Rogers, J.N., Parrish, C.E., Ward, L.G., Burdick, D.M., Evaluation of field-measured vertical obscuration and full waveform lidar to assess salt marsh vegetation biophysical parameters. Remote Sens. Environ. 156 (2015), 264–275.
65. Rogers, J.N., Parrish, C.E., Ward, L.G., Burdick, D.M., Assessment of elevation uncertainty in salt marsh environments using discrete-return and full-waveform lidar. J. Coast. Res. Special Issue 76 (2016), 107–122.
66. Roman, C.T., Burdick, D., Tidal Marsh Restoration: a Synthesis of Science and Practice. 2012, Island Press, Washington.
67. Rosso, P.H., Ustin, S.L., Hastings, A., Use of lidar to study changes associated with Spartina invasion in San Francisco Bay marshes. Remote Sens. Environ. 100 (2006), 295–306.
68. Sadro, S., Gastil-Buhl, M., Melack, J., Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations. Remote Sens. Environ. 110 (2007), 226–239.
69. Sanderson, E.W., Foin, T.C., Ustin, S.L., A simple empirical model of salt marsh plant spatial distributions with respect to a tidal channel network. Ecol. Model. 139 (2001), 293–307.
70. Sappington, J.M., Longshore, K.M., Thompson, D.B., Quantifying landscape ruggedness for animal habitat analysis: a case study using bighorn sheep in the Mojave Desert. J. Wildl. Manag. 71 (2007), 1419–1426.
71. Schmid, K.A., Hadley, B.C., Wijekoon, N., Vertical accuracy and use of topographic LIDAR data in coastal marshes. J. Coast. Res. 27 (2011), 116–132.
72. Schmidt, K.S., Skidmore, A.K., Spectral discrimination of vegetation types in a coastal wetland. Remote Sens. Environ. 85 (2003), 92–108.
73. Sellars, J.D., Jolls, C.L., Habitat modeling for Amaranthus pumilus: an application of light detection and ranging (LIDAR) data. J. Coast. Res. 23 (2007), 1193–1202.
74. Stewart, J.P., Hu, J., Kayen, R.E., Lembo, A.J. Jr., Collins, B.D., Davis, C.A., O'Rourke, T.D., Use of airborne and terrestrial lidar to detect ground displacement hazards to water systems. J. Surv. Eng. 135 (2009), 113–124.
75. Stoker, J.M., Abdullah, Q.A., Nayegandhi, A., Winehouse, J., Evaluation of single photon and Geiger mode Lidar for the 3D Elevation Program. Remote Sens., 8, 2016, 767.
76. Tayyebi, A., Pijanowski, B.C., Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int. J. Appl. Earth Obs. Geoinf. 28 (2014), 102–116.
77. Timm, B.C., McGarigal, K., Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests. Remote Sens. Environ. 127 (2012), 106–117.
78. Torres, R., Styles, R., Effects of topographic structure on salt marsh currents. J. Geophys. Res. Earth Surf., 112, 2007, 14.
79. Wagner, W., Hollaus, M., Briese, C., Ducic, V., 3D vegetation mapping using small-footprint full-waveform airborne laser scanners. Int. J. Remote Sens. 29 (2008), 1433–1452.
80. Wang, C., Menenti, M., Stoll, M.-P., Belluco, E., Marani, M., Mapping mixed vegetation communities in salt marshes using airborne spectral data. Remote Sens. Environ. 107 (2007), 559–570.
81. Wang, C., Menenti, M., Stoll, M.P., Feola, A., Belluco, E., Marani, M., Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments. IEEE Trans. Geosci. Remote Sens. 47 (2009), 2014–2023.
82. White, S.A., Parrish, C.E., Calder, B.R., Pe'eri, S., Rzhanov, Y., LIDAR-derived national shoreline: empirical and stochastic uncertainty analyses. J. Coast. Res., 2011, 62–74.
83. Wiegert, R.G., Freeman, B.J., Tidal Salt Marshes of the Southeast Atlantic Coast: a Community Profile. 1990, U.S. Dept. of the Interior, Fish and Wildlife Service, Washington, D.C.
84. Wright, C.W., Kranenburg, C., Battista, T.A., Parrish, C., Depth calibration and validation of the experimental advanced airborne research LiDAR, EAARL-B. J. Coast. Res. SI 76 (2016), 4–17.
85. Yang, Z., Myers, E., Jeong, I., White, S., VDatum for the Gulf of Maine: Tidal Datums and Topography of the Sea Surface. NOAA Technical Memorandum NOS CS 31, 2013.
86. Zedler, J.B., Callaway, J.C., Desmond, J.S., Vivian-Smith, G., Williams, G.D., Sullivan, G., Californian salt-marsh vegetation: an improved model of spatial pattern. Ecosystems 2 (1999), 19–35.
87. Zevenbergen, L.W., Thorne, C.R., Quantitative analysis of land surface topography. Earth Surf. Process. Landforms 12 (1987), 47–56.
88. Zhang, C.S., Liu, C.C., Zhang, X.L., Almpanidis, G., An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst. Appl. 82 (2017), 128–150.