Wetlands

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Fine-Scale Monitoring of Long-term Wetland Loss Using LiDAR Data and Historical Aerial Photographs: the Example of the Couesnon Floodplain, France

  • Sébastien Rapinel
  • Bernard Clément
  • Simon Dufour
  • Laurence Hubert-Moy
Original Research

Abstract

Wetland area has decreased in most parts of the world and remains threatened by human pressures. However, wetland loss is difficult to accurately detect, delineate and quantify. While wetland distribution is influenced mainly by landform, LiDAR data provide accurate digital elevation models that can be used to delineate wetlands. Our objective was to map wetland loss at a fine-scale using LiDAR data and historical aerial photographs based on a functional typology that identifies potential, existing and efficient wetlands. The study focused on a 132 km2 site with valley bottom wetlands located in western France. Boundaries of potential wetlands were extracted from a LiDAR-derived Digital Terrain Model that was standardized according to channel network elevation. We identified existing wetlands using interpretation of aerial photographs acquired in 1952, 1978 and 2012. We used multiple correspondence analysis to identify different types of wetland loss. Results show that potential wetlands were successfully delineated at 1:5000 (88–90% overall accuracy) and that 14% of existing wetland area was lost. This highlights the importance of identifying “negotiation areas” where wetland restoration is a priority. The results also reveal two main types of wetland loss based on area, geomorphic context, land cover and period of loss.

Keywords

Conservation Digital terrain model Environmental management Remote sensing Wetland delineation 

Notes

Acknowledgments

This study was funded by the Centre National de la Recherche Scientifique (Zone Atelier program) and the French Ministry of Ecology and Sustainable Development (CarHAB program). The authors are grateful to Jean Nabucet (LETG Rennes) for his help with fieldwork.

References

  1. Adams PN, Slingerland RL, Smith ND (2004) Variations in natural levee morphology in anastomosed channel flood plain complexes. Geomorphology 61:127–142.  https://doi.org/10.1016/j.geomorph.2003.10.005 CrossRefGoogle Scholar
  2. Alber A, Piégay H (2011) Spatial disaggregation and aggregation procedures for characterizing fluvial features at the network-scale: Application to the Rhône basin (France). Geomorphology 125:343–360.  https://doi.org/10.1016/j.geomorph.2010.09.009
  3. Beven K, Kirkby M (1979) A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24:43–69CrossRefGoogle Scholar
  4. Berthier, L., Guzmova, L., Laroche, B., Lehmann, S., Squivident, H., Martin, M., Chenu, J.-P., Thiry, E., Lemercier, B., Bardy, M., Mérot, P., & Walter, C. (2014) Spatial prediction of potential wetlands at the French national scale based on hydroecoregions stratification and inference modelling. EGU General Assembly Conference Abstracts, 16, 12780.Google Scholar
  5. Boon PI, Raulings E, Roach M, Morris K (2008) Vegetation changes over a four decade period in Dowd morass, a brackish-water wetland of the Gippsland Lakes, south-eastern Australia. Proceedings of the Royal Society of Victoria 120:403–418Google Scholar
  6. Brinson MM (1993) Changes in the functioning of wetlands along environmental gradients. Wetlands 13:65–74CrossRefGoogle Scholar
  7. Brinson M (2009) The United States HGM (Hydrogeomorphic) approach. The Wetlands Handbook, Wiley-Blackwell. Edward Maltby & Tom Barker, Oxford, pp 486–512Google Scholar
  8. Cazals C, Rapinel S, Frison P-L et al (2016) Mapping and characterization of hydrological dynamics in a coastal marsh using high temporal resolution sentinel-1A images. Remote Sensing 8:570.  https://doi.org/10.3390/rs8070570 CrossRefGoogle Scholar
  9. Chasmer L, Hopkinson C, Veness T et al (2014) A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost. Remote Sensing of Environment 143:73–84.  https://doi.org/10.1016/j.rse.2013.12.016 CrossRefGoogle Scholar
  10. Chasmer L, Hopkinson C, Montgomery J, Petrone R (2016) A physically based terrain morphology and vegetation structural classification for wetlands of the Boreal Plains, Alberta, Canada. Canadian Journal of Remote Sensing 42:521–540.  https://doi.org/10.1080/07038992.2016.1196583 CrossRefGoogle Scholar
  11. Clément J-C, Aquilina L, Bour O et al (2003) Hydrological flowpaths and nitrate removal rates within a riparian floodplain along a fourth-order stream in Brittany (France). Hydrological Processes 17:1177–1195.  https://doi.org/10.1002/hyp.1192 CrossRefGoogle Scholar
  12. Congalton RG, Green K (2008) Assessing the accuracy of remotely sensed data: principles and practices, Second edn. CRC Press, Boca RatonGoogle Scholar
  13. Corcoran J, Knight J, Pelletier K et al (2015) The effects of point or polygon based training data on RandomForest classification accuracy of wetlands. Remote Sensing 7:4002–4025.  https://doi.org/10.3390/rs70404002 CrossRefGoogle Scholar
  14. Cowardin LM (1979) Classification of wetlands and deepwater habitats of the United States. Fish and Wildlife Service, US Dept. of the Interior, Washington, DCGoogle Scholar
  15. Cowardin LM, Myers VI (1974) Remote sensing for identification and classification of wetland vegetation. Journal of Wildlife Management 38:308–314CrossRefGoogle Scholar
  16. Dimitrakopoulos PG, Jones N, Iosifides T et al (2010) Local attitudes on protected areas: evidence from three Natura 2000 wetland sites in Greece. Journal of Environmental Management 91:1847–1854.  https://doi.org/10.1016/j.jenvman.2010.04.010 CrossRefPubMedGoogle Scholar
  17. Dvorett D, Davis C, Papeş M (2016) Mapping and hydrologic attribution of temporary wetlands using recurrent Landsat imagery. Wetlands 36:431–443.  https://doi.org/10.1007/s13157-016-0752-9 CrossRefGoogle Scholar
  18. Emmerson M, Morales MB, Oñate JJ et al (2016) Chapter two - how agricultural intensification affects biodiversity and ecosystem services. In: Alex J, Dumbrell RLK, Woodward G (eds) Large-scale ecology: model systems to global perspectives. Academic Press, Cambridge, pp 43–97CrossRefGoogle Scholar
  19. Ewel KC (2009) Introduction – how do wetlands fail? In: Maltby E, Barker T (eds) The wetlands handbook. Wiley-Blackwell, Oxford.  https://doi.org/10.1002/9781444315813.ch27
  20. Frieswyk CB, Zedler JB (2007) Vegetation Change in Great Lakes Coastal Wetlands: Deviation from the Historical Cycle. Journal of Great Lakes Research 33:366–380.  https://doi.org/10.3394/0380-1330(2007)33[366:VCIGLC]2.0.CO;2
  21. Gabrielsen CG, Murphy MA, Evans JS (2016) Using a multiscale, probabilistic approach to identify spatial-temporal wetland gradients. Remote Sensing of Environment 184:522–538CrossRefGoogle Scholar
  22. Gallant AL (2015) The challenges of remote monitoring of wetlands. Remote Sensing 7:10938–10950.  https://doi.org/10.3390/rs70810938 CrossRefGoogle Scholar
  23. Gerakis A, Kalburtji K (1998) Agricultural activities affecting the functions and values of Ramsar wetland sites of Greece. Agriculture, Ecosystems and Environment 70:119–128.  https://doi.org/10.1016/S0167-8809(98)00119-4 CrossRefGoogle Scholar
  24. Gerard F, Petit S, Smith G, et al (2010) Land cover change in Europe between 1950 and 2000 determined employing aerial photography. Progress in Physical Geography 34:183–205.  https://doi.org/10.1177/030913330936014
  25. Godet L, Thomas A (2013) Three centuries of land cover changes in the largest French Atlantic wetland provide new insights for wetland conservation. Applied Geography 42:133–139.  https://doi.org/10.1016/j.apgeog.2013.05.011
  26. Goodale R, Hopkinson C, Colville D, Amirault-Langlais D (2007) Mapping piping plover (Charadrius melodus melodus) habitat in coastal areas using airborne lidar data. Canadian Journal of Remote Sensing 33:519–533.  https://doi.org/10.5589/m07-058 CrossRefGoogle Scholar
  27. Hettiarachchi M, Morrison TH, McAlpine C (2015) Forty-three years of Ramsar and urban wetlands. Global Environmental Change 32:57–66.  https://doi.org/10.1016/j.gloenvcha.2015.02.009
  28. Hogg A, Todd K (2007) Automated discrimination of upland and wetland using terrain derivatives. Canadian Journal of Remote Sensing 33:S68–S83CrossRefGoogle Scholar
  29. Hopkinson C, Chasmer LE, Sass G et al (2005) Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment. Canadian Journal of Remote Sensing 31:191–206CrossRefGoogle Scholar
  30. Hopkinson C, Crasto N, Marsh P et al (2011) Investigating the spatial distribution of water levels in the Mackenzie Delta using airborne LiDAR. Hydrological Processes 25:2995–3011Google Scholar
  31. Houet T, Loveland T, Hubert-Moy L et al (2010) Exploring subtle land use and land cover changes: a framework for future landscape studies. Landscape Ecology 25:249–266CrossRefGoogle Scholar
  32. Kadmon R, Harari-Kremer R (1999) Studying Long-Term Vegetation Dynamics Using Digital Processing of Historical Aerial Photographs. Remote Sensing of Environment 68:164–176.  https://doi.org/10.1016/S0034-4257(98)00109-6
  33. Kamlun KU, Bürger Arndt R, Phua M-H (2016) Monitoring deforestation in Malaysia between 1985 and 2013: insight from south-western Sabah and its protected peat swamp area. Land Use Policy 57:418–430.  https://doi.org/10.1016/j.landusepol.2016.06.011 CrossRefGoogle Scholar
  34. Kloiber SM, Macleod RD, Smith AJ et al (2015) A semi-automated, multi-source data fusion update of a wetland inventory for east-Central Minnesota, USA. Wetlands 35:335–348.  https://doi.org/10.1007/s13157-014-0621-3 CrossRefGoogle Scholar
  35. Knight JF, Tolcser BP, Corcoran JM, Rampi LP (2013) The effects of data selection and thematic detail on the accuracy of high spatial resolution wetland classifications. Photogrammetric Engineering and Remote Sensing 79:613–623.  10.14358/PERS.79.7.613 CrossRefGoogle Scholar
  36. Lague D, Launeau P, Michon C et al (2016) A geomorphologist’s dream come true: synoptic high resolution river bathymetry with the latest generation of airborne dual wavelength lidar. EGU General Assembly Conference Abstracts 18:EGU2016–EG12238Google Scholar
  37. Lê S, Josse J, Husson F (2008) FactoMineR: a package for multivariate analysis. Journal of Statistical Software 25:1–18.  10.18637/jss.v025.i01 CrossRefGoogle Scholar
  38. Legleiter CJ, Overstreet BT, Glennie CL et al (2015) Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments. Earth Surface Processes and Landforms 41(3):344–363CrossRefGoogle Scholar
  39. Leonard PB, Baldwin RF, Homyack JA, Wigley TB (2012) Remote detection of small wetlands in the Atlantic coastal plain of North America: local relief models, ground validation, and high-throughput computing. Forest Ecology and Management 284:107–115.  https://doi.org/10.1016/j.foreco.2012.07.034 CrossRefGoogle Scholar
  40. Lespez L, Viel V, Rollet AJ, Delahaye D (2015) The anthropogenic nature of present-day low energy rivers in western France and implications for current restoration projects. Geomorphology 251:64–76.  https://doi.org/10.1016/j.geomorph.2015.05.015
  41. Lindsay J, Dhun K (2015) Modelling surface drainage patterns in altered landscapes using LiDAR. International Journal of Geographical Information Science 29:397–411CrossRefGoogle Scholar
  42. Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Progress in Physical Geography 32:31–49.  https://doi.org/10.1177/0309133308089496 CrossRefGoogle Scholar
  43. Mallinis G, Emmanoloudis D, Giannakopoulos V et al (2011) Mapping and interpreting historical land cover/land use changes in a Natura 2000 site using earth observational data: the case of Nestos delta, Greece. Applied Geography 31:312–320.  https://doi.org/10.1016/j.apgeog.2010.07.002 CrossRefGoogle Scholar
  44. Maltby E (ed) (2009) Functional assessment of wetlands: towards evaluation of ecosystem services. Woodhead Publishing, CambridgeGoogle Scholar
  45. Maltby E, Barker T (2009) The wetlands handbook. Wiley-Blackwell, OxfordCrossRefGoogle Scholar
  46. Maltby E, Acreman MC (2011) Ecosystem services of wetlands: pathfinder for a new paradigm. Hydrological Sciences Journal 56:1341–1359.  https://doi.org/10.1080/02626667.2011.631014
  47. McGarigal K, Cushman SA, Neel MC, Ene E (2002) FRAGSTATS: spatial pattern analysis program for categorical maps. University of Massachusetts, AmherstGoogle Scholar
  48. Mérot P, Hubert-Moy L, Gascuel-Odoux C et al (2006) A method for improving the management of controversial wetland. Environmental Management 37:258–270CrossRefPubMedGoogle Scholar
  49. Millard K, Richardson M (2013) Wetland mapping with LiDAR derivatives, SAR polarimetric decompositions, and LiDAR--SAR fusion using a random forest classifier. Canadian Journal of Remote Sensing 39:290–307CrossRefGoogle Scholar
  50. Millard K, Richardson M (2015) On the importance of training data sample selection in random Forest image classification: a case study in peatland ecosystem mapping. Remote Sensing 7:8489–8515.  https://doi.org/10.3390/rs70708489 CrossRefGoogle Scholar
  51. Moser L, Schmitt A, Wendleder A, Roth A (2016) Monitoring of the lac bam wetland extent using dual-polarized X-band SAR data. Remote Sensing 8:302.  https://doi.org/10.3390/rs8040302 CrossRefGoogle Scholar
  52. Mui A, He Y, Weng Q (2015) An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 109:30–46.  https://doi.org/10.1016/j.isprsjprs.2015.08.005 CrossRefGoogle Scholar
  53. Murphy PNC, Ogilvie J, Connor K, Arp PA (2007) Mapping wetlands: a comparison of two different approaches for New Brunswick, Canada. Wetlands 27:846–854.  https://doi.org/10.1672/0277-5212(2007)27[846:MWACOT]2.0.CO;2 CrossRefGoogle Scholar
  54. Murphy PNC, Ogilvie J, Arp P (2009) Topographic modelling of soil moisture conditions: a comparison and verification of two models. European Journal of Soil Science 60:94–109.  https://doi.org/10.1111/j.1365-2389.2008.01094.x CrossRefGoogle Scholar
  55. Necsoiu M, Dinwiddie CL, Walter GR et al (2013) Multi-temporal image analysis of historical aerial photographs and recent satellite imagery reveals evolution of water body surface area and polygonal terrain morphology in Kobuk Valley National Park, Alaska. Environmental Research Letters 8:025007.  https://doi.org/10.1088/1748-9326/8/2/025007 CrossRefGoogle Scholar
  56. Niculescu S, Lardeux C, Grigoras I, et al (2016) Synergy between LiDAR, RADARSAT-2, and Spot-5 images for the detection and mapping of wetland vegetation in the Danube Delta. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing pp:1–16. doi: https://doi.org/10.1109/JSTARS.2016.2545242
  57. Ottinger M, Kuenzer C, Liu G et al (2013) Monitoring land cover dynamics in the Yellow River Delta from 1995 to 2010 based on Landsat 5 TM. Applied Geography 44:53–68.  https://doi.org/10.1016/j.apgeog.2013.07.003 CrossRefGoogle Scholar
  58. Pingel TJ, Clarke KC, McBride WA (2013) An improved simple morphological filter for the terrain classification of airborne LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing 77:21–30.  https://doi.org/10.1016/j.isprsjprs.2012.12.002 CrossRefGoogle Scholar
  59. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  60. Rampi LP, Knight JF, Pelletier KC (2014) Wetland mapping in the upper Midwest United States. Photogrammetric Engineering and Remote Sensing 80:439–448.  10.14358/PERS.80.5.439 CrossRefGoogle Scholar
  61. Rapinel S, Hubert-Moy L, Clément B et al (2015a) Ditches’ network extraction and hydrogeomorphological characterization using LiDAR-derived DTM in wetlands. Hydrology Research 46:276–289.  https://doi.org/10.2166/nh.2013.121 CrossRefGoogle Scholar
  62. Rapinel S, Hubert-Moy L, Clément B (2015b) Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping. International Journal of Applied Earth Observation and Geoinformation 37:56–64.  https://doi.org/10.1016/j.jag.2014.09.002 CrossRefGoogle Scholar
  63. Rapinel S, Hubert-Moy L, Clément B, Maltby E (2016) Mapping wetland functions using earth observation data and multi-criteria analysis. Environmental Monitoring and Assessment 188:641.  https://doi.org/10.1007/s10661-016-5644-1 CrossRefPubMedGoogle Scholar
  64. Robinson JS, Sivapalan M, Snell JD (1995) On the relative roles of hillslope processes, channel routing, and network geomorphology in the hydrologic response of natural catchments. Water Resources Research 31:3089–3101CrossRefGoogle Scholar
  65. Szporak-Wasilewska S, Mirosław-Świątek D, Grygoruk M, et al (2015) Processing of airborne laser scanning data to generate accurate DTM for floodplain wetland. Proceedings of SPIE 9637:963720–963720–11. doi: https://doi.org/10.1117/12.2195223
  66. Tian B, Zhou Y-X, Thom RM, et al (2015) Detecting wetland changes in shanghai, China using FORMOSAT and Landsat TM imagery. Journal of Hydrology 529, Part 1:1–10. doi: https://doi.org/10.1016/j.jhydrol.2015.07.007
  67. Töyrä J, Pietroniro A, Hopkinson C, Kalbfleisch W (2003) Assessment of airborne scanning laser altimetry (lidar) in a deltaic wetland environment. Canadian Journal of Remote Sensing 29:718–728CrossRefGoogle Scholar
  68. Vacquié L, Houet T (2012) Cartographie des zones humides de montagne par télédétection. Potentialités à très haute résolution spatiale Revue Internationale de Géomatique:497–518Google Scholar
  69. VanDerWal J, Falconi L, Januchowski S, et al (2014) Package ‘SDMTools’Google Scholar
  70. Weinstein MP, Balletto JH, Teal JM, Ludwig DF (1996) Success criteria and adaptive management for a large-scale wetland restoration project. Wetlands Ecology and Management 4:111–127.  https://doi.org/10.1007/BF01876232 CrossRefGoogle Scholar
  71. White B, Ogilvie J, Campbell DMHMH et al (2012) Using the cartographic depth-to-water index to locate small streams and associated wet areas across landscapes. Canadian Water Resources Journal / Revue canadienne des ressources hydriques 37:333–347.  https://doi.org/10.4296/cwrj2011-909 CrossRefGoogle Scholar
  72. Williams DC, Lyon JG (1997) Historical aerial photographs and a geographic information system (GIS) to determine effects of long-term water level fluctuations on wetlands along the St. Marys River, Michigan, USA. Aquatic Botany 58:363–378.  https://doi.org/10.1016/S0304-3770(97)00046-6 CrossRefGoogle Scholar
  73. Wu Q, Lane CR (2016) Delineation and quantification of wetland depressions in the prairie pothole region of North Dakota. Wetlands 36:215–227CrossRefGoogle Scholar
  74. Zedler J (2004) Compensating for wetland losses in the United States. Ibis 146:92–100CrossRefGoogle Scholar

Copyright information

© Society of Wetland Scientists 2017

Authors and Affiliations

  1. 1.Université Rennes 2, CNRS, UMR LETGRennes CedexFrance
  2. 2.Université Rennes 1, CNRS, UMR ECOBIORennes CedexFrance

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