Advertisement

Landscape metrics of post-restoration vegetation dynamics in wetland ecosystems

  • Sophie TaddeoEmail author
  • Iryna Dronova
Research Article
  • 46 Downloads

Abstract

Context

To monitor wetlands at regional scales, it is pivotal to identify metrics that show rapid and predictable responses to restoration interventions. Remote sensing can monitor such metrics at high frequency and low cost but remains underutilized in practice.

Objectives

This study sought to find a set of landscape metrics most responsive to restoration and vegetation dynamics across 11 years and 20 restored wetlands in the Sacramento-San Joaquin Delta of California, USA.

Methods

Breakpoint analysis was used to detect phases in the development of vegetated pixels as estimated from the enhanced vegetation index (EVI) derived from Landsat (2004–2017). Landscape metrics were then generated from land cover classifications based on high resolution aerial images from the National Agricultural Inventory Program (NAIP). Using hierarchical clustering, we grouped phases showing similar temporal characteristics. We selected a subset of landscape metrics that best described the spatial structure of vegetation and its dynamics in each phase type.

Results

We identified four phases in vegetation development: (1A) rapid increase; (1B) decrease; (2A) low change; (2B) low change with fluctuations. Landscape metrics showed a significant response to vegetation dynamics in our sample, suggesting their potential to expand current monitoring practices at low cost. Young sites and sites experiencing a rapid increase in greenness were characterized by a lower density of small patches, while older sites, reference sites, and low variability sites were characterized by large, clustered patches.

Conclusions

Our study demonstrates that open source remote sensing can detect patterns in wetland response to restoration and help identify factors promoting their recovery.

Keywords

Landsat NAIP Remote sensing Monitoring Lateral vegetation growth Succession Patch density 

Notes

Acknowledgements

This analysis was partially funded by California Sea Grant Delta Science Awards R/SF-71 and R/SF-52. The authors thank Erin Voss and Shehnaz Mannan for their help with preliminary assessments. We are grateful for the comments and suggestions made by two anonymous reviewers which greatly improved the manuscript.

Supplementary material

10980_2019_946_MOESM1_ESM.docx (306 kb)
Supplementary material 1 (DOCX 306 kb)

References

  1. Almeida D, Rocha J, Neto C, Arsénio P (2016) Landscape metrics applied to formerly reclaimed saltmarshes: a tool to evaluate ecosystem services? Estuar Coast Shelf Sci 181:100–113CrossRefGoogle Scholar
  2. Anderson FE, Bergamaschi B, Sturtevant C, Knox S, Hastings L, Windham-myers L, Detto M, Hestir EL, Drexler J, Miller RL et al (2016) Variation of energy and carbon fluxes from a temperate freshwater wetland and implications for carbon market verification protocols. J. Geophys. Res. Biogeoscience 121:1–19CrossRefGoogle Scholar
  3. Berkowitz JF (2013) Development of restoration trajectory metrics in reforested bottomland hardwood forests applying a rapid assessment approach. Ecol Indic 34:600–606CrossRefGoogle Scholar
  4. Bernhardt K, Koch M (2003) Restoration of a salt marsh system: temporal change of plant species diversity and composition. Basic Appl Ecol 4:441–451CrossRefGoogle Scholar
  5. Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16CrossRefGoogle Scholar
  6. Botequilha Leitao A, Ahern J (2002) Applying landscape ecological concepts and metrics in sustainable landscape planning. Landsc Urban Plan 59:65–93CrossRefGoogle Scholar
  7. Browning DM, Maynard JJ, Karl JW, Peters DC (2017) Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem: verification. Ecol Appl 27:1677–1693PubMedCrossRefGoogle Scholar
  8. Brudvig LA (2011) The restoration of biodiversity: where has research been and where does it need to go? Am J Bot 98:549PubMedCrossRefGoogle Scholar
  9. Bullock JM, Aronson J, Newton AC, Pywell RF, Rey-Benayas JM (2011) Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends Ecol Evol 26:541–549PubMedCrossRefGoogle Scholar
  10. Byrd KB, Ballanti L, Thomas N, Nguyen D, Holmquist JR, Simard M (2018) A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States. ISPRS J Photogramm Remote Sens 139:255–271CrossRefGoogle Scholar
  11. California Wetlands Monitoring Workgroup (CWMW) (2018) EcoAtlas. https://www.ecoatlas.org
  12. Chapple D, Dronova I (2017) Vegetation development in a tidal marsh restoration project during a historic drought: a remote sensing approach. Front Mar Sci.  https://doi.org/10.3389/fmars.2017.00243 CrossRefGoogle Scholar
  13. Chapple DE, Faber P, Suding KN, Merenlender AM (2017) Climate variability structures plant community dynamics in mediterranean restored and reference tidal wetlands. Water 9:209–226CrossRefGoogle Scholar
  14. Colwell RK, Lees DC (2000) The mid-domain effect: geometric constraints on the geography of species richness. Trends Ecol Evol 15:70–76PubMedCrossRefGoogle Scholar
  15. Combroux CS, Bornette G, Amoros C (2002) Plant regenerative strategies after a major disturbance: the case of a riverine wetland restoration. Wetlands 22:234–246CrossRefGoogle Scholar
  16. Cook BJ, Hauer FR (2007) Effects of hydrologic connectivity on water chemistry, soils, and vegetation structure and function in an intermontane depressional wetland landscape. Wetlands 27:719–738CrossRefGoogle Scholar
  17. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
  18. Craft C, Megonigal P, Broome S, Stevenson J, Freese R, Cornell J, Zheng L, Sacco J (2003) The pace of ecosystem development of constructed Spartina alterniflora marshes. Ecol Appl 13:1417–1432CrossRefGoogle Scholar
  19. Cushman SA, McGarigal K, Neel MC (2008) Parsimony in landscape metrics: strength, universality, and consistency. Ecol Indic 8:691–703CrossRefGoogle Scholar
  20. Dale VH, Beyeler SC (2001) Challenges in the development and use of ecological indicators. Ecol Indic 1:3–10CrossRefGoogle Scholar
  21. D’Astous A, Poulin M, Aubin I, Rochefort L (2013) Using functional diversity as an indicator of restoration success of a cut-over bog. Ecol Eng 61:519–526CrossRefGoogle Scholar
  22. Doren RF, Volin JC, Richards JH (2009) Invasive exotic plant indicators for ecosystem restoration: an example from the Everglades restoration program. Ecol Indic 9:S29–S36.  https://doi.org/10.1016/j.ecolind.2008.08.006 CrossRefGoogle Scholar
  23. Dronova I (2015) Object-based image analysis in wetland research: a review. Remote Sens 7:6380–6413CrossRefGoogle Scholar
  24. Dronova I, Beissinger SR, Burnham JW, Gong P (2016) Landscape-level associations of wintering waterbird diversity and abundance from remotely sensed wetland characteristics of poyang lake. Remote Sens 8:1–22CrossRefGoogle Scholar
  25. Dronova I, Taddeo S (2016) Canopy leaf area index in non-forested marshes of the California Delta. Wetlands 36:705–716CrossRefGoogle Scholar
  26. Dufour S, Bernez I, Betbeder J, Corgne S, Hubert-Moy L, Nabucet J, Rapinel S, Sawtschuk J, Trolle C (2013) Monitoring restored riparian vegetation: how can recent developments in remote sensing sciences help? Knowl Manag Aquat Ecosyst.  https://doi.org/10.1051/kmae/2013068
  27. Epting SM, Hosen JD, Alexander LC, Lang MW, Armstrong AW, Palmer MA (2018) Landscape metrics as predictors of hydrologic connectivity between Coastal Plain forested wetlands and streams. Hydrol Process 32:516–532PubMedPubMedCentralCrossRefGoogle Scholar
  28. Galatowitsch SM (2006) Restoring prairie pothole wetlands: does the species pool concept offer decision-making guidance for re-vegetation? Appl Veg Sci 9:261CrossRefGoogle Scholar
  29. Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27CrossRefGoogle Scholar
  30. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213CrossRefGoogle Scholar
  31. Jensen JR (2007) Remote sensing of the environment: an earth resource perspective, 2nd edn. Prentice Hall, Upper Sadle RiverGoogle Scholar
  32. Kelly M, Tuxen KA, Stralberg D (2011) Mapping changes to vegetation pattern in a restoring wetland: finding pattern metrics that are consistent across spatial scale and time. Ecol Indic 11:263–273CrossRefGoogle Scholar
  33. Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. Remote Sens Environ 114:2897–2910CrossRefGoogle Scholar
  34. Kentula ME (2000) Perspectives on setting success criteria for wetland restoration. Ecol Eng 15:199–209CrossRefGoogle Scholar
  35. Khanna S, Santos MJ, Boyer JD, Shapiro KD, Bellvert J, Ustin SL (2018) Water primrose invasion changes successional pathways in an estuarine ecosystem. Ecosphere 9:e02418CrossRefGoogle Scholar
  36. Kimmerer WJ, Murphy DD, Angermeier PJ (2005) A landscape-level model for ecosystem restoration in the San Francisco Estuary and its Watershed. San Fr Estuary Watershed.  https://doi.org/10.15447/sfews.2005v3iss1art4 CrossRefGoogle Scholar
  37. Klemas V (2013) Remote sensing of coastal and wetland biomass: an overview. J Coast Res 29:1016–1028CrossRefGoogle Scholar
  38. Knox SH, Dronova I, Sturtevant C, Oikawa PY, Matthes JH, Verfaillie J, Baldocchi D (2017) Using digital camera and Landsat imagery with eddy covariance data to model gross primary production in restored wetlands. Agric For Meteorol 237–238:233–245CrossRefGoogle Scholar
  39. Kondolf GM, Anderson S, Lave R, Pagano L, Merenlender A, Bernhardt ES (2007) Two decades of river restoration in California: what can we learn? Restor Ecol 15:516–523CrossRefGoogle Scholar
  40. Leffondré K, Abrahamowicz M, Regeasse A, Hawker GA, Badley EM, McCusker J, Belzile E (2004) Statistical measures were proposed for identifying longitudinal patterns of change in quantitative health indicators. J Clin Epidemiol 57:1049–1062CrossRefGoogle Scholar
  41. Luoma SN, Dahm CN, Healey M, Moore JN (2015) Challenges facing the Sacramento-San Joaquin Delta: complex, chaotic, or simply cantankerous? San Fr Estuary Watershed 13:1–25Google Scholar
  42. Matthews JW, Endress AG (2010) Rate of succession in restored wetlands and the role of site context. Appl Veg Sci 13:346–355Google Scholar
  43. Matthews JW, Peralta AL, Flanagan DN, Baldwin PM, Soni A, Kent AD, Endress AG (2009a) Relative influence of landscape vs. local factors on plant community assembly in restored wetlands. Ecol Appl 19:2108–2123.  https://doi.org/10.1890/08-1836.1 CrossRefPubMedGoogle Scholar
  44. Matthews JW, Peralta AL, Soni A, Baldwin P, Kent AD, Endress AG (2009b) Local and landscape correlates of non-native species invasion in restored wetlands. Ecography (Cop) 32:1031–1039CrossRefGoogle Scholar
  45. Matthews JW, Spyreas G (2010) Convergence and divergence in plant community trajectories as a framework for monitoring wetland restoration progress. J Appl Ecol 47:1128–1136CrossRefGoogle Scholar
  46. Matthews JW, Spyreas G, Endress AG (2009c) Trajectories of vegetation-based indicators used to assess wetland restoration progress. Ecol Appl 19:2093–2107PubMedCrossRefGoogle Scholar
  47. McCoy-Sulentic ME, Kolb TE, Merritt DM, Palmquist EC, Ralston BE, Sarr DA (2017) Variation in species-level plant functional traits over wetland indicator status categories. Ecol Evol 7:3732–3744PubMedPubMedCentralCrossRefGoogle Scholar
  48. McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous mapsGoogle Scholar
  49. McGarigal K, McComb WC (1995) Relationships between landscape structure and breeding birds in the Oregon coast range. Ecol Monogr 65:235–260CrossRefGoogle Scholar
  50. Meire DWSA, Kondziolka JM, Nepf HM (2014) Interaction between neighboring vegetation patches: impact on flow and deposition. Water Resour Res 50:3809–3825CrossRefGoogle Scholar
  51. Moffett KB, Gorelick SM (2016) Alternative stable states of tidal marsh vegetation patterns and channel complexity. Ecohydrology 1662:1639–1662CrossRefGoogle Scholar
  52. Moffett KB, Law J, Gorelick SM et al (2014) Alameda song sparrow abundance related to salt marsh metrics quantified from remote sensing imagery. San Fr Estuary Watershed 12:1–19Google Scholar
  53. Moreno-Mateos D, Meli P, Vara-Rodríguez MI, Aronson J (2015) Ecosystem response to interventions: lessons from restored and created wetland ecosystems. J Appl Ecol 52:1528–1537CrossRefGoogle Scholar
  54. Moyle PB, Manfree AD, Fiedler PL (2013) The future of suisun marsh: balancing policy with change. San Fr Estuary Watershed Sci 11:Google Scholar
  55. R Core Team (2017) R: a language and environment for statistical computingGoogle Scholar
  56. Rocha AV, Potts DL, Goulden ML (2008) Standing litter as a driver of interannual CO2 exchange variability in a freshwater marsh. J Geophys Res Biogeosciences 113:1–10Google Scholar
  57. Roy DP, Kovalskyy V, Zhang HK, Vermote EF, Yan L, Kumar SS, Egorov A (2016) Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens Environ 185:57–70CrossRefGoogle Scholar
  58. San Francisco Estuary Institute-Aquatic Science Center (2014) A Delta Transformed: Ecological Functions, Spatial Metrics, and Landscape Change in the Sacramento-San Joaquin Delta. SFEI Contribution No. 729. San Francisco Estuary Institute - Aquatic Science Center, Richmond, CAGoogle Scholar
  59. Schile LM, Byrd KB, Windham-Myers L, Kelly M (2013) Accounting for non-photosynthetic vegetation in remote-sensing-based estimates of carbon flux in wetlands. Remote Sens Lett 4:542–551CrossRefGoogle Scholar
  60. Shuman CS, Ambrose RF (2003) A comparison of remote sensing and ground-based methods for monitoring wetland restoration success. Restor Ecol 11:325–333CrossRefGoogle Scholar
  61. Simenstad C, Reed D, Ford M (2006) When is restoration not? Incorporating landscape-scape processes to restore self-sustaining ecosystems in coastal wetland restoration. Ecol Eng 26:27–39CrossRefGoogle Scholar
  62. Soomers H, Karssenberg D, Soons MB, Verweij PA, Verhoeven JTA, Wassen MJ (2013) Wind and water dispersal of wetland plants across fragmented landscapes. Ecosystems 16:434–451CrossRefGoogle Scholar
  63. Staszak LA, Armitage AR (2013) Evaluating salt marsh restoration success with an index of ecosystem integrity. J Coast Res 287:410–418CrossRefGoogle Scholar
  64. Suding KN (2011) Toward an era of restoration in ecology: successes, failures, and opportunities ahead. Annu Rev Ecol Evol Syst 42:465–487CrossRefGoogle Scholar
  65. Ta J, Anderson LWJ, Christman MA, Khanna S, Kratville D, Madsen JD, Moran PJ, Viers JH (2017) Invasive aquatic vegetation management in the Sacramento-San Joaquin River Delta: status and recommendations. San Fr Estuary Watershed Sci.  https://doi.org/10.15447/sfews.2017v15iss4art5
  66. Taddeo S, Dronova I (2018) Indicators of vegetation development in restored wetlands. Ecol Indic 94:454–467CrossRefGoogle Scholar
  67. Taddeo S, Dronova I (2019) Geospatial tools for the large-scale monitoring of wetlands in the San Francisco estuary: opportunities and challenges. San Fr Estuary Watershed Sci.  https://doi.org/10.15447/sfews.2019v17iss2art1 CrossRefGoogle Scholar
  68. Taddeo S, Dronova I, Depsky N (2019) Remote sensing of environment spectral vegetation indices of wetland greenness: responses to vegetation structure, composition, and spatial distribution. Remote Sens Environ 234:111467CrossRefGoogle Scholar
  69. Turner MG (1989) Landscape ecology: the effect of patern on process. Annu Rev Ecol Syst 20:171–197CrossRefGoogle Scholar
  70. Tuxen KA, Schile LM, Kelly M, Siegel SW (2008) Vegetation colonization in a restoring tidal marsh: a remote sensing approach. Restor Ecol 16:313–323CrossRefGoogle Scholar
  71. Tuxen K, Schile L, Stralberg D, Siegel S, Parker T, Vasey M, Callaway J, Kelly M (2011) Mapping changes in tidal wetland vegetation composition and pattern across a salinity gradient using high spatial resolution imagery. Wetl Ecol Manag 19:141–157CrossRefGoogle Scholar
  72. Van den Bosch K, Matthews JW (2017) An assessment of long-term compliance with performance standards in compensatory mitigation wetlands. Environ Manag 59:546–556CrossRefGoogle Scholar
  73. Van Meter KJ, Basu NB (2015) Signatures of human impact: size distributions and spatial organization of wetlands in the Prairie Pothole landscape. Ecol Appl 25:451–465PubMedCrossRefGoogle Scholar
  74. Vasey MC, Parker VT, Callaway JC, Herbert ER, Schile LM (2012) Tidal wetland vegetation in the San Francisco Bay-Delta Estuary. San Fr Estuary Watershed Sci.  https://doi.org/10.15447/sfews.2012v10iss2art2
  75. Verbesselt J, Hyndman R, Zeileis A, Culvenor D (2010) Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ 114:2970–2980CrossRefGoogle Scholar
  76. Verbesselt J, Zeileis A, Herold M (2012) Near real-time disturbance detection using satellite image time series. Remote Sens Environ 123:98–108CrossRefGoogle Scholar
  77. Villard MA, Trzcinski MK, Merriam G (1999) Fragmentation effects on forest birds: relative influence of woodland cover and configuration on landscape occupancy. Conserv Biol 13:774–783CrossRefGoogle Scholar
  78. Whipple A, Grossinger RM, Rankin D, Stanford B, Askevold RA (2012) Sacramento-San Joaquin Delta historical ecology investigation: exploring pattern and process. SFEI Contribution No. 672. SFEI, RichmondGoogle Scholar
  79. Wortley L, Hero J-M, Howes M (2013) Evaluating ecological restoration success: a review of the literature. Restor Ecol 21:537–543CrossRefGoogle Scholar
  80. Wu J (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Remote Sens 25:367–380CrossRefGoogle Scholar
  81. Xiong S, Johansson ME, Hughes FMR, Hayes A, Richards KS, Nilsson C (2003) Interactive effects of soil moisture, vegetation canopy, plant litter and seed addition on plant diversity in a wetland community. J Ecol 91:976–986CrossRefGoogle Scholar
  82. Zahawi RA, Dandois JP, Holl KD, Nadwodny D, Reid JL, Ellis EC (2015) Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biol Conserv 186:287–295CrossRefGoogle Scholar
  83. Zedler JB (2000) Progress in wetland restoration ecology. Trends Ecol Evol 15:402–407PubMedCrossRefGoogle Scholar
  84. Zhu Z (2017) Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS J Photogramm Remote Sens 130:370–384CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Landscape Architecture & Environmental Planning, College of Environmental DesignUniversity of California BerkeleyBerkeleyUSA

Personalised recommendations