Landscape Ecology

, Volume 32, Issue 9, pp 1867–1879 | Cite as

Synthetic Aperture Radar (SAR) images improve habitat suitability models

  • Julie Betbeder
  • Marianne Laslier
  • Laurence Hubert-Moy
  • Françoise Burel
  • Jacques Baudry
Research Article

Abstract

Context

The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors.

Objectives

The objective of this study was to evaluate the information provided by a synthetic aperture radar (SAR) image for landscape connectivity modeling compared to aerial photographs (APs).

Methods

We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance. More precisely, we compared how two resistance maps constructed using landscape and/or local metrics derived from AP or SAR imagery yield different connectivity values (based on graph theory), considering hedgerow networks and forest carabid beetle species as a model.

Results

We found that resistance maps using landscape and local metrics derived from SAR imagery improve landscape connectivity measures. The SAR model is the most informative, explaining 58% of the variance in forest carabid beetle abundance. This model calculates resistance values associated with homogeneous patches within hedgerows according to their suitability (canopy cover density and landscape grain) for the model species.

Conclusions

Our approach combines two important methods in landscape ecology: the construction of resistance maps and the use of buffers around sampling points to determine the importance of landscape factors. This study was carried out through an interdisciplinary approach involving remote sensing scientists and landscape ecologists. This study is a step forward in developing landscape metrics from satellites to monitor biodiversity.

Keywords

Biodiversity Remote sensing TerraSAR-X Hedgerow networks Forest carabid beetles Canopy cover density Landscape connectivity Graph theory Habitat suitability 

References

  1. Adriaensen F, Chardon JP, De Blust G, Swinnen E, Villalba S, Gulinck H, Matthysen E (2003) The application of ‘least-cost’ modelling as a functional landscape model. Landsc Urban Plan 64:233–247CrossRefGoogle Scholar
  2. Aksoy S, Akcay G, Cinbis G, Wassenaar T (2008) Automatic mapping of linear woody vegetation features in agricultural landscapes. In: IGARSS 2008—2008 IEEE international geoscience and remote sensing symposium, p IV-403–IV-406Google Scholar
  3. Aviron S, Burel F, Baudry J, Schermann N (2005) Carabid assemblages in agricultural landscapes: impacts of habitat features, landscape context at different spatial scales and farming intensity. Agric Ecosyst Environ 108:205–217CrossRefGoogle Scholar
  4. Baghdadi N, Bernier M, Gauthier R, Neeson I (2001) Evaluation of C-band SAR data for wetlands mapping. Int J Remote Sens 22:71–88CrossRefGoogle Scholar
  5. Baghdadi N, Boyer N, Todoroff P, El Hajj M, Bégué A (2009) Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island. Remote Sens Environ 113:1724–1738CrossRefGoogle Scholar
  6. Bargiel D (2013) Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes. Ecol Inform 13:9–16CrossRefGoogle Scholar
  7. Baudry J, Burel F, Thenail C, Le Cœur D (2000) A holistic landscape ecological study of the interactions between farming activities and ecological patterns in Brittany, France. Landsc Urban Plan 50:119–128CrossRefGoogle Scholar
  8. Baudry J, Jouin A (2003) De la haie aux bocages. Organisation, dynamique et gestion (Editions Quae)Google Scholar
  9. Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens 58:239–258CrossRefGoogle Scholar
  10. Betbeder J, Fieuzal R, Baup F (2016) Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield. IEEE J Sel Top Appl Earth Obs Remote Sens. doi:10.1109/JSTARS.2016.2541169 Google Scholar
  11. Betbeder J, Gond V, Frappart F, Baghdadi NN, Briant G, Bartholomé E (2014a) Mapping of Central Africa forested wetlands using remote sensing. IEEE J Sel Top Appl Earth Obs Remote Sens 7:531–542CrossRefGoogle Scholar
  12. Betbeder J, Hubert-Moy L, Burel F, Corgne S, Baudry J (2015) Assessing ecological habitat structure from local to landscape scales using synthetic aperture radar. Ecol Indic 52:545–557CrossRefGoogle Scholar
  13. Betbeder J, Nabucet J, Pottier E, Baudry J, Corgne S, Hubert-Moy L (2014b) Detection and characterization of hedgerows using TerraSAR-X imagery. Remote Sens 6:3752–3769CrossRefGoogle Scholar
  14. Blaschke T, Strobl J (2001) What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS 6:12–17Google Scholar
  15. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall/CRC, New YorkGoogle Scholar
  16. Broquet T, Ray N, Petit E, Fryxell JM, Burel F (2006) Genetic isolation by distance and landscape connectivity in the American marten (Martes americana). Landscape Ecol 21:877–889CrossRefGoogle Scholar
  17. Buckreuss S, Werninghaus R, Pitz W (2009) The German satellite mission TerraSAR-X. IEEE Aerosp Electron Syst Mag 24:4–9CrossRefGoogle Scholar
  18. Burel F (1989) Landscape structure effects on carabid beetles spatial patterns in western France. Landscape Ecol 2:215–226CrossRefGoogle Scholar
  19. Burel F, Baudry J, Butet A, Clergeau P, Delettre Y, Le Coeur D, Dubs F, Morvan N, Paillat G, Petit S, Thenail C, Brunel E, Lefeuvre JC (1998) Comparative biodiversity along a gradient of agricultural landscapes. Acta Oecol 19:47–60CrossRefGoogle Scholar
  20. Burel F, Butet A, Delettre YR, Millàn de la Peña N (2004) Differential response of selected taxa to landscape context and agricultural intensification. Landsc Urban Plan 67:195–204CrossRefGoogle Scholar
  21. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: A practical information-theoretic approach, Second edn. Springer, New York, USAGoogle Scholar
  22. Burnham KP, Anderson DR (2004) Multimodel inference, understanding AIC and BIC in model selection. Sociol Methods Res 33:261–304CrossRefGoogle Scholar
  23. Burnham KP, Anderson DR, Huyvaert KP (2010) AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav Ecol Sociobiol 65:23–35CrossRefGoogle Scholar
  24. Charrier S, Petit S, Burel F (1997) Movements of Abax parallelepipedus (Coleoptera, Carabidae) in woody habitats of a hedgerow network landscape: a radio-tracing study. Agric Ecosyst Environ 61:133–144CrossRefGoogle Scholar
  25. Coulon A, Aben J, Palmer SCF, Stevens VM, Callens T, Strubbe D, Lens L, Matthysen E, Baguette M, Travis JMJ (2015) A stochastic movement simulator improves estimates of landscape connectivity. Ecology 96:2203–2213CrossRefPubMedGoogle Scholar
  26. Definiens (2004) eCognition professional: user guide 4. Definiens Imaging Gmbh, MunichGoogle Scholar
  27. Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics. J Appl Ecol 44:714–724CrossRefGoogle Scholar
  28. Esmenjand M, Esteoule J, Guyader J (1976) Étude pédologique des différents types de talus: considérations sur la différenciation des profils; essai de systématique. Les bocages: histoire, écologie, économie. Inra ENSA Univ. Rennes, Rennes, p 167–175Google Scholar
  29. Foltête J-C, Clauzel C, Vuidel G (2012) A software tool dedicated to the modelling of landscape networks. Environ Model Softw 38:316–327CrossRefGoogle Scholar
  30. Forman RTT, Baudry J (1984) Hedgerows and hedgerow networks in landscape ecology. Environ Manag 8:495–510CrossRefGoogle Scholar
  31. Fox GA, Negrete-Yankelevich S, Sosa VJ (2015) Ecological statistics: contemporary theory and application. Oxford University Press, OxfordCrossRefGoogle Scholar
  32. Galpern P, Manseau M, Fall A (2011) Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biol Conserv 144:44–55CrossRefGoogle Scholar
  33. Gastón A, Blázquez-Cabrera S, Garrote G, Mateo-Sánchez MC, Beier P, Simón MA, Saura S (2016) Response to agriculture by a woodland species depends on cover type and behavioural state: insights from resident and dispersing Iberian lynx. J Appl Ecol 53(3):814–824CrossRefGoogle Scholar
  34. Gil-Tena A, Nabucet J, Mony C, Abadie J, Saura S, Butet A, Burel F, Ernoult A (2014) Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach. Community Ecol 15:256–268CrossRefGoogle Scholar
  35. Hagerty BE, Nussear KE, Esque TC, Tracy CR (2010) Making molehills out of mountains: landscape genetics of the Mojave Desert tortoise. Landscape Ecol 26:267–280CrossRefGoogle Scholar
  36. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  37. He KS, Bradley BA, Cord AF, Rocchini D, Tuanmu M-N, Schmidtlein S, Turner W, Wegmann M, Pettorelli N (2015) Will remote sensing shape the next generation of species distribution models? Remote Sens Ecol Conserv 1:4–18CrossRefGoogle Scholar
  38. Heinzel J, Koch B (2011) Exploring full-waveform LiDAR parameters for tree species classification. Int J Appl Earth Obs Geoinf 13:152–160CrossRefGoogle Scholar
  39. Imhoff ML, Sisk TD, Milne A, Morgan G, Orr T (1997) Remotely sensed indicators of habitat heterogeneity: use of synthetic aperture radar in mapping vegetation structure and bird habitat. Remote Sens Environ 60:217–227CrossRefGoogle Scholar
  40. Jongman RHG, Külvik M, Kristiansen I (2004) European ecological networks and greenways. Landsc Urban Plan 68:305–319CrossRefGoogle Scholar
  41. Kerr JT, Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends Ecol Evol 18:299–305CrossRefGoogle Scholar
  42. Kim Y, Jackson T, Bindlish R, Lee H, Hong S (2012) Radar vegetation index for estimating the vegetation water content of rice and soybean. IEEE Geosci Remote Sens Lett 9:564–568CrossRefGoogle Scholar
  43. Kromp B (1999) Carabid beetles in sustainable agriculture: a review on pest control efficacy, cultivation impacts and enhancement. Agric Ecosyst Environ 74:187–228CrossRefGoogle Scholar
  44. Le Coeur D, Baudry J, Burel F (1997) Field margins plant assemblages: variation partitioning between local and landscape factors. Landsc Urban Plan 37:57–71CrossRefGoogle Scholar
  45. Lee J-S (1981) Speckle analysis and smoothing of synthetic aperture radar images. Comput Graph Image Process 17:24–32CrossRefGoogle Scholar
  46. Lee J-S, Pottier E (2009) Polarimetric radar imaging: from basics to applications. CRC Press, Boca RatonCrossRefGoogle Scholar
  47. Legendre P, Legendre LFJ (2012) Numerical ecology. Elsevier, AmsterdamGoogle Scholar
  48. Levanoni O, Levin N, Pe’er G, Turbé A, Kark S (2011) Can we predict butterfly diversity along an elevation gradient from space? Ecography 34:372–383CrossRefGoogle Scholar
  49. Loreau M, Nolf C-L (1993) Occupation of space by the carabid beetle Abax ater. Acta Oecol 14:247–258Google Scholar
  50. Martín-Queller E, Saura S (2013) Landscape species pools and connectivity patterns influence tree species richness in both managed and unmanaged stands. For Ecol Manag 289:123–132CrossRefGoogle Scholar
  51. Müller J, Brandl R (2009) Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages. J Appl Ecol 46:897–905CrossRefGoogle Scholar
  52. O’Brien D, Manseau M, Fall A, Fortin M-J (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: an application of graph theory. Biol Conserv 130:70–83CrossRefGoogle Scholar
  53. Pascual-Hortal L, Saura S (2006) Comparison and development of new graph-based landscape connectivity indices: towards the prioritization of habitat patches and corridors for conservation. Landscape Ecol 21:959–967CrossRefGoogle Scholar
  54. Petit S, Burel F (1998) Effects of landscape dynamics on the metapopulation of a ground beetle (Coleoptera, Carabidae) in a hedgerow network. Agric Ecosyst Environ 69:243–252CrossRefGoogle Scholar
  55. Pettorelli N, Laurance WF, O’Brien TG, Wegmann M, Nagendra H, Turner W (2014) Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol 51:839–848CrossRefGoogle Scholar
  56. Pfeifer M, Disney M, Quaife T, Marchant R (2012) Terrestrial ecosystems from space: a review of earth observation products for macroecology applications. Glob Ecol Biogeogr 21:603–624CrossRefGoogle Scholar
  57. Pottier E, Ferro-Famil L (2012) PolSARPro V5.0: an ESA educational toolbox used for self-education in the field of POLSAR and POL-INSAR data analysis. In: 2012 IEEE international geoscience and remote sensing symposium (IGARSS), p 7377–7380Google Scholar
  58. Pu R (2009) Broadleaf species recognition with in situ hyperspectral data. Int J Remote Sens 30:2759–2779CrossRefGoogle Scholar
  59. Pullinger MG, Johnson CJ (2010) Maintaining or restoring connectivity of modified landscapes: evaluating the least-cost path model with multiple sources of ecological information. Landscape Ecol 25:1547–1560CrossRefGoogle Scholar
  60. Rainio J, Niemelä J (2003) Ground beetles (Coleoptera: Carabidae) as bioindicators. Biodivers Conserv 12:487–506CrossRefGoogle Scholar
  61. Rayfield B, Pelletier D, Dumitru M, Cardille JA, Gonzalez A (2016) Multipurpose habitat networks for short-range and long-range connectivity: a new method combining graph and circuit connectivity. Methods Ecol Evol 7:222–231CrossRefGoogle Scholar
  62. Saura S, Pascual-Hortal L (2007) A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landsc Urban Plan 83:91–103CrossRefGoogle Scholar
  63. Saura S, Torné J (2009) Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environ Model Softw 24:135–139CrossRefGoogle Scholar
  64. Schooley RL, Branch LC (2011) Habitat quality of source patches and connectivity in fragmented landscapes. Biodivers Conserv 20:1611–1623CrossRefGoogle Scholar
  65. Taylor PD, Fahrig L, Henein K, Merriam G (1993) Connectivity is a vital element of landscape structure. Oikos 68:571–573CrossRefGoogle Scholar
  66. Thiele HU (1977). Carabid beetles in their environments. A study on habitat selection by adaptation in physiology and behaviour. Springer, BerlinGoogle Scholar
  67. Tischendorf L, Wissel C (1997) Corridors as conduits for small animals: attainable distances depending on movement pattern, boundary reaction and corridor width. Oikos 79:603–611CrossRefGoogle Scholar
  68. Tournant P, Afonso E, Roué S, Giraudoux P, Foltête J-C (2013) Evaluating the effect of habitat connectivity on the distribution of lesser horseshoe bat maternity roosts using landscape graphs. Biol Conserv 164:39–49CrossRefGoogle Scholar
  69. Vannier C, Vasseur C, Hubert-Moy L, Baudry J (2011) Multiscale ecological assessment of remote sensing images. Landscape Ecol 26:1053–1069CrossRefGoogle Scholar
  70. Wade A, McKelvey KS, Schwartz M (2015) Resistance-surface-based wildlife conservation connectivity modeling: summary of efforts in the United States and guide for practitioners. Gen. Tech. Rep. RMRS-GTR-333. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  71. Wang IJ, Savage WK, Bradley Shaffer H (2009) Landscape genetics and least-cost path analysis reveal unexpected dispersal routes in the California tiger salamander (Ambystoma californiense). Mol Ecol 18:1365–1374CrossRefPubMedGoogle Scholar
  72. Wiens JA, Milne BT (1989) Scaling of “landscapes” in landscape ecology, or, landscape ecology from a beetle’s perspective. Landscape Ecol 3:87–96CrossRefGoogle Scholar
  73. Wiseman G, McNairn H, Homayouni S, Shang J (2014) RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J Sel Top Appl Earth Obs Remote Sens 7:4461–4471CrossRefGoogle Scholar
  74. Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landscape Ecol 27:777–797CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Julie Betbeder
    • 1
  • Marianne Laslier
    • 1
  • Laurence Hubert-Moy
    • 1
  • Françoise Burel
    • 2
  • Jacques Baudry
    • 3
  1. 1.LETG, CNRS UMR 6554Université Rennes 2Rennes CedexFrance
  2. 2.ECOBIO, CNRS UMR 6553Université de Rennes 1Rennes CedexFrance
  3. 3.INRA SAD-PAYSAGERennes CedexFrance

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