Remote Sensing of Vegetation for Nature Conservation

  • Sebastian Schmidtlein
  • Ulrike Faude
  • Stefanie Stenzel
  • Hannes Feilhauer
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 18)


A rapidly changing environment with land use and climate as the most dynamic components causes new challenges for nature conservation and management of protected areas. Dealing with these changes requires a systematic monitoring. To date, such monitoring programs are mostly backed by expert guess or permanent observation plots. Both have their merits but the plot-based approach is certainly more objective. However, even in the case of appropriate sampling, plots provide only punctual information and changes in the area between plots are easily missed. This gap can be closed by remote sensing.


Vegetation Type Synthetic Aperture Radar Spectral Coverage Hyperspectral Data Fuzzy Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author’s activities in remote sensing for nature conservation have been or are partly funded by the German Ministry of Economics and Technology, German Space Agency (MSAVE project, DLR FKZ 50EE1032) and by the European Community’s Seventh Framework Programme (MS-MONINA project, grant 263479).


  1. Amici V (2011) Dealing with vagueness in complex forest landscapes: a soft classification approach through a niche-based distribution model. Ecol Inform 6:371–383CrossRefGoogle Scholar
  2. Andrew ME, Ustin SL (2008) The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sens Environ 112:4301–4317CrossRefGoogle Scholar
  3. Asner GP, Martin RE (2008) Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sens Environ 112:3958–3970CrossRefGoogle Scholar
  4. Bradley BA, Olsson AD, Wang O, Dickson BG, Pelech L, Sesnie SE, Zachmann LJ (2012) Species detection vs. habitat suitability: are we biasing habitat suitability models with remotely sensed data? Ecol Model 244:57–64CrossRefGoogle Scholar
  5. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  6. Carter GA (1993) Responses of leaf spectral reflectance to plant stress. Am J Bot 80:239–243CrossRefGoogle Scholar
  7. Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88:677–684CrossRefGoogle Scholar
  8. Castro-Esau KL, Sánchez-Azofeifa GA, Rivard B, Wright SJ, Quesada M (2006) Variability in leaf optical properties of mesoamerican trees and the potential for species classification. Am J Bot 93:517–530CrossRefGoogle Scholar
  9. Council of the European Communities (1992) Council directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and floraGoogle Scholar
  10. Everitt JH, Alaniz MA, Escobar DE, Davis ME (1992) Using remote sensing to distinguish common (Isocoma coronopifolia) and Drummond goldenweed (Isocoma drummondii). Weed Sci 40:621–628Google Scholar
  11. Feilhauer H, Schmidtlein S (2011) On variable relations between vegetation patterns and canopy reflectance. Ecol Inform 6:83–92CrossRefGoogle Scholar
  12. Feilhauer H, Oerke EC, Schmidtlein S (2010) Quantifying empirical relations between planted species mixtures and canopy reflectance with PROTEST. Remote Sens Environ 114:1513–1521CrossRefGoogle Scholar
  13. Feilhauer H, Faude U, Schmidtlein S (2011) Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape. Remote Sens Environ 115:2513–2524CrossRefGoogle Scholar
  14. Feilhauer H, Thonfeld F, Faude U, He KS, Rocchini D, Schmidtlein S (2013) Assessing floristic composition with multispectral sensors – a comparison based on monotemporal and multiseasonal field spectra. Int J Appl Earth Obs Geoinf 21:218–229CrossRefGoogle Scholar
  15. Foody GM (1992) A fuzzy sets approach to the representation of vegetation continua from remotely sensed data: an example from lowland heath. Photogramm Eng Remote Sens 58:221–225Google Scholar
  16. Foody GM (1996) Fuzzy modelling of vegetation from remotely sensed imagery. Ecol Model 85:3–12CrossRefGoogle Scholar
  17. Foody GM (1999) The continuum of classification fuzziness in thematic mapping. Photogramm Eng Remote Sens 65:443–451Google Scholar
  18. Foody GM, Trodd NM (1993) Non-classificatory analysis and representation of heathland vegetation from remotely sensed imagery. GeoJournal 29:343–350CrossRefGoogle Scholar
  19. Förster M, Frick A, Walentowski H, Kleinschmit B (2008) Approaches for utilising QuickBird data for the monitoring of NATURA 2000 habitats. Community Ecol 9:155–168CrossRefGoogle Scholar
  20. Gausmann HW (1984) Evaluation of factors causing reflectance differences between sun and shade leaves. Remote Sens Environ 15:177–181CrossRefGoogle Scholar
  21. Ghioca-Robrecht DM, Johnston CA, Tulbure MG (2008) Assessing the use of multiseasonal Quickbird imagery for mapping invasive species in a Lake Erie coastal marsh. Wetlands 28:1028–1039CrossRefGoogle Scholar
  22. Goetz S, Steinberg D, Dubayah R, Blair B (2007) Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA. Remote Sens Environ 108:254–263CrossRefGoogle Scholar
  23. Gould W (2000) Remote sensing of vegetation, plant species richness and regional biodiversity hotspots. Ecol Appl 10:1861–1870CrossRefGoogle Scholar
  24. Gross JE, Goetz SJ, Cihlar J (2009) Application of remote sensing to parks and protected area monitoring: introduction to the special issue. Remote Sens Environ 113:1343–1345CrossRefGoogle Scholar
  25. Hall K, Reitalu T, Sykes MT, Prentice HC (2012) Spectral heterogeneity of QuickBird satellite data is related to fine-scale plant species spatial turnover in semi-natural grasslands. Appl Veg Sci 15:145–157CrossRefGoogle Scholar
  26. Hantson W, Kooistra L, Slim PA (2012) Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs. Appl Veg Sci 15:536–547CrossRefGoogle Scholar
  27. Harris AT, Asner GP, Miller ME (2003) Changes in vegetation structure after long-term grazing in pinyon–juniper ecosystems: integrating imaging spectroscopy and field studies. Ecosystems 6:368–383CrossRefGoogle Scholar
  28. Hernandez-Stefanoni JL, Ponce-Hernandez R (2004) Mapping the spatial distribution of plant diversity indices in a tropical forest using multi-spectral satellite image classification and field measurements. Biodivers Conserv 13:2599–2621CrossRefGoogle Scholar
  29. Kennedy RE, Townsend PA, Gross JE, Cohen WB, Bolstad P, Wang YQ, Adams P (2009) Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens Environ 113:1382–1396CrossRefGoogle Scholar
  30. Kumar L, Schmidt K, Dury S, Skidmore A (2001) Imaging spectrometry and vegetation science. In: van der Meer FD, de Jong SM (eds) Imaging spectroscopy: basic principles and prospective applications. Kluwer Academic Publishers, Dordrecht, pp 111–155Google Scholar
  31. Laba M, Tsai F, Ogurcak D, Smith S, Richmond ME (2005) Field determination of optimal dates for the discrimination of invasive wetland plant species using derivative spectral analysis. Photogramm Eng Remote Sens 71:603–611CrossRefGoogle Scholar
  32. Laliberte AS, Rango A, Havstad KM, Paris JF, Beck RF, McNeely R, Gonzalez AL (2004) Object-oriented image analysis for mapping shrub encroachment from 1937–2003 in southern New Mexico. Remote Sens Environ 93:198–210CrossRefGoogle Scholar
  33. Langley SK, Cheshire HM, Humes KS (2001) A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland. J Arid Environ 49:401–411CrossRefGoogle Scholar
  34. Li W, Guo Q (2010) A maximum entropy approach to one-class classification of remote sensing imagery. Int J Remote Sens 31:2227–2235CrossRefGoogle Scholar
  35. Nagendra A (2001) Using remote sensing to assess biodiversity. Int J Remote Sens 22:2377–2400CrossRefGoogle Scholar
  36. Nagendra H, Lucas R, Honrado J, Jongman R, Tarantino C, Adamo M, Mairota P (2013) Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol Indic 33:45–59Google Scholar
  37. Newton AC, Hill RA, Echeverria C, Golicher D, Rey Benayas JM, Cayuela L, Hinsley SA (2009) Remote sensing and the future of landscape ecology. Prog Phys Geogr 33:528–546CrossRefGoogle Scholar
  38. Noujdina NV, Ustin SL (2008) Mapping Downy Brome (Bromus tectorum) using multidate AVIRIS data. Weed Sci 56:173–179CrossRefGoogle Scholar
  39. Oldeland J, Dorigo W, Lieckfeld L, Lucier A, Jürgens N (2010) Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery. Remote Sens Environ 114:1155–1166CrossRefGoogle Scholar
  40. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  41. Roberts DA, Gardner M, Church R, Ustin S, Scheer G, Green RO (1998) Mapping Chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens Environ 65:267–279CrossRefGoogle Scholar
  42. Rocchini D, Chiarucci A, Loiselle SA (2004) Testing the spectral variation hypothesis by using satellite multispectral images. Acta Oecologica 26:117–120CrossRefGoogle Scholar
  43. Sanchez-Hernandez C, Boyd DS, Foody GM (2007a) Mapping specific habitats from remotely sensed imagery: support vector machine and support vector data description based classification of coastal saltmarsh habitats. Ecol Inform 2:83–88CrossRefGoogle Scholar
  44. Sanchez-Hernandez C, Boyd DS, Foody GM (2007b) One-class classification of a specific land-cover class: SVDD classification of fenland. IEEE Trans Geosci Remote Sens 45:1061–1073CrossRefGoogle Scholar
  45. Sanger JE (1971) Quantitative investigation of leaf pigments from their inception in buds through autumn coloration to decomposition in falling leaves. Ecology 52:1075–1089CrossRefGoogle Scholar
  46. Schmidt KS, Skidmore AK (2003) Spectral discrimination of vegetation types in a coastal wetland. Remote Sens Environ 85:92–108CrossRefGoogle Scholar
  47. Schmidtlein S, Sassin J (2004) Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sens Environ 92:126–138CrossRefGoogle Scholar
  48. Schmidtlein S, Feilhauer H, Bruelheide H (2012) Mapping plant strategy types using remote sensing. J Veg Sci 23:395–405CrossRefGoogle Scholar
  49. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443–1471CrossRefGoogle Scholar
  50. Schuster C, Ali I, Lohmann P, Frick A, Förster M, Kleinschmit B (2011) Towards detecting swath events in TerraSAR-X time series to establish NATURA 2000 grassland habitat swath management as monitoring parameter. Remote Sens 3:1308–1322CrossRefGoogle Scholar
  51. Smola AJ, Schölkopf B (2004) A tutorial on Support Vector Regression. Stat Comput 14:199–222CrossRefGoogle Scholar
  52. Somodi I, Čarni A, Ribeiro D, Podopnikar T (2012) Recognition of the invasive species Robinia pseudoacacia from combines remote sensing and GIS sources. Biol Conserv 150:59–67CrossRefGoogle Scholar
  53. Sorby HC (1873) On comparative vegetable chromatography. Proc R Soc Lond 21:442–483CrossRefGoogle Scholar
  54. Spanhove T, Van den Borre J, Delalieux S, Haest B, Paelinckx D (2012) Can remote sensing estimate fine-scale quality indicators of natural habitats? Ecol Indic 18:403–412CrossRefGoogle Scholar
  55. Tax DMJ, Duin RPW (2004) Support vector data description. Mach Learn 54:45–66CrossRefGoogle Scholar
  56. Townsend PA, Walsh SJ (2001) Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA. Plant Ecol 157:129–149CrossRefGoogle Scholar
  57. Townsend AR, Asner GP, Cleveland CC (2008) The biochemical heterogeneity of tropical forests. Trends Ecol Evolut 23:424–431CrossRefGoogle Scholar
  58. Tuanmu MN, Vina A, Bearer S, Xu W, Ouyang Z, Zhang H, Liu J (2010) Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sens Environ 114:1833–1844CrossRefGoogle Scholar
  59. Underwood E, Ustin S, DiPietro D (2003) Mapping nonnative plants using hyperspectral imagery. Remote Sens Environ 86:150–161CrossRefGoogle Scholar
  60. Van den Borre J, Paelinckx D, Mücher CA, Kooistra L, Haest B, De Blust G, Schmidt AM (2011) Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward. J Nat Conserv 19:116–125CrossRefGoogle Scholar
  61. Walker JS, Briggs JM (2007) An object-oriented approach to urban forest mapping in phoenix. Photogramm Eng Remote Sens 73:577–583CrossRefGoogle Scholar
  62. Wang F (1990) Improving remote sensing image analysis through fuzzy information representation. Photogramm Eng Remote Sens 56:1163–1169Google Scholar
  63. Waser LT, Baltsavias E, Ecker K, Eisenbeiss H, Feldmeyer-Christe E, Ginzler C, Küchler M, Thee P, Zhang L (2008) Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR-aerial images. Remote Sens Environ 112:1956–1968CrossRefGoogle Scholar
  64. Whittaker RH (1960) Vegetation of the Siksiyou Mountains, Oregon and California. Ecol Monogr 30:279–338CrossRefGoogle Scholar
  65. Wiens J, Sutter R, Anderson M, Blanchard J, Barnett A, Aguilar-Amuchastegui N, Avery C, Laine S (2009) Selecting and conserving lands for biodiversity: the role of remote sensing. Remote Sens Environ 113:1370–1381CrossRefGoogle Scholar
  66. Wold S, Sjöström M, Eriksson L (2001) PLS-regression. A basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130CrossRefGoogle Scholar
  67. Wood TF, Foody GM (1989) Analysis and representation of vegetation continua from Landsat Thematic Mapper data for lowland heaths. Int J Remote Sens 10:181–191CrossRefGoogle Scholar
  68. Wulder M, Hall RJ, Coops NC, Franklin SE (2004) High spatial resolution remotely sensed data for ecosystem characterization. BioScience 5:511–521CrossRefGoogle Scholar
  69. Wulder MA, Han T, White JC, Sweda T, Tsuzuki H (2007) Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization. Remote Sens Environ 110:123–137CrossRefGoogle Scholar
  70. Xie Y, Sha Z, Yu M (2008) Remote sensing imagery in vegetation mapping: a review. J Plant Ecol 1:9–23CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Sebastian Schmidtlein
    • 1
  • Ulrike Faude
    • 2
  • Stefanie Stenzel
    • 1
  • Hannes Feilhauer
    • 3
  1. 1.Institute of Geography and GeoecologyKIT KarlsruheKarlsruheGermany
  2. 2.EFTAS MünsterMünsterGermany
  3. 3.Institute of GeographyFAU Erlangen-NürnbergErlangenGermany

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