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Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

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Abstract

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.

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References

  • Ahti T, Hämet-Ahti L, Jalas J (1968) Vegetation zones and their sections in northwestern Europe. Annales Botanici Fennici 5:169–211

    Google Scholar 

  • Akaike H (1974) A new look at statistical model identification. IEEE Trans Autom Control AU-19:716–722

    Google Scholar 

  • Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688

    Article  Google Scholar 

  • Araújo MB, Pearson RG, Thuiller W, Erhard M (2005) Validation of species-climate impact models under climate change. Glob Change Biol 11:1504–1513

    Article  Google Scholar 

  • Astorga A, Heino J, Luoto M, Muotka T (2011) Freshwater biodiversity at regional extent: determinants of macroinvertebrate taxonomic richness in headwater streams. Ecography 34:705–713

    Article  Google Scholar 

  • Atlas of Finland (1987) Climate, Folio 131. National Board of Survey & Geographical Society of Finland, Helsinki

  • Atlas of Finland (1990) Geology, Folio 123-126. National Board of Survey & Geographical Society of Finland, Helsinki

  • Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118

    Article  Google Scholar 

  • Austin M (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200:1–19

    Article  Google Scholar 

  • Austin M, Meyers JA (1996) Current approaches to modelling the environmental niche of eucalypts: implication for management of forest biodiversity. For Ecol Manage 85:95–106

    Article  Google Scholar 

  • Austin MP, Van Niel KP (2011a) Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales, Australia. J Biogeogr 38:9–19

    Article  Google Scholar 

  • Austin MP, Van Niel KP (2011b) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8

    Article  Google Scholar 

  • Bartel RA, Sexton JO (2009) Monitoring habitat dynamics for rare and endangered species using satellite images and niche-based models. Ecography 32:888–896

    Article  Google Scholar 

  • Bonan GB, Shugart HH (1989) Environmental factors and ecological processes in boreal forests. Annu Rev Ecol Syst 20:1–28

    Article  Google Scholar 

  • Box EO, Holben BN, Kalb V (1989) Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux. Vegetatio 80:71–89

    Article  Google Scholar 

  • Buermann W, Saatchi S, Smith TB, Zutta BR, Chaves JA, Milá B, Graham CH (2008) Predicting species distributions across the Amazonian and Andean regions using remote sensing data. J Biogeogr 35:1160–1176

    Article  Google Scholar 

  • Carroll C, Johnson DS (2008) The importance of being spatial (and reserved): assessing northern spotted owl habitat relationships with hierarchical Bayesian models. Conserv Biol 22:1026–1036

    Google Scholar 

  • Cohen WB, Spies TA, Fiorella M (1995) Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, USA International. J Remote Sens 16:721–746

    Article  Google Scholar 

  • Cord A, Rödder D (2011) Inclusion of habitat availability in species distribution models through multi-temporal remote-sensing data? Ecol Appl 21:3285–3298

    Article  Google Scholar 

  • Cramer W, Kicklighter DW, Bondeau A, Moore B, Churkina C, Nemry B, Ruimy A, Schloss AL (1999) Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Glob Change Biol 5:1–15

    Article  Google Scholar 

  • Crist EP (1985) A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens Environ 17:301–306

    Article  Google Scholar 

  • Crist EP, Cicone RC (1984) A physically-based transformation of thematic mapper data—the TM tasseled cap. IEEE Trans Geosci Remote Sens 22:256–263

    Article  Google Scholar 

  • Currie DJ (1991) Energy and large-scale patterns of animal- and plant-species richness. Am Nat 137:27–49

    Article  Google Scholar 

  • de Siqueira MF, Durigan G, De Marco P Jr, Peterson AT (2009) Something from nothing: using landscape similarity and ecological niche modeling to find rare plant species. J Nat Conserv 17:25–32

    Article  Google Scholar 

  • Dymond CC, Mladenoff DJ, Radeloff VC (2002) Phenological differences in tasseled cap indices improve deciduous forest classification. Remote Sens Environ 80:460–472

    Article  Google Scholar 

  • Ekstrand S (1996) Landsat TM-based forest damage assessment: correction for topographic effects. Photogramm Eng Remote Sens 62:151–161

    Google Scholar 

  • Elith J, Leathwick JR (2009a) Conservation prioritization using species distribution models. In: Moilanen A, Wilson KA, Possingham HP (eds) Spatial conservation prioritization: quantitative methods and computational tools. Oxford University Press, Oxford, pp 70–93

    Google Scholar 

  • Elith J, Leathwick JR (2009b) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697

    Article  Google Scholar 

  • Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson KS, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    Google Scholar 

  • ESRI (1991) ARC/INFO user’s guide Cell-based modelling with GRID analysis, display and management. California, Environment Systems Research Institute, Inc, Redlands

    Google Scholar 

  • Fernandez N, Delibes M, Palomares F (2006) Landscape evaluation in conservation: molecular sampling and habitat modeling for the Iberian lynx. Ecol Appl 16:1037–1049

    Google Scholar 

  • Fielding A, Bell J (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49

    Article  Google Scholar 

  • Foody GM, Cutler MEJ (2003) Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. J Biogeogr 30:1053–1066

    Article  Google Scholar 

  • Franklin J (1995) Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19:474–499

    Article  Google Scholar 

  • Gessler PE, Chadwick OA, Chamran F, Althouse L, Holmes K (2000) Modeling soil-landscape and ecosystem properties using terrain attributes. Soil Sci Soc Am J 64:2046–2056

    Article  CAS  Google Scholar 

  • Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S (2008) Measuring and modelling biodiversity from space. Prog Phys Geogr 32:203–221

    Article  Google Scholar 

  • Gould WA (2000) Remote sensing of vegetation, plant species richness, and regional diversity hotspots. Ecol Appl 10:1861–1870

    Article  Google Scholar 

  • Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009

    Article  Google Scholar 

  • Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186

    Article  Google Scholar 

  • Guisan A, Theurillat JP, Kienast F (1998) Predicting the potential distribution of plant species in an alpine environment. J Veg Sci 9:65–74

    Article  Google Scholar 

  • Guisan A, Edwards TCJ, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89–100

    Article  Google Scholar 

  • Guisan A, Broennimann O, Engler R, Vust M, Yoccoz NG, Lehmann A, Zimmermann NE (2006) Using niche-based models to improve the sampling of rare species. Conserv Biol 20:501–511

    Article  PubMed  Google Scholar 

  • Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT (2007) What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? Ecol Monogr 77:615–630

    Article  Google Scholar 

  • H-Acevedo D, Currie DJ (2003) Does climate determine broad-scale patterns of species richness? A test of the causal link by natural experiment. Glob Ecol Biogeogr 12:461–473

    Article  Google Scholar 

  • Härmä P, Teiniranta R, Törmä M, Repo R, Järvenpää E, Kallio M (2004) Production of CORINE2000 land cover data using calibrated LANDSAT 7 ETM satellite image mosaics and digital maps in Finland. In: IEEE International Geoscience and Remote Sensing Symposium, 20–24 Sept 2004, Anchorage, Alaska, IEEE

  • Hastie T, Tibshirani R (1990) Generalized additive models. Chapman and Hall, London

    Google Scholar 

  • Heikkinen O (2005) Boreal forests and northern upper timberlines. In: Seppälä M (ed) The physical geography of fennoscandia. Oxford University Press, Oxford, pp 185–200

    Google Scholar 

  • Heikkinen RK, Birks HJB, Kalliola RJ (1998) A numerical analysis of the mesoscale distribution patterns of vascular plants in the Kevo Nature Reserve, northern Finland. J Biogeogr 25:123–146

    Article  Google Scholar 

  • Heikkinen RK, Luoto M, Araújo MB, Virkkala R, Thuiller W, Sykes MT (2006) Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog Phys Geogr 30:751–777

    Article  Google Scholar 

  • Heikkinen RK, Marmion M, Luoto M (2012) Does the interpolation accuracy of species distribution models come at the expense of transferability? Ecography 35:276–288

    Article  Google Scholar 

  • Hjort J, Luoto M (2006) Modelling patterned ground distribution in Finnish Lapland: an integration of topographical, ground and remote sensing information. Geografiska Annaler 88A:19–29

    Article  Google Scholar 

  • Huang C, Wylie B, Yang L, Homer C, Zylstra G (2002) Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. Int J Remote Sens 23:1741–1748

    Article  Google Scholar 

  • Huntley B (1995) Plant species’ response to climate change: implications for the conservation of European birds. Ibis 137(Supplement 1):127–138

    Google Scholar 

  • Huntley B, Berry PM, Cramer W, McDonald AP (1995) Modelling present and potential future ranges of some European higher plants using climate response surfaces. J Biogeogr 22:967–1001

    Article  Google Scholar 

  • Jimenez-Valverde A, Lobo JM (2007) Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecol 31:361–369

    Article  Google Scholar 

  • John R, Chen J, Lu N, Guo K, Liang C, Wei Y, Noormets A, Ma K, Han X (2008) Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia. Remote Sens Environ 112:2018–2032

    Article  Google Scholar 

  • Karka S, van Rensburgb BJ (2006) Ecotones: marginal or central areas of transition? Israel J Ecol Evol 52:29–53

    Article  Google Scholar 

  • Kerr J, Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends Ecol Evol 18:299–305

    Article  Google Scholar 

  • Kivinen S, Luoto M, Heikkinen RK, Saarinen K, Ryttäri T (2008) Threat spots and environmental determinants of red-listed plant, butterfly and bird species in boreal agricultural environments. Biodivers Conserv 17:3289–3305

    Article  Google Scholar 

  • Lehmann A, Overton J, Leathwick J (2002) GRASP: generalized regression analysis and spatial prediction. Ecol Model 157:189–207

    Article  Google Scholar 

  • Levin N, Shmida A, Levanoni O, Tamari H, Kark S (2007) Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Divers Distrib 13:692–703

    Article  Google Scholar 

  • Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600

    Article  Google Scholar 

  • Luoto M, Toivonen T, Heikkinen RK (2002) Prediction of total and rare plant species richness in agricultural landscapes from satellite images and topographic data. Landsc Ecol 17:195–217

    Article  Google Scholar 

  • Luoto M, Pöyry J, Heikkinen RK, Saarinen K (2005) Uncertainty of bioclimate envelope models based on geographical distribution of species. Glob Ecol Biogeogr 14:575–584

    Article  Google Scholar 

  • Maggini R, Lehmann A, Zimmermann NE, Guisan A (2006) Improving generalized regression analysis for the spatial prediction of forest communities. J Biogeogr 33:1729–1749

    Article  Google Scholar 

  • Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence-absence models in ecology: the need to account for prevalence. J Appl Ecol 38:921–931

    Article  Google Scholar 

  • McPherson JM, Jetz W (2007) Effect of species’ ecology on the accuracy of distribution models. Ecography 30:135–151

    Google Scholar 

  • Muldavin EH, Neville P, Harper G (2001) Indices of grassland biodiversity in the Chihuahuan desert ecoregion derived from remote sensing. Conserv Biol 15:844–855

    Article  Google Scholar 

  • Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995) The interpretation of spectral vegetation indexes. IEEE Trans Geosci Remote Sens 33:481–486

    Article  Google Scholar 

  • Nagendra H (2001) Using remote sensing to assess biodiversity. Int J Remote Sens 22:2377–2400

    Article  Google Scholar 

  • Newbold T (2010) Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Prog Phys Geogr 34:3–22

    Article  Google Scholar 

  • Palmer MW, Earls P, Hoagland BW, White PS, Wohlgemuth T (2002) Quantitative tools for perfecting species lists. Environmetrics 13:121–137

    Article  Google Scholar 

  • Parra JL, Graham CC, Freile JF (2004) Evaluating alternative data sets for ecological niche models of birds in the Andes. Ecography 27:350–360

    Article  Google Scholar 

  • Parviainen M, Luoto M, Ryttäri T, Heikkinen RK (2008) Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. J Biogeogr 35:1888–1905

    Article  Google Scholar 

  • Parviainen M, Luoto M, Heikkinen RK (2009) The role of local and landscape level productivity in modelling of boreal plant species richness. Ecol Model 220:2690–2701

    Article  Google Scholar 

  • Parviainen M, Luoto M, Heikkinen RK (2010) NDVI -based productivity and heterogeneity as indicators of species richness in boreal landscapes. Boreal Environ Res 15:301–318

    Google Scholar 

  • Pausas JG, Carreras J, Ferre A, Font X (2003) Coarse-scale plant species richness in relation to environmental heterogeneity. J Veg Sci 14:661–668

    Article  Google Scholar 

  • Pearce JL, Boyce MS (2006) Modelling distribution and abundance with presence-only data. J Appl Ecol 43:405–412

    Article  Google Scholar 

  • Pearson RG (2007) Species’ distribution modeling for conservation educators and practitioners. Synthesis. American Museum of Natural History http://ncepamnhorg

  • Pearson RG, Dawson TP, Liu C (2004) Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography 27:285–298

    Article  Google Scholar 

  • Peterson AT (2003) Projected climate change effects on Rocky Mountain and Great Plain birds: generalities on biodiversity consequences. Glob Change Biol 9:647–655

    Article  Google Scholar 

  • Ponder WF, Carter GA, Flemons P, Chapman RR (2001) Evaluation of museum collection data for use in biodiversity assessment. Conserv Biol 15:648–657

    Article  Google Scholar 

  • Pöyry J, Luoto M, Heikkinen RK, Saarinen K (2008) Species traits are associated with the quality of bioclimatic models. Glob Ecol Biogeogr 17:403–414

    Article  Google Scholar 

  • Randin CF, Dirnböck T, Dullinger S, Zimmerman NE, Zappa M, Guisan A (2006) Are niche-based species distribution models transferable in space. J Biogeogr 33:1689–1703

    Article  Google Scholar 

  • Rassi P, Hyvärinen E, Juslén A, Mannerkoski I (eds) (2010) The 2010 red list of Finnish species. Ministry of the Environment & Finnish Environment Institute, Helsinki

  • Raynolds MK, Walker DA, Maier HA (2006) NDVI patterns and phytomass distribution in the circumpolar Arctic. Remote Sens Environ 102:271–281

    Article  Google Scholar 

  • Redpath SM, Young J, Evely A, Adams WM, Sutherland WJ, Whitehouse A, Amar A, Lambert RA, Linnell JD, Watt A, Gutiérrez RJ (2013) Understanding and managing conservation conflicts. Trends Ecol Evol 28:100–109

    Article  PubMed  Google Scholar 

  • Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5:703–714

    Article  Google Scholar 

  • Rocchini D, Chiarucci A, Loiselle SA (2004) Testing the spectral variation hypothesis by using satellite multispectral images. Acta Oecol 26:117–120

    Article  Google Scholar 

  • Rocchini D, Balkenhol N, Carter GA, Foody GM, Gillespie TW, He KS, Kark S, Levin N, Lucas K, Luoto M, Nagendra H, Oldeland J, Ricotta C, Southworth J, Neteler M (2010) Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol Inform 5:318–329

    Article  Google Scholar 

  • Rogers AS, Kearney MS (2004) Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. Int J Remote Sens 25:2317–2335

    Article  Google Scholar 

  • Roura-Pascual N, Suarez AV, McNyset K, Gómez K, Pons P, Touyama Y, Wild A, Gascon F, Peterson AT (2006) Niche differentiation and fine-scale projections for Argentine ants based on remotely sensed data. Ecol Appl 16:1832–1841

    Article  PubMed  Google Scholar 

  • Rouse JW Jr, Hass RH, Deering DW, Schell JA, Harlan JC (1973) Monitoring the vernal advancement and retrogression (green wave effect) of natural vegetation NASA/GSFC Type III Final report Geenbelt, Maryland

  • Rushton SP, Ormerod SJ, Kerby G (2004) New paradigms for modelling species distributions? J Appl Ecol 41:193–200

    Article  Google Scholar 

  • Ryttäri T, Kettunen T (1997) Uhanalaiset kasvimme. Tampere, Suomen Ympäristökeskus, Kirjayhtymä Oy

  • Saatchi S, Buermann W, ter Steege H, Mori S, Smith TB (2008) Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sens Environ 112:2000–2017

    Article  Google Scholar 

  • Sala OE, Chapin FSI, Armesto JJ, Berlow E, Bloomfield J, Dirzo R (2000) Global biodiversity scenarios for the year 2100. Science 287:1770–1774

    Article  PubMed  CAS  Google Scholar 

  • Schmidtlein S, Feilhauer H, Bruelheide H (2012) Mapping plant strategy types using remote sensing. J Veg Sci 23:395–405

    Article  Google Scholar 

  • Schwarz M, Zimmermann NE (2005) A new GLM-based method for mapping tree cover continuous fields using MODIS reflectance data. Remote Sens Environ 95:428–443

    Article  Google Scholar 

  • Seoane J, Vinuela J, Díaz-Delgado R, Bustamante J (2003) The effects of land use and climate on red kite distribution in the Iberian peninsula. Biol Conserv 11:401–414

    Article  Google Scholar 

  • Seoane J, Carrascal LM, Alonso CL, Palomino D (2005) Species-specific traits associated to prediction errors in bird habitat suitability modelling. Ecol Model 185:299–308

    Article  Google Scholar 

  • Seto KC, Fleishman E, Fay JP, Betrus CJ (2004) Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI. Int J Remote Sens 25:4309–4324

    Article  Google Scholar 

  • Settele J, Hammen V, Hulme P, Karlson U, Klotz S, Kotarac M, Kunin W, Marion G, O’Connor M, Petanidou T, Peterson K, Potts S, Pritchard H, Pysek P, Rounsevell M, Spangenberg J, Steffan-Dewenter I, Sykes M, Vighi M, Zobel M, Kuhn I (2005) Alarm: assessing Large-scale environmental Risks for biodiversity with tested Methods GAIA. Ecol Perspect Sci Soc 14:69–72

    Google Scholar 

  • Skakun RS, Wulder MA, Franklin SE (2003) Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sens Environ 86:433–443

    Article  Google Scholar 

  • Skov F, Svenning J-C (2004) Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27:366–380

    Google Scholar 

  • Söyrinki N, Saari V (1980) Die Flora von Oulanka Nationalpark, Nordfinnland. Acta Botanica Fennica 114:1–149

    Google Scholar 

  • Thuiller W (2003) BIOMOD—optimizing predictions of species distributions and projecting potential future shifts under global change. Glob Change Biol 9:1353–1362

    Article  Google Scholar 

  • Thuiller W, Araújo MB, Lavorel S (2004) Do we need land-cover data to predict species distributions in Europe? J Biogeogr 31:353–361

    Article  Google Scholar 

  • Thuiller W, Lavorel S, Araújo MB (2005) Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob Ecol Biogeogr 14:347–357

    Article  Google Scholar 

  • Thuiller W, Albert C, Araújo MB, Berry PM, Cabeza M, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE (2008) Predicting global change impacts on plant species’ distributions: future challenges. Perspect Plant Ecol Evol Syst 9:137–152

    Article  Google Scholar 

  • Tucker CJ (1978) A comparison of satellite sensor bands for vegetation monitoring. Photogramm Eng Remote Sens 44:1369–1380

    Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  • Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing for biodiversity science and conservation. Trends Ecol Evol 18:306–314

    Article  Google Scholar 

  • Vajda A, Venäläinen A (2003) The influence of natural conditions on the spatial variation of climate in Lapland, northern Finland. Int J Climatol 23:1011–1022

    Article  Google Scholar 

  • Vasari Y, Tonkov S, Vasari A, Nikolova A (1996) The Late-quaternary history of the vegetation and flora in northeastern Finland in the light of a re-investigation of Aapalampi in Salla. Auqilo Ser Botany 36:27–41

    Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S. Springer-Verlag, Berlin

    Book  Google Scholar 

  • Venäläinen A, Heikinheimo M (2002) Meteorological data for agricultural applications. Phys Chem Earth 27:1045–1050

    Article  Google Scholar 

  • Virkkala R, Luoto M, Heikkinen RK, Leikola N (2005) Distribution patterns of boreal marshland birds: modelling the relationships to land cover and climate. J Biogeogr 32:1957–1970

    Article  Google Scholar 

  • Wang K, Franklin SE, Guo X, Cattet M (2010) Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists. Sensors 10:9647–9667

    Article  PubMed  Google Scholar 

  • Weiers S, Bock M, Wissen M, Rossner G (2004) Mapping and indicator approaches for the assessment of habitats at different scales using remote sensing and GIS methods. Landsc Urban Plan 67:43–65

    Article  Google Scholar 

  • Wilson CD, Roberts D, Reid N (2010) Applying species distribution modelling to identify areas of high conservation value for endangered species: A case study using Margaritifera margaritifera (L.). Biol Conserv 144:821–829

    Google Scholar 

  • Wood S, Augustin N (2002) GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol Model 157:157–177

    Article  Google Scholar 

  • Wright DH, Currie DJ, Maurer BA (1993) Energy supply and patterns of species richness on local and regional scales. In: Ricklefs RE, Schluter D (eds) Species diversity in ecological communities: historical and geographical perspectives. University of Chicago Press, Chicago, pp 66–74

    Google Scholar 

  • Wu XB, Smeins FE (2000) Multiple-scale habitat modeling approach for rare plant conservation. Landsc Urban Plan 51:11–28

    Article  Google Scholar 

  • Yee TW, Mitchell ND (1991) Generalized additive models in plant ecology. J Veg Sci 2:587–602

    Article  Google Scholar 

  • Young J, Watt A, Nowicki P, Alard D, Clitherow J, Henle K, Johnson R, Laczko E, McCracken D, Matouch S, Niemelä J, Richards C (2005) Towards sustainable land use: identifying and managing conflicts between human activities and biodiversity conservation in Europe. Biodivers Conserv 14:1641–1661

    Article  Google Scholar 

  • Zaniewski AE, Lehmann A, Overton JM (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Model 157:261–280

    Article  Google Scholar 

  • Zimmermann NE, Edwards TC, Moisen GG, Frescino TS, Blackard JA (2007) Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. J Appl Ecol 44:1057–1067

    Article  PubMed  Google Scholar 

  • Zimmermann NE, Edwards TC, Graham CH, Pearman PB, Svenning J-C (2010) New trends in species distribution modelling. Ecography 33:985–989

    Article  Google Scholar 

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Acknowledgments

A study of this nature would not have been possible without the hundreds of volunteers who contributed their data to the red-listed plant species database. M. J. Bailey helped with correction of the English text. Terhi Ryttäri helped in aggregating the species data for this study. Different parts of this research were funded by the Academy of Finland (project grant 116544) and the EC FP6 Integrated Projects ALARM (GOCE-CT-2003-506675) (Settele et al. 2005), ECOCHANGE (GOCE-2006-036866), and EU FP7 project SCALES (project #226852).

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Authors and Affiliations

  1. Finnish Forest Research Institute, University of Oulu, P.O. Box 413, 90014, Oulu, Finland

    Miia Parviainen

  2. Swiss Federal Research Institute WSL, 8903, Birmensdorf, Switzerland

    Niklaus E. Zimmermann

  3. Finnish Environment Institute, Natural Environment Centre, P.O. Box 140, 00251, Helsinki, Finland

    Risto K. Heikkinen

  4. Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014, Helsinki, Finland

    Miska Luoto

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  1. Miia Parviainen
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  2. Niklaus E. Zimmermann
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  3. Risto K. Heikkinen
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  4. Miska Luoto
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Correspondence to Miia Parviainen.

Appendix

Appendix

See Tables 6, 7, and 8.

Table 6 List of different habitats included in the five main habitat categories delimited for the study species
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Table 7 List of five Landsat 7 ETM+ images used in the study
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Table 8 Summary of the response shapes between the 28 red-listed vascular plant species and each environmental variable in the hybrid GAM models
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Parviainen, M., Zimmermann, N.E., Heikkinen, R.K. et al. Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species. Biodivers Conserv 22, 1731–1754 (2013). https://doi.org/10.1007/s10531-013-0509-1

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