Skip to main content

Advertisement

SpringerLink
Log in

Predictive Mapping of Plant Species and Communities Using GIS and Landsat Data in a Southern Mongolian Mountain Range

  • Published:
Folia Geobotanica Aims and scope Submit manuscript

Abstract

We assessed presence/absence prediction of plant species and communities in a southern Mongolian mountain range from geospatial data using a randomized sampling approach. One hundred randomized vegetation samples (3 × 3 m) were collected within the 2 × 2 km summit region of the Dund Saykhan range, which forms part of the core zone of the Gobi Gurvan Saykhan National Park in arid southern Mongolia. Using logistic regression, habitat preference models for all abundant species (n = 52) and communities (n = 5) were constructed; predictors were derived from Landsat 5 imagery and a digital elevation model. Nagelkerkes r 2 was used for an initial data mining, and all significant models were validated by splitting the data and using one half for accuracy assessment based on the AUC (Area Under the receiver operating characteristic Curve)-values. Significant models could be built for half of the species. Altitude proved to be the most important predictor followed by variables derived from Landsat data. The clear altitudinal distribution patterns most definitely reflect precipitation; overall biodiversity in this arid environment is widely controlled by moisture availability. The chosen approach may prove valuable for applied studies wherever spatial data on species distributions are required for conservation efforts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AIC:

Akaike information criterion

AUC:

area under the receiver operating characteristic curve

GAM:

generalized additive model

GIS:

geographical information system

GPS:

global positioning system

NDVI:

normalized difference vegetation index

PCA:

principal components analysis

SRTM:

shuttle radar topography mission

TC:

Tasseled Cap

TNDVI:

transformed normalized difference vegetation index

VEGIN:

vegetation index (channel 4  −  channel 3)

References

  • Akaike H (1978) A Bayesian analysis of the minimum AIC procedure. Ann Inst Stat Math 30:9–14

    Article  Google Scholar 

  • Balmford A, Gaston KJ (1999) Why biodiversity surveys are good value. Nature 398:204–205

    Article  CAS  Google Scholar 

  • Bergeron Y, Leduc A, Li TX (1997) Explaining the distribution of Pinus spp. in a Canadian boreal insular landscape. J Veg Sci 8:37–44

    Article  Google Scholar 

  • Bernstein R (1983) Image geometry and rectification. In Colwell RN (eds) Manual of remote sensing. American Society of Photogrammetry, Falls Church

    Google Scholar 

  • Beyer HL (2004) Hawth’s analysis tools for ArcGIS. Available at: http://www.spatialecology.com/htools

  • Bjelland T (2003) The influence of environmental factors on the spatial distribution of saxicolous lichens in a Norwegian coastal community. J Veg Sci 14:525–534

    Article  Google Scholar 

  • Brotons L, Thuiller W, Araujo M, Hirzel AM (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437–448

    Article  Google Scholar 

  • Brown DG (1994) Predicting vegetation at treeline using topography and biophysical disturbance variables. J Veg Sci 5:641–656

    Article  Google Scholar 

  • Bruun HH, Moen J, Virtanen R, Grytnes JA, Oksanen L, Angerbjörn A (2006) Effects of altitude and topography on species richness of vascular plants, bryophytes and lichens in alpine communities. J Veg Sci 17:37–46

    Article  Google Scholar 

  • Campbell JB (1996) Introduction to remote sensing. The Guilford Press, New York

    Google Scholar 

  • Chytrý M (2001) Phytosociological data give biased estimates of species richness. J Veg Sci 12:439–444

    Article  Google Scholar 

  • Cohen WB, Goward SN (2004) Landsat's role in ecological applications of remote sensing. Bioscience 54:535–545

    Article  Google Scholar 

  • Crawley MJ (2005) Statistics: An introduction using R. Wiley & Sons, Chichester

    Google Scholar 

  • Crist EP, Cicone RC (1984) A physically-based transformation of thematic mapper data − the Tm Tasseled Cap. IEEE Trans Geosci Remote 22:256–263

    Article  Google Scholar 

  • Dirnböck T, Dullinger S, Gottfried M, Ginzler C, Grabherr G (2003) Mapping alpine vegetation based on image analysis, topographic variables and Canonical Correspondence Analysis. Appl Veg Sci 6:85–96

    Google Scholar 

  • Dogan HM (2007) Applications of remote sensing and Geographic Information Systems to assess ferrous minerals and iron oxide of Tokat province in Turkey. Int J Remote Sensing 29:221–233

    Article  Google Scholar 

  • Epstein HE, Lauenroth WK, Burke IC, Coffin DP (1996) Ecological responses of dominant grasses along two climatic gradients in the Great Plains of the United States. J Veg Sci 7:777–788

    Article  Google Scholar 

  • Ewald J (2003) A critique for phytosociology. J Veg Sci 14:291–296

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence-absence models. Environm Conservation 24:38–49

    Article  Google Scholar 

  • Goodchild MF (1994) Integrating GIS and remote-sensing for vegetation and analysis and modeling − methodological issues. J Veg Sci 5:615–626

    Article  Google Scholar 

  • Grubov VI (2000) Plants of Central Asia. Vol. I–VIII. Science Publishers, Enfield

    Google Scholar 

  • Grubov VI (2001) Key to the vascular plants of Mongolia. Vol. I & II. Science Publishers, Enfield

    Google Scholar 

  • Gubanov IA (1996) Conspectus of the Flora of outer Mongolia (vascular plants). Valang Publishers, Moscow

    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, Weiss SB, Weiss AD (1999) GLM versus CCA spatial modelling of plant species distribution. Pl Ecol 143:107–122

    Article  Google Scholar 

  • Hanley JA, McNeil BJ (1982) The meaning and use of the area under a ROC curve. Radiology 143:29–36

    CAS  PubMed  Google Scholar 

  • Heegaard E (2002) A model of alpine species distribution in relation to snowmelt time and altitude. J Veg Sci 13:493–504

    Article  Google Scholar 

  • Hilbig W (1995) The vegetation of Mongolia. SPB Academic Publishing, Amsterdam

    Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley & Sons, New York

    Book  Google Scholar 

  • Jäger EJ (2005) The occurrence of forest plants in the desert mountains of Mongolia and their bearing on the history of the climate. Explor Biol Resources Mongolia 9:237–245

    Google Scholar 

  • Jarvis A, Reuter HI, Nelson A, Guevara E (2006) Hole-filled SRTM for the globe. Version 3. Available from the CGIAR-CSI SRTM 90m Database: http://srtm.csi.cgiar.org

  • Kauth RJ, Thomas GS (1976) The Tasseled Cap − A graphic description of the spectral-temporal development of agricultural crops as seen by LANDSAT. In Anonymous (ed) Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. Purdue University of West Lafayette, Indiana, pp. 4B-41—4B-51

  • Kürschner H (2004) Phytosociological studies in the Alashan Gobi − a contribution to the flora and vegetation of Inner Mongolia (NW China). Phytocoenologia 34:169–224

    Article  Google Scholar 

  • Lenihan JM (1993) Ecological response-surface for North-American boreal tree species and their use in forest classification. J Veg Sci 4:667–680

    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 

  • Martorell C, Ezcurra E (2002) Rosette scrub occurrence and fog availability in arid mountains of Mexico. J Veg Sci 13:651–662

    Article  Google Scholar 

  • McCune B, Grace JB, Urban DL (2002) Analysis of ecological communities. MjM Software Design, Gleneden Beach

    Google Scholar 

  • Miehe G, Opgenoorth L, Cermak J, Schlütz F, Jäger EJ, Samiya R, Wesche K (2007) Mountain forest islands and Holocene environmental changes in Central Asia: A case study from the southern Gobi Altay, Mongolia. Palaeogeogr Palaeoclimatol Palaeoecol 250:150–166

    Article  Google Scholar 

  • Moisen GG, Edwards TE Jr, Osborne PE (2006) Further advances in predicting species distributions. Ecol Modelling 199:129–131

    Article  Google Scholar 

  • Mucina L (1997) Classification of vegetation: Past, present and future. J Veg Sci 8:751–760

    Article  Google Scholar 

  • Mucina L, Schaminée JHJ, Rodwell JS (2000) Common data standards for recording relevés in field survey for vegetation classification. J Veg Sci 11:769–772

    Article  Google Scholar 

  • Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Noy-Meir I (1973) Desert ecosytems: environment and producers. Annual Rev Ecol Syst 4:25–41

    Article  Google Scholar 

  • Pausas JG (1997) Resprouting of Quercus suber in NE Spain after fire. J Veg Sci 8:703–706

    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 

  • Peppler-Lisbach C, Schröder B (2004) Predicting the species composition of Nardus stricta communities by logistic regression modelling. J Veg Sci 15:623–634

    Google Scholar 

  • Polasky S, Solow AR (2001) The value of information in reserve site selection. Biodivers Conservation 10:1051–1058

    Article  Google Scholar 

  • Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission − a new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogrammetry Remote Sensing 57:241–262

    Article  Google Scholar 

  • R Development Core Team (2007) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Reading RP, Bedunah DJ, Amgalanbaatar S (2006) Conserving biodiversity on Mongolian rangelands: implications for protected area development and pastoral uses. In Bedunah DJ, McArthur ED, Fernandez-Gimenez M (eds) Rangelands of Central Asia: Proceedings of the Conference on Transformations, Issues, and Future Challenges. United States Department of Agriculture Forest Service Rocky Mountain Research Station, Salt Lake City, pp 1–17

    Google Scholar 

  • Reading RP, Johnstad MD, Batjargal Z, Amgalanbaatar S, Mix HM (1999) Expanding Mongolia's system of protected areas. Nat Areas J 19:211–222

    Google Scholar 

  • Retzer V (2004) Carrying capacity and forage competition between livestock and a small mammal, the Mongolian Pika (Ochotona pallasi) in a non-equilibrium ecosystem, South-Gobi, Mongolia. Görich & Weiershäuser Verlag, Marburg

    Google Scholar 

  • Retzer V, Nadrowski K, Miehe G (2006) Variation of precipitation and its effect on phytomass production and consumption by livestock and large wild herbivores along an altitudinal gradient during a drought, South Gobi, Mongolia. J Arid Environm 66:135–150

    Article  Google Scholar 

  • Roback PJ, Askins RA (2005) Judicious use of multiple hypothesis tests. Conservation Biol 19:261–267

    Article  Google Scholar 

  • Rydgren K, Økland RH, Økland T (2003) Species response curves along environmental gradients. A case study from SE Norwegian swamp forests. J Veg Sci 14:869–880

    Google Scholar 

  • Shirakura F, Sasaki K, Arevalo JR, Palmer MW (2006) Tornado damage of Quercus stellata and Quercus marilandica in the cross timbers, Oklahoma, USA. J Veg Sci 17:347–352

    Google Scholar 

  • Steyerberg EW, Eijkemans MJC, Habbema JDF (2001) Application of shrinkage techniques in logistic regression analysis: a case study. Statist Neerl 55:76–88

    Article  Google Scholar 

  • Thuiller W, Araujo MB, Lavorel S (2003) Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales. J Veg Sci 14:669–680

    Article  Google Scholar 

  • Tichý L (2002) JUICE, software for vegetation classification. J Veg Sci 13:451–453

    Article  Google Scholar 

  • van de Rijt C, Hazelhoff L, Blom C (1996) Vegetation zonation in a former tidal area: A vegetation-type response model based on DCA and logistic regression using GIS. J Veg Sci 7:505–518

    Article  Google Scholar 

  • von Wehrden H, Hilbig W, Wesche K (2006) Plant communities of the Mongolian Transaltay. Feddes Repert 117:526–570

    Article  Google Scholar 

  • von Wehrden H, Wesche K (2007) Relationships between climate, productivity and vegetation in southern Mongolian drylands. Basic Appl Dryland Res 2:100–120

    Google Scholar 

  • Wesche K, Ronnenberg K (2004) Phytosociological affinities and habitat preferences of Juniperus sabina L. and Artemisia santolinifolia Turcz. ex Bess. in mountain sites of the south-eastern Gobi Altay, Mongolia. Feddes Repert 115:585–600

    Article  Google Scholar 

  • Wesche K, Miehe S, Miehe G (2005) Plant communities of the Gobi Gurvan Sayhan National Park (South Gobi Aymak, Mongolia). Candollea 60:149–205

    Google Scholar 

  • Zimmermann NE, Kienast F (1999) Predictive mapping of alpine grasslands in Switzerland: Species versus community approach. J Veg Sci 10:469–482

    Article  Google Scholar 

Download references

Acknowledgements

Tuvshin’s effort was of great help during the randomized sampling, Tsolmon & Ojuna from the Mongolian State University organized permits. The administration of the Gobi Gurvan Saykhan National Park kindly granted working permits. Determination of difficult taxa would have been impossible without the help of Prof. Sanchir from the Mongolian Academy of Sciences. The base camp of this study was financed by the DFG and the GTZ. Additional financial support was granted by the FWF (project P18624) and the German Academic Exchange Service. We are grateful to Danny McCluskey, Petr Šmilauer and two anonymous reviewers, whose comments greatly improved the manuscript.

Author information

Authors and Affiliations

  1. Martin-Luther-University Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, 06108, Halle/Saale, Germany

    Henrik von Wehrden, Heike Zimmermann, Katrin Ronnenberg & Karsten Wesche

  2. Research Institute of Wildlife Ecology, Savoyen Strasse 1, 1160, Vienna, Austria

    Henrik von Wehrden

  3. UFZ, Helmholtz-Centre for Environmental Research – UFZ, Theodor-Lieser-Str. 4, 06120, Halle, Germany

    Jan Hanspach

Authors
  1. Henrik von Wehrden
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heike Zimmermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Hanspach
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katrin Ronnenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karsten Wesche
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henrik von Wehrden.

Rights and permissions

Reprints and permissions

About this article

Cite this article

von Wehrden, H., Zimmermann, H., Hanspach, J. et al. Predictive Mapping of Plant Species and Communities Using GIS and Landsat Data in a Southern Mongolian Mountain Range. Folia Geobot 44, 211–225 (2009). https://doi.org/10.1007/s12224-009-9042-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12224-009-9042-0

Keywords

Plant nomenclature

Search

Navigation