Habitat suitability mapping for sand cat (Felis margarita) in Central Iran using remote sensing techniques

Abstract

One of the primary reason of species extinction especially rare species with very specific requirements, is habitat destruction. To protect these species, habitat suitability evaluation plays a central role. Hence, an attempt is made in this study to evaluate the suitability of sand cat’s habitat in a sand dune-dominated landscape in Iran. Four Landsat-derived indices including Normalized Difference Vegetation Index (NDVI), Weighted Difference Vegetation Index, Brightness Index (BI) and Salinity Index were combined to characterize sand cat’s habitat requirements through a land use land cover (LULC) map. Furthermore, a set of landscape metrics were employed to explore the spatial pattern LULC classes. Sand cat’s habitat suitability map was generated by linear combination of the standardized and relatively weighted NDVI and BI indices and then categorized into five classes of most suitable, highly suitable, moderately suitable, least suitable and not suitable. The results showed that about 75% of the total area is suitable for sand cat. Although this region is rich in biodiversity, it has not yet been subject to any conservation planning and should be granted more conservation attentions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Abdollahi, S. (2015). Modeling habitat requirements of leopard (Panthera pardus) using genetic algorithm in Golestan National Park. Environmental Resources Research, 3, 151–162.

    Google Scholar 

  2. 2.

    Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2), 147–186.

    Article  Google Scholar 

  3. 3.

    Crall, A. W., Jarnevich, C. S., Panke, B., Young, N., Renz, M., & Morisette, J. (2013). Using habitat suitability models to target invasive plant species surveys. Ecological Applications, 23(1), 60–72.

    Article  Google Scholar 

  4. 4.

    Pearson, R. G., Dawson, T. P., & Liu, C. (2004). Modelling species distributions in Britain: A hierarchical integration of climate and land-cover data. Ecography, 27(3), 285–298.

    Article  Google Scholar 

  5. 5.

    Gomez, J. J., Túnez, J. I., Fracassi, N., & Cassini, M. H. (2014). Habitat suitability and anthropogenic correlates of Neotropical river otter (Lontra longicaudis) distribution. Journal of Mammalogy, 95(4), 824–833.

    Article  Google Scholar 

  6. 6.

    Olivier, P. I., Aarde, R. J., & Lombard, A. T. (2013). The use of habitat suitability models and species–area relationships to predict extinction debts in coastal forests, South Africa. Diversity and distributions, 19(11), 1353–1365.

    Article  Google Scholar 

  7. 7.

    Dong, Z., Wang, Z., Liu, D., Li, L., Ren, C., Tang, X., et al. (2013). Assessment of habitat suitability for waterbirds in the West Songnen Plain, China, using remote sensing and GIS. Ecological Engineering, 55, 94–100.

    Article  Google Scholar 

  8. 8.

    Fourcade, Y., Engler, J. O., Rödder, D., & Secondi, J. (2014). Mapping species distributions with MAXENT using a geographically biased sample of presence data: A performance assessment of methods for correcting sampling bias. PLoS ONE, 9(5), e97122.

    Article  Google Scholar 

  9. 9.

    Carvalho, J. C., & Gomes, P. (2003). Habitat suitability model for European wild rabbit (Oryctolagus cuniculus) with implications for restocking. Game and Wildlife Science, 20, 287–301.

    Google Scholar 

  10. 10.

    Gavashelishvili, A., & Lukarevskiy, V. (2008). Modelling the habitat requirements of leopard Panthera pardus in west and central Asia. Journal of Applied Ecology, 45(2), 579–588.

    Article  Google Scholar 

  11. 11.

    Brotons, L., Thuiller, W., Araújo, M. B., & Hirzel, A. H. (2004). Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27(4), 437–448.

    Article  Google Scholar 

  12. 12.

    Rose, R. A., Byler, D., Eastman, J. R., Fleishman, E., Geller, G., Goetz, S., et al. (2015). Ten ways remote sensing can contribute to conservation. Conservation Biology, 29(2), 350–359.

    Article  Google Scholar 

  13. 13.

    Leitão, A. B., Miller, J., Ahern, J., & McGarigal, K. (2012). Measuring landscapes: A planner’s handbook. Washington: Island press.

    Google Scholar 

  14. 14.

    Farina, A. (2006). Principles and methods in landscape ecology: Towards a science of the landscape. Landscape series: Springer.

  15. 15.

    Bhatta, B. (2010). Analysis of urban growth and sprawl from remote sensing data. Advanced in Geographic Information Science. https://doi.org/10.1007/978-3-642-05299-6_1.

    Article  Google Scholar 

  16. 16.

    Asgarian, A., Amiri, B. J., & Sakieh, Y. (2015). Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosystems, 18(1), 209–222.

    Article  Google Scholar 

  17. 17.

    McGarigal, K., Cushman, S. A., Neel, M. C. & Ene, E. (2002). FRAGSTATS: Spatial pattern analysis program for categorical maps.

  18. 18.

    Holzkämper, A., Lausch, A., & Seppelt, R. (2006). Optimizing landscape configuration to enhance habitat suitability for species with contrasting habitat requirements. Ecological Modelling, 198(3), 277–292.

    Article  Google Scholar 

  19. 19.

    Mallon, D. P., Sliwa, A., & Strauss, M. (2011). Felis margarita. The IUCN Red List of Threatened Species., 2011, e.T8541A12917127. https://doi.org/10.2305/IUCN.UK.2011-2.RLTS.T8541A12917127.en.

    Google Scholar 

  20. 20.

    Sliwa, A., Breton, G. & Chevalier, F. (2013). Sand Cat sightings in the Moroccan Sahara. Cat news, IUCN.

  21. 21.

    Marino, J., Bennett, M., Cossios, D., Iriarte, A., Lucherini, M., Pliscoff, P., et al. (2011). Bioclimatic constraints to Andean cat distribution: A modelling application for rare species. Diversity and Distributions, 17(2), 311–322.

    Article  Google Scholar 

  22. 22.

    Hemami, M. R., Esmaeili, S. & Akbari fayzabadi, H. (2013). Distribution and presence frequency of Sand Cat in Naein Twinship, Isfahan Province. In National conference of desert biomes. Isfahan: Islamic Azad University, Najaf-Abad Branch.

  23. 23.

    Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation (7th ed.). Hoboken: Wiley.

    Google Scholar 

  24. 24.

    Hansen, M. C., & Loveland, T. R. (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66–74.

    Article  Google Scholar 

  25. 25.

    ENVI. (2009). Atmospheric correction module: QUAC and FLAASH user’s guide. Version, 4, 44.

    Google Scholar 

  26. 26.

    Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903.

    Article  Google Scholar 

  27. 27.

    Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.

    Article  Google Scholar 

  28. 28.

    Rouse Jr, J. W., Haas, R., Schell, J. & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Presented at Third ERTS symposium, NASA SP-351.

  29. 29.

    Mutanga, O., & Skidmore, A. K. (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25(19), 3999–4014.

    Article  Google Scholar 

  30. 30.

    Gorji, T., Sertel, E., & Tanik, A. (2017). Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Ecological Indicators, 74, 384–391.

    Article  Google Scholar 

  31. 31.

    Jensen, J. R. (2007). Remote sensing of the environment: An earth resource perspective (2nd ed.). Delhi: Pearson Education India.

    Google Scholar 

  32. 32.

    Ghafaripour, S., Naderi, M., & Rezaei, H. R. (2017). Investigating abundance, density and potential threats of Sand cat in the South-Eastern parts of Iran. Journal of Wildlife and Biodiversity, 1(1), 47–55.

    Google Scholar 

  33. 33.

    Eastman, J. R. (2015). Terrset manual, Clark Lab.

  34. 34.

    Jiang, H., & Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173–184.

    Article  Google Scholar 

  35. 35.

    Zohmann, M., Pennerstorfer, J., & Nopp-Mayr, U. (2013). Modelling habitat suitability for alpine rock ptarmigan (Lagopus muta helvetica) combining object-based classification of IKONOS imagery and Habitat Suitability Index modelling. Ecological Modelling, 254, 22–32.

    Article  Google Scholar 

  36. 36.

    Hemami, M. R., Ismaeeli, S. & Akbari, H. (2010) Dispersion and abundance of sand cat (Felis margarita) in Abbasabad Wildlife Refuge. In National conference on Biodiversity, Iran.

  37. 37.

    Castillo, E. M., García-Martin, A., Aladrén, L. A. L., & de Luis, M. (2015). Evaluation of forest cover change using remote sensing techniques and landscape metrics in Moncayo Natural Park (Spain). Applied Geography, 62, 247–255.

    Article  Google Scholar 

  38. 38.

    Elith, J., & Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology Evolution and Systematics, 40, 677–697.

    Article  Google Scholar 

  39. 39.

    Guillera-Arroita, G., Lahoz-Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., et al. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography, 24(3), 276–292.

    Article  Google Scholar 

  40. 40.

    Xiaofeng, L., Yi, Q., Diqiang, L., Shirong, L., Xiulei, W., Bo, W., et al. (2011). Habitat evaluation of wild Amur tiger (Panthera tigris altaica) and conservation priority setting in north-eastern China. Journal of Environmental Management, 92(1), 31–42.

    Article  Google Scholar 

Download references

Acknowledgement

We would like to thank Mohamad Reza Halvani and Ardeshir Khosravi for contribution in field surveys, Mohsen Ahmadi for his valuable comments and Behnam Rasti for improving the use of English.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Shiva Torabian.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 14 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Torabian, S., Soffianian, A., Fakheran, S. et al. Habitat suitability mapping for sand cat (Felis margarita) in Central Iran using remote sensing techniques. Spat. Inf. Res. 26, 11–20 (2018). https://doi.org/10.1007/s41324-017-0152-0

Download citation

Keywords

  • Habitat suitability mapping
  • Remote sensing
  • Landsat
  • GIS
  • Sand cat