THE METHOD FOR IDENTIFYING THE MOST VULNERABLE AREAS CAUSED BY EXOGENOUS PROCESSES UNDER ARIDIFICATION/HUMIDIFICATION (BASED ON GIS AND RS)

DOI: 10.24057/2414-9179-2017-3-23-93-104

View or download the article (Rus)

About the Authors

D. A. Chupina

Sobolev Institute of Geology and Mineralogy Siberian Branch of Russian Academy of Sciences
Russian Federation
Novosibirsk, Akademik Koptyug ave 3, 630090

I. D. Zolnikov

Sobolev Institute of Geology and Mineralogy Siberian Branch of Russian Academy of Sciences
Russian Federation
Novosibirsk, Akademik Koptyug ave 3, 630090

E. N. Smolentseva

Sobolev Institute of Geology and Mineralogy Siberian Branch of Russian Academy of Sciences
Russian Federation
Novosibirsk, Akademik Koptyug ave 3, 630090

Abstract

The paper presents the method of identifying the most vulnerable territories under exogenous processes caused by aridification/humidification. It is based on the assumption that some forms and types of relief increase resistance of terrestrial ecosystems to external influences, while other kinds of relief make them vulnerable. The relationship between landscape and moistening (ground and climatic) is of great importance to plains which have groundwater close to the surface. We have used morphometric analysis to divide the territory into hydromorphic and automorphic landscapes. Hydromorphic territories are those that are affected by additional surface moistening and groundwater, while automorphic landscapes are less dependent on groundwater under normal atmospheric moisture. The territory is ranked according to the degree of vulnerability by expert evaluation method. The developed approach is based entirely on using GIS software (ArcGIS 10.2.1) and processing the DEM SRTM. As a result, two models of vulnerability of natural terrestrial ecosystems to exogenic processes on Baraba Plain (Western Siberia) have been created for both aridification and humidification cases. The opportunity to estimate the vulnerability is the novel feature for these models of terrestrial ecosystems, in both regional and local scales. The results obtained confirm the existing ideas about the discrete mosaic character of changes in spatial landscape patterns in the area under consideration. For the southern part of Western Siberia where farming is risky the assessment of the potential degree of vulnerability for ecosystems under conditions of increasing climate aridity and extremes is relevant.

Keywords

aridification, Digital Elevation Model (DEM), SRTM, vulnerability of terrestrial ecosystems, hydromоrphic and automorphic landscapes, predictive modeling

References

  1. Bayarjargal Y., Karneli A. Indeksy NDVI i LST predstavlennye NOAA–AVHRR dlya vyyavleniya zasuh v Mongolii [NOAA–AVHRR derived NDVI and LST for detecting droughts in Mongolia]. Aridnyye ekosistemy, 2005, Vol. 11, No 26–27, pp. 73–78 (in Russian).
  2. Vtoroj ocenochnyj doklad Rosgidrometa ob izmeneniyah klimata i ih posledstviyah na territorii Rossijskoj Federacii. Tekhnicheskoe rezyume [The second assessed report about climatic changes and their impact on the Russian Federation territory by Hydromet. Technical Summary]. Moscow: Roshydromet Publ., 2014, 94 p. (in Russian).
  3. Zolotokrylin A.N., Titkova T.B. Sputnikovyy indeks klimaticheskikh ekstremumov zasushlivykh zemel [Satellite climatic extremes index of dryland]. Aridnyye ekosistemy, 2012, Vol. 18, No 4, pp. 5–12 (in Russian).
  4. Zolotokrylin A.N., Titkova T.B. Tendentsiya opustynivaniya severo-zapadnogo Prikaspiya po MODIS-dannym [Desertification tendency in North-West Caspian region according to MODIS data]. Sovremennyye problemy distantsionnogo zondirovaniya zemli iz kosmosa, 2011, Vol. 8, No 2, pp. 217–225 (in Russian).
  5. Zolnikov I.D., Glushkova N.V., Lyamina V.A., Smolentseva E.N., Korolyuk A.Yu., Bezuglova N.N., Zinchenko G.S., Puzanov A.V. Indikatsiya dinamiki prirodno–territorialnykh kompleksov yuga Zapadnoy Sibiri v svyazi s izmeneniyami klimata [Indication of dynamics of environmental complexes of the south of Western Siberia with reference to climatic changes]. Geografiya i prirodnye resursy, 2011, No 2, pp. 155–160 (in Russian).
  6. Kazakov L.K. Landshaftovedeniye s osnovami landshaftnogo planirovaniya [Landscape science with basics of landscape planning]. Moscow: Akademiya Publ., 2008, 336 p. (in Russian).
  7. Corobov R., Trombitsky I., Sirodoev G., Andreev A. Uyazvimost k izmeneniyu klimata: Moldavskaya chast basseyna Dnestra [Climate change vulnerability: Moldavian part of the Dniester River basin]. Kishinev: Elan Poligraf Publ., 2014, 336 p. (in Russian).
  8. Korolyuk A.Ju. Proyavleniye dinamiki ekosistem v prostranstvennoy strukture rastitelnogo pokrova na yuge Zapadnoy Sibiri [Relationships between ecosystem dynamics and vegetation structure on the southern part of Western Siberia]. Rastitelnyj mir Aziatskoj Rossii, 2010, No 6, pp.12–16 (in Russian).
  9. Choupina D.A. Avtomaticheskoye vydeleniye form i kompleksov relyefa na osnove morfometricheskogo GIS-analiza (na primere Vengerovskogo rayona Novosibirskoy oblasti) [An automatic identification of landforms and their complexes based on GIS analysis of morphometric parameters (Vengerovsk District of Novosibirsk Region as an example)]. Geomorfologiya, No 3, 2014, pp. 43–50 (in Russian).
  10. Duguy B., Alloza J.A., Baeza M.J., De la Riva J., Echeverria M., Ibarra P., Llovet J. Perez F., Cabello, Rovira P., Vallejo R.V. Modeling the Ecological Vulnerability to Forest Fires in Mediterranean Ecosystems Using Geographic Information Technologies. Environmental Management, 2012, Vol. 50, No 6, pp.1012–1026. DOI 10.1007/s00267–012–9933–3.
  11. Hilker T., Coops N.C., Wulder M.A., Black T.A., Guy R.D. The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements. Science of the Total Environment, 2008, Vol. 404, No 2–3, pp. 414–423. http://dx.doi.org/10.1016/j.scitotenv.2007.11.007.
  12. Hinkel J. Indicators of vulnerability and adaptive capacity: Towards a clarification of the science-policy interface. Global Environmental Change, 2011, Vol. 21, pp. 198–208.
  13. Huang C., Goward S.N., Masek J.G., Thomas N., Zhu Z., Vogelmann J.E. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 2010, Vol. 114, No 1, pp. 183–198. http://dx.doi.org/10.1016/j.rse.2009.08.017.
  14. IPCC. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. [M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden, C.E. Hanson (eds.)]. Cambridge: Cambridge University Press, 2007, 976 p.
  15. IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge: Cambridge University Press, 2012, 582 p.
  16. Kennedy R.E., CohenW.B., Schroeder T.A. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 2007, Vol. 110, No 3, pp. 370–386. http://dx.doi.org/10.1016/j.rse.2007.03.010.
  17. Kennedy R.E., Yang Z., Cohen W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – Temporal segmentation algorithms. Remote Sensing of Environment, 2010, Vol. 114, No 12, pp. 2897–2910. http://dx.doi.org/10.1016/j.rse.2010.07.008.
  18. Meyer B.C., Schreiner V., Smolentseva E.N., Smolentsev B.A. Indicators of desertification in the Kulunda steppe in the south of Western Siberia. Archives of Agronomy and Soil Science, 2008, Vol. 54, No 6, pp. 585–603. http://dx.doi.org/10.1080/03650340802342268.
  19. Michaelian M., Hogg E.H., Hall R.J., Arsenault E. Massive mortality of aspen following severe drought along the southern edge of the Canadian boreal forest. Global Change Biology, 2011, Vol. 17, No 6, pp. 2084–2094. http://dx.doi.org/10.1111/j.1365–2486.2010.02357.x.
  20. Nepstad D., Lefebvre P., Lopes da Silva U., Tomasella J., Schlesinger P., Solorzano L., Moutinho P., Ray D., Benito G.J. Amazon drought and its implications for forest flammability and tree growth: A basin‐wide analysis. Global Change Biology. 2004, Vol. 10, pp. 704–717. DOI: 10.1111/j.1529–8817.2003.00772.x.
  21. Smith A.M.S., Koldenb C.A., Tinkhama W.T., Talhelma A.F., Marshall J.D., Hudak A.T., Boschetti L., Falkowski M.J., Greenberge J.A., Anderson J.W., Kliskey A., Alessa L., Keefe R.F., Gosz J.R. Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems. Remote Sensing of Environment, 2014, Vol. 154, pp. 322–337.
  22. Zolnikov I.D., Glushkova N.V., Smolentseva E.N., Chupina D.A., Pchelnikov D.V., Lyamina V. A. GIS and Remote Sensing Data-Based Methods for Monitoring Water and Soil Objects in the Steppe Biome of Western Siberia. Novel Methods for Monitoring and Managing Land and Water Resources in Siberia. [Mueller, Lothar, Sheudshen, Askhad K., Eulenstein, Frank (eds.)]. Springer, 2016, pp. 253–268. DOI 10.1007/978–3–319–24409–9_9.

For citation: Chupina D.A., Zolnikov I.D., Smolentseva E.N. THE METHOD FOR IDENTIFYING THE MOST VULNERABLE AREAS CAUSED BY EXOGENOUS PROCESSES UNDER ARIDIFICATION/HUMIDIFICATION (BASED ON GIS AND RS). Proceedings of the International conference “InterCarto. InterGIS”. 2017;23(3):93–104 DOI: 10.24057/2414-9179-2017-3-23-93-104 (in Russian)