Hierarchical cluster analysis to identify the homogeneous desertification management units

English version

Autoři: Farhad Zolfaghari ^aff001; Hassan Khosravi ^aff002; Alireza Shahriyari ^aff003; Mitra Jabbari ^aff001; Azam Abolhasani ^aff002
Působiště autorů: Higher Educational Complex of Saravan, Saravan, Sistan and Baluchestan, Iran ^aff001; Faculty of Natural Resources, University of Tehran, Tehran, Iran ^aff002; Faculty of Environmental Science, University of Sistan and Baluchestan, Zahedan, Sistan and Baluchestan, Iran ^aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226355

Souhrn

Since in most mapping models geometric mean of different criteria are used to determine the desertification intensity, one of the most important issues in desertification studies is understanding the similar areas, which require similar management after determining the desertification intensity map. Two similar classes of desertification intensity may require different management due to differences in the criteria that affect its desertification severity. Therefore, after determining the geomorphological facies as the working units in Sistan plain, we used hierarchical cluster analysis to identify the homogeneous environmental management units (HEMUs) based on indices of MEDALUS model. According to the MEDALUS model, the studied area was divided into two categories namely medium and high desertification classes. Working units (geomorphological facies) are classified into five clusters according to HEMUs analysis based on climate, soil, vegetation, and wind erosion criteria. The first cluster (C11) include six facies with moderate and severe desertification; in all of these units the main effective factor was wind erosion, so they need the same management decisions controlling wind erosion. Two working units (1 and 4) with the same desertification severity were placed in two different clusters due to the main factors affecting each other. The results of the Mann-Whitney test showed that the value of the test statistics was 79. Also, the value of Asymp.Sig was obtained to be 0.018, which is less than 0.025 (two-tailed test), and it can be concluded that the classification of work units in the two models, clustering and desertification, is not equal (P<0.05). So It seems that using cluster analysis to identify the same units, which need the same management decision after preparing the desertification intensity, is necessary.

Klíčová slova:

Clustering algorithms – Desertification – Environmental management – Erosion – Geomorphology – Hierarchical clustering – Invasive species – Geological facies

Zdroje

1. Eskandari H, Borji M, Khosravi H, Mesbahzadeh T (2016). Desertification of forest, range and desert in Tehran province, affected by climate change. Solid Earth, 7(3): 905–915. doi: 10.5194/se-7-905-2016

2. Sadeghravesh M H, Khosravi H, Ghasemian S (2015). Application of fuzzy analytical hierarchy process for assessment of combating-desertification alternatives in central Iran. Natural Hazards, 75(1): 653–667. doi: 10.1007/s11069-014-1345-7

3. Ahmadi H (1995). Applied geomorphology.Vol.1 (water erosion). University of Tehran Press.

4. Verstappen H T (1983). Applied Geomorphology: Geomorphological Surveys for Environmental Development, Elsevier, Amsterdam, 450p.

5. Chamapira G H, Taghavi Goudarzi S (2011). Geomorphological Facies Zonation, Using GIS and RS and its Application in Natural Resources. (Case Study of Kouhdasht Watershed). Journal of Rangeland Science, 1(2): 143–151.

6. Brenner J, Jimenez A, Sarda R (2006). Definition of Homogeneous Environmental Management Units for the Catalan Coast. Environmental Management, 38: 993–1005. doi: 10.1007/s00267-005-0210-6 16990984

7. Van der Weide J. (1993). A systems view of integrated coastal management. Ocean Coast Manage, 21: 129–148. doi: 10.1016/0964-5691(93)90023-R

8. Christian C S (1958). The concept of land units and land systems. Proc Ninth Pacific Congress, 20: 74–81.

9. UNESCO (1997). Definition of the coherent management units: Stage 2. In Methodological guide to integrated coastal zone management. Manuals & guides 36. Intergovernmental Oceanographic Comisio´ n, France, pp: 16–19.

10. Mee L D (2005). Assessment and monitoring requirements for the adaptive management of Europe’s Regional Seas. In Salomons W, Vermaat J, K. Turner (eds), Managing European coasts: past, present and future. Environmental Sciences Series, Springer-Verlag, Berlin, Germany, 227–237 pp.

11. Baja S, Chapman D M, Dragovich D (2002). A conceptual model for defining and assessing land management units using a fuzzy modeling approach in a GIS environment. Envir Manage, 29: 647–661. doi: 10.1007/s00267-001-0053-8

12. Fricker A, Forbes D L (1988). A system for coastal description and classification. Coast Manage, 16: 111–137. doi: 10.1080/08920758809362052

13. Gornitz V (1990). Vulnerability of the East Coast, U.S.A. to future sea level rise. Journal of Coast Research, special issue, 9: 201–237.

14. Bartley J A, Buddemeier R W, Bennett D A (2001). Coastline complexity: a parameter for functional classification of coastal environments. Journal of Sea Research, 46: 87–97. doi: 10.1016/S1385-1101(01)00073-9

15. Escofet A (2002). Alternativas para la regionalizacio´n del espacio marino de Me´ xico. Working document prepared for the Mapping Marine and Estuarine Ecosystems of North America Project. Centro de Investigacio´n Cientı´fica y de Educacio´n Superior de Ensenada & Commission for Environmental Cooperation, NAFTA, Ensenada, Mexico, 13 pp.

16. Maxwell B A, Buddemeier R W (2002). Coastal typology development with heterogeneous data sets. Regional Environmental Change, 3: 77–87. doi: 10.1007/s10113-001-0034-8

17. Henocque Y, Andral B (2003). The French approach to managing water resources in the Mediterranean and the new European Water Framework Directive. Marine Pollut Bull, 47: 155–161. doi: 10.1016/S0025-326X(02)00413-7

18. Vafeidis A T, Nicholls R J, McFadden L, Hinkel J, Grashoff P S (2004). Developing a global database for coastal vulnerability analysis: design issues and challenges. In XX ISPRS Congress, The international archives of the photogrammetry, remote sensing and spatial information sciences, July, Istanbul, Turkey, 801–805.

19. Ya´n˜ EZ-Arancibia A, Day J W (2004). Environmental subregions in the Gulf of Mexico coastal zone: the ecosystem approach as an integrated management tool. Ocean Coast Manage, 47: 727–757. doi: 10.1016/j.ocecoaman.2004.12.010

20. Davari S, Rashki A, Akbari M, Talebanfard A (2017). Assessing intensity and risk of desertification and management programs (Case study: Ghasemabad plain of Bajestan, Khorasan Razavi Province). Desert Management Journal. No. 9, Spring & Summer, pp 91–106.

21. Arami S A and Ownagh M (2017). Assessment of desertification hazard, risk and development of management plans. Desert journal, No 22–1, pp: 51–67.

22. Akbari M, Ownagh M, Asgari H R, Sadodin A, Khosravi H (2016). Desertification risk assessment and management program. Global J. Environ. Sci. Manage. No 2(4), pp: 365–380. doi: 10.22034/gjesm.2016.02.04.006

23. Arami A H, Ownagh M, Bardishikh V (2013). Desertification Risk Management Program in Agh Band area of Golestan province. Geographical Studies of Arid Regions Journal. No 4, pp: 103–118.

24. Anon (2006). The annual reports of meteorological data (1982–2005). Iran Meteorological Organization. Tehran, Iran.

25. Negaresh H, Khosravi M (2000). Evaluation of Agricultural Climate of Sistan and Baluchestan province. Sistan and Baluchestan press. 277p.

26. Ganji M H (1974). Temporary concerns about evaporation in Iran. Sahab press, 256p.

27. Jabbary M, Shahriari A R, Zolfaghari F (2013). Evaluation of flora and life forms and chorology of plant species in Sistan region, Iran. Advanced Crop Science, l3(4): 273–279.

28. Zhang Z, Murtagh F, Van Poucke S, Lin S, Lan P (2017). Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R. Ann Transl Med, 5(4):75. doi: 10.21037/atm.2017.02.05 28275620

29. Varouchakis E A, Theodoridou P G, Karatzas G P (2019). Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background. Journal of Hydrology, doi: 10.1016/j.jhydrol.2019.05.055

30. Sadeghravesh M H, Khosravi H, Ghasemian S (2016). Assessment of combating-desertification strategies using the linear assignment method. Solid Earth, 7: 673–683. doi: 10.5194/se-7-673-2016

31. Campos M C C, Marques J, De souza Z M, Siqueira D S (2012). Discrimination of geomorphic surfaces with multivariate analysis of soil attributes in sandstone—basalt lithosequence. Revista Ciência Agronômica, 43: 429–438. doi: 10.1590/S1806-66902012000300003

32. Cunha Pedro. et al. (2005). Geomorphic surfaces and Latosol (Oxisol) characteristics on a Sandstone/Basalt sequence from the Jaboticabal region, São Paulo State, Brazil. Rev. Bras. Ciênc. Solo. v. 29, n. 1, p. 81–90. doi: 10.1590/S0100-06832005000100009

33. Sanchez R. B. et al. (2005). Spatial variability of Latosol properties in different geomorphic surfaces of coffee cultivation. Rev. bras. eng. agríc. ambient. v. 9, n. 4, p. 489–495. doi: 10.1590/S1415-43662005000400008

34. Teramoto E. R.; Lepsch I. F.; Vidal-torrado P. (2001). Soil, geological substrate and geomorphic surface relationships for the Marins river basin (Piracicaba, SP, Brazil). Sci. agric., v. 58, n. 2, p. 361–371. doi: 10.1590/S0103-90162001000200021

35. Ward Joe H (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58 (301): 236–244. doi: 10.2307/2282967

36. Lahlaoi H, Rhinane H, Hilali A, Lahssini S, Moukrim S (2017). Desertification Assessment Using MEDALUS Model in Watershed Oued El Maleh, Morocco. Geosciences. 7:50, doi: 10.3390/geosciences7030050

37. Boudjemline F, and Semar A (2018). Assessment and mapping of desertification sensitivity with MEDALUS model and GIS—Case study: Basin of Hodna, Algeria. Journal of Water and Land Development. 36. 17–26. doi: 10.2478/jwld-2018-0002

38. Momirović N, Kadović R, Perović V, Marjanović M, Baumgertel A (2019). Spatial assessment of the areas sensitive to degradation in the rural area of the municipality Čukarica. International Soil and Water Conservation Research. 7:(1) 71–80. doi: 10.1016/j.iswcr.2018.12.004

39. Zehtabian Gh R, Khosravi H, Masoudi R (2014). Models of Desertification Assessment (Criteria and indices). University of Tehran Press. 268pp.

40. Shakerian N, Zehtabian Gh R, Azarnivand H, Khosravi H (2011). Evaluation of desertification intensity based on soil and water criteria in Jarghooyeh region. Desert, 16: 23–32. doi: 10.22059/jdesert.2011.23019

41. Jafari M, Zare Chahouki M A, Ahmadi H, Abbasi H R (2011). Evaluation of the effects of soil properties on desertification (Case study: Segzi Pediment of Isfahan, Iran). Desert. 16: 1–4. doi: 10.22059/jdesert.2011.23015

42. Khosravi H, Zehtabian G R, Ahmadi H, Azarnivand H (2014). Hazard assessment of desertification as a result of soil and water recourse degradation in Kashan Region, Iran. Desert, 19(1): 45–55. doi: 10.22059/jdesert.2014.51053

43. Zolfaghari F, Shahriyari A, Fakhireh A, Rashki A R, Noori S, Khosravi H (2011). Assessment of desertification potential using IMDPA model in Sistan plain. Watershed Management Research (Pajouhesh & Sazandegi), 91: 97–107.

44. Pahlavanravi A, Bahreini F (2013). Evaluation of Current Desertification Status Based on IMDPA with Emphasis on Climate, Wind Erosion, Water, Soil and Vegetation: Case Study of Bordekhun Region of Boushehr. Desert journal, 18: 53–62. doi: 10.22059/jdesert.2013.36275

45. Azarnivand H, Zare Chahouki M A, Joneidi H (2012). Evaluation of the Effects of Vegetation Characteristics on Desertification (Case Study: Northern Hableh Roud, Iran). Desert, 17: 9–13. doi: 10.22059/jdesert.2012.32006

46. FRWO (2004). Forests, Range and Watershed Management Organization of iran Integrated report of desertification and combating wind erosion studies in Sistan region.

47. Webster R, Oliver M A (1990). Statistical methods in soil and land resource survey. Spatial Information Systems. New York: Oxford University Press, 316 p.

48. FU B J, Liu S L, Ma K M, Zhu Y G (2004). Relationships between soil characteristics, topography and plant diversity in a heterogeneous deciduous broad-leaved forest near Beijing, China. Plant and Soil, 261(1); 47–54. doi: 10.1023/B:PLSO.0000035567.97093.48

49. Adams M B, Turner R S, Schmoyer D D (1992). Evaluation of direct delayed response project soil sampling classes: Northeastern United States. Soil Science Society of America Journal, 56: 177–187. doi: 10.2136/sssaj1992.03615995005600010028x

50. Young F J, Hammer R D (2000). Defining geographic soil bodies by landscape position, soil taxonomy, and cluster analysis. Soil Science Society of America Journal, vol: 64: 989–998. doi: 10.2136/sssaj2000.643989x

51. CAMPOS, Milton César Costa et al. (2012). Discrimination of geomorphic surfaces with multivariate analysis of soil attributes in sandstone—basalt lithosequence. Revista Ciência Agronômica, vol.43, n. 3, pp. 429–438. doi: 10.1590/S1806-66902012000300003

52. Borujeni E, Salehi M H, Toomanian N, Mohammadi J, Poch R M. (2009). The effect of survey density on the results of geopedological approach in soil mapping: A case study in the Borujen region, Central Iran. Catena, vol.79, pp.18–26. doi: 10.1016/j.catena.2009.05.003

53. Minasny B. Mcbratney A. B. (2007). Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes. Geoderma, 142: 285–293. doi: 10.1016/j.geoderma.2007.08.022

Hierarchical cluster analysis to identify the homogeneous desertification management units

Souhrn

Klíčová slova:

Zdroje

PLOS One

INSIGHTS from European Respiratory Congress

Současné pohledy na riziko v parodontologii

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií

Střevní příprava před kolonoskopií