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Journal of Soils and Sediments

, Volume 19, Issue 2, pp 903–916 | Cite as

Quantitative relationships between soil landscape index and classification grain

  • Yue Pan
  • Xiaosong Lu
  • Dongsheng YuEmail author
  • Jingjing Huang
  • Xin Sun
  • Zhichao Xu
Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • 60 Downloads

Abstract

Purpose

Understanding space grain impact on soil landscape patterns is helpful to discover specific spatial heterogeneity of soil landscape. The objectives of this study were to reveal the effect of soil classification grain on soil landscape indices and establish a quantitative characterization function between soil landscape index and soil classification grain then we can reveal the sensitivity of soil landscape indices to soil classification grain and the significance of the function with respect to practical applications.

Materials and methods

A soil and land use vector map of Dongxiang County was made at a scale of 1:50,000. The vector map units were classified into five hierarchies of soil classification grain, such as soil great group (G1), subgroup (G2), family (G3), species (G4), and species + land use (G5). Using Fragstats 4.2 software, 40 soil landscape indices of the five soil classification grains were calculated from the vector map and its derived raster maps, at different grain sizes ranging from 10 m × 10 m to 6 km × 6 km. A numerically investigated method was developed to quantify and analyze values of the different soil classification grains, quantitative relationships, and sensitivities of soil landscape indices to soil classification grains.

Results and discussion

The effects of soil classification grain varied according to different soil landscape indices. The quantitative values of G1-G5 soil classification grains were quantified to be 0.7 km × 0.7 km, 0.5 km × 0.5 km, 0.2 km × 0.2 km, 0.15 km × 0.15 km, and 0.06 km × 0.06 km, respectively. The quantitative relationships of the abovementioned 28 landscape indices to soil classification grain were described by linear, logarithmic, reciprocal, quadratic, and power function with different sensitivities. The 13 landscape indices and their functions of the quantitative relationships to soil classification grain, which exhibited high sensitivity coefficients (PEAR > 0.90), should be adopted as a new approach in the future to quantitatively assess and forecast the spatial heterogeneity of soil landscape.

Conclusions

The soil classification grain strongly affects the spatial heterogeneity of the soil landscape. The quantification method of soil classification grains and the quantitative expression functions between soil landscape indices and the classification grain developed in this study could potentially provide a basic tool to quantitatively assess and forecast the spatial heterogeneity of soil landscapes. Among them, the landscape indices with high sensitivity to soil classification grains should be preferentially adopted within the new approach.

Keywords

Grain effect Sensitivity analysis Soil classification grain Soil landscape index 

Notes

Funding information

This paper received support from the Natural Science Foundation of China (No. 41571206), the Special project of the National Key Research and Development Program (No. 2016YFD0200301), and the Special project of the National Science and Technology Basic Work (No. 2015FY110700-S2).

References

  1. Alhamada MN, Alrababaha MA, Feaginb RA, Gharaibehc A (2011) Mediterranean drylands: the effect of grain size and domain of scale on landscape metrics. Ecol Indic 11(2):611–621CrossRefGoogle Scholar
  2. Antle J, Capalbo S, Mooney S, Elliott E, Paustian K (2003) Spatial heterogeneity, contract design, and the efficiency of carbon sequestration policies for agriculture. J Environ Econ Manag 46:231–250CrossRefGoogle Scholar
  3. Carter MR, Gregorich EG (1993) Soil sampling and methods of analysis. CRC Press, Boca RatonGoogle Scholar
  4. Chave J (2013) The problem of pattern and scale in ecology: what have we learned in 20years? Ecol Lett 16:4–16CrossRefGoogle Scholar
  5. Chave J, Levin S (2003) Scale and scaling in ecological and economic systems. Environ Resour Econ 26:527–557CrossRefGoogle Scholar
  6. Chefaoui RM (2014) Landscape metrics as indicators of coastal morphology: a multi-scale approach. Ecol Indic 45:139–147CrossRefGoogle Scholar
  7. Chen C, Zhang ZQ, Shi XZ, Ren HY, Pan JJ, Yu DS, Tan MZ, Sun WX (2011) Relationship of soil vectoe maps of different classification grades and the smallest dividable raster. Acta Pedol Sin 48:470–478 (in Chinese)Google Scholar
  8. Corry RC, Lafortezza R (2007) Sensitivity of landscape measurements to changing grain size for fine-scale design and management. Landsc Ecol Eng 3:47–53CrossRefGoogle Scholar
  9. Cressie NAC (1993) Statistics for spatial data, revised edition. John Wiley & Sons, New YorkGoogle Scholar
  10. Dungan JL, Perry JN, Dale MRT, Legendre P, Citron-Pousty S, Fortin M-J, Jakomulska A, Miriti M, Rosenberg MS (2002) A balanced view of scale in spatial statistical analysis. Ecography 25:626–640CrossRefGoogle Scholar
  11. Evert K, Edward BB, Elsworth DJ, Oquiñena I, Schmerber J, Stipe RE (2010) Encyclopedic dictionary of landscape and urban planning. Springer, BerlinCrossRefGoogle Scholar
  12. Francis JM, Klopatek JM (2000) Multiscale effects of grain size on landscape pattern analysis. Geogr Inf Sci 6:27–37Google Scholar
  13. Franzluebbers AJ, Stuedemann JA, Schomberg HH (2000) Spatial distribution of soil carbon and nitrogen pools under grazed tall fescue. Soil Sci Soc Am J 64:635–639CrossRefGoogle Scholar
  14. Fraterrigo JM, Turner MG, Pearson SM, Dixon P (2005) Effects of past land use on spatial heterogeneity of soil nutriente in southern appalachian forests. Ecol Monogr 75:215–230CrossRefGoogle Scholar
  15. Fu BJ, Chen LD, Ma KM, Wang YL, et al (2011) Principles of landscape ecology. Science Press Beijing (in Chinese)Google Scholar
  16. Garten CT Jr, Kang S, Brice DJ, Schadt CW, Zhou J (2007) Variability in soil properties at different spatial scales (1 m-1 km) in a deciduous forest ecosystem. Soil Biol Biochem 39:2621–2627CrossRefGoogle Scholar
  17. Gómez-Plaza A, MartíNez-Mena M, Albaladejo J, Castillo VM (2001) Factors regulating spatial distribution of soil water content in small semiarid catchments. J Hydrol 253:211–226CrossRefGoogle Scholar
  18. Herzog F, Lausch A, MÜller E, Thulke H-H, Steinhardt U, Lehmann S (2001) Landscape metrics for assessment of landscape destruction and rehabilitation. Environ Manag 27:91–107CrossRefGoogle Scholar
  19. Huang QP, Huang JJ, Yang XN, Ren LM, Tang C, Zhao LX (2017) Evaluating the scale effect of soil erosion using landscape pattern metrics and information entropy: a case study in the Danjiangkou reservoir area, China. Sustainability 9:1243CrossRefGoogle Scholar
  20. Inkoom JN, Frank S, Greve K, Walz U, Fürst C (2018) Suitability of different landscape metrics for the assessments of patchy landscapes in West Africa. Ecol Indic 85:117–127CrossRefGoogle Scholar
  21. Korres W, Reichenau TG, Schneider K (2013) Patterns and scaling properties of surface soil moisture in an agricultural landscape: an ecohydrological modeling study. J Hydrol 498:89–102CrossRefGoogle Scholar
  22. Lam NS-N, Quattrochi DA (1992) On the issues of scale, resolution, and fractal analysis in the mapping sciences. Prof Geogr 44:88–98CrossRefGoogle Scholar
  23. Land use Administration of Jiangxi Province (1991) Jiangxi soil. China Agr Sci Technol. Press, Beijing (in Chinese)Google Scholar
  24. Li HB, Wang ZQ, Wang QC (1998) Theory and methodology of spatial heterogeneity quantification. Chin J Appl Ecol 9:651–657 (in Chinese)Google Scholar
  25. Li SY, Zhou SL, Xu QC, Yin JM, Xiao JX (2007) Divisions of agricultural clmiate resources in Jiangxi province. Acta Agric Jiangxi 19:102–105 (in Chinese)Google Scholar
  26. Lin YP, Teng TP, Chang TK (2002) Multivariate analysis of soil heavy metal pollution and landscape pattern in Changhua county in Taiwan. Landsc Urban Plan 62:19–35CrossRefGoogle Scholar
  27. McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.html
  28. Meersmans J, Wesemael BV, Ridder FD, Molle MV (2009) Modelling the three-dimensional spatial distribution of soil organic carbon (SOC) at the regional scale (Flanders, Belgium). Geoderma 152:43–52CrossRefGoogle Scholar
  29. Northup BK, Brown JR, Holt JA (1999) Grazing impacts on the spatial distribution of soil microbial biomass around tussock grasses in a tropical grassland. Appl Soil Ecol 13:259–270CrossRefGoogle Scholar
  30. Pavlik HF, Hole FD (1977) Soilscape analysis of slightly contrasting terrains in southeastern Wisconsin. Soil Sci Soc Am J 41:407–413CrossRefGoogle Scholar
  31. Phillips JD, Gares PA, Slattery MC (1999) Agricultural soil redistribution and landscape complexity. Landsc Ecol 14:197–211CrossRefGoogle Scholar
  32. Plexida SG, Sfougaris AI, Ispikoudis IP, Papanastasis VP (2014) Selecting landscape metrics as indicators of spatial heterogeneity—a comparison among Greek landscapes. Int J Appl Earth Obs Geoinf 26:26–35CrossRefGoogle Scholar
  33. Qiu XX, Li DC, Zhao YC, Liu F, Song XD, Zhang GL (2016) The landscape pattern analysis based on different soil classification system: a case study of midstream of the Heihe River basin in northwest China. Soils 48:1022–1031 (in Chinese)Google Scholar
  34. Robertson GP (1987) Geostatistics in ecology: interpolating with known variance. Ecology 68:744–748CrossRefGoogle Scholar
  35. Saldaña A, Ibáñez JJ, Zinck JA (2011) Soilscape analysis at different scales using pattern indices in the Jarama–Henares interfluve and Henares River valley, Central Spain. Geomorphology 135:284–294CrossRefGoogle Scholar
  36. Saltelli A, Marivoet J (1990) Non-parametric statistics in sensitivity analysis for model output: a comparison of selected techniques. Reliab Eng Syst Saf 28:229–253CrossRefGoogle Scholar
  37. Saura S (2004) Effects of remote sensor spatial resolution and data aggregation on selected fragmentation indices. Landsc Ecol 19:197–209CrossRefGoogle Scholar
  38. Schneider DC (2001) The rise of the concept of scale in ecology. BioScience 51:545–553CrossRefGoogle Scholar
  39. Shen WJ, Wu JG, Lin YB, Ren H, Li QF (2003) Effects of changing grain size on landscape pattern analysis. Acta Ecol Sin 23(12):2506–2519 (in Chinese)Google Scholar
  40. Smith P (2005) An overview of the permanence of soil organic carbon stocks: influence of direct human-induced, indirect and natural effects. Eur J Soil Sci 56:673–680CrossRefGoogle Scholar
  41. Steudler PA, Melillo JM, Bowden RD, Castro MS, Lugo AE (1991) The effects of natural and human disturbances on soil nitrogen dynamics and trace gas fluxes in a Puerto Rican wet forest. Biotropica 23:356–363CrossRefGoogle Scholar
  42. Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial scale on the analysis of landscape pattern. Landsc Ecol 3:153–162CrossRefGoogle Scholar
  43. Turner MG, Gardner RH, O’Neill RV (2001) Landscape ecology in theory and practice. Springer, New YorkGoogle Scholar
  44. Uuemaa E, Antrop M, Roosaare J, Marja R, Mander Ü (2009) Landscape metrics and indices: an overview of their use in landscape research. Living Rev Landsc Res 3:1CrossRefGoogle Scholar
  45. Wang SJ (2017) The impact of changing grain size on soil landscape pattern indices. J Anhui Agric 45:182–185 (in Chinese)Google Scholar
  46. Wang DD, Shi XZ, Wang HJ, Weindorf DC, Yu DS, Sun WX, Ren HY, Zhao YC (2009) Scale effect of climate on soil organic carbon in the uplands of Northeast China. J Soils Sediments 10(6):1007–1017CrossRefGoogle Scholar
  47. Wang DD, Shi XZ, Lu XX, Wang HJ, Yu DS, Sun WX, Zhao YC (2010) Response of soil organic carbon spatial variability to the expansion of scale in the uplands of Northeast China. Geoderma 154:302–310CrossRefGoogle Scholar
  48. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397CrossRefGoogle Scholar
  49. Wu JG (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landsc Ecol 19:125–138CrossRefGoogle Scholar
  50. Wu JG, Shen WJ, Sun WZ, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landsc Ecol 17:761–782CrossRefGoogle Scholar
  51. Wu JG, Jones KB, Li HB, Loucks OL (2006) Scaling and uncertainty analysis in ecology: methods and applications. Springer, DordrechtCrossRefGoogle Scholar
  52. Xiao R, Su SL, Wang JQ, Zhang ZH, Jiang DW, Wu JP (2013) Local spatial modeling of paddy soil landscape patterns in response to urbanization across the urban agglomeration around Hangzhou Bay, China. Appl Geogr 39:158–171CrossRefGoogle Scholar
  53. Xiao R, Jiang DW, Christakos G, Fei XF, Wu JP (2016) Soil landscape pattern changes in response to rural anthropogenic activity across Tiaoxi watershed, China. PLoS One 11:e0166224CrossRefGoogle Scholar
  54. Xu JH (2014) Measurement geography. High Educ Press, Beijing (in Chinese)Google Scholar
  55. Xu L, Bian XQ, Qing XL, Zhang QG, Liu L (2010) Effects of grain size change on landscape pattern indices of Hefei City. Chin J Appl Ecol 21:1167–1173 (in Chinese)Google Scholar
  56. Yu DS, Yang H, Shi XZ, Warner ED, Zhang LM, Zhao QG (2011) Effects of soil spatial resolution on quantifying CH4 and N2O emissions from rice fields in the Tai Lake region of China by DNDC model. Glob Biogeochem Cycles 25:GB2004CrossRefGoogle Scholar
  57. Yu DS, Zhang LM, Shi XZ, Warner ED, Zhang ZQ, Zhao QG (2013) Soil assessment unit scale affects quantifying CH4 emissions from rice fields. Soil Sci Soc Am J 77:664–672CrossRefGoogle Scholar
  58. Yu DS, Ni YL, Shi XZ (2014) Optimal soil raster unit resolutions in estimation of soil organic carbon pool at different map scales. Soil Sci Soc Am J 78:1079–1086CrossRefGoogle Scholar
  59. Yu DS, Pan Y, Zhang HD, Wang XY, Ni YL, Zhang LM, Shi XZ (2017) Equality testing for soil raster unit resolutions to polygon unit scales with DNDC modeling of regional SOC pools. Chin Geogr Sci 27:552–568CrossRefGoogle Scholar
  60. Zhang ZL, Liu SH, Dong SK (2010a) Ecological security assessment of Yuan River watershed based on landscape pattern and soil erosion. Procedia Environ Sci 2:613–618CrossRefGoogle Scholar
  61. Zhang ZQ, Yu DS, Shi XZ, Warner E, Ren HY, Sun WX, Tan MZ, Wang HJ (2010b) Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China. Soil Sci Plant Nutr 56:307–318CrossRefGoogle Scholar
  62. Zhang ZQ, Yu DS, Shi XZ, Weindorf DC, Wang XX, Tan MZ (2010c) Effect of sampling classification patterns on SOC variability in the red soil region, China. Soil Tillage Res 110:2–7CrossRefGoogle Scholar
  63. Zhang XL, Qu YH, Ren YY, Zhao FF (2014a) Correlative analysis among pedodiversity, land use diversity and the other related ecological indexes. Ecol Environ Sci 23:923–931 (in Chinese)Google Scholar
  64. Zhang L, Yu DS, Shi XZ, Xu SX, Xing SH, Zhao YC (2014b) Effects of soil data and simulation unit resolution on quantifying changes of soil organic carbon at regional scale with a biogeochemical process model. PLoS One 9:e88622CrossRefGoogle Scholar
  65. Zhao WW, Fu BJ, Chen LD (2003) The effects of grain change on landscape indices. Quat Sci 23:326–333 (in Chinese)Google Scholar
  66. Zhou Y, Boutton TW, Wu XB, Yang CH (2017) Spatial heterogeneity of subsurface soil texture drives landscape-scale patterns of woody patches in a subtropical savanna. Landsc Ecol 32:915–929CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yue Pan
    • 1
    • 2
  • Xiaosong Lu
    • 1
    • 2
  • Dongsheng Yu
    • 1
    • 2
    Email author
  • Jingjing Huang
    • 1
    • 2
  • Xin Sun
    • 1
    • 3
  • Zhichao Xu
    • 1
    • 2
  1. 1.State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil ScienceChinese Academy of SciencesNanjingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.College of Forest Resources and EnvironmentNanjing Forestry UniversityNanjingChina

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