Estimation of soil erosion using RUSLE in Caijiamiao watershed, China
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  • 作者:Jinghu Pan (1)
    Yan Wen (1)
  • 关键词:Soil erosion ; RUSLE ; Remote sensing ; GIS ; Caijiamiao watershed
  • 刊名:Natural Hazards
  • 出版年:2014
  • 出版时间:April 2014
  • 年:2014
  • 卷:71
  • 期:3
  • 页码:2187-2205
  • 全文大小:2,144 KB
  • 参考文献:1. Alejandro M, Omasa K (2007) Estimation of vegetation parameter for modeling soil erosion using linear spectral mixture analysis of landsat ETM data. ISPRS J Photogramm Remote Sens 62:309-24 CrossRef
    2. Alice S, Christian P (2003) Erosion extension of indurate volcanic soils of Mexico by aerial photographs and remote sensing analysis. Geoderma 117:367-75 CrossRef
    3. Angima SD, Stott DE, O’Neill MK, Ong CK, Weesies GA (2003) Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agric Ecosyst Environ 97:295-08 CrossRef
    4. Bini C, Gemignani S, Zilocchi L (2006) Effect of different land use on soil erosion in the pre-alpine fringe (North–East Italy): ion budget and sediment yield. Sci Total Environ 369:433-46 CrossRef
    5. Chen T, Niu RQ, Li PX, Zhang LP, Du B (2011) Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun Watershed, North China. Environ Earth Sci 63:533-41 CrossRef
    6. Cohen J (1960) A coefficient of agreement of nominal scales. Educ Psychol Meas 20:37-6 CrossRef
    7. Committee of Qingcheng Chorography (2011) Chorography of Jingning (1986-010). China Publishing House, Shanghai (In chinese)
    8. Dang AR, Wang XD, Chen XF, Zhang JB (2003) Methods of processing of remote sensing image data under ERDAS IMAGINE. Qinghua University Press, Beijing (In Chinese)
    9. De Jong SM (1994) Derivation of vegetative variables from a landsat TM image for modelling soil erosion. Earth Surf Process Land 19:165-78 CrossRef
    10. Demirci A, Karaburun A (2012) Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece lake watershed, northwest Turkey. Environ Earth Sci 66:903-13 CrossRef
    11. Desmet PJ, Govers G (1996) A GIS-procedure for the automated calculation of the USLE LS-factor on topographically complex landscape units. J Soil Water Conserv 51:427-33
    12. Dong XF, Liu LC, Wang JH, Shi J, Pan JH (2009) Analysis of the landscape change at river basin scale based on SPOT and TM fusion remote sensing images: a case study of the Weigou River basin on the Chinese loess plateau. Int J Earth Sci 98:651-64 CrossRef
    13. Feng XM, Wang YF, Chen LD, Fu BJ, Bai GS (2010) Modeling soil erosion and its response to land–use change in hilly catchments of the Chinese loess plateau. Geomorphology 118:239-48 CrossRef
    14. Fernandez C, Wu JQ, McCool DK, Stockle CO (2003) Estimating water erosion and sediment yield with GIS, RUSLE and SEDD. J Soil Water Conserv 58:128-36
    15. Flanagan DC, Nearing MA (1995) USDA-water erosion prediction project: hillslope and watershed model documentation. NSERL Report No. 10.West Lafayette Ind. USDA-ARS National Soil Erosion Research Laboratory
    16. Foster MA, Gabel SE, Ward TS, Kleine LG, McCann PK, Moore J (1991) Morphological characters as indicators of rubber content in guayule (Parthenium argentatum-Compositae). SIDA 14:339-67
    17. Fu BJ, Liu Y, Lü YH, He CS, Zeng Y, Wu BF (2011) Assessing the soil erosion control service of ecosystems change in the loess plateau of China. Ecol Complex 8:284-93 CrossRef
    18. Gilabert MA, Garcia-Haro FJ, Melia J (2000) A mixture modeling approach to estimate vegetation parameters for heterogeneous canopies in remote sensing. Remote Sens Environ 72:328-45 CrossRef
    19. Griffin ML, Beasley DB, Fletcher JJ, Foster GR (1988) Estimating soil loss on topographically nonuniform field and farm units. J Soil Water Conserv 43:326-31
    20. Guo JP, Niu T, Pooyan R, Wang F, Zhao HY, Zhang JH (2013) Assessment of soil erosion susceptibility using empirical modeling. Acta Meteorol Sin 27:98-09 CrossRef
    21. Hill J, Stellmes M, Udelhoven T, Sommer ARS (2008) Mediterranean desertification and land degradation: mapping related land use change syndromes based on satellite observations. Global Planet Change 64:146-57 CrossRef
    22. Hu LJ, Yang HJ, Yang QK, Li R (2010) A GIS-based modeling approach for fast assessment of soil erosion by water at regional scale, loess plateau of China. Chin Geogr Sci 20:423-33 CrossRef
    23. Jiang ZS, Zheng FL (2008) Water erosion process prediction model. Science Press, Beijing (In Chinese)
    24. Julien PY (1998) Erosion and sedimentation. Cambridge University Press, Cambridge
    25. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the K?ppen-Geiger climate classification updated. Meteorol Z 15:259-63 CrossRef
    26. Kremer RG, Running SW (1993) Community type differentiation using NOAA/AVHRR data within a sagebrush–steppe ecosystem. Remote Sens Environ 46:311-18 CrossRef
    27. Krishna Bahadur KC (2009) Mapping soil erosion susceptibility using remote sensing and GIS: a case of the upper Nam Wa watershed, Nan Province, Thailand. Environ Geol 57:695-05 CrossRef
    28. Laflen JM, Colvin TS (1981) Effect of crop residue on soil loss from continuous row cropping. Trans ASAE 24:605-09 CrossRef
    29. Li CH, Pan JH (2009) Soil erosion simulation and forcast based on ANN-GeoCA model-A case study of loess plateau Qingcheng project area. Chin J Soil Sci 40:902-06 (In Chinese)
    30. Li ST, James T, Wang YN (2002) Using the discrete wavelet frame transform to merge landsat TM and SPOT panchromatic images. Inform Fusion 3:17-3 CrossRef
    31. Lin C, Zhou SL, Wu SH, Liao FQ (2012) Relationships between intensity gradation and evolution of soil erosion: a case study of Changting in Fujian Province, China. Pedosphere 22:243-53 CrossRef
    32. Liu Z (2004) Soil and conservation in China. Paper presented on the ninth international symposium on river sedimentation. Yichang, China, 18-1
    33. Liu LC, Dong XF, Wang JH (2007) Dynamic analysis of eco-environmental changes based on remote sensing and geographic information system: an example in Longdong region of the Chinese loess plateau. Environ Geol 53:589-98 CrossRef
    34. Lu D, Li G, Valladares GS, Batistella M (2004) Mapping soil erosion risk in Rondonia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land Degrad Dev 15:499-12 CrossRef
    35. Manserud R, Leemans R (1992) Comparing global vegetation maps with the kappa statistics. Ecol Model 62:275-79 CrossRef
    36. Meusburge K, Konz N, Schaub M, Alewell C (2010) Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment. Int J Appl Earth Obs 12:208-15 CrossRef
    37. Moore ID, Burch GJ (1986) Physical basis of the length-slope factor in the universal soil loss equation. Soil Sci Soc Am J 50:1294-298 CrossRef
    38. Morgan RPC, Quinton JN, Smith RE, Govers G, Poesen JWA, Auerswald K, Chisci G, Torri D, Styczen ME (1998) The European soil erosion model (EUROSEM): a process-based approach for predicting soil loss from fields and small catchments. Earth Surf Proc Land 23:527-44 CrossRef
    39. Noel DU, James AL (1998) The dynamics of soil erosion in US agriculture. Sci Total Environ 218:45-8 CrossRef
    40. Nyakatawa EZ, Reddy KC, Sistani KR (2001) Tillage, cover cropping, and poultry litter effects on selected soil chemical properties. Soil Till Res 58:69-9 CrossRef
    41. Parysow P, Wang GX, George G, Alan BA (2003) Spatial uncertainty analysis for mapping soil erodibility based on joint sequential simulation. Catena 53:65-8 CrossRef
    42. Perovic V, Zivotic L, Kadovic R, Dordevic A, Jaramaz D, Mrvic V, Todorovic M (2013) Spatial modelling of soil erosion potential in a mountainous watershed of South–eastern Serbia. Environ Earth Sci 68:115-28 CrossRef
    43. Pohl C (1998) Multi-sensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sens 19:823-54 CrossRef
    44. Prasannakumar V, Vijith H, Abinod S, Geeth N (2012) Estimation of soil erosion risk within a small mountainous sub–watershed in Kerala, India, using revised universal soil loss equation (RUSLE) and geo–information technology. Geosci Front 3:209-15 CrossRef
    45. Pu RL, Gong P, Michishita R, Sasagawa T (2008) Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data. Remote Sens Environ 87:939-54 CrossRef
    46. Qiao YL, Qiao Y (2002) Fast soil erosion investigation and dynamic analysis in the loess plateau of China by using information composite technique. Adv Space Res 29:85-8 CrossRef
    47. Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1997) Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE) (Vol. Handbook #703). US Department of Agriculture, Washington
    48. Ridd MK (1995) Exploring a V-I–S (Vegetation–Impervious Surface–Soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int J Remote Sens 16:2165-185 CrossRef
    49. Roberts DA, Smith MO, Adams JB (1993) Green vegetation, nonphotosynthetic vegetation and soils in AVIRIS data. Remote Sens Environ 44:255-69 CrossRef
    50. Rozos D, Skilodimou HD, Loupasakis C, Bathrellos GD (2013) Application of the revised universal soil loss equation model on landslide prevention. An example from N. Euboea (Evia) Island, Greece. Environ Earth Sci DOI pan class="a-plus-plus non-url-ref">10.1007/s12665-013-2390-3
    51. Soil Census Staff of Gansu Province (1993) Gansu soil. China Agriculture Press, Beijing (In Chinese)
    52. Sun XH, Yan FJ (2004) GIS-and-RS-based evaluation of soil erosion potentiality: a case study of Qingdao. Soils 36:516-21 (In Chinese)
    53. Sun WY, Shao QQ, Liu JL (2013) Soil erosion and its response to the changes of precipitation and vegetation cover on the loess plateau. J Geogr Sci 23:1091-106 CrossRef
    54. Tweddales SC, Eschlaeger CR, Seybold WF (2000) An improved method for spatial extrapolation of vegetative cover estimates (USLE/RUSLE C factor) using LCTA and remotely sensed imagery. USAEC report No. SFIM-AEC-EQ-TR-200011, ERDC/CERL TR-00-7, US Army of Engineer Research and Development Center, CERL, Champaign, Illinois
    55. Van der Knijff JM, Jones RJA, Montanarella L (2000) Soil erosion risk assessment in Europe, European Commission, European Soil Bureau. http://eusoils.jrc.ec.europa.eu/ESDB_Archive/pesera/pesera_cd/pdf/ereurnew2.pdf
    56. Wang WW (2007) Managing soil erosion potential by integrating digital elevation models with the Southern China’s revised universal soil loss equation: a case study for the west lake scenic spots area of Hangzhou, China. J Mt Sci-Engl 4:237-47 CrossRef
    57. Wang G, Wente S, Gertner GZ, Anderson A (2002) Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with landsat thematic mapper images. Int J Remote Sens 23:3649-667 CrossRef
    58. Wang MC, Pan JH, Zhao J (2007) Quantitative survey of the soil erosion change based on GIS and RS: take the Qingcheng area as an example. Agricultural Research in the Arid Areas 25:116-21 (In Chinese)
    59. Williams J, Jones C, Dyke P (1990) The EPIC model. In: Sharpley AN, Williams JR (eds) EPIC-erosion/productivity impact calculator: model documentation. USDA Tech. Bull. No. 1768. Chapter 2, pp 3-2
    60. Wischmeier WH, Smith DD (1958) Rainfall energy and its relationship to soil loss. Trans Am Geophys Union 39:285-91 CrossRef
    61. Wischmeier WH, Smith DD (1965) Predicting rainfall erosion losses from cropland east of the rocky mountains. Handbook no. 282. Agricultural Research Service, U.S. Dept of Agriculture in cooperation with Purdue Agricultural Experiment Station, Washington, DC
    62. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning. Agriculture Handbook No. 537. US Department of Agriculture Science and Education Administration, Washington, DC, USA
    63. Wischmeier WH, Johnson CB, Cross BV (1971) A soil erodibility nomograph for farmland and construction sites. J Soil Water Conserv 26:189-92
    64. Xu YQ, Shao XM, Kong XB, Peng J, Cai YL (2007) Adapting the RUSLE and GIS to model soil erosion risk in a mountains karst watershed, Guizhou Province, China. Environ Monit Assess 141:275-86
    65. Xu YQ, Peng J, Shao XM (2008) Assessment of soil erosion using RUSLE and GIS: a case study of the Maotiao River watershed, Guizhou Province, China. Environ Geol 56:1643-652
    66. Xu YQ, Luo D, Peng J (2011) Land use change and soil erosion in the Maotiao River watershed of Guizhou Province. J Geogr Sci 21:243-53
    67. Zhang YG, Nearing MA, Zhang XC, Xie Y, Wei H (2010) Projected rainfall erosivity changes under climate change from multi–model and multi–scenario projections in Northeast China. J Hydrol 384:97-06 CrossRef
  • 作者单位:Jinghu Pan (1)
    Yan Wen (1)

    1. College of Geographic and Environmental Science, Northwest Normal University, 967 Anning East Road, Lanzhou, 730070, People’s Republic of China
  • ISSN:1573-0840
文摘
Soil erosion is a serious environmental and production problem in China. In particular, natural conditions and human impact have made the Chinese Loess Plateau particularly prone to intense soil erosion area. To decrease the risk on environmental impacts, there is an increasing demand for sound, and readily applicable techniques for soil conservation planning in this area. This work aims at the assessment of soil erosion and its spatial distribution in hilly Loess Plateau watershed (northwestern China) with a surface area of approximately 416.31?km2. This study was conducted at the Caijiamiao watershed to determine the erosion hazard in the area and target locations for appropriate initiation of conservation measures using the revised universal soil loss equation (RUSLE). The erosion factors of RUSLE were collected and processed through a geographic information system (GIS)-based approach. The soil erosion parameters were evaluated in different ways: The R-factor map was developed from the rainfall data, the K-factor map was obtained from the soil map, the C-factor map was generated based on Landsat-5 Thematic Mapper image and spectral mixture analysis, and a digital elevation model with a spatial resolution of 25?m was derived from topographic map at the scale of 1:50,000 to develop the LS-factor map. Support practice P factor was from terraces that exist on slopes where crops are grown. By integrating the six-factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the study area was obtained by the RUSLE model. The results showed that spatial average soil erosion at the watershed was 78.78?ton?ha??year? in 2002 and 70.58?ton?ha??year? in 2010, while the estimated sediment yield was found to be 327.96?×?104 and 293.85?×?104?ton, respectively. Soil erosion is serious, respectively, from 15 to 35 of slope degree, elevation area from 1,126 to 1,395?m, in the particular area of soil and water loss prevention. As far as land use is concerned, soil losses are highest in barren land and those in waste grassland areas are second. The results of the study provide useful information for decision maker and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a river watershed scale on a cell basis in Chinese Loess Plateau and for planning of conservation practices.

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