Prediction of land-use change and its driving forces in an ecological restoration watershed of the Loess hilly region

Original Article

Abstract

Land-use change is one of the important topics of regional ecological restoration research, but it exhibits high interdependencies in social-ecological systems which make it difficult to predict spatiotemporal variability and identify the main drivers. We employ long-term dynamic predictions utilizing an integrated cellular automata–Markov (CA–Markov) model and geographic information system technology, and reveal trends of land-use change and the driving forces in an ecological restoration watershed of the Loess hilly region. Here, we show that dry land showed a rising transfer trend to shrub land, sparse forestland, middle-coverage grassland, low-coverage grassland, and rural settlements from 1995 to 2010. Dry land, forestland, sparse forestland, middle-coverage grassland, and rural settlements will maintain a slight decreasing trend from 2010 to 2020, but other forestland, low-coverage grassland, and reservoir or pond may have a gradual increasing trend. Farmland and forestland in 2050 decrease by 8.17% and increase by 46.3%, respectively, compared with that in 1980. Grassland shows an overall downward trend. Water area increases first and then decreases. Rural settlements increase rapidly with a growth rate of 88.8% from 1980 to 2050. The returning farmland policy and the decline of agricultural population may be the main driving forces and positively affect the transfer of dry land to forestland. Our results may provide underlying insights needed to guide the planning and management of regional land resources.

Keywords

Land-use change Prediction Cellular automata Markov chain Driving force Yangou River watershed 

Notes

Acknowledgements

Special thanks to the anonymous reviewers and the editor for their useful suggestions on the manuscript. This study was supported by the National Natural Science Foundation of China (51679206, 51309194), Tang Scholar (Z111021720), Youth Science and Technology Nova Project in Shaanxi Province (2017KJXX-91), International Science and Technology Cooperation Funds (A213021603), the Fundamental Research Funds for the Central Universities (2452016120), Special Research Foundation for Young Teachers (2452015374), and the Doctoral Fund of Ministry of Education of China (20130204120034). This study was also supported by the National Fund for Studying Abroad. Acknowledgement for the original land-use data support from “Loess Plateau Data Center, National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China (http://loess.geodata.cn).”

References

  1. Aburas MM, Ho YM, Ramli MF, Ashaari ZH (2017) Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an analytical hierarchy process and frequency ratio. Int J Appl Earth Obs Geoinf 59:65–78CrossRefGoogle Scholar
  2. Bai WQ, Yan JZ, Zhang YL (2004) Land use/land cover change and driving forces in the region of upper reaches of the Dadu River. Progr Geogr 23(1):71–77Google Scholar
  3. Bilsborrow RE, Okoth Ogondo WOH (1992) Population–driven changes in land use in developing countries. Ambio 21:37–45Google Scholar
  4. Chen RQ (2009) Application of Markov chain model on the dynamic change of land use in Qingdao. Terrotory Nat Resour Stud 1:29–30Google Scholar
  5. Chen LG, Yang XY, Chen LQ, Li L (2015) Impact assessment of land use planning driving forces on environment. Environ Impact Assess Rev 55:126–135CrossRefGoogle Scholar
  6. Eiter S, Potthoff K (2016) Landscape changes in Norwegian mountains: increased and decreased accessibility, and their driving forces. Land Use Policy 54:235–245CrossRefGoogle Scholar
  7. Feng Y, Tong X (2017) Calibrating nonparametric cellular automata with a generalized additive model to simulate dynamic urban growth. Environ Earth Sci 76(14):496CrossRefGoogle Scholar
  8. Feng Q, Zhao W, Fu B, Ding J, Wang S (2017) Ecosystem service trade-offs and their influencing factors: a case study in the loess plateau of China. Sci Total Environ 607–608:1250–1263CrossRefGoogle Scholar
  9. Fukushima T, Takahashi M, Matsushita B, Okanishi Y (2007) Land use/cover change and its drivers: a case in the watershed of Lake Kasumigaura, Japan. Landsc Ecol Eng 3(1):21–31CrossRefGoogle Scholar
  10. Gao P, Niu X, Wang B, Zheng YL (2015) Land use changes and its driving forces in hilly ecological restoration area based on GIS and RS of Northern China. Sci. Rep. 5:11038.  https://doi.org/10.1038/srep11038 CrossRefGoogle Scholar
  11. Garg V, Aggarwal SP, Gupta PK, Nikam BR, Thakur PK, Srivastav SK, Senthil Kumar A (2017) Assessment of land use land cover change impact on hydrological regime of a basin. Environ Earth Sci 76(18):635CrossRefGoogle Scholar
  12. Guang L, Jin QW, Li JY, Yao YF (2017) Policy factors impact analysis based on remote sensing data and the CLUE-S model in the Lijiang River Basin, China. CATENA 158:286–297CrossRefGoogle Scholar
  13. Gutman G, Janetos AC, Justice CO, Moran EF, Mustard JF, Rindfuss RR (2004) Land change science: observing, monitoring and understanding trajectories of change on the earth’s surface. Kluwer Academic Publishers, DordrechtCrossRefGoogle Scholar
  14. Hu XH, Yang GQ (2005) Cultivated land quantity change and its driving forces in Jianghan plain—a case study of Xiantao city. China Popul Resour Environ 15(1):32–35Google Scholar
  15. Jovanović D, Govedarica M, Sabo F, Željko Bugarinović Novović O, Beker T, Lauter M (2015) Land cover change detection by using remote sensing-a case study of Zlatibor (Serbia). Geogr Pannonica 19(4):162–173CrossRefGoogle Scholar
  16. Karimi M, Mesgari MS, Sharifi MA, Pilehforooshha P (2017) Developing a methodology for modelling land use change in space and time. J Spat Sci 62(2):261–280CrossRefGoogle Scholar
  17. Kavian A, Golshan M, Abdollahi Z (2017) Flow discharge simulation based on land use change predictions. Environ Earth Sci 76(16):588CrossRefGoogle Scholar
  18. Kleemann J, Baysal G, Bulley HN, Fürst C (2017) Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa. J Environ Manag 196:411–442Google Scholar
  19. Lambin EF, Geist H (2010) Land-use and land-cover change. Springer, Berlin 32(23):308–324Google Scholar
  20. Li XB (1996) A review of the international researches on land use/land cover change. Acta Geogr Sin 51(6):553–558Google Scholar
  21. Li Y, Liu G (2017) Characterizing spatiotemporal pattern of land use change and its driving force based on GIS and landscape analysis techniques in Tianjin during 2000–2015. Sustainability 9(6):894CrossRefGoogle Scholar
  22. Li X, Peng G (2016) Urban growth models: progress and perspective. Sci Bull 61(21):1637–1650CrossRefGoogle Scholar
  23. Li QB, Wang HB (2008) Prediction of land-use change in Zhangdu watershed based on a Markov model. Resour Sci 30(10):1541–1546Google Scholar
  24. Li P, Qian H, Wu J (2014) Accelerate research on land creation. Nature 510(7503):29–31.  https://doi.org/10.1038/510029a CrossRefGoogle Scholar
  25. Li JC, Liu HX, Liu Y, Su ZZ, Du ZQ (2015) Land use and land cover change processes in china’s eastern loess plateau. Sci Cold Arid Reg 6:722–729Google Scholar
  26. Liu PL, Zheng SQ, Ju TJ, Wang SQ, Xu Y (2005) Ecological and environmental construct ion in the Yan’gou watershed of the loess Plateau: models and benefits. Res Soil Water Conserv 12(5):88–91Google Scholar
  27. Liu C, Xu Y, Sun P, Huang A, Zheng W (2017) Land use change and its driving forces toward mutual conversion in zhangjiakou city, a farming-pastoral ecotone in Northern China. Environ Monit Assess 189(10):505CrossRefGoogle Scholar
  28. López E, Bocco G, Mendoza M (2001) Predicting land-cover and land-use change in the urban fringe a case in Morelia city, Mexico. Landsc Urb Plann 55:271–285CrossRefGoogle Scholar
  29. Lu Y, Chen B (2017) Urban ecological footprint prediction based on the markov chain. J Clean Prod 163:146–153CrossRefGoogle Scholar
  30. Ma C, Zhang GY, Zhang XC, Zhao YJ, Li HY (2012) Application of markov model in wetland change dynamics in tianjin coastal area, China. Proced Environ Sci 13(3):252–262CrossRefGoogle Scholar
  31. Meyn SP, Tweedie RL (2005) Markov chains and stochastic stability, 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  32. Mondal B, Das DN, Bhatta B (2017) Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration. Geocarto Int 32(4):401–419CrossRefGoogle Scholar
  33. Naboureh A, Moghaddam MHR, Feizizadeh B, Blaschke T (2017) An integrated object-based image analysis and ca-markov model approach for modeling land use/land cover trends in the sarab plain. Arab J Geosci 10(12):259CrossRefGoogle Scholar
  34. Palmate SS, Pandey A, Mishra SK (2017) Modelling spatiotemporal land dynamics for a trans-boundary river basin using integrated cellular automata and markov chain approach. Appl Geogr 82:11–23CrossRefGoogle Scholar
  35. Pinto N, Antunes AP, Roca J (2017) Applicability and calibration of an irregular cellular automata model for land use change. Comput Environ Urban Syst 65:93–102CrossRefGoogle Scholar
  36. Seeber C, Hartmann H, Xiang W, King L (2010) Land use change and causes in the Xiangxi catchment, Three Gorges area derived from multispectral data. J Earth Sci 21(6):846–855CrossRefGoogle Scholar
  37. Shafizadeh-Moghadam H, Asghari A, Tayyebi A, Taleai M (2017) Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Comput Environ Urban Syst 64:297–308CrossRefGoogle Scholar
  38. Sklenicka P, Kottová B, Šálek M (2017) Success in preserving historic rural landscapes under various policy measures: incentives, restrictions or planning? Environ Sci Policy 75:1–9CrossRefGoogle Scholar
  39. Song W, Deng X (2017) Land-use/land-cover change and ecosystem service provision in China. Sci Total Environ 576:705–719Google Scholar
  40. Song X, Yang G, Yan C, Duan H, Liu G, Zhu Y (2009) Driving forces behind land use and cover change in the Qinghai-Tibetan Plateau: a case study of the source region of the yellow river, Qinghai province, China. Environ Earth Sci 59(4):793CrossRefGoogle Scholar
  41. Song W, Deng X, Yuan Y, Wang Z, Li Z (2015) Impacts of land-use change on valued ecosystem service in rapidly urbanized north china plain. Ecol Model 318:245–253CrossRefGoogle Scholar
  42. Strigul N, Florescu I, Welden AR, Michalczewski F (2012) Modelling of forest stand dynamics using markov chains. Environ Model Softw 31(31):64–75CrossRefGoogle Scholar
  43. Sun X, Li F (2017) Spatiotemporal assessment and trade-offs of multiple ecosystem services based on land use changes in Zengcheng, China. Sci Total Environ 609:1569–1581CrossRefGoogle Scholar
  44. Tan SH, Ni SX (2005) Discussion on the driving force of regional land use change. Geogr Geo-Inf Sci 5(3):47–51Google Scholar
  45. Tian JL, Liu PL, Zhang Y (2000) The management of soil and water loss to rebuild a graceful Yan’an with green mountains and clean rivers. Res Soil Water Conserv 7(2):4–9Google Scholar
  46. Turner BLI, Clark WC, Kates RW, Richards JF, Mathews JT (1990) The earth as transformed by human action. Global and regional changes in the biosphere over the past 300 years. Cambridge University Press (with Clark University), CambridgeGoogle Scholar
  47. van Schrojenstein Lantman J, Verburg PH, Bregt A, Geertman S (2011) Core principles and concepts in land-use modelling: a literature review, land-use modelling in planning practice. Springer, DordrechtGoogle Scholar
  48. Verstegen JA, Karssenberg D, Hilst FVD (2014) Identifying a land use change cellular automaton by bayesian data assimilation. Environ Model Softw 53(1):121–136CrossRefGoogle Scholar
  49. Wang LJ, Wu L, Hou XY, Zheng BH, Li H, Norra S (2016) Role of reservoir construction in regional land use change in pengxi river basin upstream of the three gorges reservoir in China. Environmental Earth Sciences 75(13):1048Google Scholar
  50. Worku T, Khare D, Tripathi SK (2017) Modeling runoff–sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed. Environ Earth Sci 76(16):550CrossRefGoogle Scholar
  51. Wu L, Long TY, Cooper WJ (2012a) Temporal and spatial simulation of adsorbed nitrogen and phosphorus non-point source pollution load in Xiao Jiang watershed of three gorges Reservoir area, China. Environ Eng Sci 29(4):238–247CrossRefGoogle Scholar
  52. Wu L, Long TY, Liu X, Guo JS (2012b) Impacts of climate and land-use changes on the migration of non-point source nitrogen and phosphorus during rainfall-runoff in the Jialing River Watershed, China. J Hydrol 475:26–41CrossRefGoogle Scholar
  53. Wu L, Long TY, Liu X, Ma XY (2013) Modeling impacts of sediment delivery ratio and land management on adsorbed non-point source nitrogen and phosphorus load in a mountainous basin of the Three Gorges reservoir area, China. Environ Earth Sci 70(3):1405–1422CrossRefGoogle Scholar
  54. Wu L, Qi T, Li D, Yang HJ, Liu GQ, Ma XY, Gao JE (2015) Current status, problems and control strategies of water resources pollution in China. Water Policy 17(3):423–440CrossRefGoogle Scholar
  55. Wu L, Li PC, Ma XY (2016a) Estimating nonpoint source pollution load using four modified export coefficient models in a large easily eroded watershed of the loess hilly-gully region, China. Environ Earth Sci 75:1056CrossRefGoogle Scholar
  56. Wu L, Liu X, Ma XY (2016b) Application of a modified distributed-dynamic erosion and sediment yield model in a typical watershed of a hilly and gully region, Chinese Loess Plateau. Solid Earth 7(6):1577–1590CrossRefGoogle Scholar
  57. Wu L, Liu X, Ma XY (2016c) Spatio-temporal variation of erosion-type non-point source pollution in a small watershed of hilly and gully region, Chinese Loess Plateau. Environ Sci Pollut Res 23:10957–10967CrossRefGoogle Scholar
  58. Wu L, Liu X, Ma XY (2016d) Impacts of grain for green project on spatiotemporal variations of soil erosion in a typical watershed of Chinese loess hilly and gully region. Fresenius Environ Bull 25(11):4506–4516Google Scholar
  59. Wu L, Liu X, Ma XY (2016e) Spatio-temporal evolutions of precipitation in the Yellow River basin of China from 1981 to 2013. Water Sci Technol Water Supp 16(5):1441–1450CrossRefGoogle Scholar
  60. Wu L, Liu X, Ma XY (2016f) Spatiotemporal distribution of rainfall erosivity in the Yanhe River watershed of hilly and gully region, Chinese Loess Plateau. Environ Earth Sci 75:315CrossRefGoogle Scholar
  61. Wu L, Peng ML, Liu X (2017) An evaluation of sediment yield evolutions for different sub-catchments and land use types in the Yanhe River watershed, loess Plateau. Fresenius Environ Bull 26(12a):7860–7873Google Scholar
  62. Wu L, Liu X, Ma XY (2018) Spatio-temporal temperature variations in the Chinese Yellow River basin from 1981 to 2013. Weather 73(1):27–33CrossRefGoogle Scholar
  63. Xu Y, Sidle RC (2001) Land use change and its regulation of Yangou watershed in Loess Hilly-gully region. Acta Geogr Sin 56(6):681–710Google Scholar
  64. Xu XX, Gao ZX, Zhao JN (2012) Trends of runoff and sediment load of Yanhe River basin and their related driving forces during 1956–2009. J Sedim Res 2:12–18Google Scholar
  65. Zhao Q, Liu S, Deng L, Dong S, Yang Z, Liu Q (2013a) Determining the influencing distance of dam construction and reservoir impoundment on land use: a case study of Manwan Dam, Lancang River. Ecol Eng 53:235–242CrossRefGoogle Scholar
  66. Zhao R, Chen Y, Shi P, Zhang L, Pan J, Zhao H (2013b) Land use and land cover change and driving mechanism in the arid inland river basin: a case study of Tarim river, Xinjiang, China. Environ Earth Sci 68(2):591–604CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of EducationNorthwest A&F UniversityYanglingPeople’s Republic of China
  2. 2.Department of Civil and Environmental EngineeringUniversity of CaliforniaBerkeleyUSA
  3. 3.State Key Laboratory of Soil Erosion and Dryland Farming On the Loess Plateau, Institute of Water and Soil ConservationNorthwest A&F UniversityYanglingPeople’s Republic of China
  4. 4.College of Water Resources and Architectural EngineeringNorthwest A&F UniversityYangling DistrictPeople’s Republic of China

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