Chinese Geographical Science

, Volume 30, Issue 1, pp 115–126 | Cite as

Land Cover Changes and Drivers in the Water Source Area of the Middle Route of the South-to-North Water Diversion Project in China from 2000 to 2015

  • Wenwen Gao
  • Yuan ZengEmail author
  • Dan Zhao
  • Bingfang Wu
  • Zhiyuan Ren


The Middle Route of the South-to-North Water Diversion Project (MR-SNWDP) in China, with construction beginning in 2003, diverts water from Danjiangkou Reservoir to North China for residential, agriculture and industrial use. The water source area of the MR-SNWDP is the region that is most sensitive to and most affected by the construction of this water diversion project. In this study, we used Landsat Thematic Mapper (TM) and HJ-1A/B images from 2000 to 2015 by an object-based approach with a hierarchical classification method for mapping land cover in the water source area. The changes in land cover were illuminated by transfer matrixes, single dynamic degree, slope zones and fractional vegetation cover (FVC). The results indicated that the area of cropland decreased by 31% and was replaced mainly by shrub over the past 15 years, whereas forest and settlements showed continuous increases of 29.2% and 77.7%, respectively. The changes in cropland were obvious in all slope zones and decreased most remarkably (−43.8%) in the slope zone above 25°. Compared to the FVC of forest and shrub, significant improvement was exhibited in the FVC of grassland, with a growth rate of 16.6%. We concluded that local policies, including economic development, water conservation and immigration resulting from the construction of the MR-SNWDP, were the main drivers of land cover changes; notably, they stimulated the substantial and rapid expansion of settlements, doubled the wetlands and drove the transformation from cropland to settlements in immigration areas.


remote sensing land cover change object-based classification Middle Route of the South-to-North Water Diversion Project (MR-SNWDP) China 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We gratitude the project cooperation from Wang Zhimin, Liu Yuanshu, Cao Pengfei, Hou Kun, Meng Lingguang, Hu Guiquan. We also thank Zhao Yujin, Zheng Zhaoju, Dong Wenxue, and Yi Haiyan for their assistance in the field sample collection.


  1. Azadi H, Ho P, Hasfiati L, 2011. Agricultural land conversion drivers: a comparison between less developed, developing and developed countries. Land Degradation and Development, 22(6): 596–604. doi: CrossRefGoogle Scholar
  2. Berberoglu S, Akin A, 2009. Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. International Journal of Applied Earth Observation and Geoinformation, 11(1): 46–53. doi: CrossRefGoogle Scholar
  3. Blaschke T, 2010. Object based image analysis for remote sensing. Journal of Photogrammetry and Remote Sensing, 65(1): 2–16. doi: CrossRefGoogle Scholar
  4. Boesch D F, Burroughs R H, Baker J E et al., 2001. Marine Pollution in the United States. Pew Oceans Commission, Arlington, VirginiaGoogle Scholar
  5. Burnett C, Blaschke T, 2003. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3): 233–249. doi: CrossRefGoogle Scholar
  6. Carlson T N, Ripley D A, 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62: 241–252. doi: CrossRefGoogle Scholar
  7. Chen G, Hay G J, Carvalho L M T et al., 2012. Object-based change detection. International Journal of Remote Sensing, 33: 4434–4457. doi: CrossRefGoogle Scholar
  8. Chen H C, Du P F, 2008. Potential Ecological Benefits of the Middle Route for the South-North Water Diversion Project. Tsinghua Science and Technology, 13(5): 715–719. doi: CrossRefGoogle Scholar
  9. Chen Y H, Su W, Li J et al., 2009. Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research, 43: 1101–1110. doi: CrossRefGoogle Scholar
  10. Chi W F, Zhao Y Y, Kuang W H et al., 2019. Impacts of anthropogenic land use/cover changes on soil wind erosion in China. Science of The Total Environment, 668: 204–215. doi: CrossRefGoogle Scholar
  11. Cong Pifu, Chen Kexin, Qu Limei et al., 2019. Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data. Chinese Geographical Science, 29(3): 372–381. doi: CrossRefGoogle Scholar
  12. Congalton R G, Mead R A, 1983. A Quantitative Method to Test for Consistency and Correctness in Photointerpretation. Photogrammetric Engineering & Remote Sensing, 49(1): 69–74.Google Scholar
  13. Desclée B, Bogaert P, Defourny P, 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment, 102(1–2): 1–11. doi: CrossRefGoogle Scholar
  14. Dong Z J, Yan Y, Duan J et al., 2011. Computing payment for ecosystem services in watersheds: an analysis of the Middle Route Project of South-to-North Water Diversion in China. Journal of Environmental Sciences, 23(12): 2005–2012. doi: CrossRefGoogle Scholar
  15. Duan Z R, Zhang L P, Li L C, 2012. The Extreme Precipitation Change Characteristics of the Source Area of the Middle Route of South-North Water Transfer Project. Procedia Engineering, 28: 569–573. doi: CrossRefGoogle Scholar
  16. Duro D C, Franklin S E, Dubé M G, 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118: 2–16, doi: CrossRefGoogle Scholar
  17. Feng Qinliang, Chen Jiancheng, 2009. Sustainable Development of Natural Forest Protection Project Area. Journal of Beijing Forestry University (Social Sciences), 8(4): 28–31. (in Chinese)Google Scholar
  18. Foody G M, 1996. Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. International Journal of Remote Sensing, 17(7): 1317–1340. doi: CrossRefGoogle Scholar
  19. Gao Guoxiong, Zhang Guoliang, Liu Meixian et al., 2007. Retrospect on the research and practice of the converting cropland to forests. Journal of Northwest Forestry University, 22(2): 203–208. (in Chinese)Google Scholar
  20. Gu Z J, Wu X X, Zhou F et al., 2013. Estimating the effect of pinus massoniana Lamb plots on soil and water conservation during rainfall events using vegetation fractional coverage. Catena, 109: 225–233. doi: CrossRefGoogle Scholar
  21. Holland S P, Moore M R, 2003. Cadillac desert revisited: property rights, public policy, and water-resource depletion. Journal of Environmental Economics and Management, 46(1): 131–155. doi: CrossRefGoogle Scholar
  22. Im J, Jensen J R, Hodgson M E, 2008. Object-based land cover classification using high-posting-density lidar data. GIScience and Remote Sensing, 45(2): 209–228. doi: CrossRefGoogle Scholar
  23. Jian S Q, Zhao C Y, Fang S M et al., 2015. Effects of different vegetation restoration on soil water storage and water balance in the Chinese Loess Plateau. Agricultural and Forest Meteorology, 206: 85–96. doi: CrossRefGoogle Scholar
  24. Jing X, Yao W Q, Wang J H et al., 2011. A study on the relationship between dynamic change of vegetation Coverage and precipitation in Beijing’s mountainous areas during the last 20 years. Mathematical and Computer Modelling, 54(3–4): 1079–1085, doi: CrossRefGoogle Scholar
  25. Kallel A, Le Hégarat-Mascle S, Ottlé C et al., 2007. Determination of vegetation cover fraction by inversion of a four-parameter model based on isoline parametrization. Remote Sensing of Environment, 111(4): 553–566. doi: CrossRefGoogle Scholar
  26. Kanellopoulos I, Varfis A, Wilkinson G G et al., 1992. Land- cover discrimination in SPOT HRV imagery using an artificial neural network—a 20-class experiment. International Journal of Remote Sensing, 13(5): 917–924. doi: CrossRefGoogle Scholar
  27. Konik M, Bradtke K, 2016. Object-oriented approach to oil spill detection using ENVISAT ASAR images. Journal of Photogrammetry and Remote Sensing, 118: 37–52. doi: CrossRefGoogle Scholar
  28. Kuang W H, Liu J Y, Dong J W et al., 2016. The rapid and massive urban and industrial land expansions in China between 1990 and 2010: a CLUD-based analysis of their trajectories, patterns, and drivers. Landscape and Urban Planning, 145: 21–33. doi: CrossRefGoogle Scholar
  29. Kuo Y M, Liu W W, Zhao E M et al., 2019. Water quality variability in the middle and down streams of Han River under the influence of the Middle Route of South-North Water diversion project, China. Journal of Hydrology, 569: 218–229. doi: CrossRefGoogle Scholar
  30. Li Lu, Shi Zhihua, Zhu Dun et al., 2009. Forest Changes in the Water Source Area of Middle Route of the South-to-North Water Diversion Project. Journal of Natural Resources, 24(6): 1049–1057. (in Chinese)Google Scholar
  31. Li S, Liang W, Fu, B J et al., 2016. Vegetation changes in recent large-scale ecological restoration projects and subsequent impact on water resources in China’s Loess Plateau. Science of the Total Environment, 569–570: 1032–1039. doi: CrossRefGoogle Scholar
  32. Li Siyue, Zhang Quanfa, 2008. Main Eco-Environmental Problems and Revegetation in the Danjiangkou Reservoir Water Supplying Area of the Middle Route of the South to North Water Transfer Project. China Rural Water and Hydropower, (3): 1–4. (in Chinese)Google Scholar
  33. Li S Y, Li J, Zhang Q F, 2011. Water quality assessment in the rivers along the water conveyance system of the Middle Route of the South to North Water Transfer Project (China) using multivariate statistical techniques and receptor modeling. Journal of Hazardous Materials, 195: 306–317. doi: CrossRefGoogle Scholar
  34. Lindquist E J, Hansen M C, Roy D P et al., 2008. The suitability of decadal image data sets for mapping tropical forest cover change in the Democratic Republic of Congo: implications for the global land survey. International Journal of Remote Sensing, 29(24): 7269–7275. doi: CrossRefGoogle Scholar
  35. Liu Z J, Liu A X, Wang C Y et al., 2004. Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification. Future Generation Computer Systems, 20(7): 1119–1129. doi: CrossRefGoogle Scholar
  36. Mao D H, Wang Z M, Wu J G et al., 2018. China’s wetlands loss to urban expansion. Land Degradation and Development, 29(8): 2644–2657. doi: Scholar
  37. Meyer W B, Turner B L, 1994. Changes in Land Use and Land Cover: A Global Perspective. Cambridge, UK: Cambridge University Press.Google Scholar
  38. Miao Z, Sheng J C, Webber M et al., 2018. Measuring water use performance in the cities along China’s South-North Water Transfer Project. Applied Geography, 98: 184–200. doi: CrossRefGoogle Scholar
  39. Myint S W, Gober P, Brazel A et al., 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5): 1145–1161. doi: CrossRefGoogle Scholar
  40. M Konik, K Bradtke. Object-oriented approach to oil spill detection using ENVISAT ASAR images. Journal of Photogrammetry and Remote Sensing, 118: 37–52. doi: CrossRefGoogle Scholar
  41. National Bureau of Statistics. 2010. China Statistical Yearbook. Beijing: China Statistics Press.Google Scholar
  42. Ouyang Zhiyun, 2007. Ecological Construction and Sustainable Development in China. Beijing: Science Press. (in Chinese)Google Scholar
  43. Pabi O, 2007. Understanding land-use/cover change process for land and environmental resources use management policy in Ghana. Geojournal, 68(4): 369–383. doi: CrossRefGoogle Scholar
  44. Pouliot D, Latifovic R, Olthof I, 2009. Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985–2006. International Journal of Remote Sensing, 30(1): 149–168. doi: CrossRefGoogle Scholar
  45. Rundquist B C, 2002. The influence of canopy green vegetation fraction on spectral measurements over native tall-grass prairie. Remote Sensing of Environment, 81(1): 129–135. doi: CrossRefGoogle Scholar
  46. Shen Huaifei, Hou Gang, Zhai Shumei et al., 2013. Land Use/Cover Change and the Driving Force in the Water Supplying Area of the Middle-Route of the South-to-North Water Diversion (MR-SNWD) Project. Guizhou Agricultural Sciences, 41(6): 167–171. (in Chinese)Google Scholar
  47. Sheng J C, Webber M, 2018. Using incentives to coordinate responses to a system of payments for watershed services: the middle route of South-North Water Transfer Project, China. Ecosystem Services, 32: 1–8. doi: CrossRefGoogle Scholar
  48. Tømmervik H, Høgda J A, Solheim I, 2003. Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data. Remote Sensing of Environment, 85(3): 370–388. doi: CrossRefGoogle Scholar
  49. Tang C H, Yi Y J, Yang Z F et al., 2014. Water pollution risk simulation and prediction in the main canal of the South-to-North Water Transfer Project. Journal of Hydrology, 519: 2111–2120. doi: CrossRefGoogle Scholar
  50. Veldkamp A, Lambin E F, 2001. Predicting land-use change. Agriculture, Ecosystems and Environment, 85(1–3): 1–6. doi: CrossRefGoogle Scholar
  51. Wang Fang, Ge Quansheng, Yu Qibiao et al. 2017. Impacts of Land-use and Land-cover Changes on River Runoff in Yellow River Basin for Period of 1956–2012. Chinese Geographical Science, 27(1): 13–24. doi: CrossRefGoogle Scholar
  52. Wang L S, Ma C, 1999. A study on the environmental geology of the Middle Route Project of the South-North water transfer. Engineering Geology, 51: 153–165.CrossRefGoogle Scholar
  53. Wang Xiuli, 2004. The famous water transfer project in the basin and districts aboard. Water Resources and Electric Power, 30(1): 1–25. (in Chinese)Google Scholar
  54. Wang Xiulan, Bao Yuhai, 1999. Study on the methods of land use dynamic change research. Progress in Geography, 18(1): 81–87. (in Chinese).Google Scholar
  55. Wen Z M, Lees B G, Jiao Feng et al., 2010. Stratified vegetation cover index: a new way to assess vegetation impact on soil erosion. Catena, 83(1): 87–93. doi: CrossRefGoogle Scholar
  56. Wu Bingfang et al., 2017. Land Cover Atlas of the People’s Republic of China (1:1,000,000). Sinomaps Press.Google Scholar
  57. Yan B W, Chen L, 2013. Coincidence probability of precipitation for the middle route of South-to-North water transfer project in China. Journal of Hydrology, 499: 19–26. doi: CrossRefGoogle Scholar
  58. Yao Y Y, Zheng C M, Andrews C et al., 2019. Integration of groundwater into China’s south-north water transfer strategy. Science of The Total Environment, 658: 550–557. doi: CrossRefGoogle Scholar
  59. Zhang J X, Liu Z J, Sun X X, 2009. Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: land use/land cover, vegetation cover changes estimated using multi-source satellite data. International Journal of Applied Earth Observation and Geoinformation, 11(6): 403–412. doi: CrossRefGoogle Scholar
  60. Zhang L, Jia K, Li X S et al., 2014a. Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery. Remote Sensing Letters, 5(1): 73–82. doi: CrossRefGoogle Scholar
  61. Zhang L, Li X S, Yuan Q Z et al., 2014b. Object-based approach to national land cover mapping using HJ satellite imagery. Journal of Applied Remote Sensing, 8: 083686. doi: CrossRefGoogle Scholar
  62. Zhang X F, Liao C H, Li J H et al., 2013. Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 21: 506–512. doi: Scholar
  63. Zhou Zhiqiang, Zeng Yuan, Zhang Lei et al., 2012. Remote Sensing Monitoring and Analysis of Fractional Vegetation Cover in the Water Source Area of the Middle Route of Projects to Divert Water from the South to the North. Remote Sensing For Land & Resources, 24(1): 70–76. (in Chinese)Google Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Wenwen Gao
    • 1
  • Yuan Zeng
    • 1
    Email author
  • Dan Zhao
    • 1
  • Bingfang Wu
    • 1
  • Zhiyuan Ren
    • 2
  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.Research Center for Policy and Technology of the Office of the South-to-North Water Diversion ProjectMinistry of Water ResourcesBeijingChina

Personalised recommendations