GAO Wenwen, ZENG Yuan, ZHAO Dan, WU Bingfang, REN Zhiyuan. 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[J]. Chinese Geographical Science, 2020, 30(1): 115-126. doi: 10.1007/s11769-020-1099-y
Citation: GAO Wenwen, ZENG Yuan, ZHAO Dan, WU Bingfang, REN Zhiyuan. 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[J]. Chinese Geographical Science, 2020, 30(1): 115-126. doi: 10.1007/s11769-020-1099-y

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

doi: 10.1007/s11769-020-1099-y
Funds:

Under the auspices of the National Key Research and Development Program of China (No. 2016YFC0500201-01), National Natural Science Foundation of China (No. 41671365, 41771464), the Annual Project of the Office of the South-to-North Water Diversion Project (No. 2018-21)

  • Received Date: 2019-03-05
  • Rev Recd Date: 2019-07-01
  • 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.
  • [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: 10.1002/ldr.1037
    [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 Ob-servation and Geoinformation, 11(1):46-53. doi: 10.1016/j.jag.2008.06.002
    [3] Blaschke T, 2010. Object based image analysis for remote sensing. Journal of Photogrammetry and Remote Sensing, 65(1):2-16. doi: 10.1016/j.isprsjprs.2009.06.004
    [4] Boesch D F, Burroughs R H, Baker J E et al., 2001. Marine Pol-lution in the United States. Pew Oceans Commission, Arling-ton, Virginia
    [5] Burnett C, Blaschke T, 2003. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3):233-249. doi: 10.1016/S0304-3800(03)00139-X
    [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: 10.1016/S0034-4257(97)00104-1
    [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: 10.1080/01431161.2011.648285
    [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: 10.1016/S1007-0214(08)70116-0
    [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: 10.1016/j.asr.2008.11.008
    [10] Chi W F, Zhao Y Y, Kuang W H et al., 2019. Impacts of anthro-pogenic land use/cover changes on soil wind erosion in China. Science of The Total Environment, 668:204-215. doi: 10.1016/j.scitotenv.2019.03.015
    [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: 10.1007/s11769-019-1039-x
    [12] Congalton R G, Mead R A, 1983. A Quantitative Method to Test for Consistency and Correctness in Photointerpretation. Pho-togrammetric Engineering & Remote Sensing, 49(1):69-74.
    [13] Desclée B, Bogaert P, Defourny P, 2006. Forest change detection by statistical object-based method. Remote Sensing of Envi-ronment, 102(1-2):1-11. doi: 10.1016/j.rse.2006.01.013
    [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: 10.1016/S1001-0742(10)60663-8
    [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: 10.1016/j.proeng.2012.01.770
    [16] Duro D C, Franklin S E, DubéMG, 2012. A comparison of pix-el-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: 10.1016/j.rse.2011.11.020
    [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)
    [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: 10.1080/01431169608948706
    [19] Gao Guoxiong, Zhang Guoliang, Liu Meixian et al., 2007. Ret-rospect on the research and practice of the converting cropland to forests. Journal of Northwest Forestry University, 22(2):203-208. (in Chinese)
    [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: 10.1016/j.catena.2013.03.008
    [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: 10.1016/S0095-0696(02)00036-0
    [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:10.2747/1548-1603. 45.2.209
    [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 Meteor-ology, 206:85-96. doi: 10.1016/j.agrformet.2015.03.009
    [24] Jing X, Yao W Q, Wang J H et al., 2011. A study on the relation-ship 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: 10.1016/j.mcm.2010.11.038
    [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: 10.1016/j.rse.2007.04.006
    [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: 10.1080/01431169208904164
    [27] Konik M, Bradtke K, 2016. Object-oriented approach to oil spill detection using ENVISAT ASAR images. Journal of Photo-grammetry and Remote Sensing, 118:37-52. doi: 10.1016/j.isprsjprs.2016.04.006
    [28] Kuang W H, Liu J Y, Dong J W et al., 2016. The rapid and mas-sive 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: 10.1016/j.landurbplan.2015.10.001
    [29] Kuo Y M, Liu W W, Zhao E M et al., 2019. Water quality varia-bility 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: 10.1016/j.jhydrol.2018.12.001
    [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)
    [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: 10.1016/j.scitotenv.2016.06.141
    [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)
    [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: 10.1016/j.jhazmat.2011.08.043
    [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 Sens-ing, 29(24):7269-7275. doi: 10.1080/01431160802275890
    [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: 10.1016/j.future.2003.11.024
    [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: 10.1002/ldr.2939.
    [37] Meyer W B, Turner B L, 1994. Changes in Land Use and Land Cover:A Global Perspective. Cambridge, UK:Cambridge University Press.
    [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: 10.1016/j.apgeog.2018.07.020
    [39] Myint S W, Gober P, Brazel A et al., 2011. Per-pixel vs. ob-ject-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environ-ment, 115(5):1145-1161. doi: 10.1016/j.rse.2010.12.017
    [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: 10.1016/j.isprsjprs.2016.04.006
    [41] National Bureau of Statistics. 2010. China Statistical Yearbook. Beijing:China Statistics Press.
    [42] Ouyang Zhiyun, 2007. Ecological Construction and Sustainable Development in China. Beijing:Science Press. (in Chinese)
    [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: 10.1007/s10708-007-9090-z
    [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: 10.1080/01431160802302090
    [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: 10.1016/S0034-4257(01)00339-X
    [46] Shen Huaifei, Hou Gang, Zhai Shumei et al., 2013. Land Use/Cover Change and the Driving Force in the Water Sup-plying Area of the Middle-Route of the South-to-North Water Diversion (MR-SNWD) Project. Guizhou Agricultural Sci-ences, 41(6):167-171. (in Chinese)
    [47] Sheng J C, Webber M, 2018. Using incentives to coordinate re-sponses to a system of payments for watershed services:the middle route of South-North Water Transfer Project, China. Ecosystem Services, 32:1-8. doi: 10.1016/j.ecoser.2018.05.005
    [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: 10.1016/S0034-4257(03)00014-2
    [49] Tang C H, Yi Y J, Yang Z F et al., 2014. Water pollution risk sim-ulation and prediction in the main canal of the South-to-North Water Transfer Project. Journal of Hydrology, 519:2111-2120. doi: 10.1016/j.jhydrol.2014.10.010
    [50] Veldkamp A, Lambin E F, 2001. Predicting land-use change. Ag-riculture, Ecosystems and Environment, 85(1-3):1-6. doi: 10.1016/S0167-8809(01)00199-2
    [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: 10.1007/s11769-017-0843-3
    [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.
    [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)
    [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).
    [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 ero-sion. Catena, 83(1):87-93. doi: 10.1016/j.catena.2010.07.006
    [56] Wu Bingfang et al., 2017. Land Cover Atlas of the People's Re-public of China (1:1,000,000). Sinomaps Press.
    [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: 10.1016/j.jhydrol.2013.06.040
    [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: 10.1016/j.scitotenv.2018.12.185
    [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 Ap-plied Earth Observation and Geoinformation, 11(6):403-412. doi: 10.1016/j.jag.2009.07.004
    [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: 10.1080/2150704X.2013.875235
    [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: 10.1117/1.JRS.8.083686
    [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: 10.1016/j.jag.2012.07.003.
    [63] Zhou Zhiqiang, Zeng Yuan, Zhang Lei et al., 2012. Remote Sens-ing 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)
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(325) PDF downloads(107) Cited by()

Proportional views
Related

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

doi: 10.1007/s11769-020-1099-y
Funds:

Under the auspices of the National Key Research and Development Program of China (No. 2016YFC0500201-01), National Natural Science Foundation of China (No. 41671365, 41771464), the Annual Project of the Office of the South-to-North Water Diversion Project (No. 2018-21)

Abstract: 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.

GAO Wenwen, ZENG Yuan, ZHAO Dan, WU Bingfang, REN Zhiyuan. 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[J]. Chinese Geographical Science, 2020, 30(1): 115-126. doi: 10.1007/s11769-020-1099-y
Citation: GAO Wenwen, ZENG Yuan, ZHAO Dan, WU Bingfang, REN Zhiyuan. 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[J]. Chinese Geographical Science, 2020, 30(1): 115-126. doi: 10.1007/s11769-020-1099-y
Reference (63)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return