Advertisement

Prediction of landscape pattern changes in a coastal river basin in south-eastern China

  • X. ZhangEmail author
  • L. Zhou
  • Q. Zheng
Original Paper
  • 15 Downloads

Abstract

Different landscapes in a river basin make different contributions to non-point source pollution according to the source–sink theory. Changes in landscape pattern have a considerable impact on river water quality. Precise predictions of the future landscape pattern provide strong support for manipulating land use patterns, which is beneficial for sustainable development in the watershed. The Jiulong River Basin in south-eastern China was selected as the study area. The Random Forest classifier, which combined textural characteristics with spectral information, was applied to interpret the landscapes from Landsat images acquired in 1995, 2005 and 2015 in this paper. The overall classification accuracy reached 86%, and the Kappa index was higher than 0.83, which was better than the accuracy of classifiers based on the exclusive use of spectral information. We used a hybrid cellular automaton–Markov (CA–Markov) model to simulate the landscape pattern of the Jiulong River Basin in 2015 and verified the accuracy according to the interpreted landscape classification map in 2015. The pixels that were predicted to the correct landscape account for 88.35% of total pixels, and the Kappa coefficient was 0.88, indicating that the CA–Markov model was credible for predicting the future landscape pattern in the Jiulong River Basin. The CA–Markov model was applied to forecast the landscape pattern of this watershed in 2025, and the transition area matrix from 2015 to 2025 was obtained. The predicted results demonstrated the imbalanced migration of source–sink landscapes in the future, which indicated the deterioration of water quality in the study area. Hence, government regulators need rational manipulation of the landscape pattern for sustainable development in the Jiulong River Basin.

Keywords

Non-point source pollution Source–sink Landscape pattern Prediction CA–Markov model 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions. The study is funded by the National Key R & D Programmes of China (Grant No. 2017YFB0504201) and the Natural Science Foundation of China (Grant nos. 61473286 and 61375002).

Authors’ contribution

XZ and QZ helped in conceptualization; QZ involved in methodology; LZ, QZ and XZ contributed to validation; LZ helped in writing—original draft preparation and visualization; XZ involved in writing, review, editing, supervision, project administration and funding acquisition.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abe T, Saito T (2008) In: An approach to prediction of spatio-temporal patterns based on binary neural networks and cellular automata. IEEE international joint conference on neural networks, pp 2494–2499.  https://doi.org/10.1109/ijcnn.2008.4634146
  2. Adhikari S, Southworth J (2012) Simulating forest cover changes of Bannerghatta National Park based on a CA–Markov model: a remote sensing approach. Remote Sens 4:3215–3243.  https://doi.org/10.3390/rs4103215 CrossRefGoogle Scholar
  3. Basnyat P, Teeter LD, Flynn KM, Lockaby BG (1999) Relationships between landscape characteristics and nonpoint source pollution inputs to coastal estuaries. Environ Manag 23:539.  https://doi.org/10.1007/s002679900208 CrossRefGoogle Scholar
  4. Basnyat P, Teeter LD, Lockaby BG, Flynn KM (2000) The use of remote sensing and GIS in watershed level analyses of non-point source pollution problems. For Ecol Manag 128:65–73.  https://doi.org/10.1016/s0378-1127(99)00273-x CrossRefGoogle Scholar
  5. Chen L, Fu B, Xu J, Jie G (2003) Location-weighted landscape contrast index: a scale independent approach for landscape pattern evaluation based on source-sink ecological processes. Acta Ecol Sin 23:2406–2413Google Scholar
  6. Cheng H, Ouyang W, Hao F, Ren X, Yang S (2006) The non-point source pollution in livestock-breeding areas of the Heihe river basin in yellow river. Stoch Environ Res Risk Assess 21:213–221.  https://doi.org/10.1007/s00477-006-0057-2 CrossRefGoogle Scholar
  7. Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88:2783–2792.  https://doi.org/10.1890/07-0539.1 CrossRefGoogle Scholar
  8. Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recognit Lett 27:294–300.  https://doi.org/10.1109/igarss.2003.1294837 CrossRefGoogle Scholar
  9. Grinand C, Rakotomalala F, Gond V, Vaudry R, Bernoux M, Vieilledent G (2013) Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date landsat satellite images and the random forests classifier. Remote Sens Environ 139:68–80.  https://doi.org/10.1016/j.rse.2013.07.008 CrossRefGoogle Scholar
  10. Huang JL, Li QS, Hong HS, Lin J, Qu MC (2011) Preliminary study on linking land use and landscape pattern and water quality in the Jiulong river watershed. Environ Sci 32:64Google Scholar
  11. Jiang M, Chen H, Chen Q (2013) A method to analyze “source-sink” structure of non-point source pollution based on remote sensing technology. Environ Pollut 182:135–140.  https://doi.org/10.1016/j.envpol.2013.07.006 CrossRefGoogle Scholar
  12. Li SH, Jin BX, Wei XY, Jiang YY, Wang JL (2015) Using CA–Markov model to model the spatiotemporal change of land use/cover in Fuxian lake for decision support. ISPRS Ann Photogramm Remote Sens Spat Inf Sci II-4/W2:163–168.  https://doi.org/10.5194/isprsannals-ii-4-w2-163-2015 CrossRefGoogle Scholar
  13. Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26:217–222.  https://doi.org/10.1080/01431160412331269698 CrossRefGoogle Scholar
  14. Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199.  https://doi.org/10.1007/s10021-005-0054-1 CrossRefGoogle Scholar
  15. Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012a) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104.  https://doi.org/10.1016/j.isprsjprs.2011.11.002 CrossRefGoogle Scholar
  16. Rodriguez-Galiano VF, Chica-Olmo M, Abarca-Hernandez F, Atkinson PM, Jeganathan C (2012b) Random forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens Environ 121:93–107.  https://doi.org/10.1016/j.rse.2011.12.003 CrossRefGoogle Scholar
  17. Sang L, Zhang C, Yang J, Zhu D, Yun W (2011) Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model 54:938–943.  https://doi.org/10.1016/j.mcm.2010.11.019 CrossRefGoogle Scholar
  18. Subedi P, Subedi K, Thapa B (2013) Application of a hybrid cellular automaton–Markov (CA–Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Appl Ecol Environ Sci 1:126–132.  https://doi.org/10.12691/aees-1-6-5 CrossRefGoogle Scholar
  19. Wang Y, Yu X, He K, Li Q, Zhang Y, Song S (2011) Dynamic simulation of land use change in Jihe watershed based on CA–Markov model. Trans Chin Soc Agric Eng 27:330–336Google Scholar
  20. Wang S, Zhang Z, Wang X (2014) Land use change and prediction in the Baimahe basin using GIS and CA–Markov model. IOP Conf Ser Earth Environ Sci 17:012074.  https://doi.org/10.1088/1755-1315/17/1/012074 CrossRefGoogle Scholar
  21. Wu Q, Liu M, Wang X, Di L, Kang L, Lin L (2015) In: Assessing the water environmental capacity of pollution consumption in Jiulong River Basin. Fourth international conference on agro-geoinformatics, pp 318–323.  https://doi.org/10.1109/agro-geoinformatics.2015.7248100
  22. Xiao R, Wang G, Zhang Q, Zhang Z (2016) Multi-scale analysis of relationship between landscape pattern and urban river water quality in different seasons. Sci Rep 6:25250CrossRefGoogle Scholar
  23. Xin Z, Jintian C, Yuqi L, Lei W (2017) Geo-cognitive computing method for identifying “source-sink” landscape patterns of river basin non-point source pollution. Int J Agric Biol Eng 10:55–68.  https://doi.org/10.25165/j.ijabe.20171005.3272 CrossRefGoogle Scholar
  24. Yousheng W, Xinxiao Y, Kangning H, Qingyun L, Yousong Z, Siming S (2011) Dynamic simulation of land use change in Jihe watershed based on CA–Markov model. Trans Chin Soc Agric Eng.  https://doi.org/10.3969/j.issn.1002-6819.2011.12.062 CrossRefGoogle Scholar
  25. Zhang XM, He GJ, Zhang ZM, Peng Y, Long TF (2017) Spectral-spatial multi-feature classification of remote sensing big data based on a random forest classifier for land cover mapping. Clust Comput 20:2311–2321.  https://doi.org/10.1007/s10586-017-0950-0 CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.College of Remote Sensing Information EngineeringWuhan UniversityWuhanChina
  3. 3.College of Geoscience and Surveying EngineeringChina University of Mining and Technology (Beijing)BeijingChina

Personalised recommendations