Prediction of landscape pattern changes in a coastal river basin in south-eastern China
- 15 Downloads
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.
KeywordsNon-point source pollution Source–sink Landscape pattern Prediction CA–Markov model
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).
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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