Remote sensing study of wetlands in the Pearl River Delta during 1995---2015 with the support vector machine method

doi:10.1007/s11707-017-0672-x

Front. Earth Sci.

2018, Vol. 12

Issue (3) : 521-531 https://doi.org/10.1007/s11707-017-0672-x

RESEARCH ARTICLE

Remote sensing study of wetlands in the Pearl River Delta during 1995---2015 with the support vector machine method

Xiaosong HAN, Jiayi PAN(

), Adam T. DEVLIN

Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China

Download: PDF(2723 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In recent years, the Pearl River Delta has experienced rapid economic growth which may create a substantial burden to its ecology. In this study, the wetlands of the Pearl River Delta are investigated. Through the use of remote sensing methods, we analyze spatial and temporal variations of wetlands in this area over the past twenty years. The support vector machine (SVM) method is proven to be an effective approach for classifying the wetlands of the Pearl River Delta, and the total classification resolution reaches 94.94% with a Kappa coefficient exceeding 0.94, higher than other comparable analysis methods. Our results show that wetland areas were reduced by 36.9% during the past twenty years. The change detection analysis method shows that there was a 95.58% intertidal zone change to other land-use types, most of which (57.12%) was converted to construction land. In addition, farmland was reduced by 54.89% during the past twenty years, 47.19% of which was changed to construction land use. The inland water area increased 19.02%, but most of this growth (18.77%) was converted from the intertidal zone.

Keywords wetland Pearl River Delta support vector machine method Landsat images

Corresponding Author(s): Jiayi PAN

Just Accepted Date: 25 September 2017 Online First Date: 31 October 2017 Issue Date: 05 September 2018

Cite this article:

Xiaosong HAN,Jiayi PAN,Adam T. DEVLIN. Remote sensing study of wetlands in the Pearl River Delta during 1995---2015 with the support vector machine method[J]. Front. Earth Sci., 2018, 12(3): 521-531.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0672-x
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I3/521

Tab.1 Time and instrument of Landsat TM/OLI/TIRS images

Fig.1 The study area (red) on a Google map image (June 7, 2015).

Fig.2 11 TM/OLI/TIRS images downloaded from USGS after radiation correction and geometric rectification from 1995 to 2015.

Fig.3 Output images using the SVM classification on the original images shown in Fig. 2.

Tab.2 Statistics listing the number of testing samples used

Tab.3 Accuracy of regional classifications in Pearl River Delta for each image used

Fig.4 The change of different land classification regions in the Pearl River Delta from 1995 to 2015.

Fig.5 (a) Population statistics in the Pearl River Delta from 1995 to 2015, showing changes in urban and rural populations, as well as total population. (b) GDP statistics in the Pearl River Delta from 1995 to 2015, (c) The total change of wetland areas in the Pearl River Delta from 1995 to 2015.

Tab.4 The transformational matrix for different region classifications in the Pearl River Delta between 1995 and 2015 (unit: acre)

Fig.6 Comparisons of the SVM method to other methodologies: (a) Comparisons between SVM and IsoData (1995), (b) Comparisons between SVM and Minimum Distance (1995), (c) Comparisons between SVM and Maximum Likelihood (1995).

Tab.5 Accuracy comparisons between SVM and IsoData, minimum distance and maximum likelihood methods

1	Brennan R L, Prediger D J (1981). Coefficient kappa: some uses, misuses, alternative. Educ Psychol Meas, 41(3): 687–699 https://doi.org/10.1177/001316448104100307
2	Ceamanos X, Waske B, Benediktsson J A, Chanussot J, Fauvel M, Sveinsson R (2010). A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data. Int J Image Data Fusion, 1(4): 293–307 https://doi.org/10.1080/19479832.2010.485935
3	Cohen J (1960). A coefficient agreement for nominal data. Educ Psychol Meas, 20(1): 37–46 https://doi.org/10.1177/001316446002000104
4	Dahl T (2006). Status and Trends of Wetlands in the Conterminous United States 1998 to 2004. Fish and Wildlife Service Publication, 1–112
5	Fan H G, Yue C E, Wang D (2014). Remote sensing classification and land cover change in Yi Liang County based on support vector machine Method. Forest Inventory and Planning, 39(2): 51–56 (in Chinese)
7	Kamlun K U, Bürger Arndt R, Phua M H (2016). Monitoring deforestation in Malaysia between 1985 and 2013: insight from South-Western Sabah and its protected peat swamp area. Land Use Policy, 57: 418–430 https://doi.org/10.1016/j.landusepol.2016.06.011
8	Kristine Butera M (1983). Remote sensing of wetlands. IEEE Trans Geosci Remote Sens, GE-21(3): 383–392 https://doi.org/10.1109/TGRS.1983.350471
9	Li L K (2001). Wetland and ramsar convention. World Forestry Research, 14: 1–7
10	Liang S L, Fang H L, Chen M Z (2001). Atmospheric correction of Landsat ETM+ land surface imagery—Part I: methods. IEEE Trans Geosci Remote Sens, 39(11): 2490–2498 https://doi.org/10.1109/36.964986
11	Lillesan T M, Kiefer R W (2000). Remote Sensing and Image interpretation (4th ed). Hoboken: John and Sons Inc.
12	Lin Y, Shen M, Liu B, Ye Q (2013). Remote sensing classification method of wetland based on an improved SVM. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7(W1): 179–189
6	Melgani F, Bruzzone L (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens, 42(8): 1778–1790 https://doi.org/10.1109/TGRS.2004.831865
13	Næsset E (1996). Use of the weighted Kappa coefficient in classification error assessment of thematic maps. Int J Geogr Inf Syst, 10(5): 591–603 https://doi.org/10.1080/02693799608902099
14	Niu Z G, Zhang H Y, Wang X W, Yao W B, Zhou D M (2012). Mapping wetland changes in China between 1978 and 2008. Chinese Science Bulletin, 57(16): 1400–1411
15	Powers D M W (2011). Evaluation: from precision, recall and f-measure to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies, 2(1): 37–63
16	Pretorius L, Brown L R, Bredenkamp G J, van Huyssteen C W (2016). The ecology and classification of wetland vegetation in the Maputaland Coastal Plain, South Africa. Phytocoenologia, 46(2): 125–139 https://doi.org/10.1127/phyto/2016/0057
17	Silva J P, Phillips L, Jones W, Eldridge J, O’Hara E (2007). LIFE and Europe’s Wetlands. Luxembourg: Office for Official Publications of the European Communities
18	Stehman S V (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ, 62(1): 77–89 https://doi.org/10.1016/S0034-4257(97)00083-7
19	Vapnik V N (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag
20	Wang S G, Li X, Liu K, Zhou Y Z (2007). Dynamic analysis of the wetland resource changes in the estuary of the Pearl River Delta using remote sensing. Acta Scientiarum Naturalium Universitatis Sunyatseni, 46(2): 105–109 (in Chinese)
21	Waske B, Benediktsson J A (2007). Fusion of support vector machines for classification of multisensor data. IEEE Trans Geosci Remote Sens, 45(12): 3858–3866 https://doi.org/10.1109/TGRS.2007.898446
22	Wu W T, Zhou Y X, Tian B (2017). Coastal wetlands facing climate change and anthropogenic activities: a remote sensing analysis and modelling application. Ocean Coast Manage, 138: 1–10 https://doi.org/10.1016/j.ocecoaman.2017.01.005
23	Wu X M, Zhang H Q, Lin H, Liu P P (2012). Study on the prediction of wetland change in Beijing based on support vector machine. Chinese Agricultural Science Bulletin, 28(14): 280–284 (in Chinese)
24	Zhang C, Wang T, Atkinson P M, Pan X, Li H (2015). A novel multi-parameter support vector machine for image classification. Int J Remote Sens, 36(7): 1890–1906 https://doi.org/10.1080/01431161.2015.1029096
25	Zhao Y S (2003). Principles and Methods of Remote Sensing Application Analysis. Beijing: Science Press, 241–249

[1]	Yonghui WANG, Kexiang LIU, Zhaopeng WU, Li JIAO. Comparison and analysis of three estimation methods for soil carbon sequestration potential in the Ebinur Lake Wetland, China[J]. Front. Earth Sci., 2020, 14(1): 13-24.
[2]	Xue DAI, Rongrong WAN, Guishan YANG, Xiaolong WANG, Ligang XU, Yanyan LI, Bing LI. Impact of seasonal water-level fluctuations on autumn vegetation in Poyang Lake wetland, China[J]. Front. Earth Sci., 2019, 13(2): 398-409.
[3]	Erfu DAI, Zhuo WU, Xiaodian DU. A gradient analysis on urban sprawl and urban landscape pattern between 1985 and 2000 in the Pearl River Delta, China[J]. Front. Earth Sci., 2018, 12(4): 791-807.
[4]	Jianguo LI, Wenhui YANG, Qiang LI, Lijie PU, Yan XU, Zhongqi ZHANG, Lili LIU. Effect of reclamation on soil organic carbon pools in coastal areas of eastern China[J]. Front. Earth Sci., 2018, 12(2): 339-348.
[5]	Yanyun ZHAO, Zhaohua LU, Jingtao LIU, Shugang HU. Flora characteristics of Chenier Wetland in Bohai Bay and biogeographic relations with adjacent wetlands[J]. Front. Earth Sci., 2017, 11(4): 620-628.
[6]	Na ZHAO, Mengzhen XU, Zhiwei LI, Zhaoyin WANG, Hanmi ZHOU. Macroinvertebrate distribution and aquatic ecology in the Ruoergai (Zoige) Wetland, the Yellow River source region[J]. Front. Earth Sci., 2017, 11(3): 554-564.
[7]	Changhai WANG. Driving forces behind the construction of an eco-compensation mechanism for wetlands in China[J]. Front. Earth Sci., 2016, 10(3): 487-497.
[8]	Yintao LU,Changyuan TANG,Jianyao CHEN,Hong YAO. Assessment of major ions and heavy metals in groundwater: a case study from Guangzhou and Zhuhai of the Pearl River Delta, China[J]. Front. Earth Sci., 2016, 10(2): 340-351.
[9]	Yanyun ZHAO,Xiangming HU,Jingtao LIU,Zhaohua LU,Jiangbao XIA,Jiayi TIAN,Junsheng MA. Vegetation pattern in Shell Ridge Island in China’s Yellow River Delta[J]. Front. Earth Sci., 2015, 9(3): 567-577.
[10]	Lihong SONG,Hongkai LI,Kehong WANG,Donghui WU,Haitao WU. Ecology of testate amoebae and their potential use as palaeohydrologic indicators from peatland in Sanjiang Plain, Northeast China[J]. Front. Earth Sci., 2014, 8(4): 564-572.
[11]	Jue YUAN, Li ZENG, Yijun ZHAO, Yihong WU, Ping JI, Bin CHEN. Transport of a volatile contaminant in a free-surface wetland flow[J]. Front Earth Sci, 2014, 8(1): 115-122.
[12]	Zhiming ZHANG, Baoshan CUI, Xiaoyun FAN. Removal mechanisms of heavy metal pollution from urban runoff in wetlands[J]. Front Earth Sci, 2012, 6(4): 433-444.
[13]	Laibin HUANG, Junhong BAI, Denghua YAN, Bin CHEN, Rong XIAO, Haifeng GAO. Changes of wetland landscape patterns in Dadu River catchment from 1985 to 2000, China[J]. Front Earth Sci, 2012, 6(3): 237-249.
[14]	Xiaoyun FAN, Baoshan CUI, Zhiming ZHANG. Spatial variations of river water quality in Pearl River Delta, China[J]. Front Earth Sci, 2012, 6(3): 291-296.
[15]	Ying HU, Shihua QI, Chenxi WU, Yanping KE, Jing CHEN, Wei CHEN, Xiangyi GONG. Preliminary assessment of heavy metal contamination in surface water and sediments from Honghu Lake, East Central China[J]. Front Earth Sci, 2012, 6(1): 39-47.

Viewed

Full text

Abstract

Cited

Shared

Discussed