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

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.

[1]  T. Dahl,et al.  Status and trends of wetlands in the conterminous United States, 1986 to 1997 , 2000 .

[2]  Wenting Wu,et al.  Coastal wetlands facing climate change and anthropogenic activities: A remote sensing analysis and modelling application , 2017 .

[3]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Johannes R. Sveinsson,et al.  A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data , 2010 .

[5]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[6]  Stephen V. Stehman,et al.  Selecting and interpreting measures of thematic classification accuracy , 1997 .

[7]  Demin Zhou,et al.  Mapping wetland changes in China between 1978 and 2008 , 2012 .

[8]  Yi Lin,et al.  REMOTE SENSING CLASSIFICATION METHOD OF WETLAND BASED ON AN IMPROVED SVM , 2013 .

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[11]  Zhou Yong-zhang.  Dynamic Analysis of the Wetland Resource Changes in the Estuary of the Pearl River Delta Using Remote Sensing , 2007 .

[12]  Mui How Phua,et al.  Monitoring deforestation in Malaysia between 1985 and 2013: Insight from South-Western Sabah and its protected peat swamp area , 2016 .

[13]  Hongliang Fang,et al.  Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods , 2001, IEEE Trans. Geosci. Remote. Sens..

[14]  Dale J. Prediger,et al.  Coefficient Kappa: Some Uses, Misuses, and Alternatives , 1981 .

[15]  G. Bredenkamp,et al.  The ecology and classification of wetland vegetation in the Maputaland Coastal Plain, South Africa , 2016 .

[16]  M. Kristine Butera,et al.  Remote Sensing of Wetlands , 1983 .

[17]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[18]  Erik Næsset,et al.  Use of the Weighted Kappa Coefficient in Classification Error Assessment of Thematic Maps , 1996, Int. J. Geogr. Inf. Sci..

[19]  P. Atkinson,et al.  A novel multi-parameter support vector machine for image classification , 2015 .

[20]  Jon Atli Benediktsson,et al.  Fusion of Support Vector Machines for Classification of Multisensor Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Hongliang Fang,et al.  Atmospheric correction of Landsat ETM+ land surface imagery: Part I: Methods , 2001 .