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Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain, Northeast China

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Abstract

Increasing interest in wetlands for environmental management requires an understanding of the location, spatial extent, and configuration of the resource. The National Wetlands Inventory is the most commonly used data source for this information. However, its accuracy is limited in some contexts, such as agricultural and forested wetlands. An large number of studies have mapped wetlands worldwide from the perspective of land use and land cover change. However, information on the actual wetland planting areas annually is limited, which greatly impacts ongoing research. In this case study of the West Songnen Plain, we developed a simple algorithm for the quick mapping of wetlands by utilizing their unique physical features, such as annual display of phenological land-cover change of exposed soils, shallow flooding water, and plants from multi-temporal Landsat images. Temporal variations of the Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) derived from Landsat images in 2010 for wetlands at different growth stages were analyzed. Results show that during the ante-tillering phase, the NDVI value (above zero) is lower than the LSWI value of paddies because of flooding of shallow water; during the reproductive and ripening phases, the NDVI value is higher than the LSWI value (above zero); and during the post-harvest wetland planting phase, the NDVI value is still higher than the LSWI value, but the LSWI value is negative. Wetland areas can be detected using one or two images in the optimum time window. The algorithm based on the difference of NDVI and LSWI values derived from Landsat images was used to extract the actual wetland planting area. Validated alongside statistical data, the algorithm showed high accuracy. Therefore, this algorithm highlights the unique features of wetlands and can help in mapping the actual wetland area annually on a regional scale. Results further indicate that the new method has a classification accuracy of 92 %. In comparison, two traditional methods based on Landsat-7/ETM registered accuracy rates of only 83 % and 87 % respectively.

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Acknowledgments

This study was supported by the Key Deployment Project of Chinese Academy of Sciences (NO. KZZD-EW-08-02) and CAS/SAFEA International Partnership Program for Creative Research Teams.

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Authors and Affiliations

  1. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China

    Zhangyu Dong, Zongming Wang, Dianwei Liu, Kaishan Song, Mingming Jia & Zhi Ding

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Zhangyu Dong, Mingming Jia & Zhi Ding

  3. Department of Earth Sciences, Indiana University-Purdue University, Indianapolis, IN, 46202, USA

    Lin Li

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  1. Zhangyu Dong
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  2. Zongming Wang
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Correspondence to Zongming Wang.

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Dong, Z., Wang, Z., Liu, D. et al. Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain, Northeast China. J Indian Soc Remote Sens 42, 569–576 (2014). https://doi.org/10.1007/s12524-013-0357-1

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