Classification Feature Optimization for Global Wetlands Mapping

Abstract

Wetlands play an important role in supporting biodiversity conservation and helping sustainable development. Time series wetland classification requires a series of sample sets acquired at different times. To meet this need, we proposed a method for automatically producing global wetland samples based on 13 global and regional wetland-related datasets and millions of images from Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-1 Synthetic Aperture Radar (SAR) Ground Range Detected (GRD), and Sentinel-2 Multispectral Instrument (MSI) sensors. On this basis, we analyzed the classification capabilities of wetland features in the above four sensors and obtained a multi-sensor, full spectral band, multi-index wetland classification feature dataset at a global scale. The analysis results show that the shortwave infrared band (SWIR) and Land Surface Water Index (LSWI) can distinguish non-wetlands from other land cover types well, while the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and thermal infrared bands (TIR) could help distinguish water bodies and wetlands. C bands in Sentinel 1 perform well in identifying water bodies, but the ability to distinguish wetlands is limited. The mapping coefficients of the corresponding spectral bands and indexes between Sentinel-2, MODIS data, and Landsat were obtained to help realize the collaborative use of multi-sensor image data. This paper could provide guidance for large-scale wetland classification and mapping research.