Land cover classification using moderate resolution imaging spectrometer-enhanced vegetation index time-series data and self-organizing map neural network in Inner Mongolia, China

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) data offers a unique combination of spectral, temporal, and spatial resolution in comparison to other global sensors. The MODIS Enhanced Vegetation Index (EVI) product has several advantages, which make it suitable for regional land cover mapping. This paper investigates the application of MODIS EVI time-series data for mapping temperate arid and semi-arid land cover at a moderate resolution (500 m), for regional land-cover/land-use monitoring purposes. A 16-day composite EVI time-series data for 2003 (22 March 2003 - 30 September 2003) was adopted for the study. A land cover map was generated for the Inner Mongolia Autonomous Region using 7 tiles of MODIS EVI time-series data and Self-Organizing Map (SOM) neural network classification. Land-use GIS data, Landsat TM/ETM, and ASTER data were employed as reference data. The results show that the overall accuracy of land cover classification is about 84% with a Kappa coefficient of 0.8170. These results demonstrate that the SOM neural network model could work well for the multi-temporal MODIS EVI data, and suggest a potential of using MODIS EVI time-series remote sensing data to monitor desertification in Inner Mongolia with limited ancillary data and little labor-input in comparison with using high-spatial resolution remote sensing data.