Dynamic changes of wetland resources based on MODIS and Landsat image data fusion

Abstract

Abstract Given the seasonal dynamics of wetland ecosystem, limited available data, and technological method of wetland investigation, wetland evaluation cannot be accurately accessed. Although the remote sensing technology has been widely employed on wetland investigation and identification, changeable weather conditions especially cloud interference are the main barrier to acquire clear remote sensing image for wetland identification and information extraction. The combination and precision evaluation of remote sensing data with high temporal-spatial resolution ratio from moderate-resolution imaging spectroradiometer (MODIS) and Landsat were conducted using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM): a comprehensively temporal-spatial reflectance model was built; the high-resolution image in the time series and Modified Normalized Difference Water Index were obtained. The main data obstacles in wetland resources monitoring were invalid. The typical wetland areas in Liaoning province of China were evaluated using combination algorithm and Landsat (Thematic Mapper) images. The results show that the MODIS and Landsat data can be combined well with high correlations in different wave ranges. The maximum Normalized Difference Water Index (NDWI) is 0.9678, followed by green wave (0.9630), near-infrared wave (0.9345), and blue wave (0.9018).The wetland seasonal change of Panjin was examined using the data combination method. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and vegetation coverage index were extracted from combined images of Panjin from June 2016 to August 2016 and analyzed. Results showed that the NDVI was high in partial water area during the studied period indicating high chlorophyll contents.

Keywords

• Wetlands
• Dynamic changes
• MODIS
• Landsat
• Data fusion