Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data

摘要

The patchy and heterogeneous arrangement of vegetation in dryland areas complicates the extraction of phenological signals using existing remote sensing data. This study examined whether the phenological analysis of a range of dryland land cover classes would benefit fromthe availability of synthetic images at Landsat spatial resolution andMODIS time intervals. We assembled a series of 500mMODIS and Landsat-5 TM datasets fromApril to November, 2005-2009, over a study site in central Arizona that encompasses diverse dryland vegetation classes along an elevation gradient of 2000 m.Weapplied the spatial and temporal adaptive reflectance fusion model (STARFM) to each MODIS image to create a time series of synthetic images at 30 m resolution. We subjected a subset of the synthetic imagery to a pixel-based regression analysis with temporally coincident Landsat images to analyze the effect of the underlying vegetation class on the accuracy of the STARFM results. To evaluate the usefulness of the increased spatial resolution compared to a MODIS product, we analyzed the variability of the date of peak greenness values of all 30m pixels within unmixed MODIS pixels. Finally,we examined differences in the temporal distributions of peak greenness extracted from both the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) synthetic imagery time series. Our results indicate that characteristics of the vegetation classes strongly influence STARFM algorithm performance, with Pearson correlation coefficient values ranging from 0.72 to 0.96 depending on the Landsat band and the land cover class. Responses in the near-infrared (NIR) spectrumyielded the lowest correlations, particularly for the Ponderosa Pine class. The phenological variability exhibited by each land cover class was dependent on the precipitation patterns of each growing season, but was sufficiently high to make the application of STARFM imagery at this scale uniformly beneficial. The peak greenness dates extracted from EVI and NDVI time series were temporally synchronized for the Grassland class but diverged for the classes of mixed woody and herbaceous vegetation types.