MEASURING PHENOLOGICAL VARIABILITY FROM SATELLITE IMAGERY

Vegetation phenological phenomena are closely related to seasonal dyna mics of the lower atmosphere and are therefore important elements in g lobal models and vegetation monitoring. Normalized difference vegetati on index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) sat ellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked t o key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. Thes e measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measur es of central tendency and variability of the measures were computed a nd analyzed for various land cover types. Results from the analysis sh owed strong coincidence between the satellite-derived metrics and pred icted phenological characteristics. In particular, the metrics identif ied interannual variability of spring wheat in North Dakota, character ized the phenology of four types of grasslands, and established the ph enological consistency of deciduous and confiferous forests. These res ults have implications for large-area land cover mapping and monitorin g. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cove r types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with hig h phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perfo rm more detailed land cover analysis with higher resolution imagery.